program(1.3)
[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3510.2.1"}, {"coremlc-version", "3500.32.1"}, {"coremltools-component-milinternal", ""}, {"coremltools-version", "9.0"}})]
{
    func length_1<ios18>(tensor<fp32, [1, 2048, 1, 1]> inputs_embeds, state<tensor<fp16, [28, 2, 2048, 128]>> key_cache, tensor<int32, [1]> position_id, tensor<int32, [1]> position_index_seed, state<tensor<fp16, [28, 2, 2048, 128]>> value_cache) {
            tensor<fp16, [256, 2048, 1, 1]> layers_1_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524992))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524416))))[name = string("layers_1_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_1_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(525312))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13120640))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13108288))))[name = string("layers_1_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_2_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13126848))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13651776))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13651200))))[name = string("layers_2_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_2_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13652096))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26247424))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26235072))))[name = string("layers_2_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_3_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26253632))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26778560))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26777984))))[name = string("layers_3_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_3_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26778880))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30977408))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30973248))))[name = string("layers_3_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_3_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30979520))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43566656))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43562496))))[name = string("layers_3_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_4_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43568768))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44093696))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44093120))))[name = string("layers_4_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_4_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44094016))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48292544))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48288384))))[name = string("layers_4_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_4_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48294656))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60889984))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60877632))))[name = string("layers_4_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_4_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60896192))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73491520))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73479168))))[name = string("layers_4_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_4_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73497728))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86084864))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86080704))))[name = string("layers_4_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_5_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86086976))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86611904))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86611328))))[name = string("layers_5_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_5_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86612224))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90810752))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90806592))))[name = string("layers_5_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_5_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90812864))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103408192))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103395840))))[name = string("layers_5_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_5_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103414400))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116001536))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115997376))))[name = string("layers_5_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_6_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116003648))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116528576))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116528000))))[name = string("layers_6_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_6_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116528896))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120727424))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120723264))))[name = string("layers_6_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_6_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120729536))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133324864))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133312512))))[name = string("layers_6_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_6_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133331072))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145926400))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145914048))))[name = string("layers_6_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_6_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145932608))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158519744))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158515584))))[name = string("layers_6_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_7_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158521856))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159046784))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159046208))))[name = string("layers_7_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_7_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159047104))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163245632))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163241472))))[name = string("layers_7_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_7_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163247744))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175843072))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175830720))))[name = string("layers_7_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_8_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175849280))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176374208))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176373632))))[name = string("layers_8_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_8_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176374528))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180573056))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180568896))))[name = string("layers_8_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_8_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180575168))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193170496))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193158144))))[name = string("layers_8_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_8_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193176704))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205772032))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205759680))))[name = string("layers_8_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_8_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205778240))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218365376))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218361216))))[name = string("layers_8_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_9_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218367488))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218892416))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218891840))))[name = string("layers_9_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_9_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218892736))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223091264))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223087104))))[name = string("layers_9_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_9_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223093376))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235688704))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235676352))))[name = string("layers_9_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_9_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235694912))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248290240))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248277888))))[name = string("layers_9_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_9_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248296448))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260883584))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260879424))))[name = string("layers_9_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_10_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260885696))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261410624))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261410048))))[name = string("layers_10_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_10_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261410944))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265609472))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265605312))))[name = string("layers_10_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_10_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265611584))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278206912))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278194560))))[name = string("layers_10_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_10_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278213120))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290808448))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290796096))))[name = string("layers_10_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_10_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290814656))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303401792))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303397632))))[name = string("layers_10_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_11_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303403904))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307602432))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307598272))))[name = string("layers_11_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_11_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307604544))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308129472))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308128896))))[name = string("layers_11_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_11_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308129792))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308654720))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308654144))))[name = string("layers_11_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_11_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308655040))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312853568))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312849408))))[name = string("layers_11_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_11_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312855680))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325451008))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325438656))))[name = string("layers_11_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_11_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325457216))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338052544))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338040192))))[name = string("layers_11_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_11_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338058752))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350645888))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350641728))))[name = string("layers_11_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_12_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350648000))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354846528))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354842368))))[name = string("layers_12_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_12_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354848640))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355373568))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355372992))))[name = string("layers_12_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_12_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355373888))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355898816))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355898240))))[name = string("layers_12_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_12_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355899136))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360097664))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360093504))))[name = string("layers_12_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_12_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360099776))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372695104))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372682752))))[name = string("layers_12_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_12_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372701312))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385296640))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385284288))))[name = string("layers_12_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_12_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385302848))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397889984))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397885824))))[name = string("layers_12_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_13_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397892096))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402090624))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402086464))))[name = string("layers_13_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_13_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402092736))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402617664))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402617088))))[name = string("layers_13_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_13_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402617984))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403142912))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403142336))))[name = string("layers_13_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_13_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403143232))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407341760))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407337600))))[name = string("layers_13_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_13_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407343872))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419939200))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419926848))))[name = string("layers_13_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_13_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419945408))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432532544))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432528384))))[name = string("layers_13_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_14_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432534656))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436733184))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436729024))))[name = string("layers_14_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_14_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436735296))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437260224))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437259648))))[name = string("layers_14_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_14_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437260544))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441459072))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441454912))))[name = string("layers_14_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_14_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441461184))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454056512))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454044160))))[name = string("layers_14_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_14_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454062720))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(466658048))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(466645696))))[name = string("layers_14_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_14_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(466664256))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(479251392))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(479247232))))[name = string("layers_14_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_15_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(479253504))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483452032))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483447872))))[name = string("layers_15_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_15_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483454144))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483979072))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483978496))))[name = string("layers_15_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_15_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483979392))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484504320))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484503744))))[name = string("layers_15_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_15_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484504640))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488703168))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488699008))))[name = string("layers_15_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_15_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488705280))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(501300608))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(501288256))))[name = string("layers_15_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_15_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(501306816))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513902144))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513889792))))[name = string("layers_15_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_15_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513908352))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526495488))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526491328))))[name = string("layers_15_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_16_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526497600))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530696128))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530691968))))[name = string("layers_16_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_16_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530698240))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531223168))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531222592))))[name = string("layers_16_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_16_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531223488))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531748416))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531747840))))[name = string("layers_16_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_16_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531748736))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535947264))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535943104))))[name = string("layers_16_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_16_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535949376))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(548536512))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(548532352))))[name = string("layers_16_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_17_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(548538624))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552737152))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552732992))))[name = string("layers_17_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_17_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552739264))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553264192))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553263616))))[name = string("layers_17_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_17_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553264512))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553789440))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553788864))))[name = string("layers_17_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_17_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553789760))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557988288))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557984128))))[name = string("layers_17_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_17_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557990400))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570585728))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570573376))))[name = string("layers_17_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_17_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570591936))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583187264))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583174912))))[name = string("layers_17_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_17_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583193472))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(595780608))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(595776448))))[name = string("layers_17_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_18_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(595782720))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(599981248))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(599977088))))[name = string("layers_18_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_18_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(599983360))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(600508288))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(600507712))))[name = string("layers_18_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_18_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(600508608))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(601033536))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(601032960))))[name = string("layers_18_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_18_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(601033856))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(605232384))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(605228224))))[name = string("layers_18_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_18_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(605234496))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(617829824))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(617817472))))[name = string("layers_18_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_18_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(617836032))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(630431360))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(630419008))))[name = string("layers_18_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_18_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(630437568))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(643024704))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(643020544))))[name = string("layers_18_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_19_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(643026816))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647225344))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647221184))))[name = string("layers_19_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_19_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647227456))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647752384))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647751808))))[name = string("layers_19_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_19_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647752704))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(660348032))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(660335680))))[name = string("layers_19_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_19_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(660354240))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(672949568))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(672937216))))[name = string("layers_19_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_19_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(672955776))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(685542912))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(685538752))))[name = string("layers_19_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_20_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(685545024))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(689743552))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(689739392))))[name = string("layers_20_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_20_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(689745664))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(690270592))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(690270016))))[name = string("layers_20_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_20_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(690270912))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(694469440))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(694465280))))[name = string("layers_20_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_20_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(694471552))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(707066880))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(707054528))))[name = string("layers_20_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_20_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(707073088))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(719660224))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(719656064))))[name = string("layers_20_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_21_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(719662336))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(723860864))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(723856704))))[name = string("layers_21_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_21_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(723862976))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724387904))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724387328))))[name = string("layers_21_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_21_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724388224))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(728586752))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(728582592))))[name = string("layers_21_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_21_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(728588864))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(741184192))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(741171840))))[name = string("layers_21_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_21_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(741190400))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(753785728))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(753773376))))[name = string("layers_21_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_21_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(753791936))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(766379072))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(766374912))))[name = string("layers_21_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_22_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(766381184))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(770579712))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(770575552))))[name = string("layers_22_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_22_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(770581824))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(771106752))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(771106176))))[name = string("layers_22_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_22_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(771107072))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783702400))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783690048))))[name = string("layers_22_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_22_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783708608))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(796303936))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(796291584))))[name = string("layers_22_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_22_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(796310144))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808897280))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808893120))))[name = string("layers_22_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_23_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808899392))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813097920))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813093760))))[name = string("layers_23_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_23_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813100032))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813624960))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813624384))))[name = string("layers_23_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_23_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813625280))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(817823808))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(817819648))))[name = string("layers_23_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_23_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(817825920))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(830421248))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(830408896))))[name = string("layers_23_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_23_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(830427456))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(843022784))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(843010432))))[name = string("layers_23_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_23_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(843028992))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855616128))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855611968))))[name = string("layers_23_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_24_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855618240))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(859816768))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(859812608))))[name = string("layers_24_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_24_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(859818880))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(860343808))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(860343232))))[name = string("layers_24_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_24_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(860344128))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(864542656))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(864538496))))[name = string("layers_24_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_24_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(864544768))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(877140096))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(877127744))))[name = string("layers_24_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_24_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(877146304))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889741632))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889729280))))[name = string("layers_24_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_24_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889747840))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(902334976))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(902330816))))[name = string("layers_24_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_25_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(902337088))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(906535616))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(906531456))))[name = string("layers_25_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_25_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(906537728))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(907062656))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(907062080))))[name = string("layers_25_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_25_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(907062976))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(911261504))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(911257344))))[name = string("layers_25_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_25_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(911263616))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(923858944))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(923846592))))[name = string("layers_25_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_25_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(923865152))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936460480))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936448128))))[name = string("layers_25_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_26_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936466688))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(940665216))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(940661056))))[name = string("layers_26_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_26_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(940667328))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(941192256))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(941191680))))[name = string("layers_26_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_26_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(941192576))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(945391104))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(945386944))))[name = string("layers_26_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_26_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(945393216))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(957988544))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(957976192))))[name = string("layers_26_mlp_gate_proj_weight_cast_fp16")];
            int32 gather_0_cast_uint16_to_int32 = const()[name = string("gather_0_cast_uint16_to_int32"), val = int32(1)];
            tensor<int32, [1]> cache_position_end = add(x = position_id, y = gather_0_cast_uint16_to_int32)[name = string("cache_position_end")];
            fp16 fill_like_0_value_0_to_fp16 = const()[name = string("fill_like_0_value_0_to_fp16"), val = fp16(0x1p+0)];
            tensor<fp16, [1]> fill_like_0_cast_fp16 = fill_like(ref_tensor = position_index_seed, value = fill_like_0_value_0_to_fp16)[name = string("fill_like_0_cast_fp16")];
            int32 var_831 = const()[name = string("op_831"), val = int32(0)];
            bool var_833_exclusive_0 = const()[name = string("op_833_exclusive_0"), val = bool(false)];
            bool var_833_reverse_0 = const()[name = string("op_833_reverse_0"), val = bool(false)];
            tensor<fp16, [1]> var_833_cast_fp16 = cumsum(axis = var_831, exclusive = var_833_exclusive_0, reverse = var_833_reverse_0, x = fill_like_0_cast_fp16)[name = string("op_833_cast_fp16")];
            fp16 var_835_promoted_to_fp16 = const()[name = string("op_835_promoted_to_fp16"), val = fp16(0x1p+0)];
            tensor<fp16, [1]> position_offsets_cast_fp16 = sub(x = var_833_cast_fp16, y = var_835_promoted_to_fp16)[name = string("position_offsets_cast_fp16")];
            tensor<int32, [1]> var_838_axes_0 = const()[name = string("op_838_axes_0"), val = tensor<int32, [1]>([0])];
            tensor<fp16, [1, 1]> var_838_cast_fp16 = expand_dims(axes = var_838_axes_0, x = position_offsets_cast_fp16)[name = string("op_838_cast_fp16")];
            string position_id_promoted_to_fp16_dtype_0 = const()[name = string("position_id_promoted_to_fp16_dtype_0"), val = string("fp16")];
            tensor<fp16, [1]> position_id_to_fp16 = cast(dtype = position_id_promoted_to_fp16_dtype_0, x = position_id)[name = string("cast_7")];
            tensor<fp16, [1, 1]> position_ids_1_cast_fp16 = add(x = var_838_cast_fp16, y = position_id_to_fp16)[name = string("position_ids_1_cast_fp16")];
            string position_ids_dtype_0 = const()[name = string("position_ids_dtype_0"), val = string("int32")];
            int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)];
            tensor<int32, [1, 1]> position_ids_1_cast_fp16_to_int32 = cast(dtype = position_ids_dtype_0, x = position_ids_1_cast_fp16)[name = string("cast_6")];
            tensor<bool, [1, 1]> greater_equal_0 = greater_equal(x = position_ids_1_cast_fp16_to_int32, y = greater_equal_0_y_0)[name = string("greater_equal_0")];
            int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(32768)];
            tensor<int32, [1, 1]> add_0 = add(x = position_ids_1_cast_fp16_to_int32, y = slice_by_index_0)[name = string("add_0")];
            tensor<int32, [1, 1]> select_0 = select(a = position_ids_1_cast_fp16_to_int32, b = add_0, cond = greater_equal_0)[name = string("select_0")];
            tensor<fp16, [32768, 128]> rope_emb_cos_cached_to_fp16 = const()[name = string("rope_emb_cos_cached_to_fp16"), val = tensor<fp16, [32768, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(957994752)))];
            int32 cos_1_batch_dims_0 = const()[name = string("cos_1_batch_dims_0"), val = int32(0)];
            bool cos_1_validate_indices_0 = const()[name = string("cos_1_validate_indices_0"), val = bool(false)];
            int32 greater_equal_2_y_0 = const()[name = string("greater_equal_2_y_0"), val = int32(0)];
            tensor<bool, [1, 1]> greater_equal_2 = greater_equal(x = select_0, y = greater_equal_2_y_0)[name = string("greater_equal_2")];
            int32 slice_by_index_2 = const()[name = string("slice_by_index_2"), val = int32(32768)];
            tensor<int32, [1, 1]> add_2 = add(x = select_0, y = slice_by_index_2)[name = string("add_2")];
            tensor<int32, [1, 1]> select_2 = select(a = select_0, b = add_2, cond = greater_equal_2)[name = string("select_2")];
            int32 cos_1_cast_fp16_axis_1 = const()[name = string("cos_1_cast_fp16_axis_1"), val = int32(0)];
            tensor<fp16, [1, 1, 128]> cos_1_cast_fp16 = gather(axis = cos_1_cast_fp16_axis_1, batch_dims = cos_1_batch_dims_0, indices = select_2, validate_indices = cos_1_validate_indices_0, x = rope_emb_cos_cached_to_fp16)[name = string("cos_1_cast_fp16")];
            tensor<fp16, [32768, 128]> rope_emb_sin_cached_to_fp16 = const()[name = string("rope_emb_sin_cached_to_fp16"), val = tensor<fp16, [32768, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(966383424)))];
            int32 sin_1_batch_dims_0 = const()[name = string("sin_1_batch_dims_0"), val = int32(0)];
            bool sin_1_validate_indices_0 = const()[name = string("sin_1_validate_indices_0"), val = bool(false)];
            int32 sin_1_cast_fp16_axis_1 = const()[name = string("sin_1_cast_fp16_axis_1"), val = int32(0)];
            tensor<fp16, [1, 1, 128]> sin_1_cast_fp16 = gather(axis = sin_1_cast_fp16_axis_1, batch_dims = sin_1_batch_dims_0, indices = select_2, validate_indices = sin_1_validate_indices_0, x = rope_emb_sin_cached_to_fp16)[name = string("sin_1_cast_fp16")];
            tensor<int32, [3]> var_857_perm_0 = const()[name = string("op_857_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<int32, [1]> var_859_axes_0 = const()[name = string("op_859_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 128, 1]> var_857_cast_fp16 = transpose(perm = var_857_perm_0, x = cos_1_cast_fp16)[name = string("transpose_173")];
            tensor<fp16, [1, 1, 128, 1]> var_859_cast_fp16 = expand_dims(axes = var_859_axes_0, x = var_857_cast_fp16)[name = string("op_859_cast_fp16")];
            tensor<int32, [3]> var_864_perm_0 = const()[name = string("op_864_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<int32, [1]> var_866_axes_0 = const()[name = string("op_866_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 128, 1]> var_864_cast_fp16 = transpose(perm = var_864_perm_0, x = sin_1_cast_fp16)[name = string("transpose_172")];
            tensor<fp16, [1, 1, 128, 1]> var_866_cast_fp16 = expand_dims(axes = var_866_axes_0, x = var_864_cast_fp16)[name = string("op_866_cast_fp16")];
            tensor<int32, [1]> var_885_axes_0 = const()[name = string("op_885_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1, 1, 1]> var_885 = expand_dims(axes = var_885_axes_0, x = position_ids_1_cast_fp16_to_int32)[name = string("op_885")];
            tensor<int32, [1, 1, 2048]> var_878 = const()[name = string("op_878"), val = tensor<int32, [1, 1, 2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(974772096)))];
            tensor<bool, [1, 1, 2048]> var_886 = greater(x = var_878, y = var_885)[name = string("op_886")];
            string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")];
            tensor<int32, [1]> var_893_axes_0 = const()[name = string("op_893_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 2048]> var_886_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = var_886)[name = string("cast_5")];
            tensor<fp16, [1, 1, 1, 2048]> var_893_cast_fp16 = expand_dims(axes = var_893_axes_0, x = var_886_to_fp16)[name = string("op_893_cast_fp16")];
            tensor<int32, [4]> mask_perm_0 = const()[name = string("mask_perm_0"), val = tensor<int32, [4]>([0, 1, -1, -2])];
            fp16 var_897_promoted_to_fp16 = const()[name = string("op_897_promoted_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1, 2048, 1]> mask_cast_fp16 = transpose(perm = mask_perm_0, x = var_893_cast_fp16)[name = string("transpose_171")];
            tensor<bool, [1, 1, 2048, 1]> var_898_cast_fp16 = equal(x = mask_cast_fp16, y = var_897_promoted_to_fp16)[name = string("op_898_cast_fp16")];
            fp16 var_899_to_fp16 = const()[name = string("op_899_to_fp16"), val = fp16(-inf)];
            tensor<fp16, [1, 1, 2048, 1]> attn_mask_1_cast_fp16 = select(a = mask_cast_fp16, b = var_899_to_fp16, cond = var_898_cast_fp16)[name = string("attn_mask_1_cast_fp16")];
            string inputs_embeds_to_fp16_dtype_0 = const()[name = string("inputs_embeds_to_fp16_dtype_0"), val = string("fp16")];
            fp16 const_2_promoted_to_fp16 = const()[name = string("const_2_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> inputs_embeds_to_fp16 = cast(dtype = inputs_embeds_to_fp16_dtype_0, x = inputs_embeds)[name = string("cast_4")];
            tensor<fp16, [1, 2048, 1, 1]> var_909_cast_fp16 = mul(x = inputs_embeds_to_fp16, y = const_2_promoted_to_fp16)[name = string("op_909_cast_fp16")];
            int32 var_907 = const()[name = string("op_907"), val = int32(1)];
            bool doubled_1_interleave_0 = const()[name = string("doubled_1_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_1_cast_fp16 = concat(axis = var_907, interleave = doubled_1_interleave_0, values = (inputs_embeds_to_fp16, var_909_cast_fp16))[name = string("doubled_1_cast_fp16")];
            tensor<int32, [1]> out_1_axes_0 = const()[name = string("out_1_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_1_gamma_0_to_fp16 = const()[name = string("out_1_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(974780352)))];
            fp16 var_919_to_fp16 = const()[name = string("op_919_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_1_cast_fp16 = layer_norm(axes = out_1_axes_0, epsilon = var_919_to_fp16, gamma = out_1_gamma_0_to_fp16, x = doubled_1_cast_fp16)[name = string("out_1_cast_fp16")];
            tensor<int32, [2]> var_930_split_sizes_0 = const()[name = string("op_930_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_930_axis_0 = const()[name = string("op_930_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_930_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_930_cast_fp16_1 = split(axis = var_930_axis_0, split_sizes = var_930_split_sizes_0, x = out_1_cast_fp16)[name = string("op_930_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_0_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(974788608)))];
            tensor<int32, [2]> query_states_1_strides_0 = const()[name = string("query_states_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_1_pad_type_0 = const()[name = string("query_states_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_1_pad_0 = const()[name = string("query_states_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_1_dilations_0 = const()[name = string("query_states_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_1_groups_0 = const()[name = string("query_states_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_1_cast_fp16 = conv(dilations = query_states_1_dilations_0, groups = query_states_1_groups_0, pad = query_states_1_pad_0, pad_type = query_states_1_pad_type_0, strides = query_states_1_strides_0, weight = layers_0_self_attn_q_proj_weight_to_fp16, x = var_930_cast_fp16_0)[name = string("query_states_1_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_0_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(983177280)))];
            tensor<int32, [2]> key_states_1_strides_0 = const()[name = string("key_states_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_1_pad_type_0 = const()[name = string("key_states_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_1_pad_0 = const()[name = string("key_states_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_1_dilations_0 = const()[name = string("key_states_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_1_groups_0 = const()[name = string("key_states_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_1_cast_fp16 = conv(dilations = key_states_1_dilations_0, groups = key_states_1_groups_0, pad = key_states_1_pad_0, pad_type = key_states_1_pad_type_0, strides = key_states_1_strides_0, weight = layers_0_self_attn_k_proj_weight_to_fp16, x = var_930_cast_fp16_0)[name = string("key_states_1_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_0_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(984225920)))];
            tensor<int32, [2]> value_states_1_strides_0 = const()[name = string("value_states_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_1_pad_type_0 = const()[name = string("value_states_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_1_pad_0 = const()[name = string("value_states_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_1_dilations_0 = const()[name = string("value_states_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_1_groups_0 = const()[name = string("value_states_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_1_cast_fp16 = conv(dilations = value_states_1_dilations_0, groups = value_states_1_groups_0, pad = value_states_1_pad_0, pad_type = value_states_1_pad_type_0, strides = value_states_1_strides_0, weight = layers_0_self_attn_v_proj_weight_to_fp16, x = var_930_cast_fp16_0)[name = string("value_states_1_cast_fp16")];
            tensor<int32, [4]> concat_0x = const()[name = string("concat_0x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_1_cast_fp16 = reshape(shape = concat_0x, x = query_states_1_cast_fp16)[name = string("x_1_cast_fp16")];
            tensor<int32, [4]> concat_1x = const()[name = string("concat_1x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_987_cast_fp16 = reshape(shape = concat_1x, x = key_states_1_cast_fp16)[name = string("op_987_cast_fp16")];
            tensor<int32, [4]> concat_2x = const()[name = string("concat_2x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_994_cast_fp16 = reshape(shape = concat_2x, x = value_states_1_cast_fp16)[name = string("op_994_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_998_cast_fp16 = mul(x = x_1_cast_fp16, y = var_859_cast_fp16)[name = string("op_998_cast_fp16")];
            tensor<int32, [2]> var_999_split_sizes_0 = const()[name = string("op_999_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_999_axis_0 = const()[name = string("op_999_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_999_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_999_cast_fp16_1 = split(axis = var_999_axis_0, split_sizes = var_999_split_sizes_0, x = x_1_cast_fp16)[name = string("op_999_cast_fp16")];
            fp16 const_4_promoted_to_fp16 = const()[name = string("const_4_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_1001_cast_fp16 = mul(x = var_999_cast_fp16_1, y = const_4_promoted_to_fp16)[name = string("op_1001_cast_fp16")];
            int32 var_1003 = const()[name = string("op_1003"), val = int32(-2)];
            bool var_1004_interleave_0 = const()[name = string("op_1004_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_1004_cast_fp16 = concat(axis = var_1003, interleave = var_1004_interleave_0, values = (var_1001_cast_fp16, var_999_cast_fp16_0))[name = string("op_1004_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_1005_cast_fp16 = mul(x = var_1004_cast_fp16, y = var_866_cast_fp16)[name = string("op_1005_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_3_cast_fp16 = add(x = var_998_cast_fp16, y = var_1005_cast_fp16)[name = string("query_states_3_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_1011_cast_fp16 = mul(x = var_987_cast_fp16, y = var_859_cast_fp16)[name = string("op_1011_cast_fp16")];
            tensor<int32, [2]> var_1012_split_sizes_0 = const()[name = string("op_1012_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1012_axis_0 = const()[name = string("op_1012_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_1012_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_1012_cast_fp16_1 = split(axis = var_1012_axis_0, split_sizes = var_1012_split_sizes_0, x = var_987_cast_fp16)[name = string("op_1012_cast_fp16")];
            fp16 const_5_promoted_to_fp16 = const()[name = string("const_5_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_1014_cast_fp16 = mul(x = var_1012_cast_fp16_1, y = const_5_promoted_to_fp16)[name = string("op_1014_cast_fp16")];
            int32 var_1016 = const()[name = string("op_1016"), val = int32(-2)];
            bool var_1017_interleave_0 = const()[name = string("op_1017_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_1017_cast_fp16 = concat(axis = var_1016, interleave = var_1017_interleave_0, values = (var_1014_cast_fp16, var_1012_cast_fp16_0))[name = string("op_1017_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_1018_cast_fp16 = mul(x = var_1017_cast_fp16, y = var_866_cast_fp16)[name = string("op_1018_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_5_cast_fp16 = add(x = var_1011_cast_fp16, y = var_1018_cast_fp16)[name = string("key_states_5_cast_fp16")];
            tensor<fp16, [28, 2, 2048, 128]> read_state_0 = read_state(input = key_cache)[name = string("read_state_0")];
            tensor<int32, [1]> expand_dims_0 = const()[name = string("expand_dims_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_1 = const()[name = string("expand_dims_1"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_3 = const()[name = string("expand_dims_3"), val = tensor<int32, [1]>([0])];
            int32 concat_5_axis_0 = const()[name = string("concat_5_axis_0"), val = int32(0)];
            bool concat_5_interleave_0 = const()[name = string("concat_5_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_5 = concat(axis = concat_5_axis_0, interleave = concat_5_interleave_0, values = (expand_dims_0, expand_dims_1, position_id, expand_dims_3))[name = string("concat_5")];
            tensor<int32, [1]> expand_dims_4 = const()[name = string("expand_dims_4"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> concat_6_values1_0 = const()[name = string("concat_6_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_6_values3_0 = const()[name = string("concat_6_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_6_axis_0 = const()[name = string("concat_6_axis_0"), val = int32(0)];
            bool concat_6_interleave_0 = const()[name = string("concat_6_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_6 = concat(axis = concat_6_axis_0, interleave = concat_6_interleave_0, values = (expand_dims_4, concat_6_values1_0, cache_position_end, concat_6_values3_0))[name = string("concat_6")];
            tensor<int32, [4]> key_states_7_perm_0 = const()[name = string("key_states_7_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_1_stride_0 = const()[name = string("key_cache_internal_tensor_assign_1_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_1_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_1_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_1_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_1_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_1_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_1_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_7_cast_fp16 = transpose(perm = key_states_7_perm_0, x = key_states_5_cast_fp16)[name = string("transpose_170")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_1_cast_fp16 = slice_update(begin = concat_5, begin_mask = key_cache_internal_tensor_assign_1_begin_mask_0, end = concat_6, end_mask = key_cache_internal_tensor_assign_1_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_1_squeeze_mask_0, stride = key_cache_internal_tensor_assign_1_stride_0, update = key_states_7_cast_fp16, x = read_state_0)[name = string("key_cache_internal_tensor_assign_1_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_1_cast_fp16, input = key_cache)[name = string("coreml_update_state_56_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_56 = read_state(input = key_cache)[name = string("coreml_update_state_56")];
            tensor<fp16, [28, 2, 2048, 128]> read_state_1 = read_state(input = value_cache)[name = string("read_state_1")];
            tensor<int32, [4]> value_states_3_perm_0 = const()[name = string("value_states_3_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_1_stride_0 = const()[name = string("value_cache_internal_tensor_assign_1_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_1_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_1_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_1_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_1_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_1_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_1_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_3_cast_fp16 = transpose(perm = value_states_3_perm_0, x = var_994_cast_fp16)[name = string("transpose_169")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_1_cast_fp16 = slice_update(begin = concat_5, begin_mask = value_cache_internal_tensor_assign_1_begin_mask_0, end = concat_6, end_mask = value_cache_internal_tensor_assign_1_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_1_squeeze_mask_0, stride = value_cache_internal_tensor_assign_1_stride_0, update = value_states_3_cast_fp16, x = read_state_1)[name = string("value_cache_internal_tensor_assign_1_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_1_cast_fp16, input = value_cache)[name = string("coreml_update_state_57_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_57 = read_state(input = value_cache)[name = string("coreml_update_state_57")];
            tensor<int32, [4]> var_1088_begin_0 = const()[name = string("op_1088_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> var_1088_end_0 = const()[name = string("op_1088_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_1088_end_mask_0 = const()[name = string("op_1088_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1088_cast_fp16 = slice_by_index(begin = var_1088_begin_0, end = var_1088_end_0, end_mask = var_1088_end_mask_0, x = coreml_update_state_56)[name = string("op_1088_cast_fp16")];
            tensor<int32, [2]> tile_0 = const()[name = string("tile_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1091_axis_0 = const()[name = string("op_1091_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1091_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1091_cast_fp16_1 = split(axis = var_1091_axis_0, split_sizes = tile_0, x = var_1088_cast_fp16)[name = string("op_1091_cast_fp16")];
            tensor<int32, [4]> var_1098_begin_0 = const()[name = string("op_1098_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> var_1098_end_0 = const()[name = string("op_1098_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_1098_end_mask_0 = const()[name = string("op_1098_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1098_cast_fp16 = slice_by_index(begin = var_1098_begin_0, end = var_1098_end_0, end_mask = var_1098_end_mask_0, x = coreml_update_state_57)[name = string("op_1098_cast_fp16")];
            tensor<int32, [2]> tile_1 = const()[name = string("tile_1"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1101_axis_0 = const()[name = string("op_1101_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1101_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1101_cast_fp16_1 = split(axis = var_1101_axis_0, split_sizes = tile_1, x = var_1098_cast_fp16)[name = string("op_1101_cast_fp16")];
            tensor<int32, [2]> var_1104_split_sizes_0 = const()[name = string("op_1104_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_1104_axis_0 = const()[name = string("op_1104_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_1104_0, tensor<fp16, [1, 8, 128, 1]> var_1104_1 = split(axis = var_1104_axis_0, split_sizes = var_1104_split_sizes_0, x = query_states_3_cast_fp16)[name = string("op_1104")];
            bool attn_weights_1_transpose_x_0 = const()[name = string("attn_weights_1_transpose_x_0"), val = bool(false)];
            bool attn_weights_1_transpose_y_0 = const()[name = string("attn_weights_1_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_1_cast_fp16 = matmul(transpose_x = attn_weights_1_transpose_x_0, transpose_y = attn_weights_1_transpose_y_0, x = var_1091_cast_fp16_0, y = var_1104_0)[name = string("attn_weights_1_cast_fp16")];
            fp16 var_1107_to_fp16 = const()[name = string("op_1107_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_3_cast_fp16 = mul(x = attn_weights_1_cast_fp16, y = var_1107_to_fp16)[name = string("attn_weights_3_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_5_cast_fp16 = add(x = attn_weights_3_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_5_cast_fp16")];
            int32 var_1111 = const()[name = string("op_1111"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_7_cast_fp16 = softmax(axis = var_1111, x = attn_weights_5_cast_fp16)[name = string("attn_weights_7_cast_fp16")];
            bool var_1117_transpose_x_1 = const()[name = string("op_1117_transpose_x_1"), val = bool(true)];
            bool var_1117_transpose_y_1 = const()[name = string("op_1117_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_1117_cast_fp16 = matmul(transpose_x = var_1117_transpose_x_1, transpose_y = var_1117_transpose_y_1, x = attn_weights_7_cast_fp16, y = var_1101_cast_fp16_0)[name = string("op_1117_cast_fp16")];
            bool attn_weights_9_transpose_x_0 = const()[name = string("attn_weights_9_transpose_x_0"), val = bool(false)];
            bool attn_weights_9_transpose_y_0 = const()[name = string("attn_weights_9_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_9_cast_fp16 = matmul(transpose_x = attn_weights_9_transpose_x_0, transpose_y = attn_weights_9_transpose_y_0, x = var_1091_cast_fp16_1, y = var_1104_1)[name = string("attn_weights_9_cast_fp16")];
            fp16 var_1119_to_fp16 = const()[name = string("op_1119_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_11_cast_fp16 = mul(x = attn_weights_9_cast_fp16, y = var_1119_to_fp16)[name = string("attn_weights_11_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_13_cast_fp16 = add(x = attn_weights_11_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_13_cast_fp16")];
            int32 var_1123 = const()[name = string("op_1123"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_15_cast_fp16 = softmax(axis = var_1123, x = attn_weights_13_cast_fp16)[name = string("attn_weights_15_cast_fp16")];
            bool attn_output_1_transpose_x_1 = const()[name = string("attn_output_1_transpose_x_1"), val = bool(true)];
            bool attn_output_1_transpose_y_1 = const()[name = string("attn_output_1_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_1_cast_fp16 = matmul(transpose_x = attn_output_1_transpose_x_1, transpose_y = attn_output_1_transpose_y_1, x = attn_weights_15_cast_fp16, y = var_1101_cast_fp16_1)[name = string("attn_output_1_cast_fp16")];
            int32 var_1131 = const()[name = string("op_1131"), val = int32(1)];
            bool attn_output_3_interleave_0 = const()[name = string("attn_output_3_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_3_cast_fp16 = concat(axis = var_1131, interleave = attn_output_3_interleave_0, values = (var_1117_cast_fp16, attn_output_1_cast_fp16))[name = string("attn_output_3_cast_fp16")];
            tensor<int32, [4]> var_1135_perm_0 = const()[name = string("op_1135_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_11x = const()[name = string("concat_11x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_1135_cast_fp16 = transpose(perm = var_1135_perm_0, x = attn_output_3_cast_fp16)[name = string("transpose_168")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_7_cast_fp16 = reshape(shape = concat_11x, x = var_1135_cast_fp16)[name = string("attn_output_7_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_0_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(985274560)))];
            tensor<int32, [2]> hidden_states_3_strides_0 = const()[name = string("hidden_states_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_3_pad_type_0 = const()[name = string("hidden_states_3_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_3_pad_0 = const()[name = string("hidden_states_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_3_dilations_0 = const()[name = string("hidden_states_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_3_groups_0 = const()[name = string("hidden_states_3_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_3_cast_fp16 = conv(dilations = hidden_states_3_dilations_0, groups = hidden_states_3_groups_0, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = hidden_states_3_strides_0, weight = layers_0_self_attn_o_proj_weight_to_fp16, x = attn_output_7_cast_fp16)[name = string("hidden_states_3_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_5_cast_fp16 = add(x = inputs_embeds_to_fp16, y = hidden_states_3_cast_fp16)[name = string("hidden_states_5_cast_fp16")];
            fp16 const_10_promoted_to_fp16 = const()[name = string("const_10_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_1168_cast_fp16 = mul(x = hidden_states_5_cast_fp16, y = const_10_promoted_to_fp16)[name = string("op_1168_cast_fp16")];
            int32 var_1166 = const()[name = string("op_1166"), val = int32(1)];
            bool doubled_5_interleave_0 = const()[name = string("doubled_5_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_5_cast_fp16 = concat(axis = var_1166, interleave = doubled_5_interleave_0, values = (hidden_states_5_cast_fp16, var_1168_cast_fp16))[name = string("doubled_5_cast_fp16")];
            tensor<int32, [1]> out_3_axes_0 = const()[name = string("out_3_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_3_gamma_0_to_fp16 = const()[name = string("out_3_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(993663232)))];
            fp16 var_1178_to_fp16 = const()[name = string("op_1178_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_3_cast_fp16 = layer_norm(axes = out_3_axes_0, epsilon = var_1178_to_fp16, gamma = out_3_gamma_0_to_fp16, x = doubled_5_cast_fp16)[name = string("out_3_cast_fp16")];
            tensor<int32, [2]> var_1189_split_sizes_0 = const()[name = string("op_1189_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1189_axis_0 = const()[name = string("op_1189_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_1189_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_1189_cast_fp16_1 = split(axis = var_1189_axis_0, split_sizes = var_1189_split_sizes_0, x = out_3_cast_fp16)[name = string("op_1189_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_0_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_0_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(993671488)))];
            tensor<int32, [2]> input_1_strides_0 = const()[name = string("input_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_1_pad_type_0 = const()[name = string("input_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_1_pad_0 = const()[name = string("input_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_1_dilations_0 = const()[name = string("input_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_1_groups_0 = const()[name = string("input_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_1_cast_fp16 = conv(dilations = input_1_dilations_0, groups = input_1_groups_0, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = input_1_strides_0, weight = layers_0_mlp_gate_proj_weight_to_fp16, x = var_1189_cast_fp16_0)[name = string("input_1_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_1206_cast_fp16 = silu(x = input_1_cast_fp16)[name = string("op_1206_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_0_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_0_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1018837376)))];
            tensor<int32, [2]> var_1212_strides_0 = const()[name = string("op_1212_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_1212_pad_type_0 = const()[name = string("op_1212_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_1212_pad_0 = const()[name = string("op_1212_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_1212_dilations_0 = const()[name = string("op_1212_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1212_groups_0 = const()[name = string("op_1212_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_1212_cast_fp16 = conv(dilations = var_1212_dilations_0, groups = var_1212_groups_0, pad = var_1212_pad_0, pad_type = var_1212_pad_type_0, strides = var_1212_strides_0, weight = layers_0_mlp_up_proj_weight_to_fp16, x = var_1189_cast_fp16_0)[name = string("op_1212_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_9_cast_fp16 = mul(x = var_1206_cast_fp16, y = var_1212_cast_fp16)[name = string("x_9_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_0_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_0_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1044003264)))];
            tensor<int32, [2]> hidden_states_7_strides_0 = const()[name = string("hidden_states_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_7_pad_type_0 = const()[name = string("hidden_states_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_7_pad_0 = const()[name = string("hidden_states_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_7_dilations_0 = const()[name = string("hidden_states_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_7_groups_0 = const()[name = string("hidden_states_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_7_cast_fp16 = conv(dilations = hidden_states_7_dilations_0, groups = hidden_states_7_groups_0, pad = hidden_states_7_pad_0, pad_type = hidden_states_7_pad_type_0, strides = hidden_states_7_strides_0, weight = layers_0_mlp_down_proj_weight_to_fp16, x = x_9_cast_fp16)[name = string("hidden_states_7_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_9_cast_fp16 = add(x = hidden_states_5_cast_fp16, y = hidden_states_7_cast_fp16)[name = string("hidden_states_9_cast_fp16")];
            fp16 const_12_promoted_to_fp16 = const()[name = string("const_12_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_1230_cast_fp16 = mul(x = hidden_states_9_cast_fp16, y = const_12_promoted_to_fp16)[name = string("op_1230_cast_fp16")];
            int32 var_1228 = const()[name = string("op_1228"), val = int32(1)];
            bool doubled_9_interleave_0 = const()[name = string("doubled_9_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_9_cast_fp16 = concat(axis = var_1228, interleave = doubled_9_interleave_0, values = (hidden_states_9_cast_fp16, var_1230_cast_fp16))[name = string("doubled_9_cast_fp16")];
            tensor<int32, [1]> out_5_axes_0 = const()[name = string("out_5_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_5_gamma_0_to_fp16 = const()[name = string("out_5_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1069169152)))];
            fp16 var_1240_to_fp16 = const()[name = string("op_1240_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_5_cast_fp16 = layer_norm(axes = out_5_axes_0, epsilon = var_1240_to_fp16, gamma = out_5_gamma_0_to_fp16, x = doubled_9_cast_fp16)[name = string("out_5_cast_fp16")];
            tensor<int32, [2]> var_1251_split_sizes_0 = const()[name = string("op_1251_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1251_axis_0 = const()[name = string("op_1251_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_1251_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_1251_cast_fp16_1 = split(axis = var_1251_axis_0, split_sizes = var_1251_split_sizes_0, x = out_5_cast_fp16)[name = string("op_1251_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_1_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_1_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1069177408)))];
            tensor<int32, [2]> query_states_7_strides_0 = const()[name = string("query_states_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_7_pad_type_0 = const()[name = string("query_states_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_7_pad_0 = const()[name = string("query_states_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_7_dilations_0 = const()[name = string("query_states_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_7_groups_0 = const()[name = string("query_states_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_7_cast_fp16 = conv(dilations = query_states_7_dilations_0, groups = query_states_7_groups_0, pad = query_states_7_pad_0, pad_type = query_states_7_pad_type_0, strides = query_states_7_strides_0, weight = layers_1_self_attn_q_proj_weight_to_fp16, x = var_1251_cast_fp16_0)[name = string("query_states_7_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_1_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_1_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1077566080)))];
            tensor<int32, [2]> key_states_11_strides_0 = const()[name = string("key_states_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_11_pad_type_0 = const()[name = string("key_states_11_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_11_pad_0 = const()[name = string("key_states_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_11_dilations_0 = const()[name = string("key_states_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_11_groups_0 = const()[name = string("key_states_11_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_11_cast_fp16 = conv(dilations = key_states_11_dilations_0, groups = key_states_11_groups_0, pad = key_states_11_pad_0, pad_type = key_states_11_pad_type_0, strides = key_states_11_strides_0, weight = layers_1_self_attn_k_proj_weight_to_fp16, x = var_1251_cast_fp16_0)[name = string("key_states_11_cast_fp16")];
            tensor<int32, [2]> value_states_7_strides_0 = const()[name = string("value_states_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_7_pad_type_0 = const()[name = string("value_states_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_7_pad_0 = const()[name = string("value_states_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_7_dilations_0 = const()[name = string("value_states_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_7_groups_0 = const()[name = string("value_states_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_7_cast_fp16 = conv(dilations = value_states_7_dilations_0, groups = value_states_7_groups_0, pad = value_states_7_pad_0, pad_type = value_states_7_pad_type_0, strides = value_states_7_strides_0, weight = layers_1_self_attn_v_proj_weight_cast_fp16, x = var_1251_cast_fp16_0)[name = string("value_states_7_cast_fp16")];
            tensor<int32, [4]> concat_12x = const()[name = string("concat_12x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_11_cast_fp16 = reshape(shape = concat_12x, x = query_states_7_cast_fp16)[name = string("x_11_cast_fp16")];
            tensor<int32, [4]> concat_13x = const()[name = string("concat_13x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_1308_cast_fp16 = reshape(shape = concat_13x, x = key_states_11_cast_fp16)[name = string("op_1308_cast_fp16")];
            tensor<int32, [4]> concat_14x = const()[name = string("concat_14x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_1315_cast_fp16 = reshape(shape = concat_14x, x = value_states_7_cast_fp16)[name = string("op_1315_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_1319_cast_fp16 = mul(x = x_11_cast_fp16, y = var_859_cast_fp16)[name = string("op_1319_cast_fp16")];
            tensor<int32, [2]> var_1320_split_sizes_0 = const()[name = string("op_1320_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1320_axis_0 = const()[name = string("op_1320_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_1320_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_1320_cast_fp16_1 = split(axis = var_1320_axis_0, split_sizes = var_1320_split_sizes_0, x = x_11_cast_fp16)[name = string("op_1320_cast_fp16")];
            fp16 const_14_promoted_to_fp16 = const()[name = string("const_14_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_1322_cast_fp16 = mul(x = var_1320_cast_fp16_1, y = const_14_promoted_to_fp16)[name = string("op_1322_cast_fp16")];
            int32 var_1324 = const()[name = string("op_1324"), val = int32(-2)];
            bool var_1325_interleave_0 = const()[name = string("op_1325_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_1325_cast_fp16 = concat(axis = var_1324, interleave = var_1325_interleave_0, values = (var_1322_cast_fp16, var_1320_cast_fp16_0))[name = string("op_1325_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_1326_cast_fp16 = mul(x = var_1325_cast_fp16, y = var_866_cast_fp16)[name = string("op_1326_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_9_cast_fp16 = add(x = var_1319_cast_fp16, y = var_1326_cast_fp16)[name = string("query_states_9_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_1332_cast_fp16 = mul(x = var_1308_cast_fp16, y = var_859_cast_fp16)[name = string("op_1332_cast_fp16")];
            tensor<int32, [2]> var_1333_split_sizes_0 = const()[name = string("op_1333_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1333_axis_0 = const()[name = string("op_1333_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_1333_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_1333_cast_fp16_1 = split(axis = var_1333_axis_0, split_sizes = var_1333_split_sizes_0, x = var_1308_cast_fp16)[name = string("op_1333_cast_fp16")];
            fp16 const_15_promoted_to_fp16 = const()[name = string("const_15_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_1335_cast_fp16 = mul(x = var_1333_cast_fp16_1, y = const_15_promoted_to_fp16)[name = string("op_1335_cast_fp16")];
            int32 var_1337 = const()[name = string("op_1337"), val = int32(-2)];
            bool var_1338_interleave_0 = const()[name = string("op_1338_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_1338_cast_fp16 = concat(axis = var_1337, interleave = var_1338_interleave_0, values = (var_1335_cast_fp16, var_1333_cast_fp16_0))[name = string("op_1338_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_1339_cast_fp16 = mul(x = var_1338_cast_fp16, y = var_866_cast_fp16)[name = string("op_1339_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_15_cast_fp16 = add(x = var_1332_cast_fp16, y = var_1339_cast_fp16)[name = string("key_states_15_cast_fp16")];
            tensor<int32, [1]> expand_dims_12 = const()[name = string("expand_dims_12"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> expand_dims_13 = const()[name = string("expand_dims_13"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_15 = const()[name = string("expand_dims_15"), val = tensor<int32, [1]>([0])];
            int32 concat_17_axis_0 = const()[name = string("concat_17_axis_0"), val = int32(0)];
            bool concat_17_interleave_0 = const()[name = string("concat_17_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_17 = concat(axis = concat_17_axis_0, interleave = concat_17_interleave_0, values = (expand_dims_12, expand_dims_13, position_id, expand_dims_15))[name = string("concat_17")];
            tensor<int32, [1]> expand_dims_16 = const()[name = string("expand_dims_16"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> concat_18_values1_0 = const()[name = string("concat_18_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_18_values3_0 = const()[name = string("concat_18_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_18_axis_0 = const()[name = string("concat_18_axis_0"), val = int32(0)];
            bool concat_18_interleave_0 = const()[name = string("concat_18_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_18 = concat(axis = concat_18_axis_0, interleave = concat_18_interleave_0, values = (expand_dims_16, concat_18_values1_0, cache_position_end, concat_18_values3_0))[name = string("concat_18")];
            tensor<int32, [4]> key_states_17_perm_0 = const()[name = string("key_states_17_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_2_stride_0 = const()[name = string("key_cache_internal_tensor_assign_2_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_2_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_2_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_2_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_2_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_2_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_2_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_17_cast_fp16 = transpose(perm = key_states_17_perm_0, x = key_states_15_cast_fp16)[name = string("transpose_167")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_2_cast_fp16 = slice_update(begin = concat_17, begin_mask = key_cache_internal_tensor_assign_2_begin_mask_0, end = concat_18, end_mask = key_cache_internal_tensor_assign_2_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_2_squeeze_mask_0, stride = key_cache_internal_tensor_assign_2_stride_0, update = key_states_17_cast_fp16, x = coreml_update_state_56)[name = string("key_cache_internal_tensor_assign_2_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_2_cast_fp16, input = key_cache)[name = string("coreml_update_state_58_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_58 = read_state(input = key_cache)[name = string("coreml_update_state_58")];
            tensor<int32, [4]> value_states_9_perm_0 = const()[name = string("value_states_9_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_2_stride_0 = const()[name = string("value_cache_internal_tensor_assign_2_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_2_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_2_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_2_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_2_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_2_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_2_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_9_cast_fp16 = transpose(perm = value_states_9_perm_0, x = var_1315_cast_fp16)[name = string("transpose_166")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_2_cast_fp16 = slice_update(begin = concat_17, begin_mask = value_cache_internal_tensor_assign_2_begin_mask_0, end = concat_18, end_mask = value_cache_internal_tensor_assign_2_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_2_squeeze_mask_0, stride = value_cache_internal_tensor_assign_2_stride_0, update = value_states_9_cast_fp16, x = coreml_update_state_57)[name = string("value_cache_internal_tensor_assign_2_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_2_cast_fp16, input = value_cache)[name = string("coreml_update_state_59_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_59 = read_state(input = value_cache)[name = string("coreml_update_state_59")];
            tensor<int32, [4]> var_1409_begin_0 = const()[name = string("op_1409_begin_0"), val = tensor<int32, [4]>([1, 0, 0, 0])];
            tensor<int32, [4]> var_1409_end_0 = const()[name = string("op_1409_end_0"), val = tensor<int32, [4]>([2, 2, 2048, 128])];
            tensor<bool, [4]> var_1409_end_mask_0 = const()[name = string("op_1409_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1409_cast_fp16 = slice_by_index(begin = var_1409_begin_0, end = var_1409_end_0, end_mask = var_1409_end_mask_0, x = coreml_update_state_58)[name = string("op_1409_cast_fp16")];
            tensor<int32, [2]> tile_2 = const()[name = string("tile_2"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1412_axis_0 = const()[name = string("op_1412_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1412_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1412_cast_fp16_1 = split(axis = var_1412_axis_0, split_sizes = tile_2, x = var_1409_cast_fp16)[name = string("op_1412_cast_fp16")];
            tensor<int32, [4]> var_1419_begin_0 = const()[name = string("op_1419_begin_0"), val = tensor<int32, [4]>([1, 0, 0, 0])];
            tensor<int32, [4]> var_1419_end_0 = const()[name = string("op_1419_end_0"), val = tensor<int32, [4]>([2, 2, 2048, 128])];
            tensor<bool, [4]> var_1419_end_mask_0 = const()[name = string("op_1419_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1419_cast_fp16 = slice_by_index(begin = var_1419_begin_0, end = var_1419_end_0, end_mask = var_1419_end_mask_0, x = coreml_update_state_59)[name = string("op_1419_cast_fp16")];
            tensor<int32, [2]> tile_3 = const()[name = string("tile_3"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1422_axis_0 = const()[name = string("op_1422_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1422_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1422_cast_fp16_1 = split(axis = var_1422_axis_0, split_sizes = tile_3, x = var_1419_cast_fp16)[name = string("op_1422_cast_fp16")];
            tensor<int32, [2]> var_1425_split_sizes_0 = const()[name = string("op_1425_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_1425_axis_0 = const()[name = string("op_1425_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_1425_0, tensor<fp16, [1, 8, 128, 1]> var_1425_1 = split(axis = var_1425_axis_0, split_sizes = var_1425_split_sizes_0, x = query_states_9_cast_fp16)[name = string("op_1425")];
            bool attn_weights_17_transpose_x_0 = const()[name = string("attn_weights_17_transpose_x_0"), val = bool(false)];
            bool attn_weights_17_transpose_y_0 = const()[name = string("attn_weights_17_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_17_cast_fp16 = matmul(transpose_x = attn_weights_17_transpose_x_0, transpose_y = attn_weights_17_transpose_y_0, x = var_1412_cast_fp16_0, y = var_1425_0)[name = string("attn_weights_17_cast_fp16")];
            fp16 var_1428_to_fp16 = const()[name = string("op_1428_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_19_cast_fp16 = mul(x = attn_weights_17_cast_fp16, y = var_1428_to_fp16)[name = string("attn_weights_19_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_21_cast_fp16 = add(x = attn_weights_19_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_21_cast_fp16")];
            int32 var_1432 = const()[name = string("op_1432"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_23_cast_fp16 = softmax(axis = var_1432, x = attn_weights_21_cast_fp16)[name = string("attn_weights_23_cast_fp16")];
            bool var_1438_transpose_x_1 = const()[name = string("op_1438_transpose_x_1"), val = bool(true)];
            bool var_1438_transpose_y_1 = const()[name = string("op_1438_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_1438_cast_fp16 = matmul(transpose_x = var_1438_transpose_x_1, transpose_y = var_1438_transpose_y_1, x = attn_weights_23_cast_fp16, y = var_1422_cast_fp16_0)[name = string("op_1438_cast_fp16")];
            bool attn_weights_25_transpose_x_0 = const()[name = string("attn_weights_25_transpose_x_0"), val = bool(false)];
            bool attn_weights_25_transpose_y_0 = const()[name = string("attn_weights_25_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_25_cast_fp16 = matmul(transpose_x = attn_weights_25_transpose_x_0, transpose_y = attn_weights_25_transpose_y_0, x = var_1412_cast_fp16_1, y = var_1425_1)[name = string("attn_weights_25_cast_fp16")];
            fp16 var_1440_to_fp16 = const()[name = string("op_1440_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_27_cast_fp16 = mul(x = attn_weights_25_cast_fp16, y = var_1440_to_fp16)[name = string("attn_weights_27_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_29_cast_fp16 = add(x = attn_weights_27_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_29_cast_fp16")];
            int32 var_1444 = const()[name = string("op_1444"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_31_cast_fp16 = softmax(axis = var_1444, x = attn_weights_29_cast_fp16)[name = string("attn_weights_31_cast_fp16")];
            bool attn_output_9_transpose_x_1 = const()[name = string("attn_output_9_transpose_x_1"), val = bool(true)];
            bool attn_output_9_transpose_y_1 = const()[name = string("attn_output_9_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_9_cast_fp16 = matmul(transpose_x = attn_output_9_transpose_x_1, transpose_y = attn_output_9_transpose_y_1, x = attn_weights_31_cast_fp16, y = var_1422_cast_fp16_1)[name = string("attn_output_9_cast_fp16")];
            int32 var_1452 = const()[name = string("op_1452"), val = int32(1)];
            bool attn_output_11_interleave_0 = const()[name = string("attn_output_11_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_11_cast_fp16 = concat(axis = var_1452, interleave = attn_output_11_interleave_0, values = (var_1438_cast_fp16, attn_output_9_cast_fp16))[name = string("attn_output_11_cast_fp16")];
            tensor<int32, [4]> var_1456_perm_0 = const()[name = string("op_1456_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_23x = const()[name = string("concat_23x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_1456_cast_fp16 = transpose(perm = var_1456_perm_0, x = attn_output_11_cast_fp16)[name = string("transpose_165")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_15_cast_fp16 = reshape(shape = concat_23x, x = var_1456_cast_fp16)[name = string("attn_output_15_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_1_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_1_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1078614720)))];
            tensor<int32, [2]> hidden_states_13_strides_0 = const()[name = string("hidden_states_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_13_pad_type_0 = const()[name = string("hidden_states_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_13_pad_0 = const()[name = string("hidden_states_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_13_dilations_0 = const()[name = string("hidden_states_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_13_groups_0 = const()[name = string("hidden_states_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_13_cast_fp16 = conv(dilations = hidden_states_13_dilations_0, groups = hidden_states_13_groups_0, pad = hidden_states_13_pad_0, pad_type = hidden_states_13_pad_type_0, strides = hidden_states_13_strides_0, weight = layers_1_self_attn_o_proj_weight_to_fp16, x = attn_output_15_cast_fp16)[name = string("hidden_states_13_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_15_cast_fp16 = add(x = hidden_states_9_cast_fp16, y = hidden_states_13_cast_fp16)[name = string("hidden_states_15_cast_fp16")];
            fp16 const_20_promoted_to_fp16 = const()[name = string("const_20_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_1489_cast_fp16 = mul(x = hidden_states_15_cast_fp16, y = const_20_promoted_to_fp16)[name = string("op_1489_cast_fp16")];
            int32 var_1487 = const()[name = string("op_1487"), val = int32(1)];
            bool doubled_13_interleave_0 = const()[name = string("doubled_13_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_13_cast_fp16 = concat(axis = var_1487, interleave = doubled_13_interleave_0, values = (hidden_states_15_cast_fp16, var_1489_cast_fp16))[name = string("doubled_13_cast_fp16")];
            tensor<int32, [1]> out_7_axes_0 = const()[name = string("out_7_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_7_gamma_0_to_fp16 = const()[name = string("out_7_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1087003392)))];
            fp16 var_1499_to_fp16 = const()[name = string("op_1499_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_7_cast_fp16 = layer_norm(axes = out_7_axes_0, epsilon = var_1499_to_fp16, gamma = out_7_gamma_0_to_fp16, x = doubled_13_cast_fp16)[name = string("out_7_cast_fp16")];
            tensor<int32, [2]> var_1510_split_sizes_0 = const()[name = string("op_1510_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1510_axis_0 = const()[name = string("op_1510_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_1510_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_1510_cast_fp16_1 = split(axis = var_1510_axis_0, split_sizes = var_1510_split_sizes_0, x = out_7_cast_fp16)[name = string("op_1510_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_1_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_1_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1087011648)))];
            tensor<int32, [2]> input_3_strides_0 = const()[name = string("input_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_3_dilations_0 = const()[name = string("input_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_3_groups_0 = const()[name = string("input_3_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_3_cast_fp16 = conv(dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = layers_1_mlp_gate_proj_weight_to_fp16, x = var_1510_cast_fp16_0)[name = string("input_3_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_1527_cast_fp16 = silu(x = input_3_cast_fp16)[name = string("op_1527_cast_fp16")];
            tensor<int32, [2]> var_1533_strides_0 = const()[name = string("op_1533_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_1533_pad_type_0 = const()[name = string("op_1533_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_1533_pad_0 = const()[name = string("op_1533_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_1533_dilations_0 = const()[name = string("op_1533_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1533_groups_0 = const()[name = string("op_1533_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_1533_cast_fp16 = conv(dilations = var_1533_dilations_0, groups = var_1533_groups_0, pad = var_1533_pad_0, pad_type = var_1533_pad_type_0, strides = var_1533_strides_0, weight = layers_1_mlp_up_proj_weight_cast_fp16, x = var_1510_cast_fp16_0)[name = string("op_1533_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_19_cast_fp16 = mul(x = var_1527_cast_fp16, y = var_1533_cast_fp16)[name = string("x_19_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_1_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_1_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1112177536)))];
            tensor<int32, [2]> hidden_states_17_strides_0 = const()[name = string("hidden_states_17_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_17_pad_type_0 = const()[name = string("hidden_states_17_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_17_pad_0 = const()[name = string("hidden_states_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_17_dilations_0 = const()[name = string("hidden_states_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_17_groups_0 = const()[name = string("hidden_states_17_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_17_cast_fp16 = conv(dilations = hidden_states_17_dilations_0, groups = hidden_states_17_groups_0, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = hidden_states_17_strides_0, weight = layers_1_mlp_down_proj_weight_to_fp16, x = x_19_cast_fp16)[name = string("hidden_states_17_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_19_cast_fp16 = add(x = hidden_states_15_cast_fp16, y = hidden_states_17_cast_fp16)[name = string("hidden_states_19_cast_fp16")];
            fp16 const_22_promoted_to_fp16 = const()[name = string("const_22_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_1551_cast_fp16 = mul(x = hidden_states_19_cast_fp16, y = const_22_promoted_to_fp16)[name = string("op_1551_cast_fp16")];
            int32 var_1549 = const()[name = string("op_1549"), val = int32(1)];
            bool doubled_17_interleave_0 = const()[name = string("doubled_17_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_17_cast_fp16 = concat(axis = var_1549, interleave = doubled_17_interleave_0, values = (hidden_states_19_cast_fp16, var_1551_cast_fp16))[name = string("doubled_17_cast_fp16")];
            tensor<int32, [1]> out_9_axes_0 = const()[name = string("out_9_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_9_gamma_0_to_fp16 = const()[name = string("out_9_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1137343424)))];
            fp16 var_1561_to_fp16 = const()[name = string("op_1561_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_9_cast_fp16 = layer_norm(axes = out_9_axes_0, epsilon = var_1561_to_fp16, gamma = out_9_gamma_0_to_fp16, x = doubled_17_cast_fp16)[name = string("out_9_cast_fp16")];
            tensor<int32, [2]> var_1572_split_sizes_0 = const()[name = string("op_1572_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1572_axis_0 = const()[name = string("op_1572_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_1572_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_1572_cast_fp16_1 = split(axis = var_1572_axis_0, split_sizes = var_1572_split_sizes_0, x = out_9_cast_fp16)[name = string("op_1572_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_2_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_2_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1137351680)))];
            tensor<int32, [2]> query_states_13_strides_0 = const()[name = string("query_states_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_13_pad_type_0 = const()[name = string("query_states_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_13_pad_0 = const()[name = string("query_states_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_13_dilations_0 = const()[name = string("query_states_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_13_groups_0 = const()[name = string("query_states_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_13_cast_fp16 = conv(dilations = query_states_13_dilations_0, groups = query_states_13_groups_0, pad = query_states_13_pad_0, pad_type = query_states_13_pad_type_0, strides = query_states_13_strides_0, weight = layers_2_self_attn_q_proj_weight_to_fp16, x = var_1572_cast_fp16_0)[name = string("query_states_13_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_2_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_2_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1145740352)))];
            tensor<int32, [2]> key_states_21_strides_0 = const()[name = string("key_states_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_21_pad_type_0 = const()[name = string("key_states_21_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_21_pad_0 = const()[name = string("key_states_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_21_dilations_0 = const()[name = string("key_states_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_21_groups_0 = const()[name = string("key_states_21_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_21_cast_fp16 = conv(dilations = key_states_21_dilations_0, groups = key_states_21_groups_0, pad = key_states_21_pad_0, pad_type = key_states_21_pad_type_0, strides = key_states_21_strides_0, weight = layers_2_self_attn_k_proj_weight_to_fp16, x = var_1572_cast_fp16_0)[name = string("key_states_21_cast_fp16")];
            tensor<int32, [2]> value_states_13_strides_0 = const()[name = string("value_states_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_13_pad_type_0 = const()[name = string("value_states_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_13_pad_0 = const()[name = string("value_states_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_13_dilations_0 = const()[name = string("value_states_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_13_groups_0 = const()[name = string("value_states_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_13_cast_fp16 = conv(dilations = value_states_13_dilations_0, groups = value_states_13_groups_0, pad = value_states_13_pad_0, pad_type = value_states_13_pad_type_0, strides = value_states_13_strides_0, weight = layers_2_self_attn_v_proj_weight_cast_fp16, x = var_1572_cast_fp16_0)[name = string("value_states_13_cast_fp16")];
            tensor<int32, [4]> concat_24x = const()[name = string("concat_24x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_21_cast_fp16 = reshape(shape = concat_24x, x = query_states_13_cast_fp16)[name = string("x_21_cast_fp16")];
            tensor<int32, [4]> concat_25x = const()[name = string("concat_25x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_1629_cast_fp16 = reshape(shape = concat_25x, x = key_states_21_cast_fp16)[name = string("op_1629_cast_fp16")];
            tensor<int32, [4]> concat_26x = const()[name = string("concat_26x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_1636_cast_fp16 = reshape(shape = concat_26x, x = value_states_13_cast_fp16)[name = string("op_1636_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_1640_cast_fp16 = mul(x = x_21_cast_fp16, y = var_859_cast_fp16)[name = string("op_1640_cast_fp16")];
            tensor<int32, [2]> var_1641_split_sizes_0 = const()[name = string("op_1641_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1641_axis_0 = const()[name = string("op_1641_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_1641_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_1641_cast_fp16_1 = split(axis = var_1641_axis_0, split_sizes = var_1641_split_sizes_0, x = x_21_cast_fp16)[name = string("op_1641_cast_fp16")];
            fp16 const_24_promoted_to_fp16 = const()[name = string("const_24_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_1643_cast_fp16 = mul(x = var_1641_cast_fp16_1, y = const_24_promoted_to_fp16)[name = string("op_1643_cast_fp16")];
            int32 var_1645 = const()[name = string("op_1645"), val = int32(-2)];
            bool var_1646_interleave_0 = const()[name = string("op_1646_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_1646_cast_fp16 = concat(axis = var_1645, interleave = var_1646_interleave_0, values = (var_1643_cast_fp16, var_1641_cast_fp16_0))[name = string("op_1646_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_1647_cast_fp16 = mul(x = var_1646_cast_fp16, y = var_866_cast_fp16)[name = string("op_1647_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_15_cast_fp16 = add(x = var_1640_cast_fp16, y = var_1647_cast_fp16)[name = string("query_states_15_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_1653_cast_fp16 = mul(x = var_1629_cast_fp16, y = var_859_cast_fp16)[name = string("op_1653_cast_fp16")];
            tensor<int32, [2]> var_1654_split_sizes_0 = const()[name = string("op_1654_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1654_axis_0 = const()[name = string("op_1654_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_1654_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_1654_cast_fp16_1 = split(axis = var_1654_axis_0, split_sizes = var_1654_split_sizes_0, x = var_1629_cast_fp16)[name = string("op_1654_cast_fp16")];
            fp16 const_25_promoted_to_fp16 = const()[name = string("const_25_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_1656_cast_fp16 = mul(x = var_1654_cast_fp16_1, y = const_25_promoted_to_fp16)[name = string("op_1656_cast_fp16")];
            int32 var_1658 = const()[name = string("op_1658"), val = int32(-2)];
            bool var_1659_interleave_0 = const()[name = string("op_1659_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_1659_cast_fp16 = concat(axis = var_1658, interleave = var_1659_interleave_0, values = (var_1656_cast_fp16, var_1654_cast_fp16_0))[name = string("op_1659_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_1660_cast_fp16 = mul(x = var_1659_cast_fp16, y = var_866_cast_fp16)[name = string("op_1660_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_25_cast_fp16 = add(x = var_1653_cast_fp16, y = var_1660_cast_fp16)[name = string("key_states_25_cast_fp16")];
            tensor<int32, [1]> expand_dims_24 = const()[name = string("expand_dims_24"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> expand_dims_25 = const()[name = string("expand_dims_25"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_27 = const()[name = string("expand_dims_27"), val = tensor<int32, [1]>([0])];
            int32 concat_29_axis_0 = const()[name = string("concat_29_axis_0"), val = int32(0)];
            bool concat_29_interleave_0 = const()[name = string("concat_29_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_29 = concat(axis = concat_29_axis_0, interleave = concat_29_interleave_0, values = (expand_dims_24, expand_dims_25, position_id, expand_dims_27))[name = string("concat_29")];
            tensor<int32, [1]> expand_dims_28 = const()[name = string("expand_dims_28"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> concat_30_values1_0 = const()[name = string("concat_30_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_30_values3_0 = const()[name = string("concat_30_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_30_axis_0 = const()[name = string("concat_30_axis_0"), val = int32(0)];
            bool concat_30_interleave_0 = const()[name = string("concat_30_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_30 = concat(axis = concat_30_axis_0, interleave = concat_30_interleave_0, values = (expand_dims_28, concat_30_values1_0, cache_position_end, concat_30_values3_0))[name = string("concat_30")];
            tensor<int32, [4]> key_states_27_perm_0 = const()[name = string("key_states_27_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_3_stride_0 = const()[name = string("key_cache_internal_tensor_assign_3_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_3_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_3_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_3_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_3_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_3_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_3_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_27_cast_fp16 = transpose(perm = key_states_27_perm_0, x = key_states_25_cast_fp16)[name = string("transpose_164")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_3_cast_fp16 = slice_update(begin = concat_29, begin_mask = key_cache_internal_tensor_assign_3_begin_mask_0, end = concat_30, end_mask = key_cache_internal_tensor_assign_3_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_3_squeeze_mask_0, stride = key_cache_internal_tensor_assign_3_stride_0, update = key_states_27_cast_fp16, x = coreml_update_state_58)[name = string("key_cache_internal_tensor_assign_3_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_3_cast_fp16, input = key_cache)[name = string("coreml_update_state_60_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_60 = read_state(input = key_cache)[name = string("coreml_update_state_60")];
            tensor<int32, [4]> value_states_15_perm_0 = const()[name = string("value_states_15_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_3_stride_0 = const()[name = string("value_cache_internal_tensor_assign_3_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_3_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_3_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_3_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_3_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_3_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_3_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_15_cast_fp16 = transpose(perm = value_states_15_perm_0, x = var_1636_cast_fp16)[name = string("transpose_163")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_3_cast_fp16 = slice_update(begin = concat_29, begin_mask = value_cache_internal_tensor_assign_3_begin_mask_0, end = concat_30, end_mask = value_cache_internal_tensor_assign_3_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_3_squeeze_mask_0, stride = value_cache_internal_tensor_assign_3_stride_0, update = value_states_15_cast_fp16, x = coreml_update_state_59)[name = string("value_cache_internal_tensor_assign_3_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_3_cast_fp16, input = value_cache)[name = string("coreml_update_state_61_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_61 = read_state(input = value_cache)[name = string("coreml_update_state_61")];
            tensor<int32, [4]> var_1730_begin_0 = const()[name = string("op_1730_begin_0"), val = tensor<int32, [4]>([2, 0, 0, 0])];
            tensor<int32, [4]> var_1730_end_0 = const()[name = string("op_1730_end_0"), val = tensor<int32, [4]>([3, 2, 2048, 128])];
            tensor<bool, [4]> var_1730_end_mask_0 = const()[name = string("op_1730_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1730_cast_fp16 = slice_by_index(begin = var_1730_begin_0, end = var_1730_end_0, end_mask = var_1730_end_mask_0, x = coreml_update_state_60)[name = string("op_1730_cast_fp16")];
            tensor<int32, [2]> tile_4 = const()[name = string("tile_4"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1733_axis_0 = const()[name = string("op_1733_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1733_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1733_cast_fp16_1 = split(axis = var_1733_axis_0, split_sizes = tile_4, x = var_1730_cast_fp16)[name = string("op_1733_cast_fp16")];
            tensor<int32, [4]> var_1740_begin_0 = const()[name = string("op_1740_begin_0"), val = tensor<int32, [4]>([2, 0, 0, 0])];
            tensor<int32, [4]> var_1740_end_0 = const()[name = string("op_1740_end_0"), val = tensor<int32, [4]>([3, 2, 2048, 128])];
            tensor<bool, [4]> var_1740_end_mask_0 = const()[name = string("op_1740_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1740_cast_fp16 = slice_by_index(begin = var_1740_begin_0, end = var_1740_end_0, end_mask = var_1740_end_mask_0, x = coreml_update_state_61)[name = string("op_1740_cast_fp16")];
            tensor<int32, [2]> tile_5 = const()[name = string("tile_5"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1743_axis_0 = const()[name = string("op_1743_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1743_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1743_cast_fp16_1 = split(axis = var_1743_axis_0, split_sizes = tile_5, x = var_1740_cast_fp16)[name = string("op_1743_cast_fp16")];
            tensor<int32, [2]> var_1746_split_sizes_0 = const()[name = string("op_1746_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_1746_axis_0 = const()[name = string("op_1746_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_1746_0, tensor<fp16, [1, 8, 128, 1]> var_1746_1 = split(axis = var_1746_axis_0, split_sizes = var_1746_split_sizes_0, x = query_states_15_cast_fp16)[name = string("op_1746")];
            bool attn_weights_33_transpose_x_0 = const()[name = string("attn_weights_33_transpose_x_0"), val = bool(false)];
            bool attn_weights_33_transpose_y_0 = const()[name = string("attn_weights_33_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_33_cast_fp16 = matmul(transpose_x = attn_weights_33_transpose_x_0, transpose_y = attn_weights_33_transpose_y_0, x = var_1733_cast_fp16_0, y = var_1746_0)[name = string("attn_weights_33_cast_fp16")];
            fp16 var_1749_to_fp16 = const()[name = string("op_1749_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_35_cast_fp16 = mul(x = attn_weights_33_cast_fp16, y = var_1749_to_fp16)[name = string("attn_weights_35_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_37_cast_fp16 = add(x = attn_weights_35_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_37_cast_fp16")];
            int32 var_1753 = const()[name = string("op_1753"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_39_cast_fp16 = softmax(axis = var_1753, x = attn_weights_37_cast_fp16)[name = string("attn_weights_39_cast_fp16")];
            bool var_1759_transpose_x_1 = const()[name = string("op_1759_transpose_x_1"), val = bool(true)];
            bool var_1759_transpose_y_1 = const()[name = string("op_1759_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_1759_cast_fp16 = matmul(transpose_x = var_1759_transpose_x_1, transpose_y = var_1759_transpose_y_1, x = attn_weights_39_cast_fp16, y = var_1743_cast_fp16_0)[name = string("op_1759_cast_fp16")];
            bool attn_weights_41_transpose_x_0 = const()[name = string("attn_weights_41_transpose_x_0"), val = bool(false)];
            bool attn_weights_41_transpose_y_0 = const()[name = string("attn_weights_41_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_41_cast_fp16 = matmul(transpose_x = attn_weights_41_transpose_x_0, transpose_y = attn_weights_41_transpose_y_0, x = var_1733_cast_fp16_1, y = var_1746_1)[name = string("attn_weights_41_cast_fp16")];
            fp16 var_1761_to_fp16 = const()[name = string("op_1761_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_43_cast_fp16 = mul(x = attn_weights_41_cast_fp16, y = var_1761_to_fp16)[name = string("attn_weights_43_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_45_cast_fp16 = add(x = attn_weights_43_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_45_cast_fp16")];
            int32 var_1765 = const()[name = string("op_1765"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_47_cast_fp16 = softmax(axis = var_1765, x = attn_weights_45_cast_fp16)[name = string("attn_weights_47_cast_fp16")];
            bool attn_output_17_transpose_x_1 = const()[name = string("attn_output_17_transpose_x_1"), val = bool(true)];
            bool attn_output_17_transpose_y_1 = const()[name = string("attn_output_17_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_17_cast_fp16 = matmul(transpose_x = attn_output_17_transpose_x_1, transpose_y = attn_output_17_transpose_y_1, x = attn_weights_47_cast_fp16, y = var_1743_cast_fp16_1)[name = string("attn_output_17_cast_fp16")];
            int32 var_1773 = const()[name = string("op_1773"), val = int32(1)];
            bool attn_output_19_interleave_0 = const()[name = string("attn_output_19_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_19_cast_fp16 = concat(axis = var_1773, interleave = attn_output_19_interleave_0, values = (var_1759_cast_fp16, attn_output_17_cast_fp16))[name = string("attn_output_19_cast_fp16")];
            tensor<int32, [4]> var_1777_perm_0 = const()[name = string("op_1777_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_35x = const()[name = string("concat_35x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_1777_cast_fp16 = transpose(perm = var_1777_perm_0, x = attn_output_19_cast_fp16)[name = string("transpose_162")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_23_cast_fp16 = reshape(shape = concat_35x, x = var_1777_cast_fp16)[name = string("attn_output_23_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_2_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_2_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1146788992)))];
            tensor<int32, [2]> hidden_states_23_strides_0 = const()[name = string("hidden_states_23_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_23_pad_type_0 = const()[name = string("hidden_states_23_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_23_pad_0 = const()[name = string("hidden_states_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_23_dilations_0 = const()[name = string("hidden_states_23_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_23_groups_0 = const()[name = string("hidden_states_23_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_23_cast_fp16 = conv(dilations = hidden_states_23_dilations_0, groups = hidden_states_23_groups_0, pad = hidden_states_23_pad_0, pad_type = hidden_states_23_pad_type_0, strides = hidden_states_23_strides_0, weight = layers_2_self_attn_o_proj_weight_to_fp16, x = attn_output_23_cast_fp16)[name = string("hidden_states_23_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_25_cast_fp16 = add(x = hidden_states_19_cast_fp16, y = hidden_states_23_cast_fp16)[name = string("hidden_states_25_cast_fp16")];
            fp16 const_30_promoted_to_fp16 = const()[name = string("const_30_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_1810_cast_fp16 = mul(x = hidden_states_25_cast_fp16, y = const_30_promoted_to_fp16)[name = string("op_1810_cast_fp16")];
            int32 var_1808 = const()[name = string("op_1808"), val = int32(1)];
            bool doubled_21_interleave_0 = const()[name = string("doubled_21_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_21_cast_fp16 = concat(axis = var_1808, interleave = doubled_21_interleave_0, values = (hidden_states_25_cast_fp16, var_1810_cast_fp16))[name = string("doubled_21_cast_fp16")];
            tensor<int32, [1]> out_11_axes_0 = const()[name = string("out_11_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_11_gamma_0_to_fp16 = const()[name = string("out_11_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1155177664)))];
            fp16 var_1820_to_fp16 = const()[name = string("op_1820_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_11_cast_fp16 = layer_norm(axes = out_11_axes_0, epsilon = var_1820_to_fp16, gamma = out_11_gamma_0_to_fp16, x = doubled_21_cast_fp16)[name = string("out_11_cast_fp16")];
            tensor<int32, [2]> var_1831_split_sizes_0 = const()[name = string("op_1831_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1831_axis_0 = const()[name = string("op_1831_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_1831_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_1831_cast_fp16_1 = split(axis = var_1831_axis_0, split_sizes = var_1831_split_sizes_0, x = out_11_cast_fp16)[name = string("op_1831_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_2_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_2_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1155185920)))];
            tensor<int32, [2]> input_5_strides_0 = const()[name = string("input_5_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_5_pad_type_0 = const()[name = string("input_5_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_5_dilations_0 = const()[name = string("input_5_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_5_groups_0 = const()[name = string("input_5_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_5_cast_fp16 = conv(dilations = input_5_dilations_0, groups = input_5_groups_0, pad = input_5_pad_0, pad_type = input_5_pad_type_0, strides = input_5_strides_0, weight = layers_2_mlp_gate_proj_weight_to_fp16, x = var_1831_cast_fp16_0)[name = string("input_5_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_1848_cast_fp16 = silu(x = input_5_cast_fp16)[name = string("op_1848_cast_fp16")];
            tensor<int32, [2]> var_1854_strides_0 = const()[name = string("op_1854_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_1854_pad_type_0 = const()[name = string("op_1854_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_1854_pad_0 = const()[name = string("op_1854_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_1854_dilations_0 = const()[name = string("op_1854_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1854_groups_0 = const()[name = string("op_1854_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_1854_cast_fp16 = conv(dilations = var_1854_dilations_0, groups = var_1854_groups_0, pad = var_1854_pad_0, pad_type = var_1854_pad_type_0, strides = var_1854_strides_0, weight = layers_2_mlp_up_proj_weight_cast_fp16, x = var_1831_cast_fp16_0)[name = string("op_1854_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_29_cast_fp16 = mul(x = var_1848_cast_fp16, y = var_1854_cast_fp16)[name = string("x_29_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_2_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_2_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1180351808)))];
            tensor<int32, [2]> hidden_states_27_strides_0 = const()[name = string("hidden_states_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_27_pad_type_0 = const()[name = string("hidden_states_27_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_27_pad_0 = const()[name = string("hidden_states_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_27_dilations_0 = const()[name = string("hidden_states_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_27_groups_0 = const()[name = string("hidden_states_27_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_27_cast_fp16 = conv(dilations = hidden_states_27_dilations_0, groups = hidden_states_27_groups_0, pad = hidden_states_27_pad_0, pad_type = hidden_states_27_pad_type_0, strides = hidden_states_27_strides_0, weight = layers_2_mlp_down_proj_weight_to_fp16, x = x_29_cast_fp16)[name = string("hidden_states_27_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_29_cast_fp16 = add(x = hidden_states_25_cast_fp16, y = hidden_states_27_cast_fp16)[name = string("hidden_states_29_cast_fp16")];
            fp16 const_32_promoted_to_fp16 = const()[name = string("const_32_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_1872_cast_fp16 = mul(x = hidden_states_29_cast_fp16, y = const_32_promoted_to_fp16)[name = string("op_1872_cast_fp16")];
            int32 var_1870 = const()[name = string("op_1870"), val = int32(1)];
            bool doubled_25_interleave_0 = const()[name = string("doubled_25_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_25_cast_fp16 = concat(axis = var_1870, interleave = doubled_25_interleave_0, values = (hidden_states_29_cast_fp16, var_1872_cast_fp16))[name = string("doubled_25_cast_fp16")];
            tensor<int32, [1]> out_13_axes_0 = const()[name = string("out_13_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_13_gamma_0_to_fp16 = const()[name = string("out_13_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1205517696)))];
            fp16 var_1882_to_fp16 = const()[name = string("op_1882_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_13_cast_fp16 = layer_norm(axes = out_13_axes_0, epsilon = var_1882_to_fp16, gamma = out_13_gamma_0_to_fp16, x = doubled_25_cast_fp16)[name = string("out_13_cast_fp16")];
            tensor<int32, [2]> var_1893_split_sizes_0 = const()[name = string("op_1893_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1893_axis_0 = const()[name = string("op_1893_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_1893_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_1893_cast_fp16_1 = split(axis = var_1893_axis_0, split_sizes = var_1893_split_sizes_0, x = out_13_cast_fp16)[name = string("op_1893_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_3_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_3_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1205525952)))];
            tensor<int32, [2]> query_states_19_strides_0 = const()[name = string("query_states_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_19_pad_type_0 = const()[name = string("query_states_19_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_19_pad_0 = const()[name = string("query_states_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_19_dilations_0 = const()[name = string("query_states_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_19_groups_0 = const()[name = string("query_states_19_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_19_cast_fp16 = conv(dilations = query_states_19_dilations_0, groups = query_states_19_groups_0, pad = query_states_19_pad_0, pad_type = query_states_19_pad_type_0, strides = query_states_19_strides_0, weight = layers_3_self_attn_q_proj_weight_to_fp16, x = var_1893_cast_fp16_0)[name = string("query_states_19_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_3_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_3_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1213914624)))];
            tensor<int32, [2]> key_states_31_strides_0 = const()[name = string("key_states_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_31_pad_type_0 = const()[name = string("key_states_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_31_pad_0 = const()[name = string("key_states_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_31_dilations_0 = const()[name = string("key_states_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_31_groups_0 = const()[name = string("key_states_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_31_cast_fp16 = conv(dilations = key_states_31_dilations_0, groups = key_states_31_groups_0, pad = key_states_31_pad_0, pad_type = key_states_31_pad_type_0, strides = key_states_31_strides_0, weight = layers_3_self_attn_k_proj_weight_to_fp16, x = var_1893_cast_fp16_0)[name = string("key_states_31_cast_fp16")];
            tensor<int32, [2]> value_states_19_strides_0 = const()[name = string("value_states_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_19_pad_type_0 = const()[name = string("value_states_19_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_19_pad_0 = const()[name = string("value_states_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_19_dilations_0 = const()[name = string("value_states_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_19_groups_0 = const()[name = string("value_states_19_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_19_cast_fp16 = conv(dilations = value_states_19_dilations_0, groups = value_states_19_groups_0, pad = value_states_19_pad_0, pad_type = value_states_19_pad_type_0, strides = value_states_19_strides_0, weight = layers_3_self_attn_v_proj_weight_cast_fp16, x = var_1893_cast_fp16_0)[name = string("value_states_19_cast_fp16")];
            tensor<int32, [4]> concat_36x = const()[name = string("concat_36x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_31_cast_fp16 = reshape(shape = concat_36x, x = query_states_19_cast_fp16)[name = string("x_31_cast_fp16")];
            tensor<int32, [4]> concat_37x = const()[name = string("concat_37x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_1950_cast_fp16 = reshape(shape = concat_37x, x = key_states_31_cast_fp16)[name = string("op_1950_cast_fp16")];
            tensor<int32, [4]> concat_38x = const()[name = string("concat_38x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_1957_cast_fp16 = reshape(shape = concat_38x, x = value_states_19_cast_fp16)[name = string("op_1957_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_1961_cast_fp16 = mul(x = x_31_cast_fp16, y = var_859_cast_fp16)[name = string("op_1961_cast_fp16")];
            tensor<int32, [2]> var_1962_split_sizes_0 = const()[name = string("op_1962_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1962_axis_0 = const()[name = string("op_1962_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_1962_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_1962_cast_fp16_1 = split(axis = var_1962_axis_0, split_sizes = var_1962_split_sizes_0, x = x_31_cast_fp16)[name = string("op_1962_cast_fp16")];
            fp16 const_34_promoted_to_fp16 = const()[name = string("const_34_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_1964_cast_fp16 = mul(x = var_1962_cast_fp16_1, y = const_34_promoted_to_fp16)[name = string("op_1964_cast_fp16")];
            int32 var_1966 = const()[name = string("op_1966"), val = int32(-2)];
            bool var_1967_interleave_0 = const()[name = string("op_1967_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_1967_cast_fp16 = concat(axis = var_1966, interleave = var_1967_interleave_0, values = (var_1964_cast_fp16, var_1962_cast_fp16_0))[name = string("op_1967_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_1968_cast_fp16 = mul(x = var_1967_cast_fp16, y = var_866_cast_fp16)[name = string("op_1968_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_21_cast_fp16 = add(x = var_1961_cast_fp16, y = var_1968_cast_fp16)[name = string("query_states_21_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_1974_cast_fp16 = mul(x = var_1950_cast_fp16, y = var_859_cast_fp16)[name = string("op_1974_cast_fp16")];
            tensor<int32, [2]> var_1975_split_sizes_0 = const()[name = string("op_1975_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1975_axis_0 = const()[name = string("op_1975_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_1975_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_1975_cast_fp16_1 = split(axis = var_1975_axis_0, split_sizes = var_1975_split_sizes_0, x = var_1950_cast_fp16)[name = string("op_1975_cast_fp16")];
            fp16 const_35_promoted_to_fp16 = const()[name = string("const_35_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_1977_cast_fp16 = mul(x = var_1975_cast_fp16_1, y = const_35_promoted_to_fp16)[name = string("op_1977_cast_fp16")];
            int32 var_1979 = const()[name = string("op_1979"), val = int32(-2)];
            bool var_1980_interleave_0 = const()[name = string("op_1980_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_1980_cast_fp16 = concat(axis = var_1979, interleave = var_1980_interleave_0, values = (var_1977_cast_fp16, var_1975_cast_fp16_0))[name = string("op_1980_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_1981_cast_fp16 = mul(x = var_1980_cast_fp16, y = var_866_cast_fp16)[name = string("op_1981_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_35_cast_fp16 = add(x = var_1974_cast_fp16, y = var_1981_cast_fp16)[name = string("key_states_35_cast_fp16")];
            tensor<int32, [1]> expand_dims_36 = const()[name = string("expand_dims_36"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> expand_dims_37 = const()[name = string("expand_dims_37"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_39 = const()[name = string("expand_dims_39"), val = tensor<int32, [1]>([0])];
            int32 concat_41_axis_0 = const()[name = string("concat_41_axis_0"), val = int32(0)];
            bool concat_41_interleave_0 = const()[name = string("concat_41_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_41 = concat(axis = concat_41_axis_0, interleave = concat_41_interleave_0, values = (expand_dims_36, expand_dims_37, position_id, expand_dims_39))[name = string("concat_41")];
            tensor<int32, [1]> expand_dims_40 = const()[name = string("expand_dims_40"), val = tensor<int32, [1]>([4])];
            tensor<int32, [1]> concat_42_values1_0 = const()[name = string("concat_42_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_42_values3_0 = const()[name = string("concat_42_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_42_axis_0 = const()[name = string("concat_42_axis_0"), val = int32(0)];
            bool concat_42_interleave_0 = const()[name = string("concat_42_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_42 = concat(axis = concat_42_axis_0, interleave = concat_42_interleave_0, values = (expand_dims_40, concat_42_values1_0, cache_position_end, concat_42_values3_0))[name = string("concat_42")];
            tensor<int32, [4]> key_states_37_perm_0 = const()[name = string("key_states_37_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_4_stride_0 = const()[name = string("key_cache_internal_tensor_assign_4_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_4_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_4_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_4_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_4_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_4_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_4_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_37_cast_fp16 = transpose(perm = key_states_37_perm_0, x = key_states_35_cast_fp16)[name = string("transpose_161")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_4_cast_fp16 = slice_update(begin = concat_41, begin_mask = key_cache_internal_tensor_assign_4_begin_mask_0, end = concat_42, end_mask = key_cache_internal_tensor_assign_4_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_4_squeeze_mask_0, stride = key_cache_internal_tensor_assign_4_stride_0, update = key_states_37_cast_fp16, x = coreml_update_state_60)[name = string("key_cache_internal_tensor_assign_4_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_4_cast_fp16, input = key_cache)[name = string("coreml_update_state_62_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_62 = read_state(input = key_cache)[name = string("coreml_update_state_62")];
            tensor<int32, [4]> value_states_21_perm_0 = const()[name = string("value_states_21_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_4_stride_0 = const()[name = string("value_cache_internal_tensor_assign_4_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_4_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_4_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_4_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_4_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_4_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_4_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_21_cast_fp16 = transpose(perm = value_states_21_perm_0, x = var_1957_cast_fp16)[name = string("transpose_160")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_4_cast_fp16 = slice_update(begin = concat_41, begin_mask = value_cache_internal_tensor_assign_4_begin_mask_0, end = concat_42, end_mask = value_cache_internal_tensor_assign_4_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_4_squeeze_mask_0, stride = value_cache_internal_tensor_assign_4_stride_0, update = value_states_21_cast_fp16, x = coreml_update_state_61)[name = string("value_cache_internal_tensor_assign_4_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_4_cast_fp16, input = value_cache)[name = string("coreml_update_state_63_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_63 = read_state(input = value_cache)[name = string("coreml_update_state_63")];
            tensor<int32, [4]> var_2051_begin_0 = const()[name = string("op_2051_begin_0"), val = tensor<int32, [4]>([3, 0, 0, 0])];
            tensor<int32, [4]> var_2051_end_0 = const()[name = string("op_2051_end_0"), val = tensor<int32, [4]>([4, 2, 2048, 128])];
            tensor<bool, [4]> var_2051_end_mask_0 = const()[name = string("op_2051_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2051_cast_fp16 = slice_by_index(begin = var_2051_begin_0, end = var_2051_end_0, end_mask = var_2051_end_mask_0, x = coreml_update_state_62)[name = string("op_2051_cast_fp16")];
            tensor<int32, [2]> tile_6 = const()[name = string("tile_6"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2054_axis_0 = const()[name = string("op_2054_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2054_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2054_cast_fp16_1 = split(axis = var_2054_axis_0, split_sizes = tile_6, x = var_2051_cast_fp16)[name = string("op_2054_cast_fp16")];
            tensor<int32, [4]> var_2061_begin_0 = const()[name = string("op_2061_begin_0"), val = tensor<int32, [4]>([3, 0, 0, 0])];
            tensor<int32, [4]> var_2061_end_0 = const()[name = string("op_2061_end_0"), val = tensor<int32, [4]>([4, 2, 2048, 128])];
            tensor<bool, [4]> var_2061_end_mask_0 = const()[name = string("op_2061_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2061_cast_fp16 = slice_by_index(begin = var_2061_begin_0, end = var_2061_end_0, end_mask = var_2061_end_mask_0, x = coreml_update_state_63)[name = string("op_2061_cast_fp16")];
            tensor<int32, [2]> tile_7 = const()[name = string("tile_7"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2064_axis_0 = const()[name = string("op_2064_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2064_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2064_cast_fp16_1 = split(axis = var_2064_axis_0, split_sizes = tile_7, x = var_2061_cast_fp16)[name = string("op_2064_cast_fp16")];
            tensor<int32, [2]> var_2067_split_sizes_0 = const()[name = string("op_2067_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_2067_axis_0 = const()[name = string("op_2067_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_2067_0, tensor<fp16, [1, 8, 128, 1]> var_2067_1 = split(axis = var_2067_axis_0, split_sizes = var_2067_split_sizes_0, x = query_states_21_cast_fp16)[name = string("op_2067")];
            bool attn_weights_49_transpose_x_0 = const()[name = string("attn_weights_49_transpose_x_0"), val = bool(false)];
            bool attn_weights_49_transpose_y_0 = const()[name = string("attn_weights_49_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_49_cast_fp16 = matmul(transpose_x = attn_weights_49_transpose_x_0, transpose_y = attn_weights_49_transpose_y_0, x = var_2054_cast_fp16_0, y = var_2067_0)[name = string("attn_weights_49_cast_fp16")];
            fp16 var_2070_to_fp16 = const()[name = string("op_2070_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_51_cast_fp16 = mul(x = attn_weights_49_cast_fp16, y = var_2070_to_fp16)[name = string("attn_weights_51_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_53_cast_fp16 = add(x = attn_weights_51_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_53_cast_fp16")];
            int32 var_2074 = const()[name = string("op_2074"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_55_cast_fp16 = softmax(axis = var_2074, x = attn_weights_53_cast_fp16)[name = string("attn_weights_55_cast_fp16")];
            bool var_2080_transpose_x_1 = const()[name = string("op_2080_transpose_x_1"), val = bool(true)];
            bool var_2080_transpose_y_1 = const()[name = string("op_2080_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_2080_cast_fp16 = matmul(transpose_x = var_2080_transpose_x_1, transpose_y = var_2080_transpose_y_1, x = attn_weights_55_cast_fp16, y = var_2064_cast_fp16_0)[name = string("op_2080_cast_fp16")];
            bool attn_weights_57_transpose_x_0 = const()[name = string("attn_weights_57_transpose_x_0"), val = bool(false)];
            bool attn_weights_57_transpose_y_0 = const()[name = string("attn_weights_57_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_57_cast_fp16 = matmul(transpose_x = attn_weights_57_transpose_x_0, transpose_y = attn_weights_57_transpose_y_0, x = var_2054_cast_fp16_1, y = var_2067_1)[name = string("attn_weights_57_cast_fp16")];
            fp16 var_2082_to_fp16 = const()[name = string("op_2082_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_59_cast_fp16 = mul(x = attn_weights_57_cast_fp16, y = var_2082_to_fp16)[name = string("attn_weights_59_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_61_cast_fp16 = add(x = attn_weights_59_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_61_cast_fp16")];
            int32 var_2086 = const()[name = string("op_2086"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_63_cast_fp16 = softmax(axis = var_2086, x = attn_weights_61_cast_fp16)[name = string("attn_weights_63_cast_fp16")];
            bool attn_output_25_transpose_x_1 = const()[name = string("attn_output_25_transpose_x_1"), val = bool(true)];
            bool attn_output_25_transpose_y_1 = const()[name = string("attn_output_25_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_25_cast_fp16 = matmul(transpose_x = attn_output_25_transpose_x_1, transpose_y = attn_output_25_transpose_y_1, x = attn_weights_63_cast_fp16, y = var_2064_cast_fp16_1)[name = string("attn_output_25_cast_fp16")];
            int32 var_2094 = const()[name = string("op_2094"), val = int32(1)];
            bool attn_output_27_interleave_0 = const()[name = string("attn_output_27_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_27_cast_fp16 = concat(axis = var_2094, interleave = attn_output_27_interleave_0, values = (var_2080_cast_fp16, attn_output_25_cast_fp16))[name = string("attn_output_27_cast_fp16")];
            tensor<int32, [4]> var_2098_perm_0 = const()[name = string("op_2098_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_47x = const()[name = string("concat_47x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_2098_cast_fp16 = transpose(perm = var_2098_perm_0, x = attn_output_27_cast_fp16)[name = string("transpose_159")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_31_cast_fp16 = reshape(shape = concat_47x, x = var_2098_cast_fp16)[name = string("attn_output_31_cast_fp16")];
            tensor<int32, [2]> hidden_states_33_strides_0 = const()[name = string("hidden_states_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_33_pad_type_0 = const()[name = string("hidden_states_33_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_33_pad_0 = const()[name = string("hidden_states_33_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_33_dilations_0 = const()[name = string("hidden_states_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_33_groups_0 = const()[name = string("hidden_states_33_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_33_cast_fp16 = conv(dilations = hidden_states_33_dilations_0, groups = hidden_states_33_groups_0, pad = hidden_states_33_pad_0, pad_type = hidden_states_33_pad_type_0, strides = hidden_states_33_strides_0, weight = layers_3_self_attn_o_proj_weight_cast_fp16, x = attn_output_31_cast_fp16)[name = string("hidden_states_33_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_35_cast_fp16 = add(x = hidden_states_29_cast_fp16, y = hidden_states_33_cast_fp16)[name = string("hidden_states_35_cast_fp16")];
            fp16 const_40_promoted_to_fp16 = const()[name = string("const_40_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_2131_cast_fp16 = mul(x = hidden_states_35_cast_fp16, y = const_40_promoted_to_fp16)[name = string("op_2131_cast_fp16")];
            int32 var_2129 = const()[name = string("op_2129"), val = int32(1)];
            bool doubled_29_interleave_0 = const()[name = string("doubled_29_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_29_cast_fp16 = concat(axis = var_2129, interleave = doubled_29_interleave_0, values = (hidden_states_35_cast_fp16, var_2131_cast_fp16))[name = string("doubled_29_cast_fp16")];
            tensor<int32, [1]> out_15_axes_0 = const()[name = string("out_15_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_15_gamma_0_to_fp16 = const()[name = string("out_15_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1214963264)))];
            fp16 var_2141_to_fp16 = const()[name = string("op_2141_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_15_cast_fp16 = layer_norm(axes = out_15_axes_0, epsilon = var_2141_to_fp16, gamma = out_15_gamma_0_to_fp16, x = doubled_29_cast_fp16)[name = string("out_15_cast_fp16")];
            tensor<int32, [2]> var_2152_split_sizes_0 = const()[name = string("op_2152_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2152_axis_0 = const()[name = string("op_2152_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_2152_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_2152_cast_fp16_1 = split(axis = var_2152_axis_0, split_sizes = var_2152_split_sizes_0, x = out_15_cast_fp16)[name = string("op_2152_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_3_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_3_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1214971520)))];
            tensor<int32, [2]> input_7_strides_0 = const()[name = string("input_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_7_pad_type_0 = const()[name = string("input_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_7_pad_0 = const()[name = string("input_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_7_dilations_0 = const()[name = string("input_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_7_groups_0 = const()[name = string("input_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_7_cast_fp16 = conv(dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = layers_3_mlp_gate_proj_weight_to_fp16, x = var_2152_cast_fp16_0)[name = string("input_7_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_2169_cast_fp16 = silu(x = input_7_cast_fp16)[name = string("op_2169_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_3_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_3_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1240137408)))];
            tensor<int32, [2]> var_2175_strides_0 = const()[name = string("op_2175_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_2175_pad_type_0 = const()[name = string("op_2175_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_2175_pad_0 = const()[name = string("op_2175_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_2175_dilations_0 = const()[name = string("op_2175_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2175_groups_0 = const()[name = string("op_2175_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_2175_cast_fp16 = conv(dilations = var_2175_dilations_0, groups = var_2175_groups_0, pad = var_2175_pad_0, pad_type = var_2175_pad_type_0, strides = var_2175_strides_0, weight = layers_3_mlp_up_proj_weight_to_fp16, x = var_2152_cast_fp16_0)[name = string("op_2175_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_39_cast_fp16 = mul(x = var_2169_cast_fp16, y = var_2175_cast_fp16)[name = string("x_39_cast_fp16")];
            tensor<int32, [2]> hidden_states_37_strides_0 = const()[name = string("hidden_states_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_37_pad_type_0 = const()[name = string("hidden_states_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_37_pad_0 = const()[name = string("hidden_states_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_37_dilations_0 = const()[name = string("hidden_states_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_37_groups_0 = const()[name = string("hidden_states_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_37_cast_fp16 = conv(dilations = hidden_states_37_dilations_0, groups = hidden_states_37_groups_0, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = hidden_states_37_strides_0, weight = layers_3_mlp_down_proj_weight_cast_fp16, x = x_39_cast_fp16)[name = string("hidden_states_37_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_39_cast_fp16 = add(x = hidden_states_35_cast_fp16, y = hidden_states_37_cast_fp16)[name = string("hidden_states_39_cast_fp16")];
            fp16 const_42_promoted_to_fp16 = const()[name = string("const_42_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_2193_cast_fp16 = mul(x = hidden_states_39_cast_fp16, y = const_42_promoted_to_fp16)[name = string("op_2193_cast_fp16")];
            int32 var_2191 = const()[name = string("op_2191"), val = int32(1)];
            bool doubled_33_interleave_0 = const()[name = string("doubled_33_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_33_cast_fp16 = concat(axis = var_2191, interleave = doubled_33_interleave_0, values = (hidden_states_39_cast_fp16, var_2193_cast_fp16))[name = string("doubled_33_cast_fp16")];
            tensor<int32, [1]> out_17_axes_0 = const()[name = string("out_17_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_17_gamma_0_to_fp16 = const()[name = string("out_17_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1265303296)))];
            fp16 var_2203_to_fp16 = const()[name = string("op_2203_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_17_cast_fp16 = layer_norm(axes = out_17_axes_0, epsilon = var_2203_to_fp16, gamma = out_17_gamma_0_to_fp16, x = doubled_33_cast_fp16)[name = string("out_17_cast_fp16")];
            tensor<int32, [2]> var_2214_split_sizes_0 = const()[name = string("op_2214_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2214_axis_0 = const()[name = string("op_2214_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_2214_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_2214_cast_fp16_1 = split(axis = var_2214_axis_0, split_sizes = var_2214_split_sizes_0, x = out_17_cast_fp16)[name = string("op_2214_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_4_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_4_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1265311552)))];
            tensor<int32, [2]> query_states_25_strides_0 = const()[name = string("query_states_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_25_pad_type_0 = const()[name = string("query_states_25_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_25_pad_0 = const()[name = string("query_states_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_25_dilations_0 = const()[name = string("query_states_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_25_groups_0 = const()[name = string("query_states_25_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_25_cast_fp16 = conv(dilations = query_states_25_dilations_0, groups = query_states_25_groups_0, pad = query_states_25_pad_0, pad_type = query_states_25_pad_type_0, strides = query_states_25_strides_0, weight = layers_4_self_attn_q_proj_weight_to_fp16, x = var_2214_cast_fp16_0)[name = string("query_states_25_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_4_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_4_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1273700224)))];
            tensor<int32, [2]> key_states_41_strides_0 = const()[name = string("key_states_41_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_41_pad_type_0 = const()[name = string("key_states_41_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_41_pad_0 = const()[name = string("key_states_41_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_41_dilations_0 = const()[name = string("key_states_41_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_41_groups_0 = const()[name = string("key_states_41_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_41_cast_fp16 = conv(dilations = key_states_41_dilations_0, groups = key_states_41_groups_0, pad = key_states_41_pad_0, pad_type = key_states_41_pad_type_0, strides = key_states_41_strides_0, weight = layers_4_self_attn_k_proj_weight_to_fp16, x = var_2214_cast_fp16_0)[name = string("key_states_41_cast_fp16")];
            tensor<int32, [2]> value_states_25_strides_0 = const()[name = string("value_states_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_25_pad_type_0 = const()[name = string("value_states_25_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_25_pad_0 = const()[name = string("value_states_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_25_dilations_0 = const()[name = string("value_states_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_25_groups_0 = const()[name = string("value_states_25_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_25_cast_fp16 = conv(dilations = value_states_25_dilations_0, groups = value_states_25_groups_0, pad = value_states_25_pad_0, pad_type = value_states_25_pad_type_0, strides = value_states_25_strides_0, weight = layers_4_self_attn_v_proj_weight_cast_fp16, x = var_2214_cast_fp16_0)[name = string("value_states_25_cast_fp16")];
            tensor<int32, [4]> concat_48x = const()[name = string("concat_48x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_41_cast_fp16 = reshape(shape = concat_48x, x = query_states_25_cast_fp16)[name = string("x_41_cast_fp16")];
            tensor<int32, [4]> concat_49x = const()[name = string("concat_49x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_2271_cast_fp16 = reshape(shape = concat_49x, x = key_states_41_cast_fp16)[name = string("op_2271_cast_fp16")];
            tensor<int32, [4]> concat_50x = const()[name = string("concat_50x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_2278_cast_fp16 = reshape(shape = concat_50x, x = value_states_25_cast_fp16)[name = string("op_2278_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_2282_cast_fp16 = mul(x = x_41_cast_fp16, y = var_859_cast_fp16)[name = string("op_2282_cast_fp16")];
            tensor<int32, [2]> var_2283_split_sizes_0 = const()[name = string("op_2283_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2283_axis_0 = const()[name = string("op_2283_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_2283_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_2283_cast_fp16_1 = split(axis = var_2283_axis_0, split_sizes = var_2283_split_sizes_0, x = x_41_cast_fp16)[name = string("op_2283_cast_fp16")];
            fp16 const_44_promoted_to_fp16 = const()[name = string("const_44_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_2285_cast_fp16 = mul(x = var_2283_cast_fp16_1, y = const_44_promoted_to_fp16)[name = string("op_2285_cast_fp16")];
            int32 var_2287 = const()[name = string("op_2287"), val = int32(-2)];
            bool var_2288_interleave_0 = const()[name = string("op_2288_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_2288_cast_fp16 = concat(axis = var_2287, interleave = var_2288_interleave_0, values = (var_2285_cast_fp16, var_2283_cast_fp16_0))[name = string("op_2288_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_2289_cast_fp16 = mul(x = var_2288_cast_fp16, y = var_866_cast_fp16)[name = string("op_2289_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_27_cast_fp16 = add(x = var_2282_cast_fp16, y = var_2289_cast_fp16)[name = string("query_states_27_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_2295_cast_fp16 = mul(x = var_2271_cast_fp16, y = var_859_cast_fp16)[name = string("op_2295_cast_fp16")];
            tensor<int32, [2]> var_2296_split_sizes_0 = const()[name = string("op_2296_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2296_axis_0 = const()[name = string("op_2296_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_2296_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_2296_cast_fp16_1 = split(axis = var_2296_axis_0, split_sizes = var_2296_split_sizes_0, x = var_2271_cast_fp16)[name = string("op_2296_cast_fp16")];
            fp16 const_45_promoted_to_fp16 = const()[name = string("const_45_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_2298_cast_fp16 = mul(x = var_2296_cast_fp16_1, y = const_45_promoted_to_fp16)[name = string("op_2298_cast_fp16")];
            int32 var_2300 = const()[name = string("op_2300"), val = int32(-2)];
            bool var_2301_interleave_0 = const()[name = string("op_2301_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_2301_cast_fp16 = concat(axis = var_2300, interleave = var_2301_interleave_0, values = (var_2298_cast_fp16, var_2296_cast_fp16_0))[name = string("op_2301_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_2302_cast_fp16 = mul(x = var_2301_cast_fp16, y = var_866_cast_fp16)[name = string("op_2302_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_45_cast_fp16 = add(x = var_2295_cast_fp16, y = var_2302_cast_fp16)[name = string("key_states_45_cast_fp16")];
            tensor<int32, [1]> expand_dims_48 = const()[name = string("expand_dims_48"), val = tensor<int32, [1]>([4])];
            tensor<int32, [1]> expand_dims_49 = const()[name = string("expand_dims_49"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_51 = const()[name = string("expand_dims_51"), val = tensor<int32, [1]>([0])];
            int32 concat_53_axis_0 = const()[name = string("concat_53_axis_0"), val = int32(0)];
            bool concat_53_interleave_0 = const()[name = string("concat_53_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_53 = concat(axis = concat_53_axis_0, interleave = concat_53_interleave_0, values = (expand_dims_48, expand_dims_49, position_id, expand_dims_51))[name = string("concat_53")];
            tensor<int32, [1]> expand_dims_52 = const()[name = string("expand_dims_52"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> concat_54_values1_0 = const()[name = string("concat_54_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_54_values3_0 = const()[name = string("concat_54_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_54_axis_0 = const()[name = string("concat_54_axis_0"), val = int32(0)];
            bool concat_54_interleave_0 = const()[name = string("concat_54_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_54 = concat(axis = concat_54_axis_0, interleave = concat_54_interleave_0, values = (expand_dims_52, concat_54_values1_0, cache_position_end, concat_54_values3_0))[name = string("concat_54")];
            tensor<int32, [4]> key_states_47_perm_0 = const()[name = string("key_states_47_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_5_stride_0 = const()[name = string("key_cache_internal_tensor_assign_5_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_5_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_5_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_5_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_5_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_5_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_5_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_47_cast_fp16 = transpose(perm = key_states_47_perm_0, x = key_states_45_cast_fp16)[name = string("transpose_158")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_5_cast_fp16 = slice_update(begin = concat_53, begin_mask = key_cache_internal_tensor_assign_5_begin_mask_0, end = concat_54, end_mask = key_cache_internal_tensor_assign_5_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_5_squeeze_mask_0, stride = key_cache_internal_tensor_assign_5_stride_0, update = key_states_47_cast_fp16, x = coreml_update_state_62)[name = string("key_cache_internal_tensor_assign_5_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_5_cast_fp16, input = key_cache)[name = string("coreml_update_state_64_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_64 = read_state(input = key_cache)[name = string("coreml_update_state_64")];
            tensor<int32, [4]> value_states_27_perm_0 = const()[name = string("value_states_27_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_5_stride_0 = const()[name = string("value_cache_internal_tensor_assign_5_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_5_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_5_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_5_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_5_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_5_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_5_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_27_cast_fp16 = transpose(perm = value_states_27_perm_0, x = var_2278_cast_fp16)[name = string("transpose_157")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_5_cast_fp16 = slice_update(begin = concat_53, begin_mask = value_cache_internal_tensor_assign_5_begin_mask_0, end = concat_54, end_mask = value_cache_internal_tensor_assign_5_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_5_squeeze_mask_0, stride = value_cache_internal_tensor_assign_5_stride_0, update = value_states_27_cast_fp16, x = coreml_update_state_63)[name = string("value_cache_internal_tensor_assign_5_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_5_cast_fp16, input = value_cache)[name = string("coreml_update_state_65_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_65 = read_state(input = value_cache)[name = string("coreml_update_state_65")];
            tensor<int32, [4]> var_2372_begin_0 = const()[name = string("op_2372_begin_0"), val = tensor<int32, [4]>([4, 0, 0, 0])];
            tensor<int32, [4]> var_2372_end_0 = const()[name = string("op_2372_end_0"), val = tensor<int32, [4]>([5, 2, 2048, 128])];
            tensor<bool, [4]> var_2372_end_mask_0 = const()[name = string("op_2372_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2372_cast_fp16 = slice_by_index(begin = var_2372_begin_0, end = var_2372_end_0, end_mask = var_2372_end_mask_0, x = coreml_update_state_64)[name = string("op_2372_cast_fp16")];
            tensor<int32, [2]> tile_8 = const()[name = string("tile_8"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2375_axis_0 = const()[name = string("op_2375_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2375_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2375_cast_fp16_1 = split(axis = var_2375_axis_0, split_sizes = tile_8, x = var_2372_cast_fp16)[name = string("op_2375_cast_fp16")];
            tensor<int32, [4]> var_2382_begin_0 = const()[name = string("op_2382_begin_0"), val = tensor<int32, [4]>([4, 0, 0, 0])];
            tensor<int32, [4]> var_2382_end_0 = const()[name = string("op_2382_end_0"), val = tensor<int32, [4]>([5, 2, 2048, 128])];
            tensor<bool, [4]> var_2382_end_mask_0 = const()[name = string("op_2382_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2382_cast_fp16 = slice_by_index(begin = var_2382_begin_0, end = var_2382_end_0, end_mask = var_2382_end_mask_0, x = coreml_update_state_65)[name = string("op_2382_cast_fp16")];
            tensor<int32, [2]> tile_9 = const()[name = string("tile_9"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2385_axis_0 = const()[name = string("op_2385_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2385_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2385_cast_fp16_1 = split(axis = var_2385_axis_0, split_sizes = tile_9, x = var_2382_cast_fp16)[name = string("op_2385_cast_fp16")];
            tensor<int32, [2]> var_2388_split_sizes_0 = const()[name = string("op_2388_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_2388_axis_0 = const()[name = string("op_2388_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_2388_0, tensor<fp16, [1, 8, 128, 1]> var_2388_1 = split(axis = var_2388_axis_0, split_sizes = var_2388_split_sizes_0, x = query_states_27_cast_fp16)[name = string("op_2388")];
            bool attn_weights_65_transpose_x_0 = const()[name = string("attn_weights_65_transpose_x_0"), val = bool(false)];
            bool attn_weights_65_transpose_y_0 = const()[name = string("attn_weights_65_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_65_cast_fp16 = matmul(transpose_x = attn_weights_65_transpose_x_0, transpose_y = attn_weights_65_transpose_y_0, x = var_2375_cast_fp16_0, y = var_2388_0)[name = string("attn_weights_65_cast_fp16")];
            fp16 var_2391_to_fp16 = const()[name = string("op_2391_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_67_cast_fp16 = mul(x = attn_weights_65_cast_fp16, y = var_2391_to_fp16)[name = string("attn_weights_67_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_69_cast_fp16 = add(x = attn_weights_67_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_69_cast_fp16")];
            int32 var_2395 = const()[name = string("op_2395"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_71_cast_fp16 = softmax(axis = var_2395, x = attn_weights_69_cast_fp16)[name = string("attn_weights_71_cast_fp16")];
            bool var_2401_transpose_x_1 = const()[name = string("op_2401_transpose_x_1"), val = bool(true)];
            bool var_2401_transpose_y_1 = const()[name = string("op_2401_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_2401_cast_fp16 = matmul(transpose_x = var_2401_transpose_x_1, transpose_y = var_2401_transpose_y_1, x = attn_weights_71_cast_fp16, y = var_2385_cast_fp16_0)[name = string("op_2401_cast_fp16")];
            bool attn_weights_73_transpose_x_0 = const()[name = string("attn_weights_73_transpose_x_0"), val = bool(false)];
            bool attn_weights_73_transpose_y_0 = const()[name = string("attn_weights_73_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_73_cast_fp16 = matmul(transpose_x = attn_weights_73_transpose_x_0, transpose_y = attn_weights_73_transpose_y_0, x = var_2375_cast_fp16_1, y = var_2388_1)[name = string("attn_weights_73_cast_fp16")];
            fp16 var_2403_to_fp16 = const()[name = string("op_2403_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_75_cast_fp16 = mul(x = attn_weights_73_cast_fp16, y = var_2403_to_fp16)[name = string("attn_weights_75_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_77_cast_fp16 = add(x = attn_weights_75_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_77_cast_fp16")];
            int32 var_2407 = const()[name = string("op_2407"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_79_cast_fp16 = softmax(axis = var_2407, x = attn_weights_77_cast_fp16)[name = string("attn_weights_79_cast_fp16")];
            bool attn_output_33_transpose_x_1 = const()[name = string("attn_output_33_transpose_x_1"), val = bool(true)];
            bool attn_output_33_transpose_y_1 = const()[name = string("attn_output_33_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_33_cast_fp16 = matmul(transpose_x = attn_output_33_transpose_x_1, transpose_y = attn_output_33_transpose_y_1, x = attn_weights_79_cast_fp16, y = var_2385_cast_fp16_1)[name = string("attn_output_33_cast_fp16")];
            int32 var_2415 = const()[name = string("op_2415"), val = int32(1)];
            bool attn_output_35_interleave_0 = const()[name = string("attn_output_35_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_35_cast_fp16 = concat(axis = var_2415, interleave = attn_output_35_interleave_0, values = (var_2401_cast_fp16, attn_output_33_cast_fp16))[name = string("attn_output_35_cast_fp16")];
            tensor<int32, [4]> var_2419_perm_0 = const()[name = string("op_2419_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_59x = const()[name = string("concat_59x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_2419_cast_fp16 = transpose(perm = var_2419_perm_0, x = attn_output_35_cast_fp16)[name = string("transpose_156")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_39_cast_fp16 = reshape(shape = concat_59x, x = var_2419_cast_fp16)[name = string("attn_output_39_cast_fp16")];
            tensor<int32, [2]> hidden_states_43_strides_0 = const()[name = string("hidden_states_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_43_pad_type_0 = const()[name = string("hidden_states_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_43_pad_0 = const()[name = string("hidden_states_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_43_dilations_0 = const()[name = string("hidden_states_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_43_groups_0 = const()[name = string("hidden_states_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_43_cast_fp16 = conv(dilations = hidden_states_43_dilations_0, groups = hidden_states_43_groups_0, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = hidden_states_43_strides_0, weight = layers_4_self_attn_o_proj_weight_cast_fp16, x = attn_output_39_cast_fp16)[name = string("hidden_states_43_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_45_cast_fp16 = add(x = hidden_states_39_cast_fp16, y = hidden_states_43_cast_fp16)[name = string("hidden_states_45_cast_fp16")];
            fp16 const_50_promoted_to_fp16 = const()[name = string("const_50_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_2452_cast_fp16 = mul(x = hidden_states_45_cast_fp16, y = const_50_promoted_to_fp16)[name = string("op_2452_cast_fp16")];
            int32 var_2450 = const()[name = string("op_2450"), val = int32(1)];
            bool doubled_37_interleave_0 = const()[name = string("doubled_37_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_37_cast_fp16 = concat(axis = var_2450, interleave = doubled_37_interleave_0, values = (hidden_states_45_cast_fp16, var_2452_cast_fp16))[name = string("doubled_37_cast_fp16")];
            tensor<int32, [1]> out_19_axes_0 = const()[name = string("out_19_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_19_gamma_0_to_fp16 = const()[name = string("out_19_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1274748864)))];
            fp16 var_2462_to_fp16 = const()[name = string("op_2462_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_19_cast_fp16 = layer_norm(axes = out_19_axes_0, epsilon = var_2462_to_fp16, gamma = out_19_gamma_0_to_fp16, x = doubled_37_cast_fp16)[name = string("out_19_cast_fp16")];
            tensor<int32, [2]> var_2473_split_sizes_0 = const()[name = string("op_2473_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2473_axis_0 = const()[name = string("op_2473_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_2473_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_2473_cast_fp16_1 = split(axis = var_2473_axis_0, split_sizes = var_2473_split_sizes_0, x = out_19_cast_fp16)[name = string("op_2473_cast_fp16")];
            tensor<int32, [2]> input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_9_cast_fp16 = conv(dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = layers_4_mlp_gate_proj_weight_cast_fp16, x = var_2473_cast_fp16_0)[name = string("input_9_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_2490_cast_fp16 = silu(x = input_9_cast_fp16)[name = string("op_2490_cast_fp16")];
            tensor<int32, [2]> var_2496_strides_0 = const()[name = string("op_2496_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_2496_pad_type_0 = const()[name = string("op_2496_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_2496_pad_0 = const()[name = string("op_2496_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_2496_dilations_0 = const()[name = string("op_2496_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2496_groups_0 = const()[name = string("op_2496_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_2496_cast_fp16 = conv(dilations = var_2496_dilations_0, groups = var_2496_groups_0, pad = var_2496_pad_0, pad_type = var_2496_pad_type_0, strides = var_2496_strides_0, weight = layers_4_mlp_up_proj_weight_cast_fp16, x = var_2473_cast_fp16_0)[name = string("op_2496_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_49_cast_fp16 = mul(x = var_2490_cast_fp16, y = var_2496_cast_fp16)[name = string("x_49_cast_fp16")];
            tensor<int32, [2]> hidden_states_47_strides_0 = const()[name = string("hidden_states_47_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_47_pad_type_0 = const()[name = string("hidden_states_47_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_47_pad_0 = const()[name = string("hidden_states_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_47_dilations_0 = const()[name = string("hidden_states_47_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_47_groups_0 = const()[name = string("hidden_states_47_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_47_cast_fp16 = conv(dilations = hidden_states_47_dilations_0, groups = hidden_states_47_groups_0, pad = hidden_states_47_pad_0, pad_type = hidden_states_47_pad_type_0, strides = hidden_states_47_strides_0, weight = layers_4_mlp_down_proj_weight_cast_fp16, x = x_49_cast_fp16)[name = string("hidden_states_47_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_49_cast_fp16 = add(x = hidden_states_45_cast_fp16, y = hidden_states_47_cast_fp16)[name = string("hidden_states_49_cast_fp16")];
            fp16 const_52_promoted_to_fp16 = const()[name = string("const_52_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_2514_cast_fp16 = mul(x = hidden_states_49_cast_fp16, y = const_52_promoted_to_fp16)[name = string("op_2514_cast_fp16")];
            int32 var_2512 = const()[name = string("op_2512"), val = int32(1)];
            bool doubled_41_interleave_0 = const()[name = string("doubled_41_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_41_cast_fp16 = concat(axis = var_2512, interleave = doubled_41_interleave_0, values = (hidden_states_49_cast_fp16, var_2514_cast_fp16))[name = string("doubled_41_cast_fp16")];
            tensor<int32, [1]> out_21_axes_0 = const()[name = string("out_21_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_21_gamma_0_to_fp16 = const()[name = string("out_21_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1274757120)))];
            fp16 var_2524_to_fp16 = const()[name = string("op_2524_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_21_cast_fp16 = layer_norm(axes = out_21_axes_0, epsilon = var_2524_to_fp16, gamma = out_21_gamma_0_to_fp16, x = doubled_41_cast_fp16)[name = string("out_21_cast_fp16")];
            tensor<int32, [2]> var_2535_split_sizes_0 = const()[name = string("op_2535_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2535_axis_0 = const()[name = string("op_2535_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_2535_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_2535_cast_fp16_1 = split(axis = var_2535_axis_0, split_sizes = var_2535_split_sizes_0, x = out_21_cast_fp16)[name = string("op_2535_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_5_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_5_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1274765376)))];
            tensor<int32, [2]> query_states_31_strides_0 = const()[name = string("query_states_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_31_pad_type_0 = const()[name = string("query_states_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_31_pad_0 = const()[name = string("query_states_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_31_dilations_0 = const()[name = string("query_states_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_31_groups_0 = const()[name = string("query_states_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_31_cast_fp16 = conv(dilations = query_states_31_dilations_0, groups = query_states_31_groups_0, pad = query_states_31_pad_0, pad_type = query_states_31_pad_type_0, strides = query_states_31_strides_0, weight = layers_5_self_attn_q_proj_weight_to_fp16, x = var_2535_cast_fp16_0)[name = string("query_states_31_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_5_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_5_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1283154048)))];
            tensor<int32, [2]> key_states_51_strides_0 = const()[name = string("key_states_51_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_51_pad_type_0 = const()[name = string("key_states_51_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_51_pad_0 = const()[name = string("key_states_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_51_dilations_0 = const()[name = string("key_states_51_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_51_groups_0 = const()[name = string("key_states_51_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_51_cast_fp16 = conv(dilations = key_states_51_dilations_0, groups = key_states_51_groups_0, pad = key_states_51_pad_0, pad_type = key_states_51_pad_type_0, strides = key_states_51_strides_0, weight = layers_5_self_attn_k_proj_weight_to_fp16, x = var_2535_cast_fp16_0)[name = string("key_states_51_cast_fp16")];
            tensor<int32, [2]> value_states_31_strides_0 = const()[name = string("value_states_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_31_pad_type_0 = const()[name = string("value_states_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_31_pad_0 = const()[name = string("value_states_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_31_dilations_0 = const()[name = string("value_states_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_31_groups_0 = const()[name = string("value_states_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_31_cast_fp16 = conv(dilations = value_states_31_dilations_0, groups = value_states_31_groups_0, pad = value_states_31_pad_0, pad_type = value_states_31_pad_type_0, strides = value_states_31_strides_0, weight = layers_5_self_attn_v_proj_weight_cast_fp16, x = var_2535_cast_fp16_0)[name = string("value_states_31_cast_fp16")];
            tensor<int32, [4]> concat_60x = const()[name = string("concat_60x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_51_cast_fp16 = reshape(shape = concat_60x, x = query_states_31_cast_fp16)[name = string("x_51_cast_fp16")];
            tensor<int32, [4]> concat_61x = const()[name = string("concat_61x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_2592_cast_fp16 = reshape(shape = concat_61x, x = key_states_51_cast_fp16)[name = string("op_2592_cast_fp16")];
            tensor<int32, [4]> concat_62x = const()[name = string("concat_62x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_2599_cast_fp16 = reshape(shape = concat_62x, x = value_states_31_cast_fp16)[name = string("op_2599_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_2603_cast_fp16 = mul(x = x_51_cast_fp16, y = var_859_cast_fp16)[name = string("op_2603_cast_fp16")];
            tensor<int32, [2]> var_2604_split_sizes_0 = const()[name = string("op_2604_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2604_axis_0 = const()[name = string("op_2604_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_2604_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_2604_cast_fp16_1 = split(axis = var_2604_axis_0, split_sizes = var_2604_split_sizes_0, x = x_51_cast_fp16)[name = string("op_2604_cast_fp16")];
            fp16 const_54_promoted_to_fp16 = const()[name = string("const_54_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_2606_cast_fp16 = mul(x = var_2604_cast_fp16_1, y = const_54_promoted_to_fp16)[name = string("op_2606_cast_fp16")];
            int32 var_2608 = const()[name = string("op_2608"), val = int32(-2)];
            bool var_2609_interleave_0 = const()[name = string("op_2609_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_2609_cast_fp16 = concat(axis = var_2608, interleave = var_2609_interleave_0, values = (var_2606_cast_fp16, var_2604_cast_fp16_0))[name = string("op_2609_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_2610_cast_fp16 = mul(x = var_2609_cast_fp16, y = var_866_cast_fp16)[name = string("op_2610_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_33_cast_fp16 = add(x = var_2603_cast_fp16, y = var_2610_cast_fp16)[name = string("query_states_33_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_2616_cast_fp16 = mul(x = var_2592_cast_fp16, y = var_859_cast_fp16)[name = string("op_2616_cast_fp16")];
            tensor<int32, [2]> var_2617_split_sizes_0 = const()[name = string("op_2617_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2617_axis_0 = const()[name = string("op_2617_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_2617_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_2617_cast_fp16_1 = split(axis = var_2617_axis_0, split_sizes = var_2617_split_sizes_0, x = var_2592_cast_fp16)[name = string("op_2617_cast_fp16")];
            fp16 const_55_promoted_to_fp16 = const()[name = string("const_55_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_2619_cast_fp16 = mul(x = var_2617_cast_fp16_1, y = const_55_promoted_to_fp16)[name = string("op_2619_cast_fp16")];
            int32 var_2621 = const()[name = string("op_2621"), val = int32(-2)];
            bool var_2622_interleave_0 = const()[name = string("op_2622_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_2622_cast_fp16 = concat(axis = var_2621, interleave = var_2622_interleave_0, values = (var_2619_cast_fp16, var_2617_cast_fp16_0))[name = string("op_2622_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_2623_cast_fp16 = mul(x = var_2622_cast_fp16, y = var_866_cast_fp16)[name = string("op_2623_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_55_cast_fp16 = add(x = var_2616_cast_fp16, y = var_2623_cast_fp16)[name = string("key_states_55_cast_fp16")];
            tensor<int32, [1]> expand_dims_60 = const()[name = string("expand_dims_60"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> expand_dims_61 = const()[name = string("expand_dims_61"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_63 = const()[name = string("expand_dims_63"), val = tensor<int32, [1]>([0])];
            int32 concat_65_axis_0 = const()[name = string("concat_65_axis_0"), val = int32(0)];
            bool concat_65_interleave_0 = const()[name = string("concat_65_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_65 = concat(axis = concat_65_axis_0, interleave = concat_65_interleave_0, values = (expand_dims_60, expand_dims_61, position_id, expand_dims_63))[name = string("concat_65")];
            tensor<int32, [1]> expand_dims_64 = const()[name = string("expand_dims_64"), val = tensor<int32, [1]>([6])];
            tensor<int32, [1]> concat_66_values1_0 = const()[name = string("concat_66_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_66_values3_0 = const()[name = string("concat_66_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_66_axis_0 = const()[name = string("concat_66_axis_0"), val = int32(0)];
            bool concat_66_interleave_0 = const()[name = string("concat_66_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_66 = concat(axis = concat_66_axis_0, interleave = concat_66_interleave_0, values = (expand_dims_64, concat_66_values1_0, cache_position_end, concat_66_values3_0))[name = string("concat_66")];
            tensor<int32, [4]> key_states_57_perm_0 = const()[name = string("key_states_57_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_6_stride_0 = const()[name = string("key_cache_internal_tensor_assign_6_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_6_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_6_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_6_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_6_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_6_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_6_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_57_cast_fp16 = transpose(perm = key_states_57_perm_0, x = key_states_55_cast_fp16)[name = string("transpose_155")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_6_cast_fp16 = slice_update(begin = concat_65, begin_mask = key_cache_internal_tensor_assign_6_begin_mask_0, end = concat_66, end_mask = key_cache_internal_tensor_assign_6_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_6_squeeze_mask_0, stride = key_cache_internal_tensor_assign_6_stride_0, update = key_states_57_cast_fp16, x = coreml_update_state_64)[name = string("key_cache_internal_tensor_assign_6_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_6_cast_fp16, input = key_cache)[name = string("coreml_update_state_66_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_66 = read_state(input = key_cache)[name = string("coreml_update_state_66")];
            tensor<int32, [4]> value_states_33_perm_0 = const()[name = string("value_states_33_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_6_stride_0 = const()[name = string("value_cache_internal_tensor_assign_6_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_6_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_6_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_6_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_6_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_6_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_6_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_33_cast_fp16 = transpose(perm = value_states_33_perm_0, x = var_2599_cast_fp16)[name = string("transpose_154")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_6_cast_fp16 = slice_update(begin = concat_65, begin_mask = value_cache_internal_tensor_assign_6_begin_mask_0, end = concat_66, end_mask = value_cache_internal_tensor_assign_6_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_6_squeeze_mask_0, stride = value_cache_internal_tensor_assign_6_stride_0, update = value_states_33_cast_fp16, x = coreml_update_state_65)[name = string("value_cache_internal_tensor_assign_6_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_6_cast_fp16, input = value_cache)[name = string("coreml_update_state_67_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_67 = read_state(input = value_cache)[name = string("coreml_update_state_67")];
            tensor<int32, [4]> var_2693_begin_0 = const()[name = string("op_2693_begin_0"), val = tensor<int32, [4]>([5, 0, 0, 0])];
            tensor<int32, [4]> var_2693_end_0 = const()[name = string("op_2693_end_0"), val = tensor<int32, [4]>([6, 2, 2048, 128])];
            tensor<bool, [4]> var_2693_end_mask_0 = const()[name = string("op_2693_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2693_cast_fp16 = slice_by_index(begin = var_2693_begin_0, end = var_2693_end_0, end_mask = var_2693_end_mask_0, x = coreml_update_state_66)[name = string("op_2693_cast_fp16")];
            tensor<int32, [2]> tile_10 = const()[name = string("tile_10"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2696_axis_0 = const()[name = string("op_2696_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2696_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2696_cast_fp16_1 = split(axis = var_2696_axis_0, split_sizes = tile_10, x = var_2693_cast_fp16)[name = string("op_2696_cast_fp16")];
            tensor<int32, [4]> var_2703_begin_0 = const()[name = string("op_2703_begin_0"), val = tensor<int32, [4]>([5, 0, 0, 0])];
            tensor<int32, [4]> var_2703_end_0 = const()[name = string("op_2703_end_0"), val = tensor<int32, [4]>([6, 2, 2048, 128])];
            tensor<bool, [4]> var_2703_end_mask_0 = const()[name = string("op_2703_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2703_cast_fp16 = slice_by_index(begin = var_2703_begin_0, end = var_2703_end_0, end_mask = var_2703_end_mask_0, x = coreml_update_state_67)[name = string("op_2703_cast_fp16")];
            tensor<int32, [2]> tile_11 = const()[name = string("tile_11"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2706_axis_0 = const()[name = string("op_2706_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2706_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2706_cast_fp16_1 = split(axis = var_2706_axis_0, split_sizes = tile_11, x = var_2703_cast_fp16)[name = string("op_2706_cast_fp16")];
            tensor<int32, [2]> var_2709_split_sizes_0 = const()[name = string("op_2709_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_2709_axis_0 = const()[name = string("op_2709_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_2709_0, tensor<fp16, [1, 8, 128, 1]> var_2709_1 = split(axis = var_2709_axis_0, split_sizes = var_2709_split_sizes_0, x = query_states_33_cast_fp16)[name = string("op_2709")];
            bool attn_weights_81_transpose_x_0 = const()[name = string("attn_weights_81_transpose_x_0"), val = bool(false)];
            bool attn_weights_81_transpose_y_0 = const()[name = string("attn_weights_81_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_81_cast_fp16 = matmul(transpose_x = attn_weights_81_transpose_x_0, transpose_y = attn_weights_81_transpose_y_0, x = var_2696_cast_fp16_0, y = var_2709_0)[name = string("attn_weights_81_cast_fp16")];
            fp16 var_2712_to_fp16 = const()[name = string("op_2712_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_83_cast_fp16 = mul(x = attn_weights_81_cast_fp16, y = var_2712_to_fp16)[name = string("attn_weights_83_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_85_cast_fp16 = add(x = attn_weights_83_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_85_cast_fp16")];
            int32 var_2716 = const()[name = string("op_2716"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_87_cast_fp16 = softmax(axis = var_2716, x = attn_weights_85_cast_fp16)[name = string("attn_weights_87_cast_fp16")];
            bool var_2722_transpose_x_1 = const()[name = string("op_2722_transpose_x_1"), val = bool(true)];
            bool var_2722_transpose_y_1 = const()[name = string("op_2722_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_2722_cast_fp16 = matmul(transpose_x = var_2722_transpose_x_1, transpose_y = var_2722_transpose_y_1, x = attn_weights_87_cast_fp16, y = var_2706_cast_fp16_0)[name = string("op_2722_cast_fp16")];
            bool attn_weights_89_transpose_x_0 = const()[name = string("attn_weights_89_transpose_x_0"), val = bool(false)];
            bool attn_weights_89_transpose_y_0 = const()[name = string("attn_weights_89_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_89_cast_fp16 = matmul(transpose_x = attn_weights_89_transpose_x_0, transpose_y = attn_weights_89_transpose_y_0, x = var_2696_cast_fp16_1, y = var_2709_1)[name = string("attn_weights_89_cast_fp16")];
            fp16 var_2724_to_fp16 = const()[name = string("op_2724_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_91_cast_fp16 = mul(x = attn_weights_89_cast_fp16, y = var_2724_to_fp16)[name = string("attn_weights_91_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_93_cast_fp16 = add(x = attn_weights_91_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_93_cast_fp16")];
            int32 var_2728 = const()[name = string("op_2728"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_95_cast_fp16 = softmax(axis = var_2728, x = attn_weights_93_cast_fp16)[name = string("attn_weights_95_cast_fp16")];
            bool attn_output_41_transpose_x_1 = const()[name = string("attn_output_41_transpose_x_1"), val = bool(true)];
            bool attn_output_41_transpose_y_1 = const()[name = string("attn_output_41_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_41_cast_fp16 = matmul(transpose_x = attn_output_41_transpose_x_1, transpose_y = attn_output_41_transpose_y_1, x = attn_weights_95_cast_fp16, y = var_2706_cast_fp16_1)[name = string("attn_output_41_cast_fp16")];
            int32 var_2736 = const()[name = string("op_2736"), val = int32(1)];
            bool attn_output_43_interleave_0 = const()[name = string("attn_output_43_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_43_cast_fp16 = concat(axis = var_2736, interleave = attn_output_43_interleave_0, values = (var_2722_cast_fp16, attn_output_41_cast_fp16))[name = string("attn_output_43_cast_fp16")];
            tensor<int32, [4]> var_2740_perm_0 = const()[name = string("op_2740_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_71x = const()[name = string("concat_71x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_2740_cast_fp16 = transpose(perm = var_2740_perm_0, x = attn_output_43_cast_fp16)[name = string("transpose_153")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_47_cast_fp16 = reshape(shape = concat_71x, x = var_2740_cast_fp16)[name = string("attn_output_47_cast_fp16")];
            tensor<int32, [2]> hidden_states_53_strides_0 = const()[name = string("hidden_states_53_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_53_pad_type_0 = const()[name = string("hidden_states_53_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_53_pad_0 = const()[name = string("hidden_states_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_53_dilations_0 = const()[name = string("hidden_states_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_53_groups_0 = const()[name = string("hidden_states_53_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_53_cast_fp16 = conv(dilations = hidden_states_53_dilations_0, groups = hidden_states_53_groups_0, pad = hidden_states_53_pad_0, pad_type = hidden_states_53_pad_type_0, strides = hidden_states_53_strides_0, weight = layers_5_self_attn_o_proj_weight_cast_fp16, x = attn_output_47_cast_fp16)[name = string("hidden_states_53_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_55_cast_fp16 = add(x = hidden_states_49_cast_fp16, y = hidden_states_53_cast_fp16)[name = string("hidden_states_55_cast_fp16")];
            fp16 const_60_promoted_to_fp16 = const()[name = string("const_60_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_2773_cast_fp16 = mul(x = hidden_states_55_cast_fp16, y = const_60_promoted_to_fp16)[name = string("op_2773_cast_fp16")];
            int32 var_2771 = const()[name = string("op_2771"), val = int32(1)];
            bool doubled_45_interleave_0 = const()[name = string("doubled_45_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_45_cast_fp16 = concat(axis = var_2771, interleave = doubled_45_interleave_0, values = (hidden_states_55_cast_fp16, var_2773_cast_fp16))[name = string("doubled_45_cast_fp16")];
            tensor<int32, [1]> out_23_axes_0 = const()[name = string("out_23_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_23_gamma_0_to_fp16 = const()[name = string("out_23_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1284202688)))];
            fp16 var_2783_to_fp16 = const()[name = string("op_2783_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_23_cast_fp16 = layer_norm(axes = out_23_axes_0, epsilon = var_2783_to_fp16, gamma = out_23_gamma_0_to_fp16, x = doubled_45_cast_fp16)[name = string("out_23_cast_fp16")];
            tensor<int32, [2]> var_2794_split_sizes_0 = const()[name = string("op_2794_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2794_axis_0 = const()[name = string("op_2794_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_2794_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_2794_cast_fp16_1 = split(axis = var_2794_axis_0, split_sizes = var_2794_split_sizes_0, x = out_23_cast_fp16)[name = string("op_2794_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_5_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_5_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1284210944)))];
            tensor<int32, [2]> input_11_strides_0 = const()[name = string("input_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_11_pad_type_0 = const()[name = string("input_11_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_11_dilations_0 = const()[name = string("input_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_11_groups_0 = const()[name = string("input_11_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_11_cast_fp16 = conv(dilations = input_11_dilations_0, groups = input_11_groups_0, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = input_11_strides_0, weight = layers_5_mlp_gate_proj_weight_to_fp16, x = var_2794_cast_fp16_0)[name = string("input_11_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_2811_cast_fp16 = silu(x = input_11_cast_fp16)[name = string("op_2811_cast_fp16")];
            tensor<int32, [2]> var_2817_strides_0 = const()[name = string("op_2817_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_2817_pad_type_0 = const()[name = string("op_2817_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_2817_pad_0 = const()[name = string("op_2817_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_2817_dilations_0 = const()[name = string("op_2817_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2817_groups_0 = const()[name = string("op_2817_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_2817_cast_fp16 = conv(dilations = var_2817_dilations_0, groups = var_2817_groups_0, pad = var_2817_pad_0, pad_type = var_2817_pad_type_0, strides = var_2817_strides_0, weight = layers_5_mlp_up_proj_weight_cast_fp16, x = var_2794_cast_fp16_0)[name = string("op_2817_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_59_cast_fp16 = mul(x = var_2811_cast_fp16, y = var_2817_cast_fp16)[name = string("x_59_cast_fp16")];
            tensor<int32, [2]> hidden_states_57_strides_0 = const()[name = string("hidden_states_57_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_57_pad_type_0 = const()[name = string("hidden_states_57_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_57_pad_0 = const()[name = string("hidden_states_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_57_dilations_0 = const()[name = string("hidden_states_57_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_57_groups_0 = const()[name = string("hidden_states_57_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_57_cast_fp16 = conv(dilations = hidden_states_57_dilations_0, groups = hidden_states_57_groups_0, pad = hidden_states_57_pad_0, pad_type = hidden_states_57_pad_type_0, strides = hidden_states_57_strides_0, weight = layers_5_mlp_down_proj_weight_cast_fp16, x = x_59_cast_fp16)[name = string("hidden_states_57_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_59_cast_fp16 = add(x = hidden_states_55_cast_fp16, y = hidden_states_57_cast_fp16)[name = string("hidden_states_59_cast_fp16")];
            fp16 const_62_promoted_to_fp16 = const()[name = string("const_62_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_2835_cast_fp16 = mul(x = hidden_states_59_cast_fp16, y = const_62_promoted_to_fp16)[name = string("op_2835_cast_fp16")];
            int32 var_2833 = const()[name = string("op_2833"), val = int32(1)];
            bool doubled_49_interleave_0 = const()[name = string("doubled_49_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_49_cast_fp16 = concat(axis = var_2833, interleave = doubled_49_interleave_0, values = (hidden_states_59_cast_fp16, var_2835_cast_fp16))[name = string("doubled_49_cast_fp16")];
            tensor<int32, [1]> out_25_axes_0 = const()[name = string("out_25_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_25_gamma_0_to_fp16 = const()[name = string("out_25_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1309376832)))];
            fp16 var_2845_to_fp16 = const()[name = string("op_2845_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_25_cast_fp16 = layer_norm(axes = out_25_axes_0, epsilon = var_2845_to_fp16, gamma = out_25_gamma_0_to_fp16, x = doubled_49_cast_fp16)[name = string("out_25_cast_fp16")];
            tensor<int32, [2]> var_2856_split_sizes_0 = const()[name = string("op_2856_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2856_axis_0 = const()[name = string("op_2856_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_2856_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_2856_cast_fp16_1 = split(axis = var_2856_axis_0, split_sizes = var_2856_split_sizes_0, x = out_25_cast_fp16)[name = string("op_2856_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_6_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_6_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1309385088)))];
            tensor<int32, [2]> query_states_37_strides_0 = const()[name = string("query_states_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_37_pad_type_0 = const()[name = string("query_states_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_37_pad_0 = const()[name = string("query_states_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_37_dilations_0 = const()[name = string("query_states_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_37_groups_0 = const()[name = string("query_states_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_37_cast_fp16 = conv(dilations = query_states_37_dilations_0, groups = query_states_37_groups_0, pad = query_states_37_pad_0, pad_type = query_states_37_pad_type_0, strides = query_states_37_strides_0, weight = layers_6_self_attn_q_proj_weight_to_fp16, x = var_2856_cast_fp16_0)[name = string("query_states_37_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_6_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_6_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1317773760)))];
            tensor<int32, [2]> key_states_61_strides_0 = const()[name = string("key_states_61_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_61_pad_type_0 = const()[name = string("key_states_61_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_61_pad_0 = const()[name = string("key_states_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_61_dilations_0 = const()[name = string("key_states_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_61_groups_0 = const()[name = string("key_states_61_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_61_cast_fp16 = conv(dilations = key_states_61_dilations_0, groups = key_states_61_groups_0, pad = key_states_61_pad_0, pad_type = key_states_61_pad_type_0, strides = key_states_61_strides_0, weight = layers_6_self_attn_k_proj_weight_to_fp16, x = var_2856_cast_fp16_0)[name = string("key_states_61_cast_fp16")];
            tensor<int32, [2]> value_states_37_strides_0 = const()[name = string("value_states_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_37_pad_type_0 = const()[name = string("value_states_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_37_pad_0 = const()[name = string("value_states_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_37_dilations_0 = const()[name = string("value_states_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_37_groups_0 = const()[name = string("value_states_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_37_cast_fp16 = conv(dilations = value_states_37_dilations_0, groups = value_states_37_groups_0, pad = value_states_37_pad_0, pad_type = value_states_37_pad_type_0, strides = value_states_37_strides_0, weight = layers_6_self_attn_v_proj_weight_cast_fp16, x = var_2856_cast_fp16_0)[name = string("value_states_37_cast_fp16")];
            tensor<int32, [4]> concat_72x = const()[name = string("concat_72x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_61_cast_fp16 = reshape(shape = concat_72x, x = query_states_37_cast_fp16)[name = string("x_61_cast_fp16")];
            tensor<int32, [4]> concat_73x = const()[name = string("concat_73x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_2913_cast_fp16 = reshape(shape = concat_73x, x = key_states_61_cast_fp16)[name = string("op_2913_cast_fp16")];
            tensor<int32, [4]> concat_74x = const()[name = string("concat_74x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_2920_cast_fp16 = reshape(shape = concat_74x, x = value_states_37_cast_fp16)[name = string("op_2920_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_2924_cast_fp16 = mul(x = x_61_cast_fp16, y = var_859_cast_fp16)[name = string("op_2924_cast_fp16")];
            tensor<int32, [2]> var_2925_split_sizes_0 = const()[name = string("op_2925_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2925_axis_0 = const()[name = string("op_2925_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_2925_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_2925_cast_fp16_1 = split(axis = var_2925_axis_0, split_sizes = var_2925_split_sizes_0, x = x_61_cast_fp16)[name = string("op_2925_cast_fp16")];
            fp16 const_64_promoted_to_fp16 = const()[name = string("const_64_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_2927_cast_fp16 = mul(x = var_2925_cast_fp16_1, y = const_64_promoted_to_fp16)[name = string("op_2927_cast_fp16")];
            int32 var_2929 = const()[name = string("op_2929"), val = int32(-2)];
            bool var_2930_interleave_0 = const()[name = string("op_2930_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_2930_cast_fp16 = concat(axis = var_2929, interleave = var_2930_interleave_0, values = (var_2927_cast_fp16, var_2925_cast_fp16_0))[name = string("op_2930_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_2931_cast_fp16 = mul(x = var_2930_cast_fp16, y = var_866_cast_fp16)[name = string("op_2931_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_39_cast_fp16 = add(x = var_2924_cast_fp16, y = var_2931_cast_fp16)[name = string("query_states_39_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_2937_cast_fp16 = mul(x = var_2913_cast_fp16, y = var_859_cast_fp16)[name = string("op_2937_cast_fp16")];
            tensor<int32, [2]> var_2938_split_sizes_0 = const()[name = string("op_2938_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2938_axis_0 = const()[name = string("op_2938_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_2938_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_2938_cast_fp16_1 = split(axis = var_2938_axis_0, split_sizes = var_2938_split_sizes_0, x = var_2913_cast_fp16)[name = string("op_2938_cast_fp16")];
            fp16 const_65_promoted_to_fp16 = const()[name = string("const_65_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_2940_cast_fp16 = mul(x = var_2938_cast_fp16_1, y = const_65_promoted_to_fp16)[name = string("op_2940_cast_fp16")];
            int32 var_2942 = const()[name = string("op_2942"), val = int32(-2)];
            bool var_2943_interleave_0 = const()[name = string("op_2943_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_2943_cast_fp16 = concat(axis = var_2942, interleave = var_2943_interleave_0, values = (var_2940_cast_fp16, var_2938_cast_fp16_0))[name = string("op_2943_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_2944_cast_fp16 = mul(x = var_2943_cast_fp16, y = var_866_cast_fp16)[name = string("op_2944_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_65_cast_fp16 = add(x = var_2937_cast_fp16, y = var_2944_cast_fp16)[name = string("key_states_65_cast_fp16")];
            tensor<int32, [1]> expand_dims_72 = const()[name = string("expand_dims_72"), val = tensor<int32, [1]>([6])];
            tensor<int32, [1]> expand_dims_73 = const()[name = string("expand_dims_73"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_75 = const()[name = string("expand_dims_75"), val = tensor<int32, [1]>([0])];
            int32 concat_77_axis_0 = const()[name = string("concat_77_axis_0"), val = int32(0)];
            bool concat_77_interleave_0 = const()[name = string("concat_77_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_77 = concat(axis = concat_77_axis_0, interleave = concat_77_interleave_0, values = (expand_dims_72, expand_dims_73, position_id, expand_dims_75))[name = string("concat_77")];
            tensor<int32, [1]> expand_dims_76 = const()[name = string("expand_dims_76"), val = tensor<int32, [1]>([7])];
            tensor<int32, [1]> concat_78_values1_0 = const()[name = string("concat_78_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_78_values3_0 = const()[name = string("concat_78_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_78_axis_0 = const()[name = string("concat_78_axis_0"), val = int32(0)];
            bool concat_78_interleave_0 = const()[name = string("concat_78_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_78 = concat(axis = concat_78_axis_0, interleave = concat_78_interleave_0, values = (expand_dims_76, concat_78_values1_0, cache_position_end, concat_78_values3_0))[name = string("concat_78")];
            tensor<int32, [4]> key_states_67_perm_0 = const()[name = string("key_states_67_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_7_stride_0 = const()[name = string("key_cache_internal_tensor_assign_7_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_7_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_7_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_7_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_7_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_7_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_7_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_67_cast_fp16 = transpose(perm = key_states_67_perm_0, x = key_states_65_cast_fp16)[name = string("transpose_152")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_7_cast_fp16 = slice_update(begin = concat_77, begin_mask = key_cache_internal_tensor_assign_7_begin_mask_0, end = concat_78, end_mask = key_cache_internal_tensor_assign_7_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_7_squeeze_mask_0, stride = key_cache_internal_tensor_assign_7_stride_0, update = key_states_67_cast_fp16, x = coreml_update_state_66)[name = string("key_cache_internal_tensor_assign_7_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_7_cast_fp16, input = key_cache)[name = string("coreml_update_state_68_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_68 = read_state(input = key_cache)[name = string("coreml_update_state_68")];
            tensor<int32, [4]> value_states_39_perm_0 = const()[name = string("value_states_39_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_7_stride_0 = const()[name = string("value_cache_internal_tensor_assign_7_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_7_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_7_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_7_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_7_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_7_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_7_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_39_cast_fp16 = transpose(perm = value_states_39_perm_0, x = var_2920_cast_fp16)[name = string("transpose_151")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_7_cast_fp16 = slice_update(begin = concat_77, begin_mask = value_cache_internal_tensor_assign_7_begin_mask_0, end = concat_78, end_mask = value_cache_internal_tensor_assign_7_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_7_squeeze_mask_0, stride = value_cache_internal_tensor_assign_7_stride_0, update = value_states_39_cast_fp16, x = coreml_update_state_67)[name = string("value_cache_internal_tensor_assign_7_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_7_cast_fp16, input = value_cache)[name = string("coreml_update_state_69_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_69 = read_state(input = value_cache)[name = string("coreml_update_state_69")];
            tensor<int32, [4]> var_3014_begin_0 = const()[name = string("op_3014_begin_0"), val = tensor<int32, [4]>([6, 0, 0, 0])];
            tensor<int32, [4]> var_3014_end_0 = const()[name = string("op_3014_end_0"), val = tensor<int32, [4]>([7, 2, 2048, 128])];
            tensor<bool, [4]> var_3014_end_mask_0 = const()[name = string("op_3014_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3014_cast_fp16 = slice_by_index(begin = var_3014_begin_0, end = var_3014_end_0, end_mask = var_3014_end_mask_0, x = coreml_update_state_68)[name = string("op_3014_cast_fp16")];
            tensor<int32, [2]> tile_12 = const()[name = string("tile_12"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3017_axis_0 = const()[name = string("op_3017_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3017_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3017_cast_fp16_1 = split(axis = var_3017_axis_0, split_sizes = tile_12, x = var_3014_cast_fp16)[name = string("op_3017_cast_fp16")];
            tensor<int32, [4]> var_3024_begin_0 = const()[name = string("op_3024_begin_0"), val = tensor<int32, [4]>([6, 0, 0, 0])];
            tensor<int32, [4]> var_3024_end_0 = const()[name = string("op_3024_end_0"), val = tensor<int32, [4]>([7, 2, 2048, 128])];
            tensor<bool, [4]> var_3024_end_mask_0 = const()[name = string("op_3024_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3024_cast_fp16 = slice_by_index(begin = var_3024_begin_0, end = var_3024_end_0, end_mask = var_3024_end_mask_0, x = coreml_update_state_69)[name = string("op_3024_cast_fp16")];
            tensor<int32, [2]> tile_13 = const()[name = string("tile_13"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3027_axis_0 = const()[name = string("op_3027_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3027_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3027_cast_fp16_1 = split(axis = var_3027_axis_0, split_sizes = tile_13, x = var_3024_cast_fp16)[name = string("op_3027_cast_fp16")];
            tensor<int32, [2]> var_3030_split_sizes_0 = const()[name = string("op_3030_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3030_axis_0 = const()[name = string("op_3030_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_3030_0, tensor<fp16, [1, 8, 128, 1]> var_3030_1 = split(axis = var_3030_axis_0, split_sizes = var_3030_split_sizes_0, x = query_states_39_cast_fp16)[name = string("op_3030")];
            bool attn_weights_97_transpose_x_0 = const()[name = string("attn_weights_97_transpose_x_0"), val = bool(false)];
            bool attn_weights_97_transpose_y_0 = const()[name = string("attn_weights_97_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_97_cast_fp16 = matmul(transpose_x = attn_weights_97_transpose_x_0, transpose_y = attn_weights_97_transpose_y_0, x = var_3017_cast_fp16_0, y = var_3030_0)[name = string("attn_weights_97_cast_fp16")];
            fp16 var_3033_to_fp16 = const()[name = string("op_3033_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_99_cast_fp16 = mul(x = attn_weights_97_cast_fp16, y = var_3033_to_fp16)[name = string("attn_weights_99_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_101_cast_fp16 = add(x = attn_weights_99_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_101_cast_fp16")];
            int32 var_3037 = const()[name = string("op_3037"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_103_cast_fp16 = softmax(axis = var_3037, x = attn_weights_101_cast_fp16)[name = string("attn_weights_103_cast_fp16")];
            bool var_3043_transpose_x_1 = const()[name = string("op_3043_transpose_x_1"), val = bool(true)];
            bool var_3043_transpose_y_1 = const()[name = string("op_3043_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_3043_cast_fp16 = matmul(transpose_x = var_3043_transpose_x_1, transpose_y = var_3043_transpose_y_1, x = attn_weights_103_cast_fp16, y = var_3027_cast_fp16_0)[name = string("op_3043_cast_fp16")];
            bool attn_weights_105_transpose_x_0 = const()[name = string("attn_weights_105_transpose_x_0"), val = bool(false)];
            bool attn_weights_105_transpose_y_0 = const()[name = string("attn_weights_105_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_105_cast_fp16 = matmul(transpose_x = attn_weights_105_transpose_x_0, transpose_y = attn_weights_105_transpose_y_0, x = var_3017_cast_fp16_1, y = var_3030_1)[name = string("attn_weights_105_cast_fp16")];
            fp16 var_3045_to_fp16 = const()[name = string("op_3045_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_107_cast_fp16 = mul(x = attn_weights_105_cast_fp16, y = var_3045_to_fp16)[name = string("attn_weights_107_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_109_cast_fp16 = add(x = attn_weights_107_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_109_cast_fp16")];
            int32 var_3049 = const()[name = string("op_3049"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_111_cast_fp16 = softmax(axis = var_3049, x = attn_weights_109_cast_fp16)[name = string("attn_weights_111_cast_fp16")];
            bool attn_output_49_transpose_x_1 = const()[name = string("attn_output_49_transpose_x_1"), val = bool(true)];
            bool attn_output_49_transpose_y_1 = const()[name = string("attn_output_49_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_49_cast_fp16 = matmul(transpose_x = attn_output_49_transpose_x_1, transpose_y = attn_output_49_transpose_y_1, x = attn_weights_111_cast_fp16, y = var_3027_cast_fp16_1)[name = string("attn_output_49_cast_fp16")];
            int32 var_3057 = const()[name = string("op_3057"), val = int32(1)];
            bool attn_output_51_interleave_0 = const()[name = string("attn_output_51_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_51_cast_fp16 = concat(axis = var_3057, interleave = attn_output_51_interleave_0, values = (var_3043_cast_fp16, attn_output_49_cast_fp16))[name = string("attn_output_51_cast_fp16")];
            tensor<int32, [4]> var_3061_perm_0 = const()[name = string("op_3061_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_83x = const()[name = string("concat_83x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_3061_cast_fp16 = transpose(perm = var_3061_perm_0, x = attn_output_51_cast_fp16)[name = string("transpose_150")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_55_cast_fp16 = reshape(shape = concat_83x, x = var_3061_cast_fp16)[name = string("attn_output_55_cast_fp16")];
            tensor<int32, [2]> hidden_states_63_strides_0 = const()[name = string("hidden_states_63_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_63_pad_type_0 = const()[name = string("hidden_states_63_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_63_pad_0 = const()[name = string("hidden_states_63_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_63_dilations_0 = const()[name = string("hidden_states_63_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_63_groups_0 = const()[name = string("hidden_states_63_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_63_cast_fp16 = conv(dilations = hidden_states_63_dilations_0, groups = hidden_states_63_groups_0, pad = hidden_states_63_pad_0, pad_type = hidden_states_63_pad_type_0, strides = hidden_states_63_strides_0, weight = layers_6_self_attn_o_proj_weight_cast_fp16, x = attn_output_55_cast_fp16)[name = string("hidden_states_63_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_65_cast_fp16 = add(x = hidden_states_59_cast_fp16, y = hidden_states_63_cast_fp16)[name = string("hidden_states_65_cast_fp16")];
            fp16 const_70_promoted_to_fp16 = const()[name = string("const_70_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_3094_cast_fp16 = mul(x = hidden_states_65_cast_fp16, y = const_70_promoted_to_fp16)[name = string("op_3094_cast_fp16")];
            int32 var_3092 = const()[name = string("op_3092"), val = int32(1)];
            bool doubled_53_interleave_0 = const()[name = string("doubled_53_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_53_cast_fp16 = concat(axis = var_3092, interleave = doubled_53_interleave_0, values = (hidden_states_65_cast_fp16, var_3094_cast_fp16))[name = string("doubled_53_cast_fp16")];
            tensor<int32, [1]> out_27_axes_0 = const()[name = string("out_27_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_27_gamma_0_to_fp16 = const()[name = string("out_27_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1318822400)))];
            fp16 var_3104_to_fp16 = const()[name = string("op_3104_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_27_cast_fp16 = layer_norm(axes = out_27_axes_0, epsilon = var_3104_to_fp16, gamma = out_27_gamma_0_to_fp16, x = doubled_53_cast_fp16)[name = string("out_27_cast_fp16")];
            tensor<int32, [2]> var_3115_split_sizes_0 = const()[name = string("op_3115_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3115_axis_0 = const()[name = string("op_3115_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_3115_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_3115_cast_fp16_1 = split(axis = var_3115_axis_0, split_sizes = var_3115_split_sizes_0, x = out_27_cast_fp16)[name = string("op_3115_cast_fp16")];
            tensor<int32, [2]> input_13_strides_0 = const()[name = string("input_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_13_pad_type_0 = const()[name = string("input_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_13_pad_0 = const()[name = string("input_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_13_dilations_0 = const()[name = string("input_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_13_groups_0 = const()[name = string("input_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_13_cast_fp16 = conv(dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = layers_6_mlp_gate_proj_weight_cast_fp16, x = var_3115_cast_fp16_0)[name = string("input_13_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_3132_cast_fp16 = silu(x = input_13_cast_fp16)[name = string("op_3132_cast_fp16")];
            tensor<int32, [2]> var_3138_strides_0 = const()[name = string("op_3138_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_3138_pad_type_0 = const()[name = string("op_3138_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_3138_pad_0 = const()[name = string("op_3138_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_3138_dilations_0 = const()[name = string("op_3138_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3138_groups_0 = const()[name = string("op_3138_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_3138_cast_fp16 = conv(dilations = var_3138_dilations_0, groups = var_3138_groups_0, pad = var_3138_pad_0, pad_type = var_3138_pad_type_0, strides = var_3138_strides_0, weight = layers_6_mlp_up_proj_weight_cast_fp16, x = var_3115_cast_fp16_0)[name = string("op_3138_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_69_cast_fp16 = mul(x = var_3132_cast_fp16, y = var_3138_cast_fp16)[name = string("x_69_cast_fp16")];
            tensor<int32, [2]> hidden_states_67_strides_0 = const()[name = string("hidden_states_67_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_67_pad_type_0 = const()[name = string("hidden_states_67_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_67_pad_0 = const()[name = string("hidden_states_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_67_dilations_0 = const()[name = string("hidden_states_67_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_67_groups_0 = const()[name = string("hidden_states_67_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_67_cast_fp16 = conv(dilations = hidden_states_67_dilations_0, groups = hidden_states_67_groups_0, pad = hidden_states_67_pad_0, pad_type = hidden_states_67_pad_type_0, strides = hidden_states_67_strides_0, weight = layers_6_mlp_down_proj_weight_cast_fp16, x = x_69_cast_fp16)[name = string("hidden_states_67_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_69_cast_fp16 = add(x = hidden_states_65_cast_fp16, y = hidden_states_67_cast_fp16)[name = string("hidden_states_69_cast_fp16")];
            fp16 const_72_promoted_to_fp16 = const()[name = string("const_72_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_3156_cast_fp16 = mul(x = hidden_states_69_cast_fp16, y = const_72_promoted_to_fp16)[name = string("op_3156_cast_fp16")];
            int32 var_3154 = const()[name = string("op_3154"), val = int32(1)];
            bool doubled_57_interleave_0 = const()[name = string("doubled_57_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_57_cast_fp16 = concat(axis = var_3154, interleave = doubled_57_interleave_0, values = (hidden_states_69_cast_fp16, var_3156_cast_fp16))[name = string("doubled_57_cast_fp16")];
            tensor<int32, [1]> out_29_axes_0 = const()[name = string("out_29_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_29_gamma_0_to_fp16 = const()[name = string("out_29_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1318830656)))];
            fp16 var_3166_to_fp16 = const()[name = string("op_3166_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_29_cast_fp16 = layer_norm(axes = out_29_axes_0, epsilon = var_3166_to_fp16, gamma = out_29_gamma_0_to_fp16, x = doubled_57_cast_fp16)[name = string("out_29_cast_fp16")];
            tensor<int32, [2]> var_3177_split_sizes_0 = const()[name = string("op_3177_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3177_axis_0 = const()[name = string("op_3177_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_3177_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_3177_cast_fp16_1 = split(axis = var_3177_axis_0, split_sizes = var_3177_split_sizes_0, x = out_29_cast_fp16)[name = string("op_3177_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_7_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_7_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1318838912)))];
            tensor<int32, [2]> query_states_43_strides_0 = const()[name = string("query_states_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_43_pad_type_0 = const()[name = string("query_states_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_43_pad_0 = const()[name = string("query_states_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_43_dilations_0 = const()[name = string("query_states_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_43_groups_0 = const()[name = string("query_states_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_43_cast_fp16 = conv(dilations = query_states_43_dilations_0, groups = query_states_43_groups_0, pad = query_states_43_pad_0, pad_type = query_states_43_pad_type_0, strides = query_states_43_strides_0, weight = layers_7_self_attn_q_proj_weight_to_fp16, x = var_3177_cast_fp16_0)[name = string("query_states_43_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_7_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_7_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1327227584)))];
            tensor<int32, [2]> key_states_71_strides_0 = const()[name = string("key_states_71_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_71_pad_type_0 = const()[name = string("key_states_71_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_71_pad_0 = const()[name = string("key_states_71_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_71_dilations_0 = const()[name = string("key_states_71_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_71_groups_0 = const()[name = string("key_states_71_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_71_cast_fp16 = conv(dilations = key_states_71_dilations_0, groups = key_states_71_groups_0, pad = key_states_71_pad_0, pad_type = key_states_71_pad_type_0, strides = key_states_71_strides_0, weight = layers_7_self_attn_k_proj_weight_to_fp16, x = var_3177_cast_fp16_0)[name = string("key_states_71_cast_fp16")];
            tensor<int32, [2]> value_states_43_strides_0 = const()[name = string("value_states_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_43_pad_type_0 = const()[name = string("value_states_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_43_pad_0 = const()[name = string("value_states_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_43_dilations_0 = const()[name = string("value_states_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_43_groups_0 = const()[name = string("value_states_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_43_cast_fp16 = conv(dilations = value_states_43_dilations_0, groups = value_states_43_groups_0, pad = value_states_43_pad_0, pad_type = value_states_43_pad_type_0, strides = value_states_43_strides_0, weight = layers_7_self_attn_v_proj_weight_cast_fp16, x = var_3177_cast_fp16_0)[name = string("value_states_43_cast_fp16")];
            tensor<int32, [4]> concat_84x = const()[name = string("concat_84x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_71_cast_fp16 = reshape(shape = concat_84x, x = query_states_43_cast_fp16)[name = string("x_71_cast_fp16")];
            tensor<int32, [4]> concat_85x = const()[name = string("concat_85x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_3234_cast_fp16 = reshape(shape = concat_85x, x = key_states_71_cast_fp16)[name = string("op_3234_cast_fp16")];
            tensor<int32, [4]> concat_86x = const()[name = string("concat_86x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_3241_cast_fp16 = reshape(shape = concat_86x, x = value_states_43_cast_fp16)[name = string("op_3241_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_3245_cast_fp16 = mul(x = x_71_cast_fp16, y = var_859_cast_fp16)[name = string("op_3245_cast_fp16")];
            tensor<int32, [2]> var_3246_split_sizes_0 = const()[name = string("op_3246_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3246_axis_0 = const()[name = string("op_3246_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_3246_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_3246_cast_fp16_1 = split(axis = var_3246_axis_0, split_sizes = var_3246_split_sizes_0, x = x_71_cast_fp16)[name = string("op_3246_cast_fp16")];
            fp16 const_74_promoted_to_fp16 = const()[name = string("const_74_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_3248_cast_fp16 = mul(x = var_3246_cast_fp16_1, y = const_74_promoted_to_fp16)[name = string("op_3248_cast_fp16")];
            int32 var_3250 = const()[name = string("op_3250"), val = int32(-2)];
            bool var_3251_interleave_0 = const()[name = string("op_3251_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_3251_cast_fp16 = concat(axis = var_3250, interleave = var_3251_interleave_0, values = (var_3248_cast_fp16, var_3246_cast_fp16_0))[name = string("op_3251_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_3252_cast_fp16 = mul(x = var_3251_cast_fp16, y = var_866_cast_fp16)[name = string("op_3252_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_45_cast_fp16 = add(x = var_3245_cast_fp16, y = var_3252_cast_fp16)[name = string("query_states_45_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_3258_cast_fp16 = mul(x = var_3234_cast_fp16, y = var_859_cast_fp16)[name = string("op_3258_cast_fp16")];
            tensor<int32, [2]> var_3259_split_sizes_0 = const()[name = string("op_3259_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3259_axis_0 = const()[name = string("op_3259_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_3259_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_3259_cast_fp16_1 = split(axis = var_3259_axis_0, split_sizes = var_3259_split_sizes_0, x = var_3234_cast_fp16)[name = string("op_3259_cast_fp16")];
            fp16 const_75_promoted_to_fp16 = const()[name = string("const_75_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_3261_cast_fp16 = mul(x = var_3259_cast_fp16_1, y = const_75_promoted_to_fp16)[name = string("op_3261_cast_fp16")];
            int32 var_3263 = const()[name = string("op_3263"), val = int32(-2)];
            bool var_3264_interleave_0 = const()[name = string("op_3264_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_3264_cast_fp16 = concat(axis = var_3263, interleave = var_3264_interleave_0, values = (var_3261_cast_fp16, var_3259_cast_fp16_0))[name = string("op_3264_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_3265_cast_fp16 = mul(x = var_3264_cast_fp16, y = var_866_cast_fp16)[name = string("op_3265_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_75_cast_fp16 = add(x = var_3258_cast_fp16, y = var_3265_cast_fp16)[name = string("key_states_75_cast_fp16")];
            tensor<int32, [1]> expand_dims_84 = const()[name = string("expand_dims_84"), val = tensor<int32, [1]>([7])];
            tensor<int32, [1]> expand_dims_85 = const()[name = string("expand_dims_85"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_87 = const()[name = string("expand_dims_87"), val = tensor<int32, [1]>([0])];
            int32 concat_89_axis_0 = const()[name = string("concat_89_axis_0"), val = int32(0)];
            bool concat_89_interleave_0 = const()[name = string("concat_89_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_89 = concat(axis = concat_89_axis_0, interleave = concat_89_interleave_0, values = (expand_dims_84, expand_dims_85, position_id, expand_dims_87))[name = string("concat_89")];
            tensor<int32, [1]> expand_dims_88 = const()[name = string("expand_dims_88"), val = tensor<int32, [1]>([8])];
            tensor<int32, [1]> concat_90_values1_0 = const()[name = string("concat_90_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_90_values3_0 = const()[name = string("concat_90_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_90_axis_0 = const()[name = string("concat_90_axis_0"), val = int32(0)];
            bool concat_90_interleave_0 = const()[name = string("concat_90_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_90 = concat(axis = concat_90_axis_0, interleave = concat_90_interleave_0, values = (expand_dims_88, concat_90_values1_0, cache_position_end, concat_90_values3_0))[name = string("concat_90")];
            tensor<int32, [4]> key_states_77_perm_0 = const()[name = string("key_states_77_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_8_stride_0 = const()[name = string("key_cache_internal_tensor_assign_8_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_8_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_8_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_8_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_8_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_8_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_8_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_77_cast_fp16 = transpose(perm = key_states_77_perm_0, x = key_states_75_cast_fp16)[name = string("transpose_149")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_8_cast_fp16 = slice_update(begin = concat_89, begin_mask = key_cache_internal_tensor_assign_8_begin_mask_0, end = concat_90, end_mask = key_cache_internal_tensor_assign_8_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_8_squeeze_mask_0, stride = key_cache_internal_tensor_assign_8_stride_0, update = key_states_77_cast_fp16, x = coreml_update_state_68)[name = string("key_cache_internal_tensor_assign_8_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_8_cast_fp16, input = key_cache)[name = string("coreml_update_state_70_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_70 = read_state(input = key_cache)[name = string("coreml_update_state_70")];
            tensor<int32, [4]> value_states_45_perm_0 = const()[name = string("value_states_45_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_8_stride_0 = const()[name = string("value_cache_internal_tensor_assign_8_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_8_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_8_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_8_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_8_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_8_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_8_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_45_cast_fp16 = transpose(perm = value_states_45_perm_0, x = var_3241_cast_fp16)[name = string("transpose_148")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_8_cast_fp16 = slice_update(begin = concat_89, begin_mask = value_cache_internal_tensor_assign_8_begin_mask_0, end = concat_90, end_mask = value_cache_internal_tensor_assign_8_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_8_squeeze_mask_0, stride = value_cache_internal_tensor_assign_8_stride_0, update = value_states_45_cast_fp16, x = coreml_update_state_69)[name = string("value_cache_internal_tensor_assign_8_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_8_cast_fp16, input = value_cache)[name = string("coreml_update_state_71_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_71 = read_state(input = value_cache)[name = string("coreml_update_state_71")];
            tensor<int32, [4]> var_3335_begin_0 = const()[name = string("op_3335_begin_0"), val = tensor<int32, [4]>([7, 0, 0, 0])];
            tensor<int32, [4]> var_3335_end_0 = const()[name = string("op_3335_end_0"), val = tensor<int32, [4]>([8, 2, 2048, 128])];
            tensor<bool, [4]> var_3335_end_mask_0 = const()[name = string("op_3335_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3335_cast_fp16 = slice_by_index(begin = var_3335_begin_0, end = var_3335_end_0, end_mask = var_3335_end_mask_0, x = coreml_update_state_70)[name = string("op_3335_cast_fp16")];
            tensor<int32, [2]> tile_14 = const()[name = string("tile_14"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3338_axis_0 = const()[name = string("op_3338_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3338_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3338_cast_fp16_1 = split(axis = var_3338_axis_0, split_sizes = tile_14, x = var_3335_cast_fp16)[name = string("op_3338_cast_fp16")];
            tensor<int32, [4]> var_3345_begin_0 = const()[name = string("op_3345_begin_0"), val = tensor<int32, [4]>([7, 0, 0, 0])];
            tensor<int32, [4]> var_3345_end_0 = const()[name = string("op_3345_end_0"), val = tensor<int32, [4]>([8, 2, 2048, 128])];
            tensor<bool, [4]> var_3345_end_mask_0 = const()[name = string("op_3345_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3345_cast_fp16 = slice_by_index(begin = var_3345_begin_0, end = var_3345_end_0, end_mask = var_3345_end_mask_0, x = coreml_update_state_71)[name = string("op_3345_cast_fp16")];
            tensor<int32, [2]> tile_15 = const()[name = string("tile_15"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3348_axis_0 = const()[name = string("op_3348_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3348_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3348_cast_fp16_1 = split(axis = var_3348_axis_0, split_sizes = tile_15, x = var_3345_cast_fp16)[name = string("op_3348_cast_fp16")];
            tensor<int32, [2]> var_3351_split_sizes_0 = const()[name = string("op_3351_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3351_axis_0 = const()[name = string("op_3351_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_3351_0, tensor<fp16, [1, 8, 128, 1]> var_3351_1 = split(axis = var_3351_axis_0, split_sizes = var_3351_split_sizes_0, x = query_states_45_cast_fp16)[name = string("op_3351")];
            bool attn_weights_113_transpose_x_0 = const()[name = string("attn_weights_113_transpose_x_0"), val = bool(false)];
            bool attn_weights_113_transpose_y_0 = const()[name = string("attn_weights_113_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_113_cast_fp16 = matmul(transpose_x = attn_weights_113_transpose_x_0, transpose_y = attn_weights_113_transpose_y_0, x = var_3338_cast_fp16_0, y = var_3351_0)[name = string("attn_weights_113_cast_fp16")];
            fp16 var_3354_to_fp16 = const()[name = string("op_3354_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_115_cast_fp16 = mul(x = attn_weights_113_cast_fp16, y = var_3354_to_fp16)[name = string("attn_weights_115_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_117_cast_fp16 = add(x = attn_weights_115_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_117_cast_fp16")];
            int32 var_3358 = const()[name = string("op_3358"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_119_cast_fp16 = softmax(axis = var_3358, x = attn_weights_117_cast_fp16)[name = string("attn_weights_119_cast_fp16")];
            bool var_3364_transpose_x_1 = const()[name = string("op_3364_transpose_x_1"), val = bool(true)];
            bool var_3364_transpose_y_1 = const()[name = string("op_3364_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_3364_cast_fp16 = matmul(transpose_x = var_3364_transpose_x_1, transpose_y = var_3364_transpose_y_1, x = attn_weights_119_cast_fp16, y = var_3348_cast_fp16_0)[name = string("op_3364_cast_fp16")];
            bool attn_weights_121_transpose_x_0 = const()[name = string("attn_weights_121_transpose_x_0"), val = bool(false)];
            bool attn_weights_121_transpose_y_0 = const()[name = string("attn_weights_121_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_121_cast_fp16 = matmul(transpose_x = attn_weights_121_transpose_x_0, transpose_y = attn_weights_121_transpose_y_0, x = var_3338_cast_fp16_1, y = var_3351_1)[name = string("attn_weights_121_cast_fp16")];
            fp16 var_3366_to_fp16 = const()[name = string("op_3366_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_123_cast_fp16 = mul(x = attn_weights_121_cast_fp16, y = var_3366_to_fp16)[name = string("attn_weights_123_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_125_cast_fp16 = add(x = attn_weights_123_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_125_cast_fp16")];
            int32 var_3370 = const()[name = string("op_3370"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_127_cast_fp16 = softmax(axis = var_3370, x = attn_weights_125_cast_fp16)[name = string("attn_weights_127_cast_fp16")];
            bool attn_output_57_transpose_x_1 = const()[name = string("attn_output_57_transpose_x_1"), val = bool(true)];
            bool attn_output_57_transpose_y_1 = const()[name = string("attn_output_57_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_57_cast_fp16 = matmul(transpose_x = attn_output_57_transpose_x_1, transpose_y = attn_output_57_transpose_y_1, x = attn_weights_127_cast_fp16, y = var_3348_cast_fp16_1)[name = string("attn_output_57_cast_fp16")];
            int32 var_3378 = const()[name = string("op_3378"), val = int32(1)];
            bool attn_output_59_interleave_0 = const()[name = string("attn_output_59_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_59_cast_fp16 = concat(axis = var_3378, interleave = attn_output_59_interleave_0, values = (var_3364_cast_fp16, attn_output_57_cast_fp16))[name = string("attn_output_59_cast_fp16")];
            tensor<int32, [4]> var_3382_perm_0 = const()[name = string("op_3382_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_95x = const()[name = string("concat_95x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_3382_cast_fp16 = transpose(perm = var_3382_perm_0, x = attn_output_59_cast_fp16)[name = string("transpose_147")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_63_cast_fp16 = reshape(shape = concat_95x, x = var_3382_cast_fp16)[name = string("attn_output_63_cast_fp16")];
            tensor<int32, [2]> hidden_states_73_strides_0 = const()[name = string("hidden_states_73_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_73_pad_type_0 = const()[name = string("hidden_states_73_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_73_pad_0 = const()[name = string("hidden_states_73_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_73_dilations_0 = const()[name = string("hidden_states_73_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_73_groups_0 = const()[name = string("hidden_states_73_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_73_cast_fp16 = conv(dilations = hidden_states_73_dilations_0, groups = hidden_states_73_groups_0, pad = hidden_states_73_pad_0, pad_type = hidden_states_73_pad_type_0, strides = hidden_states_73_strides_0, weight = layers_7_self_attn_o_proj_weight_cast_fp16, x = attn_output_63_cast_fp16)[name = string("hidden_states_73_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_75_cast_fp16 = add(x = hidden_states_69_cast_fp16, y = hidden_states_73_cast_fp16)[name = string("hidden_states_75_cast_fp16")];
            fp16 const_80_promoted_to_fp16 = const()[name = string("const_80_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_3415_cast_fp16 = mul(x = hidden_states_75_cast_fp16, y = const_80_promoted_to_fp16)[name = string("op_3415_cast_fp16")];
            int32 var_3413 = const()[name = string("op_3413"), val = int32(1)];
            bool doubled_61_interleave_0 = const()[name = string("doubled_61_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_61_cast_fp16 = concat(axis = var_3413, interleave = doubled_61_interleave_0, values = (hidden_states_75_cast_fp16, var_3415_cast_fp16))[name = string("doubled_61_cast_fp16")];
            tensor<int32, [1]> out_31_axes_0 = const()[name = string("out_31_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_31_gamma_0_to_fp16 = const()[name = string("out_31_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1328276224)))];
            fp16 var_3425_to_fp16 = const()[name = string("op_3425_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_31_cast_fp16 = layer_norm(axes = out_31_axes_0, epsilon = var_3425_to_fp16, gamma = out_31_gamma_0_to_fp16, x = doubled_61_cast_fp16)[name = string("out_31_cast_fp16")];
            tensor<int32, [2]> var_3436_split_sizes_0 = const()[name = string("op_3436_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3436_axis_0 = const()[name = string("op_3436_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_3436_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_3436_cast_fp16_1 = split(axis = var_3436_axis_0, split_sizes = var_3436_split_sizes_0, x = out_31_cast_fp16)[name = string("op_3436_cast_fp16")];
            tensor<int32, [2]> input_15_strides_0 = const()[name = string("input_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_15_pad_type_0 = const()[name = string("input_15_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_15_dilations_0 = const()[name = string("input_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_15_groups_0 = const()[name = string("input_15_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_15_cast_fp16 = conv(dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = layers_7_mlp_gate_proj_weight_cast_fp16, x = var_3436_cast_fp16_0)[name = string("input_15_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_3453_cast_fp16 = silu(x = input_15_cast_fp16)[name = string("op_3453_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_7_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_7_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1328284480)))];
            tensor<int32, [2]> var_3459_strides_0 = const()[name = string("op_3459_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_3459_pad_type_0 = const()[name = string("op_3459_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_3459_pad_0 = const()[name = string("op_3459_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_3459_dilations_0 = const()[name = string("op_3459_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3459_groups_0 = const()[name = string("op_3459_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_3459_cast_fp16 = conv(dilations = var_3459_dilations_0, groups = var_3459_groups_0, pad = var_3459_pad_0, pad_type = var_3459_pad_type_0, strides = var_3459_strides_0, weight = layers_7_mlp_up_proj_weight_to_fp16, x = var_3436_cast_fp16_0)[name = string("op_3459_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_79_cast_fp16 = mul(x = var_3453_cast_fp16, y = var_3459_cast_fp16)[name = string("x_79_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_7_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_7_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353450368)))];
            tensor<int32, [2]> hidden_states_77_strides_0 = const()[name = string("hidden_states_77_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_77_pad_type_0 = const()[name = string("hidden_states_77_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_77_pad_0 = const()[name = string("hidden_states_77_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_77_dilations_0 = const()[name = string("hidden_states_77_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_77_groups_0 = const()[name = string("hidden_states_77_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_77_cast_fp16 = conv(dilations = hidden_states_77_dilations_0, groups = hidden_states_77_groups_0, pad = hidden_states_77_pad_0, pad_type = hidden_states_77_pad_type_0, strides = hidden_states_77_strides_0, weight = layers_7_mlp_down_proj_weight_to_fp16, x = x_79_cast_fp16)[name = string("hidden_states_77_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_79_cast_fp16 = add(x = hidden_states_75_cast_fp16, y = hidden_states_77_cast_fp16)[name = string("hidden_states_79_cast_fp16")];
            fp16 const_82_promoted_to_fp16 = const()[name = string("const_82_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_3477_cast_fp16 = mul(x = hidden_states_79_cast_fp16, y = const_82_promoted_to_fp16)[name = string("op_3477_cast_fp16")];
            int32 var_3475 = const()[name = string("op_3475"), val = int32(1)];
            bool doubled_65_interleave_0 = const()[name = string("doubled_65_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_65_cast_fp16 = concat(axis = var_3475, interleave = doubled_65_interleave_0, values = (hidden_states_79_cast_fp16, var_3477_cast_fp16))[name = string("doubled_65_cast_fp16")];
            tensor<int32, [1]> out_33_axes_0 = const()[name = string("out_33_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_33_gamma_0_to_fp16 = const()[name = string("out_33_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1378616256)))];
            fp16 var_3487_to_fp16 = const()[name = string("op_3487_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_33_cast_fp16 = layer_norm(axes = out_33_axes_0, epsilon = var_3487_to_fp16, gamma = out_33_gamma_0_to_fp16, x = doubled_65_cast_fp16)[name = string("out_33_cast_fp16")];
            tensor<int32, [2]> var_3498_split_sizes_0 = const()[name = string("op_3498_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3498_axis_0 = const()[name = string("op_3498_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_3498_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_3498_cast_fp16_1 = split(axis = var_3498_axis_0, split_sizes = var_3498_split_sizes_0, x = out_33_cast_fp16)[name = string("op_3498_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_8_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_8_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1378624512)))];
            tensor<int32, [2]> query_states_49_strides_0 = const()[name = string("query_states_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_49_pad_type_0 = const()[name = string("query_states_49_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_49_pad_0 = const()[name = string("query_states_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_49_dilations_0 = const()[name = string("query_states_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_49_groups_0 = const()[name = string("query_states_49_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_49_cast_fp16 = conv(dilations = query_states_49_dilations_0, groups = query_states_49_groups_0, pad = query_states_49_pad_0, pad_type = query_states_49_pad_type_0, strides = query_states_49_strides_0, weight = layers_8_self_attn_q_proj_weight_to_fp16, x = var_3498_cast_fp16_0)[name = string("query_states_49_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_8_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_8_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1387013184)))];
            tensor<int32, [2]> key_states_81_strides_0 = const()[name = string("key_states_81_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_81_pad_type_0 = const()[name = string("key_states_81_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_81_pad_0 = const()[name = string("key_states_81_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_81_dilations_0 = const()[name = string("key_states_81_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_81_groups_0 = const()[name = string("key_states_81_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_81_cast_fp16 = conv(dilations = key_states_81_dilations_0, groups = key_states_81_groups_0, pad = key_states_81_pad_0, pad_type = key_states_81_pad_type_0, strides = key_states_81_strides_0, weight = layers_8_self_attn_k_proj_weight_to_fp16, x = var_3498_cast_fp16_0)[name = string("key_states_81_cast_fp16")];
            tensor<int32, [2]> value_states_49_strides_0 = const()[name = string("value_states_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_49_pad_type_0 = const()[name = string("value_states_49_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_49_pad_0 = const()[name = string("value_states_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_49_dilations_0 = const()[name = string("value_states_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_49_groups_0 = const()[name = string("value_states_49_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_49_cast_fp16 = conv(dilations = value_states_49_dilations_0, groups = value_states_49_groups_0, pad = value_states_49_pad_0, pad_type = value_states_49_pad_type_0, strides = value_states_49_strides_0, weight = layers_8_self_attn_v_proj_weight_cast_fp16, x = var_3498_cast_fp16_0)[name = string("value_states_49_cast_fp16")];
            tensor<int32, [4]> concat_96x = const()[name = string("concat_96x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_81_cast_fp16 = reshape(shape = concat_96x, x = query_states_49_cast_fp16)[name = string("x_81_cast_fp16")];
            tensor<int32, [4]> concat_97x = const()[name = string("concat_97x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_3555_cast_fp16 = reshape(shape = concat_97x, x = key_states_81_cast_fp16)[name = string("op_3555_cast_fp16")];
            tensor<int32, [4]> concat_98x = const()[name = string("concat_98x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_3562_cast_fp16 = reshape(shape = concat_98x, x = value_states_49_cast_fp16)[name = string("op_3562_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_3566_cast_fp16 = mul(x = x_81_cast_fp16, y = var_859_cast_fp16)[name = string("op_3566_cast_fp16")];
            tensor<int32, [2]> var_3567_split_sizes_0 = const()[name = string("op_3567_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3567_axis_0 = const()[name = string("op_3567_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_3567_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_3567_cast_fp16_1 = split(axis = var_3567_axis_0, split_sizes = var_3567_split_sizes_0, x = x_81_cast_fp16)[name = string("op_3567_cast_fp16")];
            fp16 const_84_promoted_to_fp16 = const()[name = string("const_84_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_3569_cast_fp16 = mul(x = var_3567_cast_fp16_1, y = const_84_promoted_to_fp16)[name = string("op_3569_cast_fp16")];
            int32 var_3571 = const()[name = string("op_3571"), val = int32(-2)];
            bool var_3572_interleave_0 = const()[name = string("op_3572_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_3572_cast_fp16 = concat(axis = var_3571, interleave = var_3572_interleave_0, values = (var_3569_cast_fp16, var_3567_cast_fp16_0))[name = string("op_3572_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_3573_cast_fp16 = mul(x = var_3572_cast_fp16, y = var_866_cast_fp16)[name = string("op_3573_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_51_cast_fp16 = add(x = var_3566_cast_fp16, y = var_3573_cast_fp16)[name = string("query_states_51_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_3579_cast_fp16 = mul(x = var_3555_cast_fp16, y = var_859_cast_fp16)[name = string("op_3579_cast_fp16")];
            tensor<int32, [2]> var_3580_split_sizes_0 = const()[name = string("op_3580_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3580_axis_0 = const()[name = string("op_3580_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_3580_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_3580_cast_fp16_1 = split(axis = var_3580_axis_0, split_sizes = var_3580_split_sizes_0, x = var_3555_cast_fp16)[name = string("op_3580_cast_fp16")];
            fp16 const_85_promoted_to_fp16 = const()[name = string("const_85_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_3582_cast_fp16 = mul(x = var_3580_cast_fp16_1, y = const_85_promoted_to_fp16)[name = string("op_3582_cast_fp16")];
            int32 var_3584 = const()[name = string("op_3584"), val = int32(-2)];
            bool var_3585_interleave_0 = const()[name = string("op_3585_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_3585_cast_fp16 = concat(axis = var_3584, interleave = var_3585_interleave_0, values = (var_3582_cast_fp16, var_3580_cast_fp16_0))[name = string("op_3585_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_3586_cast_fp16 = mul(x = var_3585_cast_fp16, y = var_866_cast_fp16)[name = string("op_3586_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_85_cast_fp16 = add(x = var_3579_cast_fp16, y = var_3586_cast_fp16)[name = string("key_states_85_cast_fp16")];
            tensor<int32, [1]> expand_dims_96 = const()[name = string("expand_dims_96"), val = tensor<int32, [1]>([8])];
            tensor<int32, [1]> expand_dims_97 = const()[name = string("expand_dims_97"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_99 = const()[name = string("expand_dims_99"), val = tensor<int32, [1]>([0])];
            int32 concat_101_axis_0 = const()[name = string("concat_101_axis_0"), val = int32(0)];
            bool concat_101_interleave_0 = const()[name = string("concat_101_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_101 = concat(axis = concat_101_axis_0, interleave = concat_101_interleave_0, values = (expand_dims_96, expand_dims_97, position_id, expand_dims_99))[name = string("concat_101")];
            tensor<int32, [1]> expand_dims_100 = const()[name = string("expand_dims_100"), val = tensor<int32, [1]>([9])];
            tensor<int32, [1]> concat_102_values1_0 = const()[name = string("concat_102_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_102_values3_0 = const()[name = string("concat_102_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_102_axis_0 = const()[name = string("concat_102_axis_0"), val = int32(0)];
            bool concat_102_interleave_0 = const()[name = string("concat_102_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_102 = concat(axis = concat_102_axis_0, interleave = concat_102_interleave_0, values = (expand_dims_100, concat_102_values1_0, cache_position_end, concat_102_values3_0))[name = string("concat_102")];
            tensor<int32, [4]> key_states_87_perm_0 = const()[name = string("key_states_87_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_9_stride_0 = const()[name = string("key_cache_internal_tensor_assign_9_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_9_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_9_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_9_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_9_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_9_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_9_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_87_cast_fp16 = transpose(perm = key_states_87_perm_0, x = key_states_85_cast_fp16)[name = string("transpose_146")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_9_cast_fp16 = slice_update(begin = concat_101, begin_mask = key_cache_internal_tensor_assign_9_begin_mask_0, end = concat_102, end_mask = key_cache_internal_tensor_assign_9_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_9_squeeze_mask_0, stride = key_cache_internal_tensor_assign_9_stride_0, update = key_states_87_cast_fp16, x = coreml_update_state_70)[name = string("key_cache_internal_tensor_assign_9_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_9_cast_fp16, input = key_cache)[name = string("coreml_update_state_72_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_72 = read_state(input = key_cache)[name = string("coreml_update_state_72")];
            tensor<int32, [4]> value_states_51_perm_0 = const()[name = string("value_states_51_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_9_stride_0 = const()[name = string("value_cache_internal_tensor_assign_9_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_9_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_9_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_9_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_9_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_9_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_9_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_51_cast_fp16 = transpose(perm = value_states_51_perm_0, x = var_3562_cast_fp16)[name = string("transpose_145")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_9_cast_fp16 = slice_update(begin = concat_101, begin_mask = value_cache_internal_tensor_assign_9_begin_mask_0, end = concat_102, end_mask = value_cache_internal_tensor_assign_9_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_9_squeeze_mask_0, stride = value_cache_internal_tensor_assign_9_stride_0, update = value_states_51_cast_fp16, x = coreml_update_state_71)[name = string("value_cache_internal_tensor_assign_9_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_9_cast_fp16, input = value_cache)[name = string("coreml_update_state_73_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_73 = read_state(input = value_cache)[name = string("coreml_update_state_73")];
            tensor<int32, [4]> var_3656_begin_0 = const()[name = string("op_3656_begin_0"), val = tensor<int32, [4]>([8, 0, 0, 0])];
            tensor<int32, [4]> var_3656_end_0 = const()[name = string("op_3656_end_0"), val = tensor<int32, [4]>([9, 2, 2048, 128])];
            tensor<bool, [4]> var_3656_end_mask_0 = const()[name = string("op_3656_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3656_cast_fp16 = slice_by_index(begin = var_3656_begin_0, end = var_3656_end_0, end_mask = var_3656_end_mask_0, x = coreml_update_state_72)[name = string("op_3656_cast_fp16")];
            tensor<int32, [2]> tile_16 = const()[name = string("tile_16"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3659_axis_0 = const()[name = string("op_3659_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3659_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3659_cast_fp16_1 = split(axis = var_3659_axis_0, split_sizes = tile_16, x = var_3656_cast_fp16)[name = string("op_3659_cast_fp16")];
            tensor<int32, [4]> var_3666_begin_0 = const()[name = string("op_3666_begin_0"), val = tensor<int32, [4]>([8, 0, 0, 0])];
            tensor<int32, [4]> var_3666_end_0 = const()[name = string("op_3666_end_0"), val = tensor<int32, [4]>([9, 2, 2048, 128])];
            tensor<bool, [4]> var_3666_end_mask_0 = const()[name = string("op_3666_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3666_cast_fp16 = slice_by_index(begin = var_3666_begin_0, end = var_3666_end_0, end_mask = var_3666_end_mask_0, x = coreml_update_state_73)[name = string("op_3666_cast_fp16")];
            tensor<int32, [2]> tile_17 = const()[name = string("tile_17"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3669_axis_0 = const()[name = string("op_3669_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3669_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3669_cast_fp16_1 = split(axis = var_3669_axis_0, split_sizes = tile_17, x = var_3666_cast_fp16)[name = string("op_3669_cast_fp16")];
            tensor<int32, [2]> var_3672_split_sizes_0 = const()[name = string("op_3672_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3672_axis_0 = const()[name = string("op_3672_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_3672_0, tensor<fp16, [1, 8, 128, 1]> var_3672_1 = split(axis = var_3672_axis_0, split_sizes = var_3672_split_sizes_0, x = query_states_51_cast_fp16)[name = string("op_3672")];
            bool attn_weights_129_transpose_x_0 = const()[name = string("attn_weights_129_transpose_x_0"), val = bool(false)];
            bool attn_weights_129_transpose_y_0 = const()[name = string("attn_weights_129_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_129_cast_fp16 = matmul(transpose_x = attn_weights_129_transpose_x_0, transpose_y = attn_weights_129_transpose_y_0, x = var_3659_cast_fp16_0, y = var_3672_0)[name = string("attn_weights_129_cast_fp16")];
            fp16 var_3675_to_fp16 = const()[name = string("op_3675_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_131_cast_fp16 = mul(x = attn_weights_129_cast_fp16, y = var_3675_to_fp16)[name = string("attn_weights_131_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_133_cast_fp16 = add(x = attn_weights_131_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_133_cast_fp16")];
            int32 var_3679 = const()[name = string("op_3679"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_135_cast_fp16 = softmax(axis = var_3679, x = attn_weights_133_cast_fp16)[name = string("attn_weights_135_cast_fp16")];
            bool var_3685_transpose_x_1 = const()[name = string("op_3685_transpose_x_1"), val = bool(true)];
            bool var_3685_transpose_y_1 = const()[name = string("op_3685_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_3685_cast_fp16 = matmul(transpose_x = var_3685_transpose_x_1, transpose_y = var_3685_transpose_y_1, x = attn_weights_135_cast_fp16, y = var_3669_cast_fp16_0)[name = string("op_3685_cast_fp16")];
            bool attn_weights_137_transpose_x_0 = const()[name = string("attn_weights_137_transpose_x_0"), val = bool(false)];
            bool attn_weights_137_transpose_y_0 = const()[name = string("attn_weights_137_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_137_cast_fp16 = matmul(transpose_x = attn_weights_137_transpose_x_0, transpose_y = attn_weights_137_transpose_y_0, x = var_3659_cast_fp16_1, y = var_3672_1)[name = string("attn_weights_137_cast_fp16")];
            fp16 var_3687_to_fp16 = const()[name = string("op_3687_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_139_cast_fp16 = mul(x = attn_weights_137_cast_fp16, y = var_3687_to_fp16)[name = string("attn_weights_139_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_141_cast_fp16 = add(x = attn_weights_139_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_141_cast_fp16")];
            int32 var_3691 = const()[name = string("op_3691"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_143_cast_fp16 = softmax(axis = var_3691, x = attn_weights_141_cast_fp16)[name = string("attn_weights_143_cast_fp16")];
            bool attn_output_65_transpose_x_1 = const()[name = string("attn_output_65_transpose_x_1"), val = bool(true)];
            bool attn_output_65_transpose_y_1 = const()[name = string("attn_output_65_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_65_cast_fp16 = matmul(transpose_x = attn_output_65_transpose_x_1, transpose_y = attn_output_65_transpose_y_1, x = attn_weights_143_cast_fp16, y = var_3669_cast_fp16_1)[name = string("attn_output_65_cast_fp16")];
            int32 var_3699 = const()[name = string("op_3699"), val = int32(1)];
            bool attn_output_67_interleave_0 = const()[name = string("attn_output_67_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_67_cast_fp16 = concat(axis = var_3699, interleave = attn_output_67_interleave_0, values = (var_3685_cast_fp16, attn_output_65_cast_fp16))[name = string("attn_output_67_cast_fp16")];
            tensor<int32, [4]> var_3703_perm_0 = const()[name = string("op_3703_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_107x = const()[name = string("concat_107x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_3703_cast_fp16 = transpose(perm = var_3703_perm_0, x = attn_output_67_cast_fp16)[name = string("transpose_144")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_71_cast_fp16 = reshape(shape = concat_107x, x = var_3703_cast_fp16)[name = string("attn_output_71_cast_fp16")];
            tensor<int32, [2]> hidden_states_83_strides_0 = const()[name = string("hidden_states_83_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_83_pad_type_0 = const()[name = string("hidden_states_83_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_83_pad_0 = const()[name = string("hidden_states_83_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_83_dilations_0 = const()[name = string("hidden_states_83_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_83_groups_0 = const()[name = string("hidden_states_83_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_83_cast_fp16 = conv(dilations = hidden_states_83_dilations_0, groups = hidden_states_83_groups_0, pad = hidden_states_83_pad_0, pad_type = hidden_states_83_pad_type_0, strides = hidden_states_83_strides_0, weight = layers_8_self_attn_o_proj_weight_cast_fp16, x = attn_output_71_cast_fp16)[name = string("hidden_states_83_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_85_cast_fp16 = add(x = hidden_states_79_cast_fp16, y = hidden_states_83_cast_fp16)[name = string("hidden_states_85_cast_fp16")];
            fp16 const_90_promoted_to_fp16 = const()[name = string("const_90_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_3736_cast_fp16 = mul(x = hidden_states_85_cast_fp16, y = const_90_promoted_to_fp16)[name = string("op_3736_cast_fp16")];
            int32 var_3734 = const()[name = string("op_3734"), val = int32(1)];
            bool doubled_69_interleave_0 = const()[name = string("doubled_69_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_69_cast_fp16 = concat(axis = var_3734, interleave = doubled_69_interleave_0, values = (hidden_states_85_cast_fp16, var_3736_cast_fp16))[name = string("doubled_69_cast_fp16")];
            tensor<int32, [1]> out_35_axes_0 = const()[name = string("out_35_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_35_gamma_0_to_fp16 = const()[name = string("out_35_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1388061824)))];
            fp16 var_3746_to_fp16 = const()[name = string("op_3746_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_35_cast_fp16 = layer_norm(axes = out_35_axes_0, epsilon = var_3746_to_fp16, gamma = out_35_gamma_0_to_fp16, x = doubled_69_cast_fp16)[name = string("out_35_cast_fp16")];
            tensor<int32, [2]> var_3757_split_sizes_0 = const()[name = string("op_3757_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3757_axis_0 = const()[name = string("op_3757_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_3757_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_3757_cast_fp16_1 = split(axis = var_3757_axis_0, split_sizes = var_3757_split_sizes_0, x = out_35_cast_fp16)[name = string("op_3757_cast_fp16")];
            tensor<int32, [2]> input_17_strides_0 = const()[name = string("input_17_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_17_pad_type_0 = const()[name = string("input_17_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_17_pad_0 = const()[name = string("input_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_17_dilations_0 = const()[name = string("input_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_17_groups_0 = const()[name = string("input_17_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_17_cast_fp16 = conv(dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = layers_8_mlp_gate_proj_weight_cast_fp16, x = var_3757_cast_fp16_0)[name = string("input_17_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_3774_cast_fp16 = silu(x = input_17_cast_fp16)[name = string("op_3774_cast_fp16")];
            tensor<int32, [2]> var_3780_strides_0 = const()[name = string("op_3780_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_3780_pad_type_0 = const()[name = string("op_3780_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_3780_pad_0 = const()[name = string("op_3780_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_3780_dilations_0 = const()[name = string("op_3780_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3780_groups_0 = const()[name = string("op_3780_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_3780_cast_fp16 = conv(dilations = var_3780_dilations_0, groups = var_3780_groups_0, pad = var_3780_pad_0, pad_type = var_3780_pad_type_0, strides = var_3780_strides_0, weight = layers_8_mlp_up_proj_weight_cast_fp16, x = var_3757_cast_fp16_0)[name = string("op_3780_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_89_cast_fp16 = mul(x = var_3774_cast_fp16, y = var_3780_cast_fp16)[name = string("x_89_cast_fp16")];
            tensor<int32, [2]> hidden_states_87_strides_0 = const()[name = string("hidden_states_87_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_87_pad_type_0 = const()[name = string("hidden_states_87_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_87_pad_0 = const()[name = string("hidden_states_87_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_87_dilations_0 = const()[name = string("hidden_states_87_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_87_groups_0 = const()[name = string("hidden_states_87_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_87_cast_fp16 = conv(dilations = hidden_states_87_dilations_0, groups = hidden_states_87_groups_0, pad = hidden_states_87_pad_0, pad_type = hidden_states_87_pad_type_0, strides = hidden_states_87_strides_0, weight = layers_8_mlp_down_proj_weight_cast_fp16, x = x_89_cast_fp16)[name = string("hidden_states_87_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_89_cast_fp16 = add(x = hidden_states_85_cast_fp16, y = hidden_states_87_cast_fp16)[name = string("hidden_states_89_cast_fp16")];
            fp16 const_92_promoted_to_fp16 = const()[name = string("const_92_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_3798_cast_fp16 = mul(x = hidden_states_89_cast_fp16, y = const_92_promoted_to_fp16)[name = string("op_3798_cast_fp16")];
            int32 var_3796 = const()[name = string("op_3796"), val = int32(1)];
            bool doubled_73_interleave_0 = const()[name = string("doubled_73_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_73_cast_fp16 = concat(axis = var_3796, interleave = doubled_73_interleave_0, values = (hidden_states_89_cast_fp16, var_3798_cast_fp16))[name = string("doubled_73_cast_fp16")];
            tensor<int32, [1]> out_37_axes_0 = const()[name = string("out_37_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_37_gamma_0_to_fp16 = const()[name = string("out_37_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1388070080)))];
            fp16 var_3808_to_fp16 = const()[name = string("op_3808_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_37_cast_fp16 = layer_norm(axes = out_37_axes_0, epsilon = var_3808_to_fp16, gamma = out_37_gamma_0_to_fp16, x = doubled_73_cast_fp16)[name = string("out_37_cast_fp16")];
            tensor<int32, [2]> var_3819_split_sizes_0 = const()[name = string("op_3819_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3819_axis_0 = const()[name = string("op_3819_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_3819_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_3819_cast_fp16_1 = split(axis = var_3819_axis_0, split_sizes = var_3819_split_sizes_0, x = out_37_cast_fp16)[name = string("op_3819_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_9_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_9_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1388078336)))];
            tensor<int32, [2]> query_states_55_strides_0 = const()[name = string("query_states_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_55_pad_type_0 = const()[name = string("query_states_55_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_55_pad_0 = const()[name = string("query_states_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_55_dilations_0 = const()[name = string("query_states_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_55_groups_0 = const()[name = string("query_states_55_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_55_cast_fp16 = conv(dilations = query_states_55_dilations_0, groups = query_states_55_groups_0, pad = query_states_55_pad_0, pad_type = query_states_55_pad_type_0, strides = query_states_55_strides_0, weight = layers_9_self_attn_q_proj_weight_to_fp16, x = var_3819_cast_fp16_0)[name = string("query_states_55_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_9_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_9_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1396467008)))];
            tensor<int32, [2]> key_states_91_strides_0 = const()[name = string("key_states_91_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_91_pad_type_0 = const()[name = string("key_states_91_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_91_pad_0 = const()[name = string("key_states_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_91_dilations_0 = const()[name = string("key_states_91_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_91_groups_0 = const()[name = string("key_states_91_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_91_cast_fp16 = conv(dilations = key_states_91_dilations_0, groups = key_states_91_groups_0, pad = key_states_91_pad_0, pad_type = key_states_91_pad_type_0, strides = key_states_91_strides_0, weight = layers_9_self_attn_k_proj_weight_to_fp16, x = var_3819_cast_fp16_0)[name = string("key_states_91_cast_fp16")];
            tensor<int32, [2]> value_states_55_strides_0 = const()[name = string("value_states_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_55_pad_type_0 = const()[name = string("value_states_55_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_55_pad_0 = const()[name = string("value_states_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_55_dilations_0 = const()[name = string("value_states_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_55_groups_0 = const()[name = string("value_states_55_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_55_cast_fp16 = conv(dilations = value_states_55_dilations_0, groups = value_states_55_groups_0, pad = value_states_55_pad_0, pad_type = value_states_55_pad_type_0, strides = value_states_55_strides_0, weight = layers_9_self_attn_v_proj_weight_cast_fp16, x = var_3819_cast_fp16_0)[name = string("value_states_55_cast_fp16")];
            tensor<int32, [4]> concat_108x = const()[name = string("concat_108x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_91_cast_fp16 = reshape(shape = concat_108x, x = query_states_55_cast_fp16)[name = string("x_91_cast_fp16")];
            tensor<int32, [4]> concat_109x = const()[name = string("concat_109x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_3876_cast_fp16 = reshape(shape = concat_109x, x = key_states_91_cast_fp16)[name = string("op_3876_cast_fp16")];
            tensor<int32, [4]> concat_110x = const()[name = string("concat_110x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_3883_cast_fp16 = reshape(shape = concat_110x, x = value_states_55_cast_fp16)[name = string("op_3883_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_3887_cast_fp16 = mul(x = x_91_cast_fp16, y = var_859_cast_fp16)[name = string("op_3887_cast_fp16")];
            tensor<int32, [2]> var_3888_split_sizes_0 = const()[name = string("op_3888_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3888_axis_0 = const()[name = string("op_3888_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_3888_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_3888_cast_fp16_1 = split(axis = var_3888_axis_0, split_sizes = var_3888_split_sizes_0, x = x_91_cast_fp16)[name = string("op_3888_cast_fp16")];
            fp16 const_94_promoted_to_fp16 = const()[name = string("const_94_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_3890_cast_fp16 = mul(x = var_3888_cast_fp16_1, y = const_94_promoted_to_fp16)[name = string("op_3890_cast_fp16")];
            int32 var_3892 = const()[name = string("op_3892"), val = int32(-2)];
            bool var_3893_interleave_0 = const()[name = string("op_3893_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_3893_cast_fp16 = concat(axis = var_3892, interleave = var_3893_interleave_0, values = (var_3890_cast_fp16, var_3888_cast_fp16_0))[name = string("op_3893_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_3894_cast_fp16 = mul(x = var_3893_cast_fp16, y = var_866_cast_fp16)[name = string("op_3894_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_57_cast_fp16 = add(x = var_3887_cast_fp16, y = var_3894_cast_fp16)[name = string("query_states_57_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_3900_cast_fp16 = mul(x = var_3876_cast_fp16, y = var_859_cast_fp16)[name = string("op_3900_cast_fp16")];
            tensor<int32, [2]> var_3901_split_sizes_0 = const()[name = string("op_3901_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3901_axis_0 = const()[name = string("op_3901_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_3901_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_3901_cast_fp16_1 = split(axis = var_3901_axis_0, split_sizes = var_3901_split_sizes_0, x = var_3876_cast_fp16)[name = string("op_3901_cast_fp16")];
            fp16 const_95_promoted_to_fp16 = const()[name = string("const_95_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_3903_cast_fp16 = mul(x = var_3901_cast_fp16_1, y = const_95_promoted_to_fp16)[name = string("op_3903_cast_fp16")];
            int32 var_3905 = const()[name = string("op_3905"), val = int32(-2)];
            bool var_3906_interleave_0 = const()[name = string("op_3906_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_3906_cast_fp16 = concat(axis = var_3905, interleave = var_3906_interleave_0, values = (var_3903_cast_fp16, var_3901_cast_fp16_0))[name = string("op_3906_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_3907_cast_fp16 = mul(x = var_3906_cast_fp16, y = var_866_cast_fp16)[name = string("op_3907_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_95_cast_fp16 = add(x = var_3900_cast_fp16, y = var_3907_cast_fp16)[name = string("key_states_95_cast_fp16")];
            tensor<int32, [1]> expand_dims_108 = const()[name = string("expand_dims_108"), val = tensor<int32, [1]>([9])];
            tensor<int32, [1]> expand_dims_109 = const()[name = string("expand_dims_109"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_111 = const()[name = string("expand_dims_111"), val = tensor<int32, [1]>([0])];
            int32 concat_113_axis_0 = const()[name = string("concat_113_axis_0"), val = int32(0)];
            bool concat_113_interleave_0 = const()[name = string("concat_113_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_113 = concat(axis = concat_113_axis_0, interleave = concat_113_interleave_0, values = (expand_dims_108, expand_dims_109, position_id, expand_dims_111))[name = string("concat_113")];
            tensor<int32, [1]> expand_dims_112 = const()[name = string("expand_dims_112"), val = tensor<int32, [1]>([10])];
            tensor<int32, [1]> concat_114_values1_0 = const()[name = string("concat_114_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_114_values3_0 = const()[name = string("concat_114_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_114_axis_0 = const()[name = string("concat_114_axis_0"), val = int32(0)];
            bool concat_114_interleave_0 = const()[name = string("concat_114_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_114 = concat(axis = concat_114_axis_0, interleave = concat_114_interleave_0, values = (expand_dims_112, concat_114_values1_0, cache_position_end, concat_114_values3_0))[name = string("concat_114")];
            tensor<int32, [4]> key_states_97_perm_0 = const()[name = string("key_states_97_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_10_stride_0 = const()[name = string("key_cache_internal_tensor_assign_10_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_10_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_10_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_10_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_10_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_10_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_10_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_97_cast_fp16 = transpose(perm = key_states_97_perm_0, x = key_states_95_cast_fp16)[name = string("transpose_143")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_10_cast_fp16 = slice_update(begin = concat_113, begin_mask = key_cache_internal_tensor_assign_10_begin_mask_0, end = concat_114, end_mask = key_cache_internal_tensor_assign_10_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_10_squeeze_mask_0, stride = key_cache_internal_tensor_assign_10_stride_0, update = key_states_97_cast_fp16, x = coreml_update_state_72)[name = string("key_cache_internal_tensor_assign_10_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_10_cast_fp16, input = key_cache)[name = string("coreml_update_state_74_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_74 = read_state(input = key_cache)[name = string("coreml_update_state_74")];
            tensor<int32, [4]> value_states_57_perm_0 = const()[name = string("value_states_57_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_10_stride_0 = const()[name = string("value_cache_internal_tensor_assign_10_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_10_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_10_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_10_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_10_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_10_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_10_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_57_cast_fp16 = transpose(perm = value_states_57_perm_0, x = var_3883_cast_fp16)[name = string("transpose_142")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_10_cast_fp16 = slice_update(begin = concat_113, begin_mask = value_cache_internal_tensor_assign_10_begin_mask_0, end = concat_114, end_mask = value_cache_internal_tensor_assign_10_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_10_squeeze_mask_0, stride = value_cache_internal_tensor_assign_10_stride_0, update = value_states_57_cast_fp16, x = coreml_update_state_73)[name = string("value_cache_internal_tensor_assign_10_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_10_cast_fp16, input = value_cache)[name = string("coreml_update_state_75_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_75 = read_state(input = value_cache)[name = string("coreml_update_state_75")];
            tensor<int32, [4]> var_3977_begin_0 = const()[name = string("op_3977_begin_0"), val = tensor<int32, [4]>([9, 0, 0, 0])];
            tensor<int32, [4]> var_3977_end_0 = const()[name = string("op_3977_end_0"), val = tensor<int32, [4]>([10, 2, 2048, 128])];
            tensor<bool, [4]> var_3977_end_mask_0 = const()[name = string("op_3977_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3977_cast_fp16 = slice_by_index(begin = var_3977_begin_0, end = var_3977_end_0, end_mask = var_3977_end_mask_0, x = coreml_update_state_74)[name = string("op_3977_cast_fp16")];
            tensor<int32, [2]> tile_18 = const()[name = string("tile_18"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3980_axis_0 = const()[name = string("op_3980_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3980_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3980_cast_fp16_1 = split(axis = var_3980_axis_0, split_sizes = tile_18, x = var_3977_cast_fp16)[name = string("op_3980_cast_fp16")];
            tensor<int32, [4]> var_3987_begin_0 = const()[name = string("op_3987_begin_0"), val = tensor<int32, [4]>([9, 0, 0, 0])];
            tensor<int32, [4]> var_3987_end_0 = const()[name = string("op_3987_end_0"), val = tensor<int32, [4]>([10, 2, 2048, 128])];
            tensor<bool, [4]> var_3987_end_mask_0 = const()[name = string("op_3987_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3987_cast_fp16 = slice_by_index(begin = var_3987_begin_0, end = var_3987_end_0, end_mask = var_3987_end_mask_0, x = coreml_update_state_75)[name = string("op_3987_cast_fp16")];
            tensor<int32, [2]> tile_19 = const()[name = string("tile_19"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3990_axis_0 = const()[name = string("op_3990_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3990_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3990_cast_fp16_1 = split(axis = var_3990_axis_0, split_sizes = tile_19, x = var_3987_cast_fp16)[name = string("op_3990_cast_fp16")];
            tensor<int32, [2]> var_3993_split_sizes_0 = const()[name = string("op_3993_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3993_axis_0 = const()[name = string("op_3993_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_3993_0, tensor<fp16, [1, 8, 128, 1]> var_3993_1 = split(axis = var_3993_axis_0, split_sizes = var_3993_split_sizes_0, x = query_states_57_cast_fp16)[name = string("op_3993")];
            bool attn_weights_145_transpose_x_0 = const()[name = string("attn_weights_145_transpose_x_0"), val = bool(false)];
            bool attn_weights_145_transpose_y_0 = const()[name = string("attn_weights_145_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_145_cast_fp16 = matmul(transpose_x = attn_weights_145_transpose_x_0, transpose_y = attn_weights_145_transpose_y_0, x = var_3980_cast_fp16_0, y = var_3993_0)[name = string("attn_weights_145_cast_fp16")];
            fp16 var_3996_to_fp16 = const()[name = string("op_3996_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_147_cast_fp16 = mul(x = attn_weights_145_cast_fp16, y = var_3996_to_fp16)[name = string("attn_weights_147_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_149_cast_fp16 = add(x = attn_weights_147_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_149_cast_fp16")];
            int32 var_4000 = const()[name = string("op_4000"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_151_cast_fp16 = softmax(axis = var_4000, x = attn_weights_149_cast_fp16)[name = string("attn_weights_151_cast_fp16")];
            bool var_4006_transpose_x_1 = const()[name = string("op_4006_transpose_x_1"), val = bool(true)];
            bool var_4006_transpose_y_1 = const()[name = string("op_4006_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_4006_cast_fp16 = matmul(transpose_x = var_4006_transpose_x_1, transpose_y = var_4006_transpose_y_1, x = attn_weights_151_cast_fp16, y = var_3990_cast_fp16_0)[name = string("op_4006_cast_fp16")];
            bool attn_weights_153_transpose_x_0 = const()[name = string("attn_weights_153_transpose_x_0"), val = bool(false)];
            bool attn_weights_153_transpose_y_0 = const()[name = string("attn_weights_153_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_153_cast_fp16 = matmul(transpose_x = attn_weights_153_transpose_x_0, transpose_y = attn_weights_153_transpose_y_0, x = var_3980_cast_fp16_1, y = var_3993_1)[name = string("attn_weights_153_cast_fp16")];
            fp16 var_4008_to_fp16 = const()[name = string("op_4008_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_155_cast_fp16 = mul(x = attn_weights_153_cast_fp16, y = var_4008_to_fp16)[name = string("attn_weights_155_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_157_cast_fp16 = add(x = attn_weights_155_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_157_cast_fp16")];
            int32 var_4012 = const()[name = string("op_4012"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_159_cast_fp16 = softmax(axis = var_4012, x = attn_weights_157_cast_fp16)[name = string("attn_weights_159_cast_fp16")];
            bool attn_output_73_transpose_x_1 = const()[name = string("attn_output_73_transpose_x_1"), val = bool(true)];
            bool attn_output_73_transpose_y_1 = const()[name = string("attn_output_73_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_73_cast_fp16 = matmul(transpose_x = attn_output_73_transpose_x_1, transpose_y = attn_output_73_transpose_y_1, x = attn_weights_159_cast_fp16, y = var_3990_cast_fp16_1)[name = string("attn_output_73_cast_fp16")];
            int32 var_4020 = const()[name = string("op_4020"), val = int32(1)];
            bool attn_output_75_interleave_0 = const()[name = string("attn_output_75_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_75_cast_fp16 = concat(axis = var_4020, interleave = attn_output_75_interleave_0, values = (var_4006_cast_fp16, attn_output_73_cast_fp16))[name = string("attn_output_75_cast_fp16")];
            tensor<int32, [4]> var_4024_perm_0 = const()[name = string("op_4024_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_119x = const()[name = string("concat_119x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_4024_cast_fp16 = transpose(perm = var_4024_perm_0, x = attn_output_75_cast_fp16)[name = string("transpose_141")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_79_cast_fp16 = reshape(shape = concat_119x, x = var_4024_cast_fp16)[name = string("attn_output_79_cast_fp16")];
            tensor<int32, [2]> hidden_states_93_strides_0 = const()[name = string("hidden_states_93_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_93_pad_type_0 = const()[name = string("hidden_states_93_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_93_pad_0 = const()[name = string("hidden_states_93_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_93_dilations_0 = const()[name = string("hidden_states_93_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_93_groups_0 = const()[name = string("hidden_states_93_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_93_cast_fp16 = conv(dilations = hidden_states_93_dilations_0, groups = hidden_states_93_groups_0, pad = hidden_states_93_pad_0, pad_type = hidden_states_93_pad_type_0, strides = hidden_states_93_strides_0, weight = layers_9_self_attn_o_proj_weight_cast_fp16, x = attn_output_79_cast_fp16)[name = string("hidden_states_93_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_95_cast_fp16 = add(x = hidden_states_89_cast_fp16, y = hidden_states_93_cast_fp16)[name = string("hidden_states_95_cast_fp16")];
            fp16 const_100_promoted_to_fp16 = const()[name = string("const_100_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_4057_cast_fp16 = mul(x = hidden_states_95_cast_fp16, y = const_100_promoted_to_fp16)[name = string("op_4057_cast_fp16")];
            int32 var_4055 = const()[name = string("op_4055"), val = int32(1)];
            bool doubled_77_interleave_0 = const()[name = string("doubled_77_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_77_cast_fp16 = concat(axis = var_4055, interleave = doubled_77_interleave_0, values = (hidden_states_95_cast_fp16, var_4057_cast_fp16))[name = string("doubled_77_cast_fp16")];
            tensor<int32, [1]> out_39_axes_0 = const()[name = string("out_39_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_39_gamma_0_to_fp16 = const()[name = string("out_39_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1397515648)))];
            fp16 var_4067_to_fp16 = const()[name = string("op_4067_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_39_cast_fp16 = layer_norm(axes = out_39_axes_0, epsilon = var_4067_to_fp16, gamma = out_39_gamma_0_to_fp16, x = doubled_77_cast_fp16)[name = string("out_39_cast_fp16")];
            tensor<int32, [2]> var_4078_split_sizes_0 = const()[name = string("op_4078_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4078_axis_0 = const()[name = string("op_4078_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_4078_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_4078_cast_fp16_1 = split(axis = var_4078_axis_0, split_sizes = var_4078_split_sizes_0, x = out_39_cast_fp16)[name = string("op_4078_cast_fp16")];
            tensor<int32, [2]> input_19_strides_0 = const()[name = string("input_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_19_pad_type_0 = const()[name = string("input_19_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_19_pad_0 = const()[name = string("input_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_19_dilations_0 = const()[name = string("input_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_19_groups_0 = const()[name = string("input_19_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_19_cast_fp16 = conv(dilations = input_19_dilations_0, groups = input_19_groups_0, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = input_19_strides_0, weight = layers_9_mlp_gate_proj_weight_cast_fp16, x = var_4078_cast_fp16_0)[name = string("input_19_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_4095_cast_fp16 = silu(x = input_19_cast_fp16)[name = string("op_4095_cast_fp16")];
            tensor<int32, [2]> var_4101_strides_0 = const()[name = string("op_4101_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_4101_pad_type_0 = const()[name = string("op_4101_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_4101_pad_0 = const()[name = string("op_4101_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_4101_dilations_0 = const()[name = string("op_4101_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4101_groups_0 = const()[name = string("op_4101_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_4101_cast_fp16 = conv(dilations = var_4101_dilations_0, groups = var_4101_groups_0, pad = var_4101_pad_0, pad_type = var_4101_pad_type_0, strides = var_4101_strides_0, weight = layers_9_mlp_up_proj_weight_cast_fp16, x = var_4078_cast_fp16_0)[name = string("op_4101_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_99_cast_fp16 = mul(x = var_4095_cast_fp16, y = var_4101_cast_fp16)[name = string("x_99_cast_fp16")];
            tensor<int32, [2]> hidden_states_97_strides_0 = const()[name = string("hidden_states_97_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_97_pad_type_0 = const()[name = string("hidden_states_97_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_97_pad_0 = const()[name = string("hidden_states_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_97_dilations_0 = const()[name = string("hidden_states_97_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_97_groups_0 = const()[name = string("hidden_states_97_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_97_cast_fp16 = conv(dilations = hidden_states_97_dilations_0, groups = hidden_states_97_groups_0, pad = hidden_states_97_pad_0, pad_type = hidden_states_97_pad_type_0, strides = hidden_states_97_strides_0, weight = layers_9_mlp_down_proj_weight_cast_fp16, x = x_99_cast_fp16)[name = string("hidden_states_97_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_99_cast_fp16 = add(x = hidden_states_95_cast_fp16, y = hidden_states_97_cast_fp16)[name = string("hidden_states_99_cast_fp16")];
            fp16 const_102_promoted_to_fp16 = const()[name = string("const_102_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_4119_cast_fp16 = mul(x = hidden_states_99_cast_fp16, y = const_102_promoted_to_fp16)[name = string("op_4119_cast_fp16")];
            int32 var_4117 = const()[name = string("op_4117"), val = int32(1)];
            bool doubled_81_interleave_0 = const()[name = string("doubled_81_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_81_cast_fp16 = concat(axis = var_4117, interleave = doubled_81_interleave_0, values = (hidden_states_99_cast_fp16, var_4119_cast_fp16))[name = string("doubled_81_cast_fp16")];
            tensor<int32, [1]> out_41_axes_0 = const()[name = string("out_41_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_41_gamma_0_to_fp16 = const()[name = string("out_41_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1397523904)))];
            fp16 var_4129_to_fp16 = const()[name = string("op_4129_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_41_cast_fp16 = layer_norm(axes = out_41_axes_0, epsilon = var_4129_to_fp16, gamma = out_41_gamma_0_to_fp16, x = doubled_81_cast_fp16)[name = string("out_41_cast_fp16")];
            tensor<int32, [2]> var_4140_split_sizes_0 = const()[name = string("op_4140_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4140_axis_0 = const()[name = string("op_4140_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_4140_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_4140_cast_fp16_1 = split(axis = var_4140_axis_0, split_sizes = var_4140_split_sizes_0, x = out_41_cast_fp16)[name = string("op_4140_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_10_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_10_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1397532160)))];
            tensor<int32, [2]> query_states_61_strides_0 = const()[name = string("query_states_61_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_61_pad_type_0 = const()[name = string("query_states_61_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_61_pad_0 = const()[name = string("query_states_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_61_dilations_0 = const()[name = string("query_states_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_61_groups_0 = const()[name = string("query_states_61_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_61_cast_fp16 = conv(dilations = query_states_61_dilations_0, groups = query_states_61_groups_0, pad = query_states_61_pad_0, pad_type = query_states_61_pad_type_0, strides = query_states_61_strides_0, weight = layers_10_self_attn_q_proj_weight_to_fp16, x = var_4140_cast_fp16_0)[name = string("query_states_61_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_10_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_10_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1405920832)))];
            tensor<int32, [2]> key_states_101_strides_0 = const()[name = string("key_states_101_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_101_pad_type_0 = const()[name = string("key_states_101_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_101_pad_0 = const()[name = string("key_states_101_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_101_dilations_0 = const()[name = string("key_states_101_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_101_groups_0 = const()[name = string("key_states_101_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_101_cast_fp16 = conv(dilations = key_states_101_dilations_0, groups = key_states_101_groups_0, pad = key_states_101_pad_0, pad_type = key_states_101_pad_type_0, strides = key_states_101_strides_0, weight = layers_10_self_attn_k_proj_weight_to_fp16, x = var_4140_cast_fp16_0)[name = string("key_states_101_cast_fp16")];
            tensor<int32, [2]> value_states_61_strides_0 = const()[name = string("value_states_61_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_61_pad_type_0 = const()[name = string("value_states_61_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_61_pad_0 = const()[name = string("value_states_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_61_dilations_0 = const()[name = string("value_states_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_61_groups_0 = const()[name = string("value_states_61_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_61_cast_fp16 = conv(dilations = value_states_61_dilations_0, groups = value_states_61_groups_0, pad = value_states_61_pad_0, pad_type = value_states_61_pad_type_0, strides = value_states_61_strides_0, weight = layers_10_self_attn_v_proj_weight_cast_fp16, x = var_4140_cast_fp16_0)[name = string("value_states_61_cast_fp16")];
            tensor<int32, [4]> concat_120x = const()[name = string("concat_120x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_101_cast_fp16 = reshape(shape = concat_120x, x = query_states_61_cast_fp16)[name = string("x_101_cast_fp16")];
            tensor<int32, [4]> concat_121x = const()[name = string("concat_121x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_4197_cast_fp16 = reshape(shape = concat_121x, x = key_states_101_cast_fp16)[name = string("op_4197_cast_fp16")];
            tensor<int32, [4]> concat_122x = const()[name = string("concat_122x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_4204_cast_fp16 = reshape(shape = concat_122x, x = value_states_61_cast_fp16)[name = string("op_4204_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_4208_cast_fp16 = mul(x = x_101_cast_fp16, y = var_859_cast_fp16)[name = string("op_4208_cast_fp16")];
            tensor<int32, [2]> var_4209_split_sizes_0 = const()[name = string("op_4209_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4209_axis_0 = const()[name = string("op_4209_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_4209_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_4209_cast_fp16_1 = split(axis = var_4209_axis_0, split_sizes = var_4209_split_sizes_0, x = x_101_cast_fp16)[name = string("op_4209_cast_fp16")];
            fp16 const_104_promoted_to_fp16 = const()[name = string("const_104_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_4211_cast_fp16 = mul(x = var_4209_cast_fp16_1, y = const_104_promoted_to_fp16)[name = string("op_4211_cast_fp16")];
            int32 var_4213 = const()[name = string("op_4213"), val = int32(-2)];
            bool var_4214_interleave_0 = const()[name = string("op_4214_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_4214_cast_fp16 = concat(axis = var_4213, interleave = var_4214_interleave_0, values = (var_4211_cast_fp16, var_4209_cast_fp16_0))[name = string("op_4214_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_4215_cast_fp16 = mul(x = var_4214_cast_fp16, y = var_866_cast_fp16)[name = string("op_4215_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_63_cast_fp16 = add(x = var_4208_cast_fp16, y = var_4215_cast_fp16)[name = string("query_states_63_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_4221_cast_fp16 = mul(x = var_4197_cast_fp16, y = var_859_cast_fp16)[name = string("op_4221_cast_fp16")];
            tensor<int32, [2]> var_4222_split_sizes_0 = const()[name = string("op_4222_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4222_axis_0 = const()[name = string("op_4222_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_4222_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_4222_cast_fp16_1 = split(axis = var_4222_axis_0, split_sizes = var_4222_split_sizes_0, x = var_4197_cast_fp16)[name = string("op_4222_cast_fp16")];
            fp16 const_105_promoted_to_fp16 = const()[name = string("const_105_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_4224_cast_fp16 = mul(x = var_4222_cast_fp16_1, y = const_105_promoted_to_fp16)[name = string("op_4224_cast_fp16")];
            int32 var_4226 = const()[name = string("op_4226"), val = int32(-2)];
            bool var_4227_interleave_0 = const()[name = string("op_4227_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_4227_cast_fp16 = concat(axis = var_4226, interleave = var_4227_interleave_0, values = (var_4224_cast_fp16, var_4222_cast_fp16_0))[name = string("op_4227_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_4228_cast_fp16 = mul(x = var_4227_cast_fp16, y = var_866_cast_fp16)[name = string("op_4228_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_105_cast_fp16 = add(x = var_4221_cast_fp16, y = var_4228_cast_fp16)[name = string("key_states_105_cast_fp16")];
            tensor<int32, [1]> expand_dims_120 = const()[name = string("expand_dims_120"), val = tensor<int32, [1]>([10])];
            tensor<int32, [1]> expand_dims_121 = const()[name = string("expand_dims_121"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_123 = const()[name = string("expand_dims_123"), val = tensor<int32, [1]>([0])];
            int32 concat_125_axis_0 = const()[name = string("concat_125_axis_0"), val = int32(0)];
            bool concat_125_interleave_0 = const()[name = string("concat_125_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_125 = concat(axis = concat_125_axis_0, interleave = concat_125_interleave_0, values = (expand_dims_120, expand_dims_121, position_id, expand_dims_123))[name = string("concat_125")];
            tensor<int32, [1]> expand_dims_124 = const()[name = string("expand_dims_124"), val = tensor<int32, [1]>([11])];
            tensor<int32, [1]> concat_126_values1_0 = const()[name = string("concat_126_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_126_values3_0 = const()[name = string("concat_126_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_126_axis_0 = const()[name = string("concat_126_axis_0"), val = int32(0)];
            bool concat_126_interleave_0 = const()[name = string("concat_126_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_126 = concat(axis = concat_126_axis_0, interleave = concat_126_interleave_0, values = (expand_dims_124, concat_126_values1_0, cache_position_end, concat_126_values3_0))[name = string("concat_126")];
            tensor<int32, [4]> key_states_107_perm_0 = const()[name = string("key_states_107_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_11_stride_0 = const()[name = string("key_cache_internal_tensor_assign_11_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_11_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_11_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_11_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_11_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_11_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_11_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_107_cast_fp16 = transpose(perm = key_states_107_perm_0, x = key_states_105_cast_fp16)[name = string("transpose_140")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_11_cast_fp16 = slice_update(begin = concat_125, begin_mask = key_cache_internal_tensor_assign_11_begin_mask_0, end = concat_126, end_mask = key_cache_internal_tensor_assign_11_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_11_squeeze_mask_0, stride = key_cache_internal_tensor_assign_11_stride_0, update = key_states_107_cast_fp16, x = coreml_update_state_74)[name = string("key_cache_internal_tensor_assign_11_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_11_cast_fp16, input = key_cache)[name = string("coreml_update_state_76_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_76 = read_state(input = key_cache)[name = string("coreml_update_state_76")];
            tensor<int32, [4]> value_states_63_perm_0 = const()[name = string("value_states_63_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_11_stride_0 = const()[name = string("value_cache_internal_tensor_assign_11_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_11_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_11_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_11_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_11_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_11_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_11_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_63_cast_fp16 = transpose(perm = value_states_63_perm_0, x = var_4204_cast_fp16)[name = string("transpose_139")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_11_cast_fp16 = slice_update(begin = concat_125, begin_mask = value_cache_internal_tensor_assign_11_begin_mask_0, end = concat_126, end_mask = value_cache_internal_tensor_assign_11_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_11_squeeze_mask_0, stride = value_cache_internal_tensor_assign_11_stride_0, update = value_states_63_cast_fp16, x = coreml_update_state_75)[name = string("value_cache_internal_tensor_assign_11_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_11_cast_fp16, input = value_cache)[name = string("coreml_update_state_77_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_77 = read_state(input = value_cache)[name = string("coreml_update_state_77")];
            tensor<int32, [4]> var_4298_begin_0 = const()[name = string("op_4298_begin_0"), val = tensor<int32, [4]>([10, 0, 0, 0])];
            tensor<int32, [4]> var_4298_end_0 = const()[name = string("op_4298_end_0"), val = tensor<int32, [4]>([11, 2, 2048, 128])];
            tensor<bool, [4]> var_4298_end_mask_0 = const()[name = string("op_4298_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4298_cast_fp16 = slice_by_index(begin = var_4298_begin_0, end = var_4298_end_0, end_mask = var_4298_end_mask_0, x = coreml_update_state_76)[name = string("op_4298_cast_fp16")];
            tensor<int32, [2]> tile_20 = const()[name = string("tile_20"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4301_axis_0 = const()[name = string("op_4301_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4301_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4301_cast_fp16_1 = split(axis = var_4301_axis_0, split_sizes = tile_20, x = var_4298_cast_fp16)[name = string("op_4301_cast_fp16")];
            tensor<int32, [4]> var_4308_begin_0 = const()[name = string("op_4308_begin_0"), val = tensor<int32, [4]>([10, 0, 0, 0])];
            tensor<int32, [4]> var_4308_end_0 = const()[name = string("op_4308_end_0"), val = tensor<int32, [4]>([11, 2, 2048, 128])];
            tensor<bool, [4]> var_4308_end_mask_0 = const()[name = string("op_4308_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4308_cast_fp16 = slice_by_index(begin = var_4308_begin_0, end = var_4308_end_0, end_mask = var_4308_end_mask_0, x = coreml_update_state_77)[name = string("op_4308_cast_fp16")];
            tensor<int32, [2]> tile_21 = const()[name = string("tile_21"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4311_axis_0 = const()[name = string("op_4311_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4311_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4311_cast_fp16_1 = split(axis = var_4311_axis_0, split_sizes = tile_21, x = var_4308_cast_fp16)[name = string("op_4311_cast_fp16")];
            tensor<int32, [2]> var_4314_split_sizes_0 = const()[name = string("op_4314_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_4314_axis_0 = const()[name = string("op_4314_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_4314_0, tensor<fp16, [1, 8, 128, 1]> var_4314_1 = split(axis = var_4314_axis_0, split_sizes = var_4314_split_sizes_0, x = query_states_63_cast_fp16)[name = string("op_4314")];
            bool attn_weights_161_transpose_x_0 = const()[name = string("attn_weights_161_transpose_x_0"), val = bool(false)];
            bool attn_weights_161_transpose_y_0 = const()[name = string("attn_weights_161_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_161_cast_fp16 = matmul(transpose_x = attn_weights_161_transpose_x_0, transpose_y = attn_weights_161_transpose_y_0, x = var_4301_cast_fp16_0, y = var_4314_0)[name = string("attn_weights_161_cast_fp16")];
            fp16 var_4317_to_fp16 = const()[name = string("op_4317_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_163_cast_fp16 = mul(x = attn_weights_161_cast_fp16, y = var_4317_to_fp16)[name = string("attn_weights_163_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_165_cast_fp16 = add(x = attn_weights_163_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_165_cast_fp16")];
            int32 var_4321 = const()[name = string("op_4321"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_167_cast_fp16 = softmax(axis = var_4321, x = attn_weights_165_cast_fp16)[name = string("attn_weights_167_cast_fp16")];
            bool var_4327_transpose_x_1 = const()[name = string("op_4327_transpose_x_1"), val = bool(true)];
            bool var_4327_transpose_y_1 = const()[name = string("op_4327_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_4327_cast_fp16 = matmul(transpose_x = var_4327_transpose_x_1, transpose_y = var_4327_transpose_y_1, x = attn_weights_167_cast_fp16, y = var_4311_cast_fp16_0)[name = string("op_4327_cast_fp16")];
            bool attn_weights_169_transpose_x_0 = const()[name = string("attn_weights_169_transpose_x_0"), val = bool(false)];
            bool attn_weights_169_transpose_y_0 = const()[name = string("attn_weights_169_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_169_cast_fp16 = matmul(transpose_x = attn_weights_169_transpose_x_0, transpose_y = attn_weights_169_transpose_y_0, x = var_4301_cast_fp16_1, y = var_4314_1)[name = string("attn_weights_169_cast_fp16")];
            fp16 var_4329_to_fp16 = const()[name = string("op_4329_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_171_cast_fp16 = mul(x = attn_weights_169_cast_fp16, y = var_4329_to_fp16)[name = string("attn_weights_171_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_173_cast_fp16 = add(x = attn_weights_171_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_173_cast_fp16")];
            int32 var_4333 = const()[name = string("op_4333"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_175_cast_fp16 = softmax(axis = var_4333, x = attn_weights_173_cast_fp16)[name = string("attn_weights_175_cast_fp16")];
            bool attn_output_81_transpose_x_1 = const()[name = string("attn_output_81_transpose_x_1"), val = bool(true)];
            bool attn_output_81_transpose_y_1 = const()[name = string("attn_output_81_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_81_cast_fp16 = matmul(transpose_x = attn_output_81_transpose_x_1, transpose_y = attn_output_81_transpose_y_1, x = attn_weights_175_cast_fp16, y = var_4311_cast_fp16_1)[name = string("attn_output_81_cast_fp16")];
            int32 var_4341 = const()[name = string("op_4341"), val = int32(1)];
            bool attn_output_83_interleave_0 = const()[name = string("attn_output_83_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_83_cast_fp16 = concat(axis = var_4341, interleave = attn_output_83_interleave_0, values = (var_4327_cast_fp16, attn_output_81_cast_fp16))[name = string("attn_output_83_cast_fp16")];
            tensor<int32, [4]> var_4345_perm_0 = const()[name = string("op_4345_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_131x = const()[name = string("concat_131x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_4345_cast_fp16 = transpose(perm = var_4345_perm_0, x = attn_output_83_cast_fp16)[name = string("transpose_138")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_87_cast_fp16 = reshape(shape = concat_131x, x = var_4345_cast_fp16)[name = string("attn_output_87_cast_fp16")];
            tensor<int32, [2]> hidden_states_103_strides_0 = const()[name = string("hidden_states_103_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_103_pad_type_0 = const()[name = string("hidden_states_103_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_103_pad_0 = const()[name = string("hidden_states_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_103_dilations_0 = const()[name = string("hidden_states_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_103_groups_0 = const()[name = string("hidden_states_103_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_103_cast_fp16 = conv(dilations = hidden_states_103_dilations_0, groups = hidden_states_103_groups_0, pad = hidden_states_103_pad_0, pad_type = hidden_states_103_pad_type_0, strides = hidden_states_103_strides_0, weight = layers_10_self_attn_o_proj_weight_cast_fp16, x = attn_output_87_cast_fp16)[name = string("hidden_states_103_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_105_cast_fp16 = add(x = hidden_states_99_cast_fp16, y = hidden_states_103_cast_fp16)[name = string("hidden_states_105_cast_fp16")];
            fp16 const_110_promoted_to_fp16 = const()[name = string("const_110_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_4378_cast_fp16 = mul(x = hidden_states_105_cast_fp16, y = const_110_promoted_to_fp16)[name = string("op_4378_cast_fp16")];
            int32 var_4376 = const()[name = string("op_4376"), val = int32(1)];
            bool doubled_85_interleave_0 = const()[name = string("doubled_85_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_85_cast_fp16 = concat(axis = var_4376, interleave = doubled_85_interleave_0, values = (hidden_states_105_cast_fp16, var_4378_cast_fp16))[name = string("doubled_85_cast_fp16")];
            tensor<int32, [1]> out_43_axes_0 = const()[name = string("out_43_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_43_gamma_0_to_fp16 = const()[name = string("out_43_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406969472)))];
            fp16 var_4388_to_fp16 = const()[name = string("op_4388_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_43_cast_fp16 = layer_norm(axes = out_43_axes_0, epsilon = var_4388_to_fp16, gamma = out_43_gamma_0_to_fp16, x = doubled_85_cast_fp16)[name = string("out_43_cast_fp16")];
            tensor<int32, [2]> var_4399_split_sizes_0 = const()[name = string("op_4399_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4399_axis_0 = const()[name = string("op_4399_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_4399_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_4399_cast_fp16_1 = split(axis = var_4399_axis_0, split_sizes = var_4399_split_sizes_0, x = out_43_cast_fp16)[name = string("op_4399_cast_fp16")];
            tensor<int32, [2]> input_21_strides_0 = const()[name = string("input_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_21_pad_type_0 = const()[name = string("input_21_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_21_pad_0 = const()[name = string("input_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_21_dilations_0 = const()[name = string("input_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_21_groups_0 = const()[name = string("input_21_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_21_cast_fp16 = conv(dilations = input_21_dilations_0, groups = input_21_groups_0, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = input_21_strides_0, weight = layers_10_mlp_gate_proj_weight_cast_fp16, x = var_4399_cast_fp16_0)[name = string("input_21_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_4416_cast_fp16 = silu(x = input_21_cast_fp16)[name = string("op_4416_cast_fp16")];
            tensor<int32, [2]> var_4422_strides_0 = const()[name = string("op_4422_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_4422_pad_type_0 = const()[name = string("op_4422_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_4422_pad_0 = const()[name = string("op_4422_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_4422_dilations_0 = const()[name = string("op_4422_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4422_groups_0 = const()[name = string("op_4422_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_4422_cast_fp16 = conv(dilations = var_4422_dilations_0, groups = var_4422_groups_0, pad = var_4422_pad_0, pad_type = var_4422_pad_type_0, strides = var_4422_strides_0, weight = layers_10_mlp_up_proj_weight_cast_fp16, x = var_4399_cast_fp16_0)[name = string("op_4422_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_109_cast_fp16 = mul(x = var_4416_cast_fp16, y = var_4422_cast_fp16)[name = string("x_109_cast_fp16")];
            tensor<int32, [2]> hidden_states_107_strides_0 = const()[name = string("hidden_states_107_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_107_pad_type_0 = const()[name = string("hidden_states_107_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_107_pad_0 = const()[name = string("hidden_states_107_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_107_dilations_0 = const()[name = string("hidden_states_107_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_107_groups_0 = const()[name = string("hidden_states_107_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_107_cast_fp16 = conv(dilations = hidden_states_107_dilations_0, groups = hidden_states_107_groups_0, pad = hidden_states_107_pad_0, pad_type = hidden_states_107_pad_type_0, strides = hidden_states_107_strides_0, weight = layers_10_mlp_down_proj_weight_cast_fp16, x = x_109_cast_fp16)[name = string("hidden_states_107_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_109_cast_fp16 = add(x = hidden_states_105_cast_fp16, y = hidden_states_107_cast_fp16)[name = string("hidden_states_109_cast_fp16")];
            fp16 const_112_promoted_to_fp16 = const()[name = string("const_112_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_4440_cast_fp16 = mul(x = hidden_states_109_cast_fp16, y = const_112_promoted_to_fp16)[name = string("op_4440_cast_fp16")];
            int32 var_4438 = const()[name = string("op_4438"), val = int32(1)];
            bool doubled_89_interleave_0 = const()[name = string("doubled_89_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_89_cast_fp16 = concat(axis = var_4438, interleave = doubled_89_interleave_0, values = (hidden_states_109_cast_fp16, var_4440_cast_fp16))[name = string("doubled_89_cast_fp16")];
            tensor<int32, [1]> out_45_axes_0 = const()[name = string("out_45_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_45_gamma_0_to_fp16 = const()[name = string("out_45_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406977728)))];
            fp16 var_4450_to_fp16 = const()[name = string("op_4450_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_45_cast_fp16 = layer_norm(axes = out_45_axes_0, epsilon = var_4450_to_fp16, gamma = out_45_gamma_0_to_fp16, x = doubled_89_cast_fp16)[name = string("out_45_cast_fp16")];
            tensor<int32, [2]> var_4461_split_sizes_0 = const()[name = string("op_4461_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4461_axis_0 = const()[name = string("op_4461_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_4461_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_4461_cast_fp16_1 = split(axis = var_4461_axis_0, split_sizes = var_4461_split_sizes_0, x = out_45_cast_fp16)[name = string("op_4461_cast_fp16")];
            tensor<int32, [2]> query_states_67_strides_0 = const()[name = string("query_states_67_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_67_pad_type_0 = const()[name = string("query_states_67_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_67_pad_0 = const()[name = string("query_states_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_67_dilations_0 = const()[name = string("query_states_67_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_67_groups_0 = const()[name = string("query_states_67_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_67_cast_fp16 = conv(dilations = query_states_67_dilations_0, groups = query_states_67_groups_0, pad = query_states_67_pad_0, pad_type = query_states_67_pad_type_0, strides = query_states_67_strides_0, weight = layers_11_self_attn_q_proj_weight_cast_fp16, x = var_4461_cast_fp16_0)[name = string("query_states_67_cast_fp16")];
            tensor<int32, [2]> key_states_111_strides_0 = const()[name = string("key_states_111_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_111_pad_type_0 = const()[name = string("key_states_111_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_111_pad_0 = const()[name = string("key_states_111_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_111_dilations_0 = const()[name = string("key_states_111_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_111_groups_0 = const()[name = string("key_states_111_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_111_cast_fp16 = conv(dilations = key_states_111_dilations_0, groups = key_states_111_groups_0, pad = key_states_111_pad_0, pad_type = key_states_111_pad_type_0, strides = key_states_111_strides_0, weight = layers_11_self_attn_k_proj_weight_cast_fp16, x = var_4461_cast_fp16_0)[name = string("key_states_111_cast_fp16")];
            tensor<int32, [2]> value_states_67_strides_0 = const()[name = string("value_states_67_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_67_pad_type_0 = const()[name = string("value_states_67_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_67_pad_0 = const()[name = string("value_states_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_67_dilations_0 = const()[name = string("value_states_67_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_67_groups_0 = const()[name = string("value_states_67_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_67_cast_fp16 = conv(dilations = value_states_67_dilations_0, groups = value_states_67_groups_0, pad = value_states_67_pad_0, pad_type = value_states_67_pad_type_0, strides = value_states_67_strides_0, weight = layers_11_self_attn_v_proj_weight_cast_fp16, x = var_4461_cast_fp16_0)[name = string("value_states_67_cast_fp16")];
            tensor<int32, [4]> concat_132x = const()[name = string("concat_132x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_111_cast_fp16 = reshape(shape = concat_132x, x = query_states_67_cast_fp16)[name = string("x_111_cast_fp16")];
            tensor<int32, [4]> concat_133x = const()[name = string("concat_133x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_4518_cast_fp16 = reshape(shape = concat_133x, x = key_states_111_cast_fp16)[name = string("op_4518_cast_fp16")];
            tensor<int32, [4]> concat_134x = const()[name = string("concat_134x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_4525_cast_fp16 = reshape(shape = concat_134x, x = value_states_67_cast_fp16)[name = string("op_4525_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_4529_cast_fp16 = mul(x = x_111_cast_fp16, y = var_859_cast_fp16)[name = string("op_4529_cast_fp16")];
            tensor<int32, [2]> var_4530_split_sizes_0 = const()[name = string("op_4530_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4530_axis_0 = const()[name = string("op_4530_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_4530_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_4530_cast_fp16_1 = split(axis = var_4530_axis_0, split_sizes = var_4530_split_sizes_0, x = x_111_cast_fp16)[name = string("op_4530_cast_fp16")];
            fp16 const_114_promoted_to_fp16 = const()[name = string("const_114_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_4532_cast_fp16 = mul(x = var_4530_cast_fp16_1, y = const_114_promoted_to_fp16)[name = string("op_4532_cast_fp16")];
            int32 var_4534 = const()[name = string("op_4534"), val = int32(-2)];
            bool var_4535_interleave_0 = const()[name = string("op_4535_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_4535_cast_fp16 = concat(axis = var_4534, interleave = var_4535_interleave_0, values = (var_4532_cast_fp16, var_4530_cast_fp16_0))[name = string("op_4535_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_4536_cast_fp16 = mul(x = var_4535_cast_fp16, y = var_866_cast_fp16)[name = string("op_4536_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_69_cast_fp16 = add(x = var_4529_cast_fp16, y = var_4536_cast_fp16)[name = string("query_states_69_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_4542_cast_fp16 = mul(x = var_4518_cast_fp16, y = var_859_cast_fp16)[name = string("op_4542_cast_fp16")];
            tensor<int32, [2]> var_4543_split_sizes_0 = const()[name = string("op_4543_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4543_axis_0 = const()[name = string("op_4543_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_4543_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_4543_cast_fp16_1 = split(axis = var_4543_axis_0, split_sizes = var_4543_split_sizes_0, x = var_4518_cast_fp16)[name = string("op_4543_cast_fp16")];
            fp16 const_115_promoted_to_fp16 = const()[name = string("const_115_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_4545_cast_fp16 = mul(x = var_4543_cast_fp16_1, y = const_115_promoted_to_fp16)[name = string("op_4545_cast_fp16")];
            int32 var_4547 = const()[name = string("op_4547"), val = int32(-2)];
            bool var_4548_interleave_0 = const()[name = string("op_4548_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_4548_cast_fp16 = concat(axis = var_4547, interleave = var_4548_interleave_0, values = (var_4545_cast_fp16, var_4543_cast_fp16_0))[name = string("op_4548_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_4549_cast_fp16 = mul(x = var_4548_cast_fp16, y = var_866_cast_fp16)[name = string("op_4549_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_115_cast_fp16 = add(x = var_4542_cast_fp16, y = var_4549_cast_fp16)[name = string("key_states_115_cast_fp16")];
            tensor<int32, [1]> expand_dims_132 = const()[name = string("expand_dims_132"), val = tensor<int32, [1]>([11])];
            tensor<int32, [1]> expand_dims_133 = const()[name = string("expand_dims_133"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_135 = const()[name = string("expand_dims_135"), val = tensor<int32, [1]>([0])];
            int32 concat_137_axis_0 = const()[name = string("concat_137_axis_0"), val = int32(0)];
            bool concat_137_interleave_0 = const()[name = string("concat_137_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_137 = concat(axis = concat_137_axis_0, interleave = concat_137_interleave_0, values = (expand_dims_132, expand_dims_133, position_id, expand_dims_135))[name = string("concat_137")];
            tensor<int32, [1]> expand_dims_136 = const()[name = string("expand_dims_136"), val = tensor<int32, [1]>([12])];
            tensor<int32, [1]> concat_138_values1_0 = const()[name = string("concat_138_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_138_values3_0 = const()[name = string("concat_138_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_138_axis_0 = const()[name = string("concat_138_axis_0"), val = int32(0)];
            bool concat_138_interleave_0 = const()[name = string("concat_138_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_138 = concat(axis = concat_138_axis_0, interleave = concat_138_interleave_0, values = (expand_dims_136, concat_138_values1_0, cache_position_end, concat_138_values3_0))[name = string("concat_138")];
            tensor<int32, [4]> key_states_117_perm_0 = const()[name = string("key_states_117_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_12_stride_0 = const()[name = string("key_cache_internal_tensor_assign_12_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_12_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_12_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_12_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_12_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_12_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_12_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_117_cast_fp16 = transpose(perm = key_states_117_perm_0, x = key_states_115_cast_fp16)[name = string("transpose_137")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_12_cast_fp16 = slice_update(begin = concat_137, begin_mask = key_cache_internal_tensor_assign_12_begin_mask_0, end = concat_138, end_mask = key_cache_internal_tensor_assign_12_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_12_squeeze_mask_0, stride = key_cache_internal_tensor_assign_12_stride_0, update = key_states_117_cast_fp16, x = coreml_update_state_76)[name = string("key_cache_internal_tensor_assign_12_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_12_cast_fp16, input = key_cache)[name = string("coreml_update_state_78_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_78 = read_state(input = key_cache)[name = string("coreml_update_state_78")];
            tensor<int32, [4]> value_states_69_perm_0 = const()[name = string("value_states_69_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_12_stride_0 = const()[name = string("value_cache_internal_tensor_assign_12_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_12_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_12_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_12_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_12_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_12_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_12_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_69_cast_fp16 = transpose(perm = value_states_69_perm_0, x = var_4525_cast_fp16)[name = string("transpose_136")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_12_cast_fp16 = slice_update(begin = concat_137, begin_mask = value_cache_internal_tensor_assign_12_begin_mask_0, end = concat_138, end_mask = value_cache_internal_tensor_assign_12_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_12_squeeze_mask_0, stride = value_cache_internal_tensor_assign_12_stride_0, update = value_states_69_cast_fp16, x = coreml_update_state_77)[name = string("value_cache_internal_tensor_assign_12_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_12_cast_fp16, input = value_cache)[name = string("coreml_update_state_79_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_79 = read_state(input = value_cache)[name = string("coreml_update_state_79")];
            tensor<int32, [4]> var_4619_begin_0 = const()[name = string("op_4619_begin_0"), val = tensor<int32, [4]>([11, 0, 0, 0])];
            tensor<int32, [4]> var_4619_end_0 = const()[name = string("op_4619_end_0"), val = tensor<int32, [4]>([12, 2, 2048, 128])];
            tensor<bool, [4]> var_4619_end_mask_0 = const()[name = string("op_4619_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4619_cast_fp16 = slice_by_index(begin = var_4619_begin_0, end = var_4619_end_0, end_mask = var_4619_end_mask_0, x = coreml_update_state_78)[name = string("op_4619_cast_fp16")];
            tensor<int32, [2]> tile_22 = const()[name = string("tile_22"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4622_axis_0 = const()[name = string("op_4622_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4622_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4622_cast_fp16_1 = split(axis = var_4622_axis_0, split_sizes = tile_22, x = var_4619_cast_fp16)[name = string("op_4622_cast_fp16")];
            tensor<int32, [4]> var_4629_begin_0 = const()[name = string("op_4629_begin_0"), val = tensor<int32, [4]>([11, 0, 0, 0])];
            tensor<int32, [4]> var_4629_end_0 = const()[name = string("op_4629_end_0"), val = tensor<int32, [4]>([12, 2, 2048, 128])];
            tensor<bool, [4]> var_4629_end_mask_0 = const()[name = string("op_4629_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4629_cast_fp16 = slice_by_index(begin = var_4629_begin_0, end = var_4629_end_0, end_mask = var_4629_end_mask_0, x = coreml_update_state_79)[name = string("op_4629_cast_fp16")];
            tensor<int32, [2]> tile_23 = const()[name = string("tile_23"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4632_axis_0 = const()[name = string("op_4632_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4632_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4632_cast_fp16_1 = split(axis = var_4632_axis_0, split_sizes = tile_23, x = var_4629_cast_fp16)[name = string("op_4632_cast_fp16")];
            tensor<int32, [2]> var_4635_split_sizes_0 = const()[name = string("op_4635_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_4635_axis_0 = const()[name = string("op_4635_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_4635_0, tensor<fp16, [1, 8, 128, 1]> var_4635_1 = split(axis = var_4635_axis_0, split_sizes = var_4635_split_sizes_0, x = query_states_69_cast_fp16)[name = string("op_4635")];
            bool attn_weights_177_transpose_x_0 = const()[name = string("attn_weights_177_transpose_x_0"), val = bool(false)];
            bool attn_weights_177_transpose_y_0 = const()[name = string("attn_weights_177_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_177_cast_fp16 = matmul(transpose_x = attn_weights_177_transpose_x_0, transpose_y = attn_weights_177_transpose_y_0, x = var_4622_cast_fp16_0, y = var_4635_0)[name = string("attn_weights_177_cast_fp16")];
            fp16 var_4638_to_fp16 = const()[name = string("op_4638_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_179_cast_fp16 = mul(x = attn_weights_177_cast_fp16, y = var_4638_to_fp16)[name = string("attn_weights_179_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_181_cast_fp16 = add(x = attn_weights_179_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_181_cast_fp16")];
            int32 var_4642 = const()[name = string("op_4642"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_183_cast_fp16 = softmax(axis = var_4642, x = attn_weights_181_cast_fp16)[name = string("attn_weights_183_cast_fp16")];
            bool var_4648_transpose_x_1 = const()[name = string("op_4648_transpose_x_1"), val = bool(true)];
            bool var_4648_transpose_y_1 = const()[name = string("op_4648_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_4648_cast_fp16 = matmul(transpose_x = var_4648_transpose_x_1, transpose_y = var_4648_transpose_y_1, x = attn_weights_183_cast_fp16, y = var_4632_cast_fp16_0)[name = string("op_4648_cast_fp16")];
            bool attn_weights_185_transpose_x_0 = const()[name = string("attn_weights_185_transpose_x_0"), val = bool(false)];
            bool attn_weights_185_transpose_y_0 = const()[name = string("attn_weights_185_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_185_cast_fp16 = matmul(transpose_x = attn_weights_185_transpose_x_0, transpose_y = attn_weights_185_transpose_y_0, x = var_4622_cast_fp16_1, y = var_4635_1)[name = string("attn_weights_185_cast_fp16")];
            fp16 var_4650_to_fp16 = const()[name = string("op_4650_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_187_cast_fp16 = mul(x = attn_weights_185_cast_fp16, y = var_4650_to_fp16)[name = string("attn_weights_187_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_189_cast_fp16 = add(x = attn_weights_187_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_189_cast_fp16")];
            int32 var_4654 = const()[name = string("op_4654"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_191_cast_fp16 = softmax(axis = var_4654, x = attn_weights_189_cast_fp16)[name = string("attn_weights_191_cast_fp16")];
            bool attn_output_89_transpose_x_1 = const()[name = string("attn_output_89_transpose_x_1"), val = bool(true)];
            bool attn_output_89_transpose_y_1 = const()[name = string("attn_output_89_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_89_cast_fp16 = matmul(transpose_x = attn_output_89_transpose_x_1, transpose_y = attn_output_89_transpose_y_1, x = attn_weights_191_cast_fp16, y = var_4632_cast_fp16_1)[name = string("attn_output_89_cast_fp16")];
            int32 var_4662 = const()[name = string("op_4662"), val = int32(1)];
            bool attn_output_91_interleave_0 = const()[name = string("attn_output_91_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_91_cast_fp16 = concat(axis = var_4662, interleave = attn_output_91_interleave_0, values = (var_4648_cast_fp16, attn_output_89_cast_fp16))[name = string("attn_output_91_cast_fp16")];
            tensor<int32, [4]> var_4666_perm_0 = const()[name = string("op_4666_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_143x = const()[name = string("concat_143x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_4666_cast_fp16 = transpose(perm = var_4666_perm_0, x = attn_output_91_cast_fp16)[name = string("transpose_135")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_95_cast_fp16 = reshape(shape = concat_143x, x = var_4666_cast_fp16)[name = string("attn_output_95_cast_fp16")];
            tensor<int32, [2]> hidden_states_113_strides_0 = const()[name = string("hidden_states_113_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_113_pad_type_0 = const()[name = string("hidden_states_113_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_113_pad_0 = const()[name = string("hidden_states_113_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_113_dilations_0 = const()[name = string("hidden_states_113_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_113_groups_0 = const()[name = string("hidden_states_113_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_113_cast_fp16 = conv(dilations = hidden_states_113_dilations_0, groups = hidden_states_113_groups_0, pad = hidden_states_113_pad_0, pad_type = hidden_states_113_pad_type_0, strides = hidden_states_113_strides_0, weight = layers_11_self_attn_o_proj_weight_cast_fp16, x = attn_output_95_cast_fp16)[name = string("hidden_states_113_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_115_cast_fp16 = add(x = hidden_states_109_cast_fp16, y = hidden_states_113_cast_fp16)[name = string("hidden_states_115_cast_fp16")];
            fp16 const_120_promoted_to_fp16 = const()[name = string("const_120_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_4699_cast_fp16 = mul(x = hidden_states_115_cast_fp16, y = const_120_promoted_to_fp16)[name = string("op_4699_cast_fp16")];
            int32 var_4697 = const()[name = string("op_4697"), val = int32(1)];
            bool doubled_93_interleave_0 = const()[name = string("doubled_93_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_93_cast_fp16 = concat(axis = var_4697, interleave = doubled_93_interleave_0, values = (hidden_states_115_cast_fp16, var_4699_cast_fp16))[name = string("doubled_93_cast_fp16")];
            tensor<int32, [1]> out_47_axes_0 = const()[name = string("out_47_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_47_gamma_0_to_fp16 = const()[name = string("out_47_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406985984)))];
            fp16 var_4709_to_fp16 = const()[name = string("op_4709_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_47_cast_fp16 = layer_norm(axes = out_47_axes_0, epsilon = var_4709_to_fp16, gamma = out_47_gamma_0_to_fp16, x = doubled_93_cast_fp16)[name = string("out_47_cast_fp16")];
            tensor<int32, [2]> var_4720_split_sizes_0 = const()[name = string("op_4720_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4720_axis_0 = const()[name = string("op_4720_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_4720_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_4720_cast_fp16_1 = split(axis = var_4720_axis_0, split_sizes = var_4720_split_sizes_0, x = out_47_cast_fp16)[name = string("op_4720_cast_fp16")];
            tensor<int32, [2]> input_23_strides_0 = const()[name = string("input_23_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_23_pad_type_0 = const()[name = string("input_23_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_23_pad_0 = const()[name = string("input_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_23_dilations_0 = const()[name = string("input_23_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_23_groups_0 = const()[name = string("input_23_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_23_cast_fp16 = conv(dilations = input_23_dilations_0, groups = input_23_groups_0, pad = input_23_pad_0, pad_type = input_23_pad_type_0, strides = input_23_strides_0, weight = layers_11_mlp_gate_proj_weight_cast_fp16, x = var_4720_cast_fp16_0)[name = string("input_23_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_4737_cast_fp16 = silu(x = input_23_cast_fp16)[name = string("op_4737_cast_fp16")];
            tensor<int32, [2]> var_4743_strides_0 = const()[name = string("op_4743_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_4743_pad_type_0 = const()[name = string("op_4743_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_4743_pad_0 = const()[name = string("op_4743_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_4743_dilations_0 = const()[name = string("op_4743_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4743_groups_0 = const()[name = string("op_4743_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_4743_cast_fp16 = conv(dilations = var_4743_dilations_0, groups = var_4743_groups_0, pad = var_4743_pad_0, pad_type = var_4743_pad_type_0, strides = var_4743_strides_0, weight = layers_11_mlp_up_proj_weight_cast_fp16, x = var_4720_cast_fp16_0)[name = string("op_4743_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_119_cast_fp16 = mul(x = var_4737_cast_fp16, y = var_4743_cast_fp16)[name = string("x_119_cast_fp16")];
            tensor<int32, [2]> hidden_states_117_strides_0 = const()[name = string("hidden_states_117_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_117_pad_type_0 = const()[name = string("hidden_states_117_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_117_pad_0 = const()[name = string("hidden_states_117_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_117_dilations_0 = const()[name = string("hidden_states_117_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_117_groups_0 = const()[name = string("hidden_states_117_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_117_cast_fp16 = conv(dilations = hidden_states_117_dilations_0, groups = hidden_states_117_groups_0, pad = hidden_states_117_pad_0, pad_type = hidden_states_117_pad_type_0, strides = hidden_states_117_strides_0, weight = layers_11_mlp_down_proj_weight_cast_fp16, x = x_119_cast_fp16)[name = string("hidden_states_117_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_119_cast_fp16 = add(x = hidden_states_115_cast_fp16, y = hidden_states_117_cast_fp16)[name = string("hidden_states_119_cast_fp16")];
            fp16 const_122_promoted_to_fp16 = const()[name = string("const_122_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_4761_cast_fp16 = mul(x = hidden_states_119_cast_fp16, y = const_122_promoted_to_fp16)[name = string("op_4761_cast_fp16")];
            int32 var_4759 = const()[name = string("op_4759"), val = int32(1)];
            bool doubled_97_interleave_0 = const()[name = string("doubled_97_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_97_cast_fp16 = concat(axis = var_4759, interleave = doubled_97_interleave_0, values = (hidden_states_119_cast_fp16, var_4761_cast_fp16))[name = string("doubled_97_cast_fp16")];
            tensor<int32, [1]> out_49_axes_0 = const()[name = string("out_49_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_49_gamma_0_to_fp16 = const()[name = string("out_49_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406994240)))];
            fp16 var_4771_to_fp16 = const()[name = string("op_4771_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_49_cast_fp16 = layer_norm(axes = out_49_axes_0, epsilon = var_4771_to_fp16, gamma = out_49_gamma_0_to_fp16, x = doubled_97_cast_fp16)[name = string("out_49_cast_fp16")];
            tensor<int32, [2]> var_4782_split_sizes_0 = const()[name = string("op_4782_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4782_axis_0 = const()[name = string("op_4782_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_4782_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_4782_cast_fp16_1 = split(axis = var_4782_axis_0, split_sizes = var_4782_split_sizes_0, x = out_49_cast_fp16)[name = string("op_4782_cast_fp16")];
            tensor<int32, [2]> query_states_73_strides_0 = const()[name = string("query_states_73_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_73_pad_type_0 = const()[name = string("query_states_73_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_73_pad_0 = const()[name = string("query_states_73_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_73_dilations_0 = const()[name = string("query_states_73_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_73_groups_0 = const()[name = string("query_states_73_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_73_cast_fp16 = conv(dilations = query_states_73_dilations_0, groups = query_states_73_groups_0, pad = query_states_73_pad_0, pad_type = query_states_73_pad_type_0, strides = query_states_73_strides_0, weight = layers_12_self_attn_q_proj_weight_cast_fp16, x = var_4782_cast_fp16_0)[name = string("query_states_73_cast_fp16")];
            tensor<int32, [2]> key_states_121_strides_0 = const()[name = string("key_states_121_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_121_pad_type_0 = const()[name = string("key_states_121_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_121_pad_0 = const()[name = string("key_states_121_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_121_dilations_0 = const()[name = string("key_states_121_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_121_groups_0 = const()[name = string("key_states_121_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_121_cast_fp16 = conv(dilations = key_states_121_dilations_0, groups = key_states_121_groups_0, pad = key_states_121_pad_0, pad_type = key_states_121_pad_type_0, strides = key_states_121_strides_0, weight = layers_12_self_attn_k_proj_weight_cast_fp16, x = var_4782_cast_fp16_0)[name = string("key_states_121_cast_fp16")];
            tensor<int32, [2]> value_states_73_strides_0 = const()[name = string("value_states_73_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_73_pad_type_0 = const()[name = string("value_states_73_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_73_pad_0 = const()[name = string("value_states_73_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_73_dilations_0 = const()[name = string("value_states_73_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_73_groups_0 = const()[name = string("value_states_73_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_73_cast_fp16 = conv(dilations = value_states_73_dilations_0, groups = value_states_73_groups_0, pad = value_states_73_pad_0, pad_type = value_states_73_pad_type_0, strides = value_states_73_strides_0, weight = layers_12_self_attn_v_proj_weight_cast_fp16, x = var_4782_cast_fp16_0)[name = string("value_states_73_cast_fp16")];
            tensor<int32, [4]> concat_144x = const()[name = string("concat_144x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_121_cast_fp16 = reshape(shape = concat_144x, x = query_states_73_cast_fp16)[name = string("x_121_cast_fp16")];
            tensor<int32, [4]> concat_145x = const()[name = string("concat_145x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_4839_cast_fp16 = reshape(shape = concat_145x, x = key_states_121_cast_fp16)[name = string("op_4839_cast_fp16")];
            tensor<int32, [4]> concat_146x = const()[name = string("concat_146x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_4846_cast_fp16 = reshape(shape = concat_146x, x = value_states_73_cast_fp16)[name = string("op_4846_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_4850_cast_fp16 = mul(x = x_121_cast_fp16, y = var_859_cast_fp16)[name = string("op_4850_cast_fp16")];
            tensor<int32, [2]> var_4851_split_sizes_0 = const()[name = string("op_4851_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4851_axis_0 = const()[name = string("op_4851_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_4851_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_4851_cast_fp16_1 = split(axis = var_4851_axis_0, split_sizes = var_4851_split_sizes_0, x = x_121_cast_fp16)[name = string("op_4851_cast_fp16")];
            fp16 const_124_promoted_to_fp16 = const()[name = string("const_124_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_4853_cast_fp16 = mul(x = var_4851_cast_fp16_1, y = const_124_promoted_to_fp16)[name = string("op_4853_cast_fp16")];
            int32 var_4855 = const()[name = string("op_4855"), val = int32(-2)];
            bool var_4856_interleave_0 = const()[name = string("op_4856_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_4856_cast_fp16 = concat(axis = var_4855, interleave = var_4856_interleave_0, values = (var_4853_cast_fp16, var_4851_cast_fp16_0))[name = string("op_4856_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_4857_cast_fp16 = mul(x = var_4856_cast_fp16, y = var_866_cast_fp16)[name = string("op_4857_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_75_cast_fp16 = add(x = var_4850_cast_fp16, y = var_4857_cast_fp16)[name = string("query_states_75_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_4863_cast_fp16 = mul(x = var_4839_cast_fp16, y = var_859_cast_fp16)[name = string("op_4863_cast_fp16")];
            tensor<int32, [2]> var_4864_split_sizes_0 = const()[name = string("op_4864_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4864_axis_0 = const()[name = string("op_4864_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_4864_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_4864_cast_fp16_1 = split(axis = var_4864_axis_0, split_sizes = var_4864_split_sizes_0, x = var_4839_cast_fp16)[name = string("op_4864_cast_fp16")];
            fp16 const_125_promoted_to_fp16 = const()[name = string("const_125_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_4866_cast_fp16 = mul(x = var_4864_cast_fp16_1, y = const_125_promoted_to_fp16)[name = string("op_4866_cast_fp16")];
            int32 var_4868 = const()[name = string("op_4868"), val = int32(-2)];
            bool var_4869_interleave_0 = const()[name = string("op_4869_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_4869_cast_fp16 = concat(axis = var_4868, interleave = var_4869_interleave_0, values = (var_4866_cast_fp16, var_4864_cast_fp16_0))[name = string("op_4869_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_4870_cast_fp16 = mul(x = var_4869_cast_fp16, y = var_866_cast_fp16)[name = string("op_4870_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_125_cast_fp16 = add(x = var_4863_cast_fp16, y = var_4870_cast_fp16)[name = string("key_states_125_cast_fp16")];
            tensor<int32, [1]> expand_dims_144 = const()[name = string("expand_dims_144"), val = tensor<int32, [1]>([12])];
            tensor<int32, [1]> expand_dims_145 = const()[name = string("expand_dims_145"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_147 = const()[name = string("expand_dims_147"), val = tensor<int32, [1]>([0])];
            int32 concat_149_axis_0 = const()[name = string("concat_149_axis_0"), val = int32(0)];
            bool concat_149_interleave_0 = const()[name = string("concat_149_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_149 = concat(axis = concat_149_axis_0, interleave = concat_149_interleave_0, values = (expand_dims_144, expand_dims_145, position_id, expand_dims_147))[name = string("concat_149")];
            tensor<int32, [1]> expand_dims_148 = const()[name = string("expand_dims_148"), val = tensor<int32, [1]>([13])];
            tensor<int32, [1]> concat_150_values1_0 = const()[name = string("concat_150_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_150_values3_0 = const()[name = string("concat_150_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_150_axis_0 = const()[name = string("concat_150_axis_0"), val = int32(0)];
            bool concat_150_interleave_0 = const()[name = string("concat_150_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_150 = concat(axis = concat_150_axis_0, interleave = concat_150_interleave_0, values = (expand_dims_148, concat_150_values1_0, cache_position_end, concat_150_values3_0))[name = string("concat_150")];
            tensor<int32, [4]> key_states_127_perm_0 = const()[name = string("key_states_127_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_13_stride_0 = const()[name = string("key_cache_internal_tensor_assign_13_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_13_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_13_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_13_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_13_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_13_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_13_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_127_cast_fp16 = transpose(perm = key_states_127_perm_0, x = key_states_125_cast_fp16)[name = string("transpose_134")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_13_cast_fp16 = slice_update(begin = concat_149, begin_mask = key_cache_internal_tensor_assign_13_begin_mask_0, end = concat_150, end_mask = key_cache_internal_tensor_assign_13_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_13_squeeze_mask_0, stride = key_cache_internal_tensor_assign_13_stride_0, update = key_states_127_cast_fp16, x = coreml_update_state_78)[name = string("key_cache_internal_tensor_assign_13_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_13_cast_fp16, input = key_cache)[name = string("coreml_update_state_80_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_80 = read_state(input = key_cache)[name = string("coreml_update_state_80")];
            tensor<int32, [4]> value_states_75_perm_0 = const()[name = string("value_states_75_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_13_stride_0 = const()[name = string("value_cache_internal_tensor_assign_13_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_13_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_13_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_13_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_13_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_13_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_13_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_75_cast_fp16 = transpose(perm = value_states_75_perm_0, x = var_4846_cast_fp16)[name = string("transpose_133")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_13_cast_fp16 = slice_update(begin = concat_149, begin_mask = value_cache_internal_tensor_assign_13_begin_mask_0, end = concat_150, end_mask = value_cache_internal_tensor_assign_13_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_13_squeeze_mask_0, stride = value_cache_internal_tensor_assign_13_stride_0, update = value_states_75_cast_fp16, x = coreml_update_state_79)[name = string("value_cache_internal_tensor_assign_13_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_13_cast_fp16, input = value_cache)[name = string("coreml_update_state_81_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_81 = read_state(input = value_cache)[name = string("coreml_update_state_81")];
            tensor<int32, [4]> var_4940_begin_0 = const()[name = string("op_4940_begin_0"), val = tensor<int32, [4]>([12, 0, 0, 0])];
            tensor<int32, [4]> var_4940_end_0 = const()[name = string("op_4940_end_0"), val = tensor<int32, [4]>([13, 2, 2048, 128])];
            tensor<bool, [4]> var_4940_end_mask_0 = const()[name = string("op_4940_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4940_cast_fp16 = slice_by_index(begin = var_4940_begin_0, end = var_4940_end_0, end_mask = var_4940_end_mask_0, x = coreml_update_state_80)[name = string("op_4940_cast_fp16")];
            tensor<int32, [2]> tile_24 = const()[name = string("tile_24"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4943_axis_0 = const()[name = string("op_4943_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4943_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4943_cast_fp16_1 = split(axis = var_4943_axis_0, split_sizes = tile_24, x = var_4940_cast_fp16)[name = string("op_4943_cast_fp16")];
            tensor<int32, [4]> var_4950_begin_0 = const()[name = string("op_4950_begin_0"), val = tensor<int32, [4]>([12, 0, 0, 0])];
            tensor<int32, [4]> var_4950_end_0 = const()[name = string("op_4950_end_0"), val = tensor<int32, [4]>([13, 2, 2048, 128])];
            tensor<bool, [4]> var_4950_end_mask_0 = const()[name = string("op_4950_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4950_cast_fp16 = slice_by_index(begin = var_4950_begin_0, end = var_4950_end_0, end_mask = var_4950_end_mask_0, x = coreml_update_state_81)[name = string("op_4950_cast_fp16")];
            tensor<int32, [2]> tile_25 = const()[name = string("tile_25"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4953_axis_0 = const()[name = string("op_4953_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4953_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4953_cast_fp16_1 = split(axis = var_4953_axis_0, split_sizes = tile_25, x = var_4950_cast_fp16)[name = string("op_4953_cast_fp16")];
            tensor<int32, [2]> var_4956_split_sizes_0 = const()[name = string("op_4956_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_4956_axis_0 = const()[name = string("op_4956_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_4956_0, tensor<fp16, [1, 8, 128, 1]> var_4956_1 = split(axis = var_4956_axis_0, split_sizes = var_4956_split_sizes_0, x = query_states_75_cast_fp16)[name = string("op_4956")];
            bool attn_weights_193_transpose_x_0 = const()[name = string("attn_weights_193_transpose_x_0"), val = bool(false)];
            bool attn_weights_193_transpose_y_0 = const()[name = string("attn_weights_193_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_193_cast_fp16 = matmul(transpose_x = attn_weights_193_transpose_x_0, transpose_y = attn_weights_193_transpose_y_0, x = var_4943_cast_fp16_0, y = var_4956_0)[name = string("attn_weights_193_cast_fp16")];
            fp16 var_4959_to_fp16 = const()[name = string("op_4959_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_195_cast_fp16 = mul(x = attn_weights_193_cast_fp16, y = var_4959_to_fp16)[name = string("attn_weights_195_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_197_cast_fp16 = add(x = attn_weights_195_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_197_cast_fp16")];
            int32 var_4963 = const()[name = string("op_4963"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_199_cast_fp16 = softmax(axis = var_4963, x = attn_weights_197_cast_fp16)[name = string("attn_weights_199_cast_fp16")];
            bool var_4969_transpose_x_1 = const()[name = string("op_4969_transpose_x_1"), val = bool(true)];
            bool var_4969_transpose_y_1 = const()[name = string("op_4969_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_4969_cast_fp16 = matmul(transpose_x = var_4969_transpose_x_1, transpose_y = var_4969_transpose_y_1, x = attn_weights_199_cast_fp16, y = var_4953_cast_fp16_0)[name = string("op_4969_cast_fp16")];
            bool attn_weights_201_transpose_x_0 = const()[name = string("attn_weights_201_transpose_x_0"), val = bool(false)];
            bool attn_weights_201_transpose_y_0 = const()[name = string("attn_weights_201_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_201_cast_fp16 = matmul(transpose_x = attn_weights_201_transpose_x_0, transpose_y = attn_weights_201_transpose_y_0, x = var_4943_cast_fp16_1, y = var_4956_1)[name = string("attn_weights_201_cast_fp16")];
            fp16 var_4971_to_fp16 = const()[name = string("op_4971_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_203_cast_fp16 = mul(x = attn_weights_201_cast_fp16, y = var_4971_to_fp16)[name = string("attn_weights_203_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_205_cast_fp16 = add(x = attn_weights_203_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_205_cast_fp16")];
            int32 var_4975 = const()[name = string("op_4975"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_207_cast_fp16 = softmax(axis = var_4975, x = attn_weights_205_cast_fp16)[name = string("attn_weights_207_cast_fp16")];
            bool attn_output_97_transpose_x_1 = const()[name = string("attn_output_97_transpose_x_1"), val = bool(true)];
            bool attn_output_97_transpose_y_1 = const()[name = string("attn_output_97_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_97_cast_fp16 = matmul(transpose_x = attn_output_97_transpose_x_1, transpose_y = attn_output_97_transpose_y_1, x = attn_weights_207_cast_fp16, y = var_4953_cast_fp16_1)[name = string("attn_output_97_cast_fp16")];
            int32 var_4983 = const()[name = string("op_4983"), val = int32(1)];
            bool attn_output_99_interleave_0 = const()[name = string("attn_output_99_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_99_cast_fp16 = concat(axis = var_4983, interleave = attn_output_99_interleave_0, values = (var_4969_cast_fp16, attn_output_97_cast_fp16))[name = string("attn_output_99_cast_fp16")];
            tensor<int32, [4]> var_4987_perm_0 = const()[name = string("op_4987_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_155x = const()[name = string("concat_155x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_4987_cast_fp16 = transpose(perm = var_4987_perm_0, x = attn_output_99_cast_fp16)[name = string("transpose_132")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_103_cast_fp16 = reshape(shape = concat_155x, x = var_4987_cast_fp16)[name = string("attn_output_103_cast_fp16")];
            tensor<int32, [2]> hidden_states_123_strides_0 = const()[name = string("hidden_states_123_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_123_pad_type_0 = const()[name = string("hidden_states_123_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_123_pad_0 = const()[name = string("hidden_states_123_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_123_dilations_0 = const()[name = string("hidden_states_123_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_123_groups_0 = const()[name = string("hidden_states_123_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_123_cast_fp16 = conv(dilations = hidden_states_123_dilations_0, groups = hidden_states_123_groups_0, pad = hidden_states_123_pad_0, pad_type = hidden_states_123_pad_type_0, strides = hidden_states_123_strides_0, weight = layers_12_self_attn_o_proj_weight_cast_fp16, x = attn_output_103_cast_fp16)[name = string("hidden_states_123_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_125_cast_fp16 = add(x = hidden_states_119_cast_fp16, y = hidden_states_123_cast_fp16)[name = string("hidden_states_125_cast_fp16")];
            fp16 const_130_promoted_to_fp16 = const()[name = string("const_130_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_5020_cast_fp16 = mul(x = hidden_states_125_cast_fp16, y = const_130_promoted_to_fp16)[name = string("op_5020_cast_fp16")];
            int32 var_5018 = const()[name = string("op_5018"), val = int32(1)];
            bool doubled_101_interleave_0 = const()[name = string("doubled_101_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_101_cast_fp16 = concat(axis = var_5018, interleave = doubled_101_interleave_0, values = (hidden_states_125_cast_fp16, var_5020_cast_fp16))[name = string("doubled_101_cast_fp16")];
            tensor<int32, [1]> out_51_axes_0 = const()[name = string("out_51_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_51_gamma_0_to_fp16 = const()[name = string("out_51_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407002496)))];
            fp16 var_5030_to_fp16 = const()[name = string("op_5030_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_51_cast_fp16 = layer_norm(axes = out_51_axes_0, epsilon = var_5030_to_fp16, gamma = out_51_gamma_0_to_fp16, x = doubled_101_cast_fp16)[name = string("out_51_cast_fp16")];
            tensor<int32, [2]> var_5041_split_sizes_0 = const()[name = string("op_5041_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5041_axis_0 = const()[name = string("op_5041_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_5041_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_5041_cast_fp16_1 = split(axis = var_5041_axis_0, split_sizes = var_5041_split_sizes_0, x = out_51_cast_fp16)[name = string("op_5041_cast_fp16")];
            tensor<int32, [2]> input_25_strides_0 = const()[name = string("input_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_25_pad_type_0 = const()[name = string("input_25_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_25_pad_0 = const()[name = string("input_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_25_dilations_0 = const()[name = string("input_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_25_groups_0 = const()[name = string("input_25_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_25_cast_fp16 = conv(dilations = input_25_dilations_0, groups = input_25_groups_0, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = input_25_strides_0, weight = layers_12_mlp_gate_proj_weight_cast_fp16, x = var_5041_cast_fp16_0)[name = string("input_25_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_5058_cast_fp16 = silu(x = input_25_cast_fp16)[name = string("op_5058_cast_fp16")];
            tensor<int32, [2]> var_5064_strides_0 = const()[name = string("op_5064_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_5064_pad_type_0 = const()[name = string("op_5064_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_5064_pad_0 = const()[name = string("op_5064_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_5064_dilations_0 = const()[name = string("op_5064_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5064_groups_0 = const()[name = string("op_5064_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_5064_cast_fp16 = conv(dilations = var_5064_dilations_0, groups = var_5064_groups_0, pad = var_5064_pad_0, pad_type = var_5064_pad_type_0, strides = var_5064_strides_0, weight = layers_12_mlp_up_proj_weight_cast_fp16, x = var_5041_cast_fp16_0)[name = string("op_5064_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_129_cast_fp16 = mul(x = var_5058_cast_fp16, y = var_5064_cast_fp16)[name = string("x_129_cast_fp16")];
            tensor<int32, [2]> hidden_states_127_strides_0 = const()[name = string("hidden_states_127_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_127_pad_type_0 = const()[name = string("hidden_states_127_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_127_pad_0 = const()[name = string("hidden_states_127_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_127_dilations_0 = const()[name = string("hidden_states_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_127_groups_0 = const()[name = string("hidden_states_127_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_127_cast_fp16 = conv(dilations = hidden_states_127_dilations_0, groups = hidden_states_127_groups_0, pad = hidden_states_127_pad_0, pad_type = hidden_states_127_pad_type_0, strides = hidden_states_127_strides_0, weight = layers_12_mlp_down_proj_weight_cast_fp16, x = x_129_cast_fp16)[name = string("hidden_states_127_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_129_cast_fp16 = add(x = hidden_states_125_cast_fp16, y = hidden_states_127_cast_fp16)[name = string("hidden_states_129_cast_fp16")];
            fp16 const_132_promoted_to_fp16 = const()[name = string("const_132_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_5082_cast_fp16 = mul(x = hidden_states_129_cast_fp16, y = const_132_promoted_to_fp16)[name = string("op_5082_cast_fp16")];
            int32 var_5080 = const()[name = string("op_5080"), val = int32(1)];
            bool doubled_105_interleave_0 = const()[name = string("doubled_105_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_105_cast_fp16 = concat(axis = var_5080, interleave = doubled_105_interleave_0, values = (hidden_states_129_cast_fp16, var_5082_cast_fp16))[name = string("doubled_105_cast_fp16")];
            tensor<int32, [1]> out_53_axes_0 = const()[name = string("out_53_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_53_gamma_0_to_fp16 = const()[name = string("out_53_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407010752)))];
            fp16 var_5092_to_fp16 = const()[name = string("op_5092_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_53_cast_fp16 = layer_norm(axes = out_53_axes_0, epsilon = var_5092_to_fp16, gamma = out_53_gamma_0_to_fp16, x = doubled_105_cast_fp16)[name = string("out_53_cast_fp16")];
            tensor<int32, [2]> var_5103_split_sizes_0 = const()[name = string("op_5103_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5103_axis_0 = const()[name = string("op_5103_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_5103_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_5103_cast_fp16_1 = split(axis = var_5103_axis_0, split_sizes = var_5103_split_sizes_0, x = out_53_cast_fp16)[name = string("op_5103_cast_fp16")];
            tensor<int32, [2]> query_states_79_strides_0 = const()[name = string("query_states_79_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_79_pad_type_0 = const()[name = string("query_states_79_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_79_pad_0 = const()[name = string("query_states_79_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_79_dilations_0 = const()[name = string("query_states_79_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_79_groups_0 = const()[name = string("query_states_79_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_79_cast_fp16 = conv(dilations = query_states_79_dilations_0, groups = query_states_79_groups_0, pad = query_states_79_pad_0, pad_type = query_states_79_pad_type_0, strides = query_states_79_strides_0, weight = layers_13_self_attn_q_proj_weight_cast_fp16, x = var_5103_cast_fp16_0)[name = string("query_states_79_cast_fp16")];
            tensor<int32, [2]> key_states_131_strides_0 = const()[name = string("key_states_131_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_131_pad_type_0 = const()[name = string("key_states_131_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_131_pad_0 = const()[name = string("key_states_131_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_131_dilations_0 = const()[name = string("key_states_131_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_131_groups_0 = const()[name = string("key_states_131_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_131_cast_fp16 = conv(dilations = key_states_131_dilations_0, groups = key_states_131_groups_0, pad = key_states_131_pad_0, pad_type = key_states_131_pad_type_0, strides = key_states_131_strides_0, weight = layers_13_self_attn_k_proj_weight_cast_fp16, x = var_5103_cast_fp16_0)[name = string("key_states_131_cast_fp16")];
            tensor<int32, [2]> value_states_79_strides_0 = const()[name = string("value_states_79_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_79_pad_type_0 = const()[name = string("value_states_79_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_79_pad_0 = const()[name = string("value_states_79_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_79_dilations_0 = const()[name = string("value_states_79_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_79_groups_0 = const()[name = string("value_states_79_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_79_cast_fp16 = conv(dilations = value_states_79_dilations_0, groups = value_states_79_groups_0, pad = value_states_79_pad_0, pad_type = value_states_79_pad_type_0, strides = value_states_79_strides_0, weight = layers_13_self_attn_v_proj_weight_cast_fp16, x = var_5103_cast_fp16_0)[name = string("value_states_79_cast_fp16")];
            tensor<int32, [4]> concat_156x = const()[name = string("concat_156x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_131_cast_fp16 = reshape(shape = concat_156x, x = query_states_79_cast_fp16)[name = string("x_131_cast_fp16")];
            tensor<int32, [4]> concat_157x = const()[name = string("concat_157x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_5160_cast_fp16 = reshape(shape = concat_157x, x = key_states_131_cast_fp16)[name = string("op_5160_cast_fp16")];
            tensor<int32, [4]> concat_158x = const()[name = string("concat_158x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_5167_cast_fp16 = reshape(shape = concat_158x, x = value_states_79_cast_fp16)[name = string("op_5167_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_5171_cast_fp16 = mul(x = x_131_cast_fp16, y = var_859_cast_fp16)[name = string("op_5171_cast_fp16")];
            tensor<int32, [2]> var_5172_split_sizes_0 = const()[name = string("op_5172_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5172_axis_0 = const()[name = string("op_5172_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_5172_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_5172_cast_fp16_1 = split(axis = var_5172_axis_0, split_sizes = var_5172_split_sizes_0, x = x_131_cast_fp16)[name = string("op_5172_cast_fp16")];
            fp16 const_134_promoted_to_fp16 = const()[name = string("const_134_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_5174_cast_fp16 = mul(x = var_5172_cast_fp16_1, y = const_134_promoted_to_fp16)[name = string("op_5174_cast_fp16")];
            int32 var_5176 = const()[name = string("op_5176"), val = int32(-2)];
            bool var_5177_interleave_0 = const()[name = string("op_5177_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_5177_cast_fp16 = concat(axis = var_5176, interleave = var_5177_interleave_0, values = (var_5174_cast_fp16, var_5172_cast_fp16_0))[name = string("op_5177_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_5178_cast_fp16 = mul(x = var_5177_cast_fp16, y = var_866_cast_fp16)[name = string("op_5178_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_81_cast_fp16 = add(x = var_5171_cast_fp16, y = var_5178_cast_fp16)[name = string("query_states_81_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_5184_cast_fp16 = mul(x = var_5160_cast_fp16, y = var_859_cast_fp16)[name = string("op_5184_cast_fp16")];
            tensor<int32, [2]> var_5185_split_sizes_0 = const()[name = string("op_5185_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5185_axis_0 = const()[name = string("op_5185_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_5185_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_5185_cast_fp16_1 = split(axis = var_5185_axis_0, split_sizes = var_5185_split_sizes_0, x = var_5160_cast_fp16)[name = string("op_5185_cast_fp16")];
            fp16 const_135_promoted_to_fp16 = const()[name = string("const_135_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_5187_cast_fp16 = mul(x = var_5185_cast_fp16_1, y = const_135_promoted_to_fp16)[name = string("op_5187_cast_fp16")];
            int32 var_5189 = const()[name = string("op_5189"), val = int32(-2)];
            bool var_5190_interleave_0 = const()[name = string("op_5190_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_5190_cast_fp16 = concat(axis = var_5189, interleave = var_5190_interleave_0, values = (var_5187_cast_fp16, var_5185_cast_fp16_0))[name = string("op_5190_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_5191_cast_fp16 = mul(x = var_5190_cast_fp16, y = var_866_cast_fp16)[name = string("op_5191_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_135_cast_fp16 = add(x = var_5184_cast_fp16, y = var_5191_cast_fp16)[name = string("key_states_135_cast_fp16")];
            tensor<int32, [1]> expand_dims_156 = const()[name = string("expand_dims_156"), val = tensor<int32, [1]>([13])];
            tensor<int32, [1]> expand_dims_157 = const()[name = string("expand_dims_157"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_159 = const()[name = string("expand_dims_159"), val = tensor<int32, [1]>([0])];
            int32 concat_161_axis_0 = const()[name = string("concat_161_axis_0"), val = int32(0)];
            bool concat_161_interleave_0 = const()[name = string("concat_161_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_161 = concat(axis = concat_161_axis_0, interleave = concat_161_interleave_0, values = (expand_dims_156, expand_dims_157, position_id, expand_dims_159))[name = string("concat_161")];
            tensor<int32, [1]> expand_dims_160 = const()[name = string("expand_dims_160"), val = tensor<int32, [1]>([14])];
            tensor<int32, [1]> concat_162_values1_0 = const()[name = string("concat_162_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_162_values3_0 = const()[name = string("concat_162_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_162_axis_0 = const()[name = string("concat_162_axis_0"), val = int32(0)];
            bool concat_162_interleave_0 = const()[name = string("concat_162_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_162 = concat(axis = concat_162_axis_0, interleave = concat_162_interleave_0, values = (expand_dims_160, concat_162_values1_0, cache_position_end, concat_162_values3_0))[name = string("concat_162")];
            tensor<int32, [4]> key_states_137_perm_0 = const()[name = string("key_states_137_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_14_stride_0 = const()[name = string("key_cache_internal_tensor_assign_14_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_14_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_14_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_14_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_14_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_14_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_14_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_137_cast_fp16 = transpose(perm = key_states_137_perm_0, x = key_states_135_cast_fp16)[name = string("transpose_131")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_14_cast_fp16 = slice_update(begin = concat_161, begin_mask = key_cache_internal_tensor_assign_14_begin_mask_0, end = concat_162, end_mask = key_cache_internal_tensor_assign_14_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_14_squeeze_mask_0, stride = key_cache_internal_tensor_assign_14_stride_0, update = key_states_137_cast_fp16, x = coreml_update_state_80)[name = string("key_cache_internal_tensor_assign_14_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_14_cast_fp16, input = key_cache)[name = string("coreml_update_state_82_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_82 = read_state(input = key_cache)[name = string("coreml_update_state_82")];
            tensor<int32, [4]> value_states_81_perm_0 = const()[name = string("value_states_81_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_14_stride_0 = const()[name = string("value_cache_internal_tensor_assign_14_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_14_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_14_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_14_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_14_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_14_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_14_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_81_cast_fp16 = transpose(perm = value_states_81_perm_0, x = var_5167_cast_fp16)[name = string("transpose_130")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_14_cast_fp16 = slice_update(begin = concat_161, begin_mask = value_cache_internal_tensor_assign_14_begin_mask_0, end = concat_162, end_mask = value_cache_internal_tensor_assign_14_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_14_squeeze_mask_0, stride = value_cache_internal_tensor_assign_14_stride_0, update = value_states_81_cast_fp16, x = coreml_update_state_81)[name = string("value_cache_internal_tensor_assign_14_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_14_cast_fp16, input = value_cache)[name = string("coreml_update_state_83_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_83 = read_state(input = value_cache)[name = string("coreml_update_state_83")];
            tensor<int32, [4]> var_5261_begin_0 = const()[name = string("op_5261_begin_0"), val = tensor<int32, [4]>([13, 0, 0, 0])];
            tensor<int32, [4]> var_5261_end_0 = const()[name = string("op_5261_end_0"), val = tensor<int32, [4]>([14, 2, 2048, 128])];
            tensor<bool, [4]> var_5261_end_mask_0 = const()[name = string("op_5261_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5261_cast_fp16 = slice_by_index(begin = var_5261_begin_0, end = var_5261_end_0, end_mask = var_5261_end_mask_0, x = coreml_update_state_82)[name = string("op_5261_cast_fp16")];
            tensor<int32, [2]> tile_26 = const()[name = string("tile_26"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5264_axis_0 = const()[name = string("op_5264_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5264_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5264_cast_fp16_1 = split(axis = var_5264_axis_0, split_sizes = tile_26, x = var_5261_cast_fp16)[name = string("op_5264_cast_fp16")];
            tensor<int32, [4]> var_5271_begin_0 = const()[name = string("op_5271_begin_0"), val = tensor<int32, [4]>([13, 0, 0, 0])];
            tensor<int32, [4]> var_5271_end_0 = const()[name = string("op_5271_end_0"), val = tensor<int32, [4]>([14, 2, 2048, 128])];
            tensor<bool, [4]> var_5271_end_mask_0 = const()[name = string("op_5271_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5271_cast_fp16 = slice_by_index(begin = var_5271_begin_0, end = var_5271_end_0, end_mask = var_5271_end_mask_0, x = coreml_update_state_83)[name = string("op_5271_cast_fp16")];
            tensor<int32, [2]> tile_27 = const()[name = string("tile_27"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5274_axis_0 = const()[name = string("op_5274_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5274_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5274_cast_fp16_1 = split(axis = var_5274_axis_0, split_sizes = tile_27, x = var_5271_cast_fp16)[name = string("op_5274_cast_fp16")];
            tensor<int32, [2]> var_5277_split_sizes_0 = const()[name = string("op_5277_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_5277_axis_0 = const()[name = string("op_5277_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_5277_0, tensor<fp16, [1, 8, 128, 1]> var_5277_1 = split(axis = var_5277_axis_0, split_sizes = var_5277_split_sizes_0, x = query_states_81_cast_fp16)[name = string("op_5277")];
            bool attn_weights_209_transpose_x_0 = const()[name = string("attn_weights_209_transpose_x_0"), val = bool(false)];
            bool attn_weights_209_transpose_y_0 = const()[name = string("attn_weights_209_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_209_cast_fp16 = matmul(transpose_x = attn_weights_209_transpose_x_0, transpose_y = attn_weights_209_transpose_y_0, x = var_5264_cast_fp16_0, y = var_5277_0)[name = string("attn_weights_209_cast_fp16")];
            fp16 var_5280_to_fp16 = const()[name = string("op_5280_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_211_cast_fp16 = mul(x = attn_weights_209_cast_fp16, y = var_5280_to_fp16)[name = string("attn_weights_211_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_213_cast_fp16 = add(x = attn_weights_211_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_213_cast_fp16")];
            int32 var_5284 = const()[name = string("op_5284"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_215_cast_fp16 = softmax(axis = var_5284, x = attn_weights_213_cast_fp16)[name = string("attn_weights_215_cast_fp16")];
            bool var_5290_transpose_x_1 = const()[name = string("op_5290_transpose_x_1"), val = bool(true)];
            bool var_5290_transpose_y_1 = const()[name = string("op_5290_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_5290_cast_fp16 = matmul(transpose_x = var_5290_transpose_x_1, transpose_y = var_5290_transpose_y_1, x = attn_weights_215_cast_fp16, y = var_5274_cast_fp16_0)[name = string("op_5290_cast_fp16")];
            bool attn_weights_217_transpose_x_0 = const()[name = string("attn_weights_217_transpose_x_0"), val = bool(false)];
            bool attn_weights_217_transpose_y_0 = const()[name = string("attn_weights_217_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_217_cast_fp16 = matmul(transpose_x = attn_weights_217_transpose_x_0, transpose_y = attn_weights_217_transpose_y_0, x = var_5264_cast_fp16_1, y = var_5277_1)[name = string("attn_weights_217_cast_fp16")];
            fp16 var_5292_to_fp16 = const()[name = string("op_5292_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_219_cast_fp16 = mul(x = attn_weights_217_cast_fp16, y = var_5292_to_fp16)[name = string("attn_weights_219_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_221_cast_fp16 = add(x = attn_weights_219_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_221_cast_fp16")];
            int32 var_5296 = const()[name = string("op_5296"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_223_cast_fp16 = softmax(axis = var_5296, x = attn_weights_221_cast_fp16)[name = string("attn_weights_223_cast_fp16")];
            bool attn_output_105_transpose_x_1 = const()[name = string("attn_output_105_transpose_x_1"), val = bool(true)];
            bool attn_output_105_transpose_y_1 = const()[name = string("attn_output_105_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_105_cast_fp16 = matmul(transpose_x = attn_output_105_transpose_x_1, transpose_y = attn_output_105_transpose_y_1, x = attn_weights_223_cast_fp16, y = var_5274_cast_fp16_1)[name = string("attn_output_105_cast_fp16")];
            int32 var_5304 = const()[name = string("op_5304"), val = int32(1)];
            bool attn_output_107_interleave_0 = const()[name = string("attn_output_107_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_107_cast_fp16 = concat(axis = var_5304, interleave = attn_output_107_interleave_0, values = (var_5290_cast_fp16, attn_output_105_cast_fp16))[name = string("attn_output_107_cast_fp16")];
            tensor<int32, [4]> var_5308_perm_0 = const()[name = string("op_5308_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_167x = const()[name = string("concat_167x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_5308_cast_fp16 = transpose(perm = var_5308_perm_0, x = attn_output_107_cast_fp16)[name = string("transpose_129")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_111_cast_fp16 = reshape(shape = concat_167x, x = var_5308_cast_fp16)[name = string("attn_output_111_cast_fp16")];
            tensor<int32, [2]> hidden_states_133_strides_0 = const()[name = string("hidden_states_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_133_pad_type_0 = const()[name = string("hidden_states_133_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_133_pad_0 = const()[name = string("hidden_states_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_133_dilations_0 = const()[name = string("hidden_states_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_133_groups_0 = const()[name = string("hidden_states_133_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_133_cast_fp16 = conv(dilations = hidden_states_133_dilations_0, groups = hidden_states_133_groups_0, pad = hidden_states_133_pad_0, pad_type = hidden_states_133_pad_type_0, strides = hidden_states_133_strides_0, weight = layers_13_self_attn_o_proj_weight_cast_fp16, x = attn_output_111_cast_fp16)[name = string("hidden_states_133_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_135_cast_fp16 = add(x = hidden_states_129_cast_fp16, y = hidden_states_133_cast_fp16)[name = string("hidden_states_135_cast_fp16")];
            fp16 const_140_promoted_to_fp16 = const()[name = string("const_140_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_5341_cast_fp16 = mul(x = hidden_states_135_cast_fp16, y = const_140_promoted_to_fp16)[name = string("op_5341_cast_fp16")];
            int32 var_5339 = const()[name = string("op_5339"), val = int32(1)];
            bool doubled_109_interleave_0 = const()[name = string("doubled_109_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_109_cast_fp16 = concat(axis = var_5339, interleave = doubled_109_interleave_0, values = (hidden_states_135_cast_fp16, var_5341_cast_fp16))[name = string("doubled_109_cast_fp16")];
            tensor<int32, [1]> out_55_axes_0 = const()[name = string("out_55_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_55_gamma_0_to_fp16 = const()[name = string("out_55_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407019008)))];
            fp16 var_5351_to_fp16 = const()[name = string("op_5351_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_55_cast_fp16 = layer_norm(axes = out_55_axes_0, epsilon = var_5351_to_fp16, gamma = out_55_gamma_0_to_fp16, x = doubled_109_cast_fp16)[name = string("out_55_cast_fp16")];
            tensor<int32, [2]> var_5362_split_sizes_0 = const()[name = string("op_5362_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5362_axis_0 = const()[name = string("op_5362_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_5362_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_5362_cast_fp16_1 = split(axis = var_5362_axis_0, split_sizes = var_5362_split_sizes_0, x = out_55_cast_fp16)[name = string("op_5362_cast_fp16")];
            tensor<int32, [2]> input_27_strides_0 = const()[name = string("input_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_27_pad_type_0 = const()[name = string("input_27_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_27_pad_0 = const()[name = string("input_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_27_dilations_0 = const()[name = string("input_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_27_groups_0 = const()[name = string("input_27_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_27_cast_fp16 = conv(dilations = input_27_dilations_0, groups = input_27_groups_0, pad = input_27_pad_0, pad_type = input_27_pad_type_0, strides = input_27_strides_0, weight = layers_13_mlp_gate_proj_weight_cast_fp16, x = var_5362_cast_fp16_0)[name = string("input_27_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_5379_cast_fp16 = silu(x = input_27_cast_fp16)[name = string("op_5379_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_13_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_13_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407027264)))];
            tensor<int32, [2]> var_5385_strides_0 = const()[name = string("op_5385_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_5385_pad_type_0 = const()[name = string("op_5385_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_5385_pad_0 = const()[name = string("op_5385_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_5385_dilations_0 = const()[name = string("op_5385_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5385_groups_0 = const()[name = string("op_5385_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_5385_cast_fp16 = conv(dilations = var_5385_dilations_0, groups = var_5385_groups_0, pad = var_5385_pad_0, pad_type = var_5385_pad_type_0, strides = var_5385_strides_0, weight = layers_13_mlp_up_proj_weight_to_fp16, x = var_5362_cast_fp16_0)[name = string("op_5385_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_139_cast_fp16 = mul(x = var_5379_cast_fp16, y = var_5385_cast_fp16)[name = string("x_139_cast_fp16")];
            tensor<int32, [2]> hidden_states_137_strides_0 = const()[name = string("hidden_states_137_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_137_pad_type_0 = const()[name = string("hidden_states_137_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_137_pad_0 = const()[name = string("hidden_states_137_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_137_dilations_0 = const()[name = string("hidden_states_137_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_137_groups_0 = const()[name = string("hidden_states_137_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_137_cast_fp16 = conv(dilations = hidden_states_137_dilations_0, groups = hidden_states_137_groups_0, pad = hidden_states_137_pad_0, pad_type = hidden_states_137_pad_type_0, strides = hidden_states_137_strides_0, weight = layers_13_mlp_down_proj_weight_cast_fp16, x = x_139_cast_fp16)[name = string("hidden_states_137_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_139_cast_fp16 = add(x = hidden_states_135_cast_fp16, y = hidden_states_137_cast_fp16)[name = string("hidden_states_139_cast_fp16")];
            fp16 const_142_promoted_to_fp16 = const()[name = string("const_142_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_5403_cast_fp16 = mul(x = hidden_states_139_cast_fp16, y = const_142_promoted_to_fp16)[name = string("op_5403_cast_fp16")];
            int32 var_5401 = const()[name = string("op_5401"), val = int32(1)];
            bool doubled_113_interleave_0 = const()[name = string("doubled_113_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_113_cast_fp16 = concat(axis = var_5401, interleave = doubled_113_interleave_0, values = (hidden_states_139_cast_fp16, var_5403_cast_fp16))[name = string("doubled_113_cast_fp16")];
            tensor<int32, [1]> out_57_axes_0 = const()[name = string("out_57_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_57_gamma_0_to_fp16 = const()[name = string("out_57_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1432193152)))];
            fp16 var_5413_to_fp16 = const()[name = string("op_5413_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_57_cast_fp16 = layer_norm(axes = out_57_axes_0, epsilon = var_5413_to_fp16, gamma = out_57_gamma_0_to_fp16, x = doubled_113_cast_fp16)[name = string("out_57_cast_fp16")];
            tensor<int32, [2]> var_5424_split_sizes_0 = const()[name = string("op_5424_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5424_axis_0 = const()[name = string("op_5424_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_5424_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_5424_cast_fp16_1 = split(axis = var_5424_axis_0, split_sizes = var_5424_split_sizes_0, x = out_57_cast_fp16)[name = string("op_5424_cast_fp16")];
            tensor<int32, [2]> query_states_85_strides_0 = const()[name = string("query_states_85_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_85_pad_type_0 = const()[name = string("query_states_85_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_85_pad_0 = const()[name = string("query_states_85_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_85_dilations_0 = const()[name = string("query_states_85_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_85_groups_0 = const()[name = string("query_states_85_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_85_cast_fp16 = conv(dilations = query_states_85_dilations_0, groups = query_states_85_groups_0, pad = query_states_85_pad_0, pad_type = query_states_85_pad_type_0, strides = query_states_85_strides_0, weight = layers_14_self_attn_q_proj_weight_cast_fp16, x = var_5424_cast_fp16_0)[name = string("query_states_85_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_14_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_14_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1432201408)))];
            tensor<int32, [2]> key_states_141_strides_0 = const()[name = string("key_states_141_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_141_pad_type_0 = const()[name = string("key_states_141_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_141_pad_0 = const()[name = string("key_states_141_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_141_dilations_0 = const()[name = string("key_states_141_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_141_groups_0 = const()[name = string("key_states_141_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_141_cast_fp16 = conv(dilations = key_states_141_dilations_0, groups = key_states_141_groups_0, pad = key_states_141_pad_0, pad_type = key_states_141_pad_type_0, strides = key_states_141_strides_0, weight = layers_14_self_attn_k_proj_weight_to_fp16, x = var_5424_cast_fp16_0)[name = string("key_states_141_cast_fp16")];
            tensor<int32, [2]> value_states_85_strides_0 = const()[name = string("value_states_85_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_85_pad_type_0 = const()[name = string("value_states_85_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_85_pad_0 = const()[name = string("value_states_85_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_85_dilations_0 = const()[name = string("value_states_85_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_85_groups_0 = const()[name = string("value_states_85_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_85_cast_fp16 = conv(dilations = value_states_85_dilations_0, groups = value_states_85_groups_0, pad = value_states_85_pad_0, pad_type = value_states_85_pad_type_0, strides = value_states_85_strides_0, weight = layers_14_self_attn_v_proj_weight_cast_fp16, x = var_5424_cast_fp16_0)[name = string("value_states_85_cast_fp16")];
            tensor<int32, [4]> concat_168x = const()[name = string("concat_168x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_141_cast_fp16 = reshape(shape = concat_168x, x = query_states_85_cast_fp16)[name = string("x_141_cast_fp16")];
            tensor<int32, [4]> concat_169x = const()[name = string("concat_169x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_5481_cast_fp16 = reshape(shape = concat_169x, x = key_states_141_cast_fp16)[name = string("op_5481_cast_fp16")];
            tensor<int32, [4]> concat_170x = const()[name = string("concat_170x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_5488_cast_fp16 = reshape(shape = concat_170x, x = value_states_85_cast_fp16)[name = string("op_5488_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_5492_cast_fp16 = mul(x = x_141_cast_fp16, y = var_859_cast_fp16)[name = string("op_5492_cast_fp16")];
            tensor<int32, [2]> var_5493_split_sizes_0 = const()[name = string("op_5493_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5493_axis_0 = const()[name = string("op_5493_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_5493_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_5493_cast_fp16_1 = split(axis = var_5493_axis_0, split_sizes = var_5493_split_sizes_0, x = x_141_cast_fp16)[name = string("op_5493_cast_fp16")];
            fp16 const_144_promoted_to_fp16 = const()[name = string("const_144_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_5495_cast_fp16 = mul(x = var_5493_cast_fp16_1, y = const_144_promoted_to_fp16)[name = string("op_5495_cast_fp16")];
            int32 var_5497 = const()[name = string("op_5497"), val = int32(-2)];
            bool var_5498_interleave_0 = const()[name = string("op_5498_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_5498_cast_fp16 = concat(axis = var_5497, interleave = var_5498_interleave_0, values = (var_5495_cast_fp16, var_5493_cast_fp16_0))[name = string("op_5498_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_5499_cast_fp16 = mul(x = var_5498_cast_fp16, y = var_866_cast_fp16)[name = string("op_5499_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_87_cast_fp16 = add(x = var_5492_cast_fp16, y = var_5499_cast_fp16)[name = string("query_states_87_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_5505_cast_fp16 = mul(x = var_5481_cast_fp16, y = var_859_cast_fp16)[name = string("op_5505_cast_fp16")];
            tensor<int32, [2]> var_5506_split_sizes_0 = const()[name = string("op_5506_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5506_axis_0 = const()[name = string("op_5506_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_5506_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_5506_cast_fp16_1 = split(axis = var_5506_axis_0, split_sizes = var_5506_split_sizes_0, x = var_5481_cast_fp16)[name = string("op_5506_cast_fp16")];
            fp16 const_145_promoted_to_fp16 = const()[name = string("const_145_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_5508_cast_fp16 = mul(x = var_5506_cast_fp16_1, y = const_145_promoted_to_fp16)[name = string("op_5508_cast_fp16")];
            int32 var_5510 = const()[name = string("op_5510"), val = int32(-2)];
            bool var_5511_interleave_0 = const()[name = string("op_5511_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_5511_cast_fp16 = concat(axis = var_5510, interleave = var_5511_interleave_0, values = (var_5508_cast_fp16, var_5506_cast_fp16_0))[name = string("op_5511_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_5512_cast_fp16 = mul(x = var_5511_cast_fp16, y = var_866_cast_fp16)[name = string("op_5512_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_145_cast_fp16 = add(x = var_5505_cast_fp16, y = var_5512_cast_fp16)[name = string("key_states_145_cast_fp16")];
            tensor<int32, [1]> expand_dims_168 = const()[name = string("expand_dims_168"), val = tensor<int32, [1]>([14])];
            tensor<int32, [1]> expand_dims_169 = const()[name = string("expand_dims_169"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_171 = const()[name = string("expand_dims_171"), val = tensor<int32, [1]>([0])];
            int32 concat_173_axis_0 = const()[name = string("concat_173_axis_0"), val = int32(0)];
            bool concat_173_interleave_0 = const()[name = string("concat_173_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_173 = concat(axis = concat_173_axis_0, interleave = concat_173_interleave_0, values = (expand_dims_168, expand_dims_169, position_id, expand_dims_171))[name = string("concat_173")];
            tensor<int32, [1]> expand_dims_172 = const()[name = string("expand_dims_172"), val = tensor<int32, [1]>([15])];
            tensor<int32, [1]> concat_174_values1_0 = const()[name = string("concat_174_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_174_values3_0 = const()[name = string("concat_174_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_174_axis_0 = const()[name = string("concat_174_axis_0"), val = int32(0)];
            bool concat_174_interleave_0 = const()[name = string("concat_174_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_174 = concat(axis = concat_174_axis_0, interleave = concat_174_interleave_0, values = (expand_dims_172, concat_174_values1_0, cache_position_end, concat_174_values3_0))[name = string("concat_174")];
            tensor<int32, [4]> key_states_147_perm_0 = const()[name = string("key_states_147_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_15_stride_0 = const()[name = string("key_cache_internal_tensor_assign_15_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_15_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_15_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_15_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_15_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_15_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_15_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_147_cast_fp16 = transpose(perm = key_states_147_perm_0, x = key_states_145_cast_fp16)[name = string("transpose_128")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_15_cast_fp16 = slice_update(begin = concat_173, begin_mask = key_cache_internal_tensor_assign_15_begin_mask_0, end = concat_174, end_mask = key_cache_internal_tensor_assign_15_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_15_squeeze_mask_0, stride = key_cache_internal_tensor_assign_15_stride_0, update = key_states_147_cast_fp16, x = coreml_update_state_82)[name = string("key_cache_internal_tensor_assign_15_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_15_cast_fp16, input = key_cache)[name = string("coreml_update_state_84_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_84 = read_state(input = key_cache)[name = string("coreml_update_state_84")];
            tensor<int32, [4]> value_states_87_perm_0 = const()[name = string("value_states_87_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_15_stride_0 = const()[name = string("value_cache_internal_tensor_assign_15_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_15_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_15_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_15_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_15_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_15_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_15_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_87_cast_fp16 = transpose(perm = value_states_87_perm_0, x = var_5488_cast_fp16)[name = string("transpose_127")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_15_cast_fp16 = slice_update(begin = concat_173, begin_mask = value_cache_internal_tensor_assign_15_begin_mask_0, end = concat_174, end_mask = value_cache_internal_tensor_assign_15_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_15_squeeze_mask_0, stride = value_cache_internal_tensor_assign_15_stride_0, update = value_states_87_cast_fp16, x = coreml_update_state_83)[name = string("value_cache_internal_tensor_assign_15_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_15_cast_fp16, input = value_cache)[name = string("coreml_update_state_85_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_85 = read_state(input = value_cache)[name = string("coreml_update_state_85")];
            tensor<int32, [4]> var_5582_begin_0 = const()[name = string("op_5582_begin_0"), val = tensor<int32, [4]>([14, 0, 0, 0])];
            tensor<int32, [4]> var_5582_end_0 = const()[name = string("op_5582_end_0"), val = tensor<int32, [4]>([15, 2, 2048, 128])];
            tensor<bool, [4]> var_5582_end_mask_0 = const()[name = string("op_5582_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5582_cast_fp16 = slice_by_index(begin = var_5582_begin_0, end = var_5582_end_0, end_mask = var_5582_end_mask_0, x = coreml_update_state_84)[name = string("op_5582_cast_fp16")];
            tensor<int32, [2]> tile_28 = const()[name = string("tile_28"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5585_axis_0 = const()[name = string("op_5585_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5585_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5585_cast_fp16_1 = split(axis = var_5585_axis_0, split_sizes = tile_28, x = var_5582_cast_fp16)[name = string("op_5585_cast_fp16")];
            tensor<int32, [4]> var_5592_begin_0 = const()[name = string("op_5592_begin_0"), val = tensor<int32, [4]>([14, 0, 0, 0])];
            tensor<int32, [4]> var_5592_end_0 = const()[name = string("op_5592_end_0"), val = tensor<int32, [4]>([15, 2, 2048, 128])];
            tensor<bool, [4]> var_5592_end_mask_0 = const()[name = string("op_5592_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5592_cast_fp16 = slice_by_index(begin = var_5592_begin_0, end = var_5592_end_0, end_mask = var_5592_end_mask_0, x = coreml_update_state_85)[name = string("op_5592_cast_fp16")];
            tensor<int32, [2]> tile_29 = const()[name = string("tile_29"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5595_axis_0 = const()[name = string("op_5595_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5595_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5595_cast_fp16_1 = split(axis = var_5595_axis_0, split_sizes = tile_29, x = var_5592_cast_fp16)[name = string("op_5595_cast_fp16")];
            tensor<int32, [2]> var_5598_split_sizes_0 = const()[name = string("op_5598_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_5598_axis_0 = const()[name = string("op_5598_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_5598_0, tensor<fp16, [1, 8, 128, 1]> var_5598_1 = split(axis = var_5598_axis_0, split_sizes = var_5598_split_sizes_0, x = query_states_87_cast_fp16)[name = string("op_5598")];
            bool attn_weights_225_transpose_x_0 = const()[name = string("attn_weights_225_transpose_x_0"), val = bool(false)];
            bool attn_weights_225_transpose_y_0 = const()[name = string("attn_weights_225_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_225_cast_fp16 = matmul(transpose_x = attn_weights_225_transpose_x_0, transpose_y = attn_weights_225_transpose_y_0, x = var_5585_cast_fp16_0, y = var_5598_0)[name = string("attn_weights_225_cast_fp16")];
            fp16 var_5601_to_fp16 = const()[name = string("op_5601_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_227_cast_fp16 = mul(x = attn_weights_225_cast_fp16, y = var_5601_to_fp16)[name = string("attn_weights_227_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_229_cast_fp16 = add(x = attn_weights_227_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_229_cast_fp16")];
            int32 var_5605 = const()[name = string("op_5605"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_231_cast_fp16 = softmax(axis = var_5605, x = attn_weights_229_cast_fp16)[name = string("attn_weights_231_cast_fp16")];
            bool var_5611_transpose_x_1 = const()[name = string("op_5611_transpose_x_1"), val = bool(true)];
            bool var_5611_transpose_y_1 = const()[name = string("op_5611_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_5611_cast_fp16 = matmul(transpose_x = var_5611_transpose_x_1, transpose_y = var_5611_transpose_y_1, x = attn_weights_231_cast_fp16, y = var_5595_cast_fp16_0)[name = string("op_5611_cast_fp16")];
            bool attn_weights_233_transpose_x_0 = const()[name = string("attn_weights_233_transpose_x_0"), val = bool(false)];
            bool attn_weights_233_transpose_y_0 = const()[name = string("attn_weights_233_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_233_cast_fp16 = matmul(transpose_x = attn_weights_233_transpose_x_0, transpose_y = attn_weights_233_transpose_y_0, x = var_5585_cast_fp16_1, y = var_5598_1)[name = string("attn_weights_233_cast_fp16")];
            fp16 var_5613_to_fp16 = const()[name = string("op_5613_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_235_cast_fp16 = mul(x = attn_weights_233_cast_fp16, y = var_5613_to_fp16)[name = string("attn_weights_235_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_237_cast_fp16 = add(x = attn_weights_235_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_237_cast_fp16")];
            int32 var_5617 = const()[name = string("op_5617"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_239_cast_fp16 = softmax(axis = var_5617, x = attn_weights_237_cast_fp16)[name = string("attn_weights_239_cast_fp16")];
            bool attn_output_113_transpose_x_1 = const()[name = string("attn_output_113_transpose_x_1"), val = bool(true)];
            bool attn_output_113_transpose_y_1 = const()[name = string("attn_output_113_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_113_cast_fp16 = matmul(transpose_x = attn_output_113_transpose_x_1, transpose_y = attn_output_113_transpose_y_1, x = attn_weights_239_cast_fp16, y = var_5595_cast_fp16_1)[name = string("attn_output_113_cast_fp16")];
            int32 var_5625 = const()[name = string("op_5625"), val = int32(1)];
            bool attn_output_115_interleave_0 = const()[name = string("attn_output_115_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_115_cast_fp16 = concat(axis = var_5625, interleave = attn_output_115_interleave_0, values = (var_5611_cast_fp16, attn_output_113_cast_fp16))[name = string("attn_output_115_cast_fp16")];
            tensor<int32, [4]> var_5629_perm_0 = const()[name = string("op_5629_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_179x = const()[name = string("concat_179x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_5629_cast_fp16 = transpose(perm = var_5629_perm_0, x = attn_output_115_cast_fp16)[name = string("transpose_126")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_119_cast_fp16 = reshape(shape = concat_179x, x = var_5629_cast_fp16)[name = string("attn_output_119_cast_fp16")];
            tensor<int32, [2]> hidden_states_143_strides_0 = const()[name = string("hidden_states_143_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_143_pad_type_0 = const()[name = string("hidden_states_143_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_143_pad_0 = const()[name = string("hidden_states_143_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_143_dilations_0 = const()[name = string("hidden_states_143_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_143_groups_0 = const()[name = string("hidden_states_143_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_143_cast_fp16 = conv(dilations = hidden_states_143_dilations_0, groups = hidden_states_143_groups_0, pad = hidden_states_143_pad_0, pad_type = hidden_states_143_pad_type_0, strides = hidden_states_143_strides_0, weight = layers_14_self_attn_o_proj_weight_cast_fp16, x = attn_output_119_cast_fp16)[name = string("hidden_states_143_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_145_cast_fp16 = add(x = hidden_states_139_cast_fp16, y = hidden_states_143_cast_fp16)[name = string("hidden_states_145_cast_fp16")];
            fp16 const_150_promoted_to_fp16 = const()[name = string("const_150_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_5662_cast_fp16 = mul(x = hidden_states_145_cast_fp16, y = const_150_promoted_to_fp16)[name = string("op_5662_cast_fp16")];
            int32 var_5660 = const()[name = string("op_5660"), val = int32(1)];
            bool doubled_117_interleave_0 = const()[name = string("doubled_117_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_117_cast_fp16 = concat(axis = var_5660, interleave = doubled_117_interleave_0, values = (hidden_states_145_cast_fp16, var_5662_cast_fp16))[name = string("doubled_117_cast_fp16")];
            tensor<int32, [1]> out_59_axes_0 = const()[name = string("out_59_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_59_gamma_0_to_fp16 = const()[name = string("out_59_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433250048)))];
            fp16 var_5672_to_fp16 = const()[name = string("op_5672_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_59_cast_fp16 = layer_norm(axes = out_59_axes_0, epsilon = var_5672_to_fp16, gamma = out_59_gamma_0_to_fp16, x = doubled_117_cast_fp16)[name = string("out_59_cast_fp16")];
            tensor<int32, [2]> var_5683_split_sizes_0 = const()[name = string("op_5683_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5683_axis_0 = const()[name = string("op_5683_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_5683_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_5683_cast_fp16_1 = split(axis = var_5683_axis_0, split_sizes = var_5683_split_sizes_0, x = out_59_cast_fp16)[name = string("op_5683_cast_fp16")];
            tensor<int32, [2]> input_29_strides_0 = const()[name = string("input_29_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_29_pad_type_0 = const()[name = string("input_29_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_29_pad_0 = const()[name = string("input_29_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_29_dilations_0 = const()[name = string("input_29_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_29_groups_0 = const()[name = string("input_29_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_29_cast_fp16 = conv(dilations = input_29_dilations_0, groups = input_29_groups_0, pad = input_29_pad_0, pad_type = input_29_pad_type_0, strides = input_29_strides_0, weight = layers_14_mlp_gate_proj_weight_cast_fp16, x = var_5683_cast_fp16_0)[name = string("input_29_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_5700_cast_fp16 = silu(x = input_29_cast_fp16)[name = string("op_5700_cast_fp16")];
            tensor<int32, [2]> var_5706_strides_0 = const()[name = string("op_5706_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_5706_pad_type_0 = const()[name = string("op_5706_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_5706_pad_0 = const()[name = string("op_5706_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_5706_dilations_0 = const()[name = string("op_5706_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5706_groups_0 = const()[name = string("op_5706_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_5706_cast_fp16 = conv(dilations = var_5706_dilations_0, groups = var_5706_groups_0, pad = var_5706_pad_0, pad_type = var_5706_pad_type_0, strides = var_5706_strides_0, weight = layers_14_mlp_up_proj_weight_cast_fp16, x = var_5683_cast_fp16_0)[name = string("op_5706_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_149_cast_fp16 = mul(x = var_5700_cast_fp16, y = var_5706_cast_fp16)[name = string("x_149_cast_fp16")];
            tensor<int32, [2]> hidden_states_147_strides_0 = const()[name = string("hidden_states_147_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_147_pad_type_0 = const()[name = string("hidden_states_147_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_147_pad_0 = const()[name = string("hidden_states_147_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_147_dilations_0 = const()[name = string("hidden_states_147_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_147_groups_0 = const()[name = string("hidden_states_147_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_147_cast_fp16 = conv(dilations = hidden_states_147_dilations_0, groups = hidden_states_147_groups_0, pad = hidden_states_147_pad_0, pad_type = hidden_states_147_pad_type_0, strides = hidden_states_147_strides_0, weight = layers_14_mlp_down_proj_weight_cast_fp16, x = x_149_cast_fp16)[name = string("hidden_states_147_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_149_cast_fp16 = add(x = hidden_states_145_cast_fp16, y = hidden_states_147_cast_fp16)[name = string("hidden_states_149_cast_fp16")];
            fp16 const_152_promoted_to_fp16 = const()[name = string("const_152_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_5724_cast_fp16 = mul(x = hidden_states_149_cast_fp16, y = const_152_promoted_to_fp16)[name = string("op_5724_cast_fp16")];
            int32 var_5722 = const()[name = string("op_5722"), val = int32(1)];
            bool doubled_121_interleave_0 = const()[name = string("doubled_121_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_121_cast_fp16 = concat(axis = var_5722, interleave = doubled_121_interleave_0, values = (hidden_states_149_cast_fp16, var_5724_cast_fp16))[name = string("doubled_121_cast_fp16")];
            tensor<int32, [1]> out_61_axes_0 = const()[name = string("out_61_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_61_gamma_0_to_fp16 = const()[name = string("out_61_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433258304)))];
            fp16 var_5734_to_fp16 = const()[name = string("op_5734_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_61_cast_fp16 = layer_norm(axes = out_61_axes_0, epsilon = var_5734_to_fp16, gamma = out_61_gamma_0_to_fp16, x = doubled_121_cast_fp16)[name = string("out_61_cast_fp16")];
            tensor<int32, [2]> var_5745_split_sizes_0 = const()[name = string("op_5745_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5745_axis_0 = const()[name = string("op_5745_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_5745_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_5745_cast_fp16_1 = split(axis = var_5745_axis_0, split_sizes = var_5745_split_sizes_0, x = out_61_cast_fp16)[name = string("op_5745_cast_fp16")];
            tensor<int32, [2]> query_states_91_strides_0 = const()[name = string("query_states_91_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_91_pad_type_0 = const()[name = string("query_states_91_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_91_pad_0 = const()[name = string("query_states_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_91_dilations_0 = const()[name = string("query_states_91_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_91_groups_0 = const()[name = string("query_states_91_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_91_cast_fp16 = conv(dilations = query_states_91_dilations_0, groups = query_states_91_groups_0, pad = query_states_91_pad_0, pad_type = query_states_91_pad_type_0, strides = query_states_91_strides_0, weight = layers_15_self_attn_q_proj_weight_cast_fp16, x = var_5745_cast_fp16_0)[name = string("query_states_91_cast_fp16")];
            tensor<int32, [2]> key_states_151_strides_0 = const()[name = string("key_states_151_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_151_pad_type_0 = const()[name = string("key_states_151_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_151_pad_0 = const()[name = string("key_states_151_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_151_dilations_0 = const()[name = string("key_states_151_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_151_groups_0 = const()[name = string("key_states_151_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_151_cast_fp16 = conv(dilations = key_states_151_dilations_0, groups = key_states_151_groups_0, pad = key_states_151_pad_0, pad_type = key_states_151_pad_type_0, strides = key_states_151_strides_0, weight = layers_15_self_attn_k_proj_weight_cast_fp16, x = var_5745_cast_fp16_0)[name = string("key_states_151_cast_fp16")];
            tensor<int32, [2]> value_states_91_strides_0 = const()[name = string("value_states_91_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_91_pad_type_0 = const()[name = string("value_states_91_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_91_pad_0 = const()[name = string("value_states_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_91_dilations_0 = const()[name = string("value_states_91_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_91_groups_0 = const()[name = string("value_states_91_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_91_cast_fp16 = conv(dilations = value_states_91_dilations_0, groups = value_states_91_groups_0, pad = value_states_91_pad_0, pad_type = value_states_91_pad_type_0, strides = value_states_91_strides_0, weight = layers_15_self_attn_v_proj_weight_cast_fp16, x = var_5745_cast_fp16_0)[name = string("value_states_91_cast_fp16")];
            tensor<int32, [4]> concat_180x = const()[name = string("concat_180x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_151_cast_fp16 = reshape(shape = concat_180x, x = query_states_91_cast_fp16)[name = string("x_151_cast_fp16")];
            tensor<int32, [4]> concat_181x = const()[name = string("concat_181x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_5802_cast_fp16 = reshape(shape = concat_181x, x = key_states_151_cast_fp16)[name = string("op_5802_cast_fp16")];
            tensor<int32, [4]> concat_182x = const()[name = string("concat_182x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_5809_cast_fp16 = reshape(shape = concat_182x, x = value_states_91_cast_fp16)[name = string("op_5809_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_5813_cast_fp16 = mul(x = x_151_cast_fp16, y = var_859_cast_fp16)[name = string("op_5813_cast_fp16")];
            tensor<int32, [2]> var_5814_split_sizes_0 = const()[name = string("op_5814_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5814_axis_0 = const()[name = string("op_5814_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_5814_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_5814_cast_fp16_1 = split(axis = var_5814_axis_0, split_sizes = var_5814_split_sizes_0, x = x_151_cast_fp16)[name = string("op_5814_cast_fp16")];
            fp16 const_154_promoted_to_fp16 = const()[name = string("const_154_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_5816_cast_fp16 = mul(x = var_5814_cast_fp16_1, y = const_154_promoted_to_fp16)[name = string("op_5816_cast_fp16")];
            int32 var_5818 = const()[name = string("op_5818"), val = int32(-2)];
            bool var_5819_interleave_0 = const()[name = string("op_5819_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_5819_cast_fp16 = concat(axis = var_5818, interleave = var_5819_interleave_0, values = (var_5816_cast_fp16, var_5814_cast_fp16_0))[name = string("op_5819_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_5820_cast_fp16 = mul(x = var_5819_cast_fp16, y = var_866_cast_fp16)[name = string("op_5820_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_93_cast_fp16 = add(x = var_5813_cast_fp16, y = var_5820_cast_fp16)[name = string("query_states_93_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_5826_cast_fp16 = mul(x = var_5802_cast_fp16, y = var_859_cast_fp16)[name = string("op_5826_cast_fp16")];
            tensor<int32, [2]> var_5827_split_sizes_0 = const()[name = string("op_5827_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5827_axis_0 = const()[name = string("op_5827_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_5827_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_5827_cast_fp16_1 = split(axis = var_5827_axis_0, split_sizes = var_5827_split_sizes_0, x = var_5802_cast_fp16)[name = string("op_5827_cast_fp16")];
            fp16 const_155_promoted_to_fp16 = const()[name = string("const_155_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_5829_cast_fp16 = mul(x = var_5827_cast_fp16_1, y = const_155_promoted_to_fp16)[name = string("op_5829_cast_fp16")];
            int32 var_5831 = const()[name = string("op_5831"), val = int32(-2)];
            bool var_5832_interleave_0 = const()[name = string("op_5832_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_5832_cast_fp16 = concat(axis = var_5831, interleave = var_5832_interleave_0, values = (var_5829_cast_fp16, var_5827_cast_fp16_0))[name = string("op_5832_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_5833_cast_fp16 = mul(x = var_5832_cast_fp16, y = var_866_cast_fp16)[name = string("op_5833_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_155_cast_fp16 = add(x = var_5826_cast_fp16, y = var_5833_cast_fp16)[name = string("key_states_155_cast_fp16")];
            tensor<int32, [1]> expand_dims_180 = const()[name = string("expand_dims_180"), val = tensor<int32, [1]>([15])];
            tensor<int32, [1]> expand_dims_181 = const()[name = string("expand_dims_181"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_183 = const()[name = string("expand_dims_183"), val = tensor<int32, [1]>([0])];
            int32 concat_185_axis_0 = const()[name = string("concat_185_axis_0"), val = int32(0)];
            bool concat_185_interleave_0 = const()[name = string("concat_185_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_185 = concat(axis = concat_185_axis_0, interleave = concat_185_interleave_0, values = (expand_dims_180, expand_dims_181, position_id, expand_dims_183))[name = string("concat_185")];
            tensor<int32, [1]> expand_dims_184 = const()[name = string("expand_dims_184"), val = tensor<int32, [1]>([16])];
            tensor<int32, [1]> concat_186_values1_0 = const()[name = string("concat_186_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_186_values3_0 = const()[name = string("concat_186_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_186_axis_0 = const()[name = string("concat_186_axis_0"), val = int32(0)];
            bool concat_186_interleave_0 = const()[name = string("concat_186_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_186 = concat(axis = concat_186_axis_0, interleave = concat_186_interleave_0, values = (expand_dims_184, concat_186_values1_0, cache_position_end, concat_186_values3_0))[name = string("concat_186")];
            tensor<int32, [4]> key_states_157_perm_0 = const()[name = string("key_states_157_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_16_stride_0 = const()[name = string("key_cache_internal_tensor_assign_16_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_16_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_16_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_16_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_16_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_16_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_16_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_157_cast_fp16 = transpose(perm = key_states_157_perm_0, x = key_states_155_cast_fp16)[name = string("transpose_125")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_16_cast_fp16 = slice_update(begin = concat_185, begin_mask = key_cache_internal_tensor_assign_16_begin_mask_0, end = concat_186, end_mask = key_cache_internal_tensor_assign_16_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_16_squeeze_mask_0, stride = key_cache_internal_tensor_assign_16_stride_0, update = key_states_157_cast_fp16, x = coreml_update_state_84)[name = string("key_cache_internal_tensor_assign_16_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_16_cast_fp16, input = key_cache)[name = string("coreml_update_state_86_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_86 = read_state(input = key_cache)[name = string("coreml_update_state_86")];
            tensor<int32, [4]> value_states_93_perm_0 = const()[name = string("value_states_93_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_16_stride_0 = const()[name = string("value_cache_internal_tensor_assign_16_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_16_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_16_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_16_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_16_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_16_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_16_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_93_cast_fp16 = transpose(perm = value_states_93_perm_0, x = var_5809_cast_fp16)[name = string("transpose_124")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_16_cast_fp16 = slice_update(begin = concat_185, begin_mask = value_cache_internal_tensor_assign_16_begin_mask_0, end = concat_186, end_mask = value_cache_internal_tensor_assign_16_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_16_squeeze_mask_0, stride = value_cache_internal_tensor_assign_16_stride_0, update = value_states_93_cast_fp16, x = coreml_update_state_85)[name = string("value_cache_internal_tensor_assign_16_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_16_cast_fp16, input = value_cache)[name = string("coreml_update_state_87_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_87 = read_state(input = value_cache)[name = string("coreml_update_state_87")];
            tensor<int32, [4]> var_5903_begin_0 = const()[name = string("op_5903_begin_0"), val = tensor<int32, [4]>([15, 0, 0, 0])];
            tensor<int32, [4]> var_5903_end_0 = const()[name = string("op_5903_end_0"), val = tensor<int32, [4]>([16, 2, 2048, 128])];
            tensor<bool, [4]> var_5903_end_mask_0 = const()[name = string("op_5903_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5903_cast_fp16 = slice_by_index(begin = var_5903_begin_0, end = var_5903_end_0, end_mask = var_5903_end_mask_0, x = coreml_update_state_86)[name = string("op_5903_cast_fp16")];
            tensor<int32, [2]> tile_30 = const()[name = string("tile_30"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5906_axis_0 = const()[name = string("op_5906_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5906_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5906_cast_fp16_1 = split(axis = var_5906_axis_0, split_sizes = tile_30, x = var_5903_cast_fp16)[name = string("op_5906_cast_fp16")];
            tensor<int32, [4]> var_5913_begin_0 = const()[name = string("op_5913_begin_0"), val = tensor<int32, [4]>([15, 0, 0, 0])];
            tensor<int32, [4]> var_5913_end_0 = const()[name = string("op_5913_end_0"), val = tensor<int32, [4]>([16, 2, 2048, 128])];
            tensor<bool, [4]> var_5913_end_mask_0 = const()[name = string("op_5913_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5913_cast_fp16 = slice_by_index(begin = var_5913_begin_0, end = var_5913_end_0, end_mask = var_5913_end_mask_0, x = coreml_update_state_87)[name = string("op_5913_cast_fp16")];
            tensor<int32, [2]> tile_31 = const()[name = string("tile_31"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5916_axis_0 = const()[name = string("op_5916_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5916_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5916_cast_fp16_1 = split(axis = var_5916_axis_0, split_sizes = tile_31, x = var_5913_cast_fp16)[name = string("op_5916_cast_fp16")];
            tensor<int32, [2]> var_5919_split_sizes_0 = const()[name = string("op_5919_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_5919_axis_0 = const()[name = string("op_5919_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_5919_0, tensor<fp16, [1, 8, 128, 1]> var_5919_1 = split(axis = var_5919_axis_0, split_sizes = var_5919_split_sizes_0, x = query_states_93_cast_fp16)[name = string("op_5919")];
            bool attn_weights_241_transpose_x_0 = const()[name = string("attn_weights_241_transpose_x_0"), val = bool(false)];
            bool attn_weights_241_transpose_y_0 = const()[name = string("attn_weights_241_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_241_cast_fp16 = matmul(transpose_x = attn_weights_241_transpose_x_0, transpose_y = attn_weights_241_transpose_y_0, x = var_5906_cast_fp16_0, y = var_5919_0)[name = string("attn_weights_241_cast_fp16")];
            fp16 var_5922_to_fp16 = const()[name = string("op_5922_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_243_cast_fp16 = mul(x = attn_weights_241_cast_fp16, y = var_5922_to_fp16)[name = string("attn_weights_243_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_245_cast_fp16 = add(x = attn_weights_243_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_245_cast_fp16")];
            int32 var_5926 = const()[name = string("op_5926"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_247_cast_fp16 = softmax(axis = var_5926, x = attn_weights_245_cast_fp16)[name = string("attn_weights_247_cast_fp16")];
            bool var_5932_transpose_x_1 = const()[name = string("op_5932_transpose_x_1"), val = bool(true)];
            bool var_5932_transpose_y_1 = const()[name = string("op_5932_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_5932_cast_fp16 = matmul(transpose_x = var_5932_transpose_x_1, transpose_y = var_5932_transpose_y_1, x = attn_weights_247_cast_fp16, y = var_5916_cast_fp16_0)[name = string("op_5932_cast_fp16")];
            bool attn_weights_249_transpose_x_0 = const()[name = string("attn_weights_249_transpose_x_0"), val = bool(false)];
            bool attn_weights_249_transpose_y_0 = const()[name = string("attn_weights_249_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_249_cast_fp16 = matmul(transpose_x = attn_weights_249_transpose_x_0, transpose_y = attn_weights_249_transpose_y_0, x = var_5906_cast_fp16_1, y = var_5919_1)[name = string("attn_weights_249_cast_fp16")];
            fp16 var_5934_to_fp16 = const()[name = string("op_5934_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_251_cast_fp16 = mul(x = attn_weights_249_cast_fp16, y = var_5934_to_fp16)[name = string("attn_weights_251_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_253_cast_fp16 = add(x = attn_weights_251_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_253_cast_fp16")];
            int32 var_5938 = const()[name = string("op_5938"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_255_cast_fp16 = softmax(axis = var_5938, x = attn_weights_253_cast_fp16)[name = string("attn_weights_255_cast_fp16")];
            bool attn_output_121_transpose_x_1 = const()[name = string("attn_output_121_transpose_x_1"), val = bool(true)];
            bool attn_output_121_transpose_y_1 = const()[name = string("attn_output_121_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_121_cast_fp16 = matmul(transpose_x = attn_output_121_transpose_x_1, transpose_y = attn_output_121_transpose_y_1, x = attn_weights_255_cast_fp16, y = var_5916_cast_fp16_1)[name = string("attn_output_121_cast_fp16")];
            int32 var_5946 = const()[name = string("op_5946"), val = int32(1)];
            bool attn_output_123_interleave_0 = const()[name = string("attn_output_123_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_123_cast_fp16 = concat(axis = var_5946, interleave = attn_output_123_interleave_0, values = (var_5932_cast_fp16, attn_output_121_cast_fp16))[name = string("attn_output_123_cast_fp16")];
            tensor<int32, [4]> var_5950_perm_0 = const()[name = string("op_5950_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_191x = const()[name = string("concat_191x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_5950_cast_fp16 = transpose(perm = var_5950_perm_0, x = attn_output_123_cast_fp16)[name = string("transpose_123")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_127_cast_fp16 = reshape(shape = concat_191x, x = var_5950_cast_fp16)[name = string("attn_output_127_cast_fp16")];
            tensor<int32, [2]> hidden_states_153_strides_0 = const()[name = string("hidden_states_153_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_153_pad_type_0 = const()[name = string("hidden_states_153_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_153_pad_0 = const()[name = string("hidden_states_153_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_153_dilations_0 = const()[name = string("hidden_states_153_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_153_groups_0 = const()[name = string("hidden_states_153_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_153_cast_fp16 = conv(dilations = hidden_states_153_dilations_0, groups = hidden_states_153_groups_0, pad = hidden_states_153_pad_0, pad_type = hidden_states_153_pad_type_0, strides = hidden_states_153_strides_0, weight = layers_15_self_attn_o_proj_weight_cast_fp16, x = attn_output_127_cast_fp16)[name = string("hidden_states_153_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_155_cast_fp16 = add(x = hidden_states_149_cast_fp16, y = hidden_states_153_cast_fp16)[name = string("hidden_states_155_cast_fp16")];
            fp16 const_160_promoted_to_fp16 = const()[name = string("const_160_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_5983_cast_fp16 = mul(x = hidden_states_155_cast_fp16, y = const_160_promoted_to_fp16)[name = string("op_5983_cast_fp16")];
            int32 var_5981 = const()[name = string("op_5981"), val = int32(1)];
            bool doubled_125_interleave_0 = const()[name = string("doubled_125_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_125_cast_fp16 = concat(axis = var_5981, interleave = doubled_125_interleave_0, values = (hidden_states_155_cast_fp16, var_5983_cast_fp16))[name = string("doubled_125_cast_fp16")];
            tensor<int32, [1]> out_63_axes_0 = const()[name = string("out_63_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_63_gamma_0_to_fp16 = const()[name = string("out_63_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433266560)))];
            fp16 var_5993_to_fp16 = const()[name = string("op_5993_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_63_cast_fp16 = layer_norm(axes = out_63_axes_0, epsilon = var_5993_to_fp16, gamma = out_63_gamma_0_to_fp16, x = doubled_125_cast_fp16)[name = string("out_63_cast_fp16")];
            tensor<int32, [2]> var_6004_split_sizes_0 = const()[name = string("op_6004_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6004_axis_0 = const()[name = string("op_6004_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_6004_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_6004_cast_fp16_1 = split(axis = var_6004_axis_0, split_sizes = var_6004_split_sizes_0, x = out_63_cast_fp16)[name = string("op_6004_cast_fp16")];
            tensor<int32, [2]> input_31_strides_0 = const()[name = string("input_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_31_pad_type_0 = const()[name = string("input_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_31_pad_0 = const()[name = string("input_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_31_dilations_0 = const()[name = string("input_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_31_groups_0 = const()[name = string("input_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_31_cast_fp16 = conv(dilations = input_31_dilations_0, groups = input_31_groups_0, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = input_31_strides_0, weight = layers_15_mlp_gate_proj_weight_cast_fp16, x = var_6004_cast_fp16_0)[name = string("input_31_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_6021_cast_fp16 = silu(x = input_31_cast_fp16)[name = string("op_6021_cast_fp16")];
            tensor<int32, [2]> var_6027_strides_0 = const()[name = string("op_6027_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6027_pad_type_0 = const()[name = string("op_6027_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6027_pad_0 = const()[name = string("op_6027_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6027_dilations_0 = const()[name = string("op_6027_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6027_groups_0 = const()[name = string("op_6027_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_6027_cast_fp16 = conv(dilations = var_6027_dilations_0, groups = var_6027_groups_0, pad = var_6027_pad_0, pad_type = var_6027_pad_type_0, strides = var_6027_strides_0, weight = layers_15_mlp_up_proj_weight_cast_fp16, x = var_6004_cast_fp16_0)[name = string("op_6027_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_159_cast_fp16 = mul(x = var_6021_cast_fp16, y = var_6027_cast_fp16)[name = string("x_159_cast_fp16")];
            tensor<int32, [2]> hidden_states_157_strides_0 = const()[name = string("hidden_states_157_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_157_pad_type_0 = const()[name = string("hidden_states_157_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_157_pad_0 = const()[name = string("hidden_states_157_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_157_dilations_0 = const()[name = string("hidden_states_157_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_157_groups_0 = const()[name = string("hidden_states_157_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_157_cast_fp16 = conv(dilations = hidden_states_157_dilations_0, groups = hidden_states_157_groups_0, pad = hidden_states_157_pad_0, pad_type = hidden_states_157_pad_type_0, strides = hidden_states_157_strides_0, weight = layers_15_mlp_down_proj_weight_cast_fp16, x = x_159_cast_fp16)[name = string("hidden_states_157_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_159_cast_fp16 = add(x = hidden_states_155_cast_fp16, y = hidden_states_157_cast_fp16)[name = string("hidden_states_159_cast_fp16")];
            fp16 const_162_promoted_to_fp16 = const()[name = string("const_162_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_6045_cast_fp16 = mul(x = hidden_states_159_cast_fp16, y = const_162_promoted_to_fp16)[name = string("op_6045_cast_fp16")];
            int32 var_6043 = const()[name = string("op_6043"), val = int32(1)];
            bool doubled_129_interleave_0 = const()[name = string("doubled_129_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_129_cast_fp16 = concat(axis = var_6043, interleave = doubled_129_interleave_0, values = (hidden_states_159_cast_fp16, var_6045_cast_fp16))[name = string("doubled_129_cast_fp16")];
            tensor<int32, [1]> out_65_axes_0 = const()[name = string("out_65_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_65_gamma_0_to_fp16 = const()[name = string("out_65_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433274816)))];
            fp16 var_6055_to_fp16 = const()[name = string("op_6055_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_65_cast_fp16 = layer_norm(axes = out_65_axes_0, epsilon = var_6055_to_fp16, gamma = out_65_gamma_0_to_fp16, x = doubled_129_cast_fp16)[name = string("out_65_cast_fp16")];
            tensor<int32, [2]> var_6066_split_sizes_0 = const()[name = string("op_6066_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6066_axis_0 = const()[name = string("op_6066_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_6066_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_6066_cast_fp16_1 = split(axis = var_6066_axis_0, split_sizes = var_6066_split_sizes_0, x = out_65_cast_fp16)[name = string("op_6066_cast_fp16")];
            tensor<int32, [2]> query_states_97_strides_0 = const()[name = string("query_states_97_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_97_pad_type_0 = const()[name = string("query_states_97_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_97_pad_0 = const()[name = string("query_states_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_97_dilations_0 = const()[name = string("query_states_97_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_97_groups_0 = const()[name = string("query_states_97_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_97_cast_fp16 = conv(dilations = query_states_97_dilations_0, groups = query_states_97_groups_0, pad = query_states_97_pad_0, pad_type = query_states_97_pad_type_0, strides = query_states_97_strides_0, weight = layers_16_self_attn_q_proj_weight_cast_fp16, x = var_6066_cast_fp16_0)[name = string("query_states_97_cast_fp16")];
            tensor<int32, [2]> key_states_161_strides_0 = const()[name = string("key_states_161_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_161_pad_type_0 = const()[name = string("key_states_161_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_161_pad_0 = const()[name = string("key_states_161_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_161_dilations_0 = const()[name = string("key_states_161_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_161_groups_0 = const()[name = string("key_states_161_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_161_cast_fp16 = conv(dilations = key_states_161_dilations_0, groups = key_states_161_groups_0, pad = key_states_161_pad_0, pad_type = key_states_161_pad_type_0, strides = key_states_161_strides_0, weight = layers_16_self_attn_k_proj_weight_cast_fp16, x = var_6066_cast_fp16_0)[name = string("key_states_161_cast_fp16")];
            tensor<int32, [2]> value_states_97_strides_0 = const()[name = string("value_states_97_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_97_pad_type_0 = const()[name = string("value_states_97_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_97_pad_0 = const()[name = string("value_states_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_97_dilations_0 = const()[name = string("value_states_97_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_97_groups_0 = const()[name = string("value_states_97_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_97_cast_fp16 = conv(dilations = value_states_97_dilations_0, groups = value_states_97_groups_0, pad = value_states_97_pad_0, pad_type = value_states_97_pad_type_0, strides = value_states_97_strides_0, weight = layers_16_self_attn_v_proj_weight_cast_fp16, x = var_6066_cast_fp16_0)[name = string("value_states_97_cast_fp16")];
            tensor<int32, [4]> concat_192x = const()[name = string("concat_192x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_161_cast_fp16 = reshape(shape = concat_192x, x = query_states_97_cast_fp16)[name = string("x_161_cast_fp16")];
            tensor<int32, [4]> concat_193x = const()[name = string("concat_193x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_6123_cast_fp16 = reshape(shape = concat_193x, x = key_states_161_cast_fp16)[name = string("op_6123_cast_fp16")];
            tensor<int32, [4]> concat_194x = const()[name = string("concat_194x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_6130_cast_fp16 = reshape(shape = concat_194x, x = value_states_97_cast_fp16)[name = string("op_6130_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_6134_cast_fp16 = mul(x = x_161_cast_fp16, y = var_859_cast_fp16)[name = string("op_6134_cast_fp16")];
            tensor<int32, [2]> var_6135_split_sizes_0 = const()[name = string("op_6135_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6135_axis_0 = const()[name = string("op_6135_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_6135_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_6135_cast_fp16_1 = split(axis = var_6135_axis_0, split_sizes = var_6135_split_sizes_0, x = x_161_cast_fp16)[name = string("op_6135_cast_fp16")];
            fp16 const_164_promoted_to_fp16 = const()[name = string("const_164_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_6137_cast_fp16 = mul(x = var_6135_cast_fp16_1, y = const_164_promoted_to_fp16)[name = string("op_6137_cast_fp16")];
            int32 var_6139 = const()[name = string("op_6139"), val = int32(-2)];
            bool var_6140_interleave_0 = const()[name = string("op_6140_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_6140_cast_fp16 = concat(axis = var_6139, interleave = var_6140_interleave_0, values = (var_6137_cast_fp16, var_6135_cast_fp16_0))[name = string("op_6140_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_6141_cast_fp16 = mul(x = var_6140_cast_fp16, y = var_866_cast_fp16)[name = string("op_6141_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_99_cast_fp16 = add(x = var_6134_cast_fp16, y = var_6141_cast_fp16)[name = string("query_states_99_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_6147_cast_fp16 = mul(x = var_6123_cast_fp16, y = var_859_cast_fp16)[name = string("op_6147_cast_fp16")];
            tensor<int32, [2]> var_6148_split_sizes_0 = const()[name = string("op_6148_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6148_axis_0 = const()[name = string("op_6148_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_6148_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_6148_cast_fp16_1 = split(axis = var_6148_axis_0, split_sizes = var_6148_split_sizes_0, x = var_6123_cast_fp16)[name = string("op_6148_cast_fp16")];
            fp16 const_165_promoted_to_fp16 = const()[name = string("const_165_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_6150_cast_fp16 = mul(x = var_6148_cast_fp16_1, y = const_165_promoted_to_fp16)[name = string("op_6150_cast_fp16")];
            int32 var_6152 = const()[name = string("op_6152"), val = int32(-2)];
            bool var_6153_interleave_0 = const()[name = string("op_6153_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_6153_cast_fp16 = concat(axis = var_6152, interleave = var_6153_interleave_0, values = (var_6150_cast_fp16, var_6148_cast_fp16_0))[name = string("op_6153_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_6154_cast_fp16 = mul(x = var_6153_cast_fp16, y = var_866_cast_fp16)[name = string("op_6154_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_165_cast_fp16 = add(x = var_6147_cast_fp16, y = var_6154_cast_fp16)[name = string("key_states_165_cast_fp16")];
            tensor<int32, [1]> expand_dims_192 = const()[name = string("expand_dims_192"), val = tensor<int32, [1]>([16])];
            tensor<int32, [1]> expand_dims_193 = const()[name = string("expand_dims_193"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_195 = const()[name = string("expand_dims_195"), val = tensor<int32, [1]>([0])];
            int32 concat_197_axis_0 = const()[name = string("concat_197_axis_0"), val = int32(0)];
            bool concat_197_interleave_0 = const()[name = string("concat_197_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_197 = concat(axis = concat_197_axis_0, interleave = concat_197_interleave_0, values = (expand_dims_192, expand_dims_193, position_id, expand_dims_195))[name = string("concat_197")];
            tensor<int32, [1]> expand_dims_196 = const()[name = string("expand_dims_196"), val = tensor<int32, [1]>([17])];
            tensor<int32, [1]> concat_198_values1_0 = const()[name = string("concat_198_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_198_values3_0 = const()[name = string("concat_198_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_198_axis_0 = const()[name = string("concat_198_axis_0"), val = int32(0)];
            bool concat_198_interleave_0 = const()[name = string("concat_198_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_198 = concat(axis = concat_198_axis_0, interleave = concat_198_interleave_0, values = (expand_dims_196, concat_198_values1_0, cache_position_end, concat_198_values3_0))[name = string("concat_198")];
            tensor<int32, [4]> key_states_167_perm_0 = const()[name = string("key_states_167_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_17_stride_0 = const()[name = string("key_cache_internal_tensor_assign_17_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_17_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_17_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_17_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_17_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_17_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_17_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_167_cast_fp16 = transpose(perm = key_states_167_perm_0, x = key_states_165_cast_fp16)[name = string("transpose_122")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_17_cast_fp16 = slice_update(begin = concat_197, begin_mask = key_cache_internal_tensor_assign_17_begin_mask_0, end = concat_198, end_mask = key_cache_internal_tensor_assign_17_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_17_squeeze_mask_0, stride = key_cache_internal_tensor_assign_17_stride_0, update = key_states_167_cast_fp16, x = coreml_update_state_86)[name = string("key_cache_internal_tensor_assign_17_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_17_cast_fp16, input = key_cache)[name = string("coreml_update_state_88_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_88 = read_state(input = key_cache)[name = string("coreml_update_state_88")];
            tensor<int32, [4]> value_states_99_perm_0 = const()[name = string("value_states_99_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_17_stride_0 = const()[name = string("value_cache_internal_tensor_assign_17_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_17_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_17_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_17_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_17_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_17_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_17_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_99_cast_fp16 = transpose(perm = value_states_99_perm_0, x = var_6130_cast_fp16)[name = string("transpose_121")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_17_cast_fp16 = slice_update(begin = concat_197, begin_mask = value_cache_internal_tensor_assign_17_begin_mask_0, end = concat_198, end_mask = value_cache_internal_tensor_assign_17_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_17_squeeze_mask_0, stride = value_cache_internal_tensor_assign_17_stride_0, update = value_states_99_cast_fp16, x = coreml_update_state_87)[name = string("value_cache_internal_tensor_assign_17_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_17_cast_fp16, input = value_cache)[name = string("coreml_update_state_89_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_89 = read_state(input = value_cache)[name = string("coreml_update_state_89")];
            tensor<int32, [4]> var_6224_begin_0 = const()[name = string("op_6224_begin_0"), val = tensor<int32, [4]>([16, 0, 0, 0])];
            tensor<int32, [4]> var_6224_end_0 = const()[name = string("op_6224_end_0"), val = tensor<int32, [4]>([17, 2, 2048, 128])];
            tensor<bool, [4]> var_6224_end_mask_0 = const()[name = string("op_6224_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6224_cast_fp16 = slice_by_index(begin = var_6224_begin_0, end = var_6224_end_0, end_mask = var_6224_end_mask_0, x = coreml_update_state_88)[name = string("op_6224_cast_fp16")];
            tensor<int32, [2]> tile_32 = const()[name = string("tile_32"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6227_axis_0 = const()[name = string("op_6227_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6227_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6227_cast_fp16_1 = split(axis = var_6227_axis_0, split_sizes = tile_32, x = var_6224_cast_fp16)[name = string("op_6227_cast_fp16")];
            tensor<int32, [4]> var_6234_begin_0 = const()[name = string("op_6234_begin_0"), val = tensor<int32, [4]>([16, 0, 0, 0])];
            tensor<int32, [4]> var_6234_end_0 = const()[name = string("op_6234_end_0"), val = tensor<int32, [4]>([17, 2, 2048, 128])];
            tensor<bool, [4]> var_6234_end_mask_0 = const()[name = string("op_6234_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6234_cast_fp16 = slice_by_index(begin = var_6234_begin_0, end = var_6234_end_0, end_mask = var_6234_end_mask_0, x = coreml_update_state_89)[name = string("op_6234_cast_fp16")];
            tensor<int32, [2]> tile_33 = const()[name = string("tile_33"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6237_axis_0 = const()[name = string("op_6237_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6237_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6237_cast_fp16_1 = split(axis = var_6237_axis_0, split_sizes = tile_33, x = var_6234_cast_fp16)[name = string("op_6237_cast_fp16")];
            tensor<int32, [2]> var_6240_split_sizes_0 = const()[name = string("op_6240_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_6240_axis_0 = const()[name = string("op_6240_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_6240_0, tensor<fp16, [1, 8, 128, 1]> var_6240_1 = split(axis = var_6240_axis_0, split_sizes = var_6240_split_sizes_0, x = query_states_99_cast_fp16)[name = string("op_6240")];
            bool attn_weights_257_transpose_x_0 = const()[name = string("attn_weights_257_transpose_x_0"), val = bool(false)];
            bool attn_weights_257_transpose_y_0 = const()[name = string("attn_weights_257_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_257_cast_fp16 = matmul(transpose_x = attn_weights_257_transpose_x_0, transpose_y = attn_weights_257_transpose_y_0, x = var_6227_cast_fp16_0, y = var_6240_0)[name = string("attn_weights_257_cast_fp16")];
            fp16 var_6243_to_fp16 = const()[name = string("op_6243_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_259_cast_fp16 = mul(x = attn_weights_257_cast_fp16, y = var_6243_to_fp16)[name = string("attn_weights_259_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_261_cast_fp16 = add(x = attn_weights_259_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_261_cast_fp16")];
            int32 var_6247 = const()[name = string("op_6247"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_263_cast_fp16 = softmax(axis = var_6247, x = attn_weights_261_cast_fp16)[name = string("attn_weights_263_cast_fp16")];
            bool var_6253_transpose_x_1 = const()[name = string("op_6253_transpose_x_1"), val = bool(true)];
            bool var_6253_transpose_y_1 = const()[name = string("op_6253_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_6253_cast_fp16 = matmul(transpose_x = var_6253_transpose_x_1, transpose_y = var_6253_transpose_y_1, x = attn_weights_263_cast_fp16, y = var_6237_cast_fp16_0)[name = string("op_6253_cast_fp16")];
            bool attn_weights_265_transpose_x_0 = const()[name = string("attn_weights_265_transpose_x_0"), val = bool(false)];
            bool attn_weights_265_transpose_y_0 = const()[name = string("attn_weights_265_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_265_cast_fp16 = matmul(transpose_x = attn_weights_265_transpose_x_0, transpose_y = attn_weights_265_transpose_y_0, x = var_6227_cast_fp16_1, y = var_6240_1)[name = string("attn_weights_265_cast_fp16")];
            fp16 var_6255_to_fp16 = const()[name = string("op_6255_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_267_cast_fp16 = mul(x = attn_weights_265_cast_fp16, y = var_6255_to_fp16)[name = string("attn_weights_267_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_269_cast_fp16 = add(x = attn_weights_267_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_269_cast_fp16")];
            int32 var_6259 = const()[name = string("op_6259"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_271_cast_fp16 = softmax(axis = var_6259, x = attn_weights_269_cast_fp16)[name = string("attn_weights_271_cast_fp16")];
            bool attn_output_129_transpose_x_1 = const()[name = string("attn_output_129_transpose_x_1"), val = bool(true)];
            bool attn_output_129_transpose_y_1 = const()[name = string("attn_output_129_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_129_cast_fp16 = matmul(transpose_x = attn_output_129_transpose_x_1, transpose_y = attn_output_129_transpose_y_1, x = attn_weights_271_cast_fp16, y = var_6237_cast_fp16_1)[name = string("attn_output_129_cast_fp16")];
            int32 var_6267 = const()[name = string("op_6267"), val = int32(1)];
            bool attn_output_131_interleave_0 = const()[name = string("attn_output_131_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_131_cast_fp16 = concat(axis = var_6267, interleave = attn_output_131_interleave_0, values = (var_6253_cast_fp16, attn_output_129_cast_fp16))[name = string("attn_output_131_cast_fp16")];
            tensor<int32, [4]> var_6271_perm_0 = const()[name = string("op_6271_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_203x = const()[name = string("concat_203x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_6271_cast_fp16 = transpose(perm = var_6271_perm_0, x = attn_output_131_cast_fp16)[name = string("transpose_120")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_135_cast_fp16 = reshape(shape = concat_203x, x = var_6271_cast_fp16)[name = string("attn_output_135_cast_fp16")];
            tensor<int32, [2]> hidden_states_163_strides_0 = const()[name = string("hidden_states_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_163_pad_type_0 = const()[name = string("hidden_states_163_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_163_pad_0 = const()[name = string("hidden_states_163_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_163_dilations_0 = const()[name = string("hidden_states_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_163_groups_0 = const()[name = string("hidden_states_163_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_163_cast_fp16 = conv(dilations = hidden_states_163_dilations_0, groups = hidden_states_163_groups_0, pad = hidden_states_163_pad_0, pad_type = hidden_states_163_pad_type_0, strides = hidden_states_163_strides_0, weight = layers_16_self_attn_o_proj_weight_cast_fp16, x = attn_output_135_cast_fp16)[name = string("hidden_states_163_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_165_cast_fp16 = add(x = hidden_states_159_cast_fp16, y = hidden_states_163_cast_fp16)[name = string("hidden_states_165_cast_fp16")];
            fp16 const_170_promoted_to_fp16 = const()[name = string("const_170_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_6304_cast_fp16 = mul(x = hidden_states_165_cast_fp16, y = const_170_promoted_to_fp16)[name = string("op_6304_cast_fp16")];
            int32 var_6302 = const()[name = string("op_6302"), val = int32(1)];
            bool doubled_133_interleave_0 = const()[name = string("doubled_133_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_133_cast_fp16 = concat(axis = var_6302, interleave = doubled_133_interleave_0, values = (hidden_states_165_cast_fp16, var_6304_cast_fp16))[name = string("doubled_133_cast_fp16")];
            tensor<int32, [1]> out_67_axes_0 = const()[name = string("out_67_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_67_gamma_0_to_fp16 = const()[name = string("out_67_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433283072)))];
            fp16 var_6314_to_fp16 = const()[name = string("op_6314_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_67_cast_fp16 = layer_norm(axes = out_67_axes_0, epsilon = var_6314_to_fp16, gamma = out_67_gamma_0_to_fp16, x = doubled_133_cast_fp16)[name = string("out_67_cast_fp16")];
            tensor<int32, [2]> var_6325_split_sizes_0 = const()[name = string("op_6325_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6325_axis_0 = const()[name = string("op_6325_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_6325_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_6325_cast_fp16_1 = split(axis = var_6325_axis_0, split_sizes = var_6325_split_sizes_0, x = out_67_cast_fp16)[name = string("op_6325_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_16_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_16_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433291328)))];
            tensor<int32, [2]> input_33_strides_0 = const()[name = string("input_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_33_pad_type_0 = const()[name = string("input_33_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_33_pad_0 = const()[name = string("input_33_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_33_dilations_0 = const()[name = string("input_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_33_groups_0 = const()[name = string("input_33_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_33_cast_fp16 = conv(dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = layers_16_mlp_gate_proj_weight_to_fp16, x = var_6325_cast_fp16_0)[name = string("input_33_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_6342_cast_fp16 = silu(x = input_33_cast_fp16)[name = string("op_6342_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_16_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_16_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1458457216)))];
            tensor<int32, [2]> var_6348_strides_0 = const()[name = string("op_6348_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6348_pad_type_0 = const()[name = string("op_6348_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6348_pad_0 = const()[name = string("op_6348_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6348_dilations_0 = const()[name = string("op_6348_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6348_groups_0 = const()[name = string("op_6348_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_6348_cast_fp16 = conv(dilations = var_6348_dilations_0, groups = var_6348_groups_0, pad = var_6348_pad_0, pad_type = var_6348_pad_type_0, strides = var_6348_strides_0, weight = layers_16_mlp_up_proj_weight_to_fp16, x = var_6325_cast_fp16_0)[name = string("op_6348_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_169_cast_fp16 = mul(x = var_6342_cast_fp16, y = var_6348_cast_fp16)[name = string("x_169_cast_fp16")];
            tensor<int32, [2]> hidden_states_167_strides_0 = const()[name = string("hidden_states_167_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_167_pad_type_0 = const()[name = string("hidden_states_167_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_167_pad_0 = const()[name = string("hidden_states_167_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_167_dilations_0 = const()[name = string("hidden_states_167_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_167_groups_0 = const()[name = string("hidden_states_167_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_167_cast_fp16 = conv(dilations = hidden_states_167_dilations_0, groups = hidden_states_167_groups_0, pad = hidden_states_167_pad_0, pad_type = hidden_states_167_pad_type_0, strides = hidden_states_167_strides_0, weight = layers_16_mlp_down_proj_weight_cast_fp16, x = x_169_cast_fp16)[name = string("hidden_states_167_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_169_cast_fp16 = add(x = hidden_states_165_cast_fp16, y = hidden_states_167_cast_fp16)[name = string("hidden_states_169_cast_fp16")];
            fp16 const_172_promoted_to_fp16 = const()[name = string("const_172_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_6366_cast_fp16 = mul(x = hidden_states_169_cast_fp16, y = const_172_promoted_to_fp16)[name = string("op_6366_cast_fp16")];
            int32 var_6364 = const()[name = string("op_6364"), val = int32(1)];
            bool doubled_137_interleave_0 = const()[name = string("doubled_137_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_137_cast_fp16 = concat(axis = var_6364, interleave = doubled_137_interleave_0, values = (hidden_states_169_cast_fp16, var_6366_cast_fp16))[name = string("doubled_137_cast_fp16")];
            tensor<int32, [1]> out_69_axes_0 = const()[name = string("out_69_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_69_gamma_0_to_fp16 = const()[name = string("out_69_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483623104)))];
            fp16 var_6376_to_fp16 = const()[name = string("op_6376_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_69_cast_fp16 = layer_norm(axes = out_69_axes_0, epsilon = var_6376_to_fp16, gamma = out_69_gamma_0_to_fp16, x = doubled_137_cast_fp16)[name = string("out_69_cast_fp16")];
            tensor<int32, [2]> var_6387_split_sizes_0 = const()[name = string("op_6387_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6387_axis_0 = const()[name = string("op_6387_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_6387_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_6387_cast_fp16_1 = split(axis = var_6387_axis_0, split_sizes = var_6387_split_sizes_0, x = out_69_cast_fp16)[name = string("op_6387_cast_fp16")];
            tensor<int32, [2]> query_states_103_strides_0 = const()[name = string("query_states_103_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_103_pad_type_0 = const()[name = string("query_states_103_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_103_pad_0 = const()[name = string("query_states_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_103_dilations_0 = const()[name = string("query_states_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_103_groups_0 = const()[name = string("query_states_103_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_103_cast_fp16 = conv(dilations = query_states_103_dilations_0, groups = query_states_103_groups_0, pad = query_states_103_pad_0, pad_type = query_states_103_pad_type_0, strides = query_states_103_strides_0, weight = layers_17_self_attn_q_proj_weight_cast_fp16, x = var_6387_cast_fp16_0)[name = string("query_states_103_cast_fp16")];
            tensor<int32, [2]> key_states_171_strides_0 = const()[name = string("key_states_171_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_171_pad_type_0 = const()[name = string("key_states_171_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_171_pad_0 = const()[name = string("key_states_171_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_171_dilations_0 = const()[name = string("key_states_171_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_171_groups_0 = const()[name = string("key_states_171_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_171_cast_fp16 = conv(dilations = key_states_171_dilations_0, groups = key_states_171_groups_0, pad = key_states_171_pad_0, pad_type = key_states_171_pad_type_0, strides = key_states_171_strides_0, weight = layers_17_self_attn_k_proj_weight_cast_fp16, x = var_6387_cast_fp16_0)[name = string("key_states_171_cast_fp16")];
            tensor<int32, [2]> value_states_103_strides_0 = const()[name = string("value_states_103_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_103_pad_type_0 = const()[name = string("value_states_103_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_103_pad_0 = const()[name = string("value_states_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_103_dilations_0 = const()[name = string("value_states_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_103_groups_0 = const()[name = string("value_states_103_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_103_cast_fp16 = conv(dilations = value_states_103_dilations_0, groups = value_states_103_groups_0, pad = value_states_103_pad_0, pad_type = value_states_103_pad_type_0, strides = value_states_103_strides_0, weight = layers_17_self_attn_v_proj_weight_cast_fp16, x = var_6387_cast_fp16_0)[name = string("value_states_103_cast_fp16")];
            tensor<int32, [4]> concat_204x = const()[name = string("concat_204x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_171_cast_fp16 = reshape(shape = concat_204x, x = query_states_103_cast_fp16)[name = string("x_171_cast_fp16")];
            tensor<int32, [4]> concat_205x = const()[name = string("concat_205x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_6444_cast_fp16 = reshape(shape = concat_205x, x = key_states_171_cast_fp16)[name = string("op_6444_cast_fp16")];
            tensor<int32, [4]> concat_206x = const()[name = string("concat_206x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_6451_cast_fp16 = reshape(shape = concat_206x, x = value_states_103_cast_fp16)[name = string("op_6451_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_6455_cast_fp16 = mul(x = x_171_cast_fp16, y = var_859_cast_fp16)[name = string("op_6455_cast_fp16")];
            tensor<int32, [2]> var_6456_split_sizes_0 = const()[name = string("op_6456_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6456_axis_0 = const()[name = string("op_6456_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_6456_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_6456_cast_fp16_1 = split(axis = var_6456_axis_0, split_sizes = var_6456_split_sizes_0, x = x_171_cast_fp16)[name = string("op_6456_cast_fp16")];
            fp16 const_174_promoted_to_fp16 = const()[name = string("const_174_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_6458_cast_fp16 = mul(x = var_6456_cast_fp16_1, y = const_174_promoted_to_fp16)[name = string("op_6458_cast_fp16")];
            int32 var_6460 = const()[name = string("op_6460"), val = int32(-2)];
            bool var_6461_interleave_0 = const()[name = string("op_6461_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_6461_cast_fp16 = concat(axis = var_6460, interleave = var_6461_interleave_0, values = (var_6458_cast_fp16, var_6456_cast_fp16_0))[name = string("op_6461_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_6462_cast_fp16 = mul(x = var_6461_cast_fp16, y = var_866_cast_fp16)[name = string("op_6462_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_105_cast_fp16 = add(x = var_6455_cast_fp16, y = var_6462_cast_fp16)[name = string("query_states_105_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_6468_cast_fp16 = mul(x = var_6444_cast_fp16, y = var_859_cast_fp16)[name = string("op_6468_cast_fp16")];
            tensor<int32, [2]> var_6469_split_sizes_0 = const()[name = string("op_6469_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6469_axis_0 = const()[name = string("op_6469_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_6469_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_6469_cast_fp16_1 = split(axis = var_6469_axis_0, split_sizes = var_6469_split_sizes_0, x = var_6444_cast_fp16)[name = string("op_6469_cast_fp16")];
            fp16 const_175_promoted_to_fp16 = const()[name = string("const_175_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_6471_cast_fp16 = mul(x = var_6469_cast_fp16_1, y = const_175_promoted_to_fp16)[name = string("op_6471_cast_fp16")];
            int32 var_6473 = const()[name = string("op_6473"), val = int32(-2)];
            bool var_6474_interleave_0 = const()[name = string("op_6474_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_6474_cast_fp16 = concat(axis = var_6473, interleave = var_6474_interleave_0, values = (var_6471_cast_fp16, var_6469_cast_fp16_0))[name = string("op_6474_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_6475_cast_fp16 = mul(x = var_6474_cast_fp16, y = var_866_cast_fp16)[name = string("op_6475_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_175_cast_fp16 = add(x = var_6468_cast_fp16, y = var_6475_cast_fp16)[name = string("key_states_175_cast_fp16")];
            tensor<int32, [1]> expand_dims_204 = const()[name = string("expand_dims_204"), val = tensor<int32, [1]>([17])];
            tensor<int32, [1]> expand_dims_205 = const()[name = string("expand_dims_205"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_207 = const()[name = string("expand_dims_207"), val = tensor<int32, [1]>([0])];
            int32 concat_209_axis_0 = const()[name = string("concat_209_axis_0"), val = int32(0)];
            bool concat_209_interleave_0 = const()[name = string("concat_209_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_209 = concat(axis = concat_209_axis_0, interleave = concat_209_interleave_0, values = (expand_dims_204, expand_dims_205, position_id, expand_dims_207))[name = string("concat_209")];
            tensor<int32, [1]> expand_dims_208 = const()[name = string("expand_dims_208"), val = tensor<int32, [1]>([18])];
            tensor<int32, [1]> concat_210_values1_0 = const()[name = string("concat_210_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_210_values3_0 = const()[name = string("concat_210_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_210_axis_0 = const()[name = string("concat_210_axis_0"), val = int32(0)];
            bool concat_210_interleave_0 = const()[name = string("concat_210_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_210 = concat(axis = concat_210_axis_0, interleave = concat_210_interleave_0, values = (expand_dims_208, concat_210_values1_0, cache_position_end, concat_210_values3_0))[name = string("concat_210")];
            tensor<int32, [4]> key_states_177_perm_0 = const()[name = string("key_states_177_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_18_stride_0 = const()[name = string("key_cache_internal_tensor_assign_18_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_18_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_18_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_18_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_18_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_18_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_18_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_177_cast_fp16 = transpose(perm = key_states_177_perm_0, x = key_states_175_cast_fp16)[name = string("transpose_119")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_18_cast_fp16 = slice_update(begin = concat_209, begin_mask = key_cache_internal_tensor_assign_18_begin_mask_0, end = concat_210, end_mask = key_cache_internal_tensor_assign_18_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_18_squeeze_mask_0, stride = key_cache_internal_tensor_assign_18_stride_0, update = key_states_177_cast_fp16, x = coreml_update_state_88)[name = string("key_cache_internal_tensor_assign_18_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_18_cast_fp16, input = key_cache)[name = string("coreml_update_state_90_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_90 = read_state(input = key_cache)[name = string("coreml_update_state_90")];
            tensor<int32, [4]> value_states_105_perm_0 = const()[name = string("value_states_105_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_18_stride_0 = const()[name = string("value_cache_internal_tensor_assign_18_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_18_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_18_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_18_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_18_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_18_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_18_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_105_cast_fp16 = transpose(perm = value_states_105_perm_0, x = var_6451_cast_fp16)[name = string("transpose_118")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_18_cast_fp16 = slice_update(begin = concat_209, begin_mask = value_cache_internal_tensor_assign_18_begin_mask_0, end = concat_210, end_mask = value_cache_internal_tensor_assign_18_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_18_squeeze_mask_0, stride = value_cache_internal_tensor_assign_18_stride_0, update = value_states_105_cast_fp16, x = coreml_update_state_89)[name = string("value_cache_internal_tensor_assign_18_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_18_cast_fp16, input = value_cache)[name = string("coreml_update_state_91_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_91 = read_state(input = value_cache)[name = string("coreml_update_state_91")];
            tensor<int32, [4]> var_6545_begin_0 = const()[name = string("op_6545_begin_0"), val = tensor<int32, [4]>([17, 0, 0, 0])];
            tensor<int32, [4]> var_6545_end_0 = const()[name = string("op_6545_end_0"), val = tensor<int32, [4]>([18, 2, 2048, 128])];
            tensor<bool, [4]> var_6545_end_mask_0 = const()[name = string("op_6545_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6545_cast_fp16 = slice_by_index(begin = var_6545_begin_0, end = var_6545_end_0, end_mask = var_6545_end_mask_0, x = coreml_update_state_90)[name = string("op_6545_cast_fp16")];
            tensor<int32, [2]> tile_34 = const()[name = string("tile_34"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6548_axis_0 = const()[name = string("op_6548_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6548_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6548_cast_fp16_1 = split(axis = var_6548_axis_0, split_sizes = tile_34, x = var_6545_cast_fp16)[name = string("op_6548_cast_fp16")];
            tensor<int32, [4]> var_6555_begin_0 = const()[name = string("op_6555_begin_0"), val = tensor<int32, [4]>([17, 0, 0, 0])];
            tensor<int32, [4]> var_6555_end_0 = const()[name = string("op_6555_end_0"), val = tensor<int32, [4]>([18, 2, 2048, 128])];
            tensor<bool, [4]> var_6555_end_mask_0 = const()[name = string("op_6555_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6555_cast_fp16 = slice_by_index(begin = var_6555_begin_0, end = var_6555_end_0, end_mask = var_6555_end_mask_0, x = coreml_update_state_91)[name = string("op_6555_cast_fp16")];
            tensor<int32, [2]> tile_35 = const()[name = string("tile_35"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6558_axis_0 = const()[name = string("op_6558_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6558_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6558_cast_fp16_1 = split(axis = var_6558_axis_0, split_sizes = tile_35, x = var_6555_cast_fp16)[name = string("op_6558_cast_fp16")];
            tensor<int32, [2]> var_6561_split_sizes_0 = const()[name = string("op_6561_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_6561_axis_0 = const()[name = string("op_6561_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_6561_0, tensor<fp16, [1, 8, 128, 1]> var_6561_1 = split(axis = var_6561_axis_0, split_sizes = var_6561_split_sizes_0, x = query_states_105_cast_fp16)[name = string("op_6561")];
            bool attn_weights_273_transpose_x_0 = const()[name = string("attn_weights_273_transpose_x_0"), val = bool(false)];
            bool attn_weights_273_transpose_y_0 = const()[name = string("attn_weights_273_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_273_cast_fp16 = matmul(transpose_x = attn_weights_273_transpose_x_0, transpose_y = attn_weights_273_transpose_y_0, x = var_6548_cast_fp16_0, y = var_6561_0)[name = string("attn_weights_273_cast_fp16")];
            fp16 var_6564_to_fp16 = const()[name = string("op_6564_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_275_cast_fp16 = mul(x = attn_weights_273_cast_fp16, y = var_6564_to_fp16)[name = string("attn_weights_275_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_277_cast_fp16 = add(x = attn_weights_275_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_277_cast_fp16")];
            int32 var_6568 = const()[name = string("op_6568"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_279_cast_fp16 = softmax(axis = var_6568, x = attn_weights_277_cast_fp16)[name = string("attn_weights_279_cast_fp16")];
            bool var_6574_transpose_x_1 = const()[name = string("op_6574_transpose_x_1"), val = bool(true)];
            bool var_6574_transpose_y_1 = const()[name = string("op_6574_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_6574_cast_fp16 = matmul(transpose_x = var_6574_transpose_x_1, transpose_y = var_6574_transpose_y_1, x = attn_weights_279_cast_fp16, y = var_6558_cast_fp16_0)[name = string("op_6574_cast_fp16")];
            bool attn_weights_281_transpose_x_0 = const()[name = string("attn_weights_281_transpose_x_0"), val = bool(false)];
            bool attn_weights_281_transpose_y_0 = const()[name = string("attn_weights_281_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_281_cast_fp16 = matmul(transpose_x = attn_weights_281_transpose_x_0, transpose_y = attn_weights_281_transpose_y_0, x = var_6548_cast_fp16_1, y = var_6561_1)[name = string("attn_weights_281_cast_fp16")];
            fp16 var_6576_to_fp16 = const()[name = string("op_6576_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_283_cast_fp16 = mul(x = attn_weights_281_cast_fp16, y = var_6576_to_fp16)[name = string("attn_weights_283_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_285_cast_fp16 = add(x = attn_weights_283_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_285_cast_fp16")];
            int32 var_6580 = const()[name = string("op_6580"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_287_cast_fp16 = softmax(axis = var_6580, x = attn_weights_285_cast_fp16)[name = string("attn_weights_287_cast_fp16")];
            bool attn_output_137_transpose_x_1 = const()[name = string("attn_output_137_transpose_x_1"), val = bool(true)];
            bool attn_output_137_transpose_y_1 = const()[name = string("attn_output_137_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_137_cast_fp16 = matmul(transpose_x = attn_output_137_transpose_x_1, transpose_y = attn_output_137_transpose_y_1, x = attn_weights_287_cast_fp16, y = var_6558_cast_fp16_1)[name = string("attn_output_137_cast_fp16")];
            int32 var_6588 = const()[name = string("op_6588"), val = int32(1)];
            bool attn_output_139_interleave_0 = const()[name = string("attn_output_139_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_139_cast_fp16 = concat(axis = var_6588, interleave = attn_output_139_interleave_0, values = (var_6574_cast_fp16, attn_output_137_cast_fp16))[name = string("attn_output_139_cast_fp16")];
            tensor<int32, [4]> var_6592_perm_0 = const()[name = string("op_6592_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_215x = const()[name = string("concat_215x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_6592_cast_fp16 = transpose(perm = var_6592_perm_0, x = attn_output_139_cast_fp16)[name = string("transpose_117")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_143_cast_fp16 = reshape(shape = concat_215x, x = var_6592_cast_fp16)[name = string("attn_output_143_cast_fp16")];
            tensor<int32, [2]> hidden_states_173_strides_0 = const()[name = string("hidden_states_173_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_173_pad_type_0 = const()[name = string("hidden_states_173_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_173_pad_0 = const()[name = string("hidden_states_173_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_173_dilations_0 = const()[name = string("hidden_states_173_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_173_groups_0 = const()[name = string("hidden_states_173_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_173_cast_fp16 = conv(dilations = hidden_states_173_dilations_0, groups = hidden_states_173_groups_0, pad = hidden_states_173_pad_0, pad_type = hidden_states_173_pad_type_0, strides = hidden_states_173_strides_0, weight = layers_17_self_attn_o_proj_weight_cast_fp16, x = attn_output_143_cast_fp16)[name = string("hidden_states_173_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_175_cast_fp16 = add(x = hidden_states_169_cast_fp16, y = hidden_states_173_cast_fp16)[name = string("hidden_states_175_cast_fp16")];
            fp16 const_180_promoted_to_fp16 = const()[name = string("const_180_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_6625_cast_fp16 = mul(x = hidden_states_175_cast_fp16, y = const_180_promoted_to_fp16)[name = string("op_6625_cast_fp16")];
            int32 var_6623 = const()[name = string("op_6623"), val = int32(1)];
            bool doubled_141_interleave_0 = const()[name = string("doubled_141_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_141_cast_fp16 = concat(axis = var_6623, interleave = doubled_141_interleave_0, values = (hidden_states_175_cast_fp16, var_6625_cast_fp16))[name = string("doubled_141_cast_fp16")];
            tensor<int32, [1]> out_71_axes_0 = const()[name = string("out_71_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_71_gamma_0_to_fp16 = const()[name = string("out_71_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483631360)))];
            fp16 var_6635_to_fp16 = const()[name = string("op_6635_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_71_cast_fp16 = layer_norm(axes = out_71_axes_0, epsilon = var_6635_to_fp16, gamma = out_71_gamma_0_to_fp16, x = doubled_141_cast_fp16)[name = string("out_71_cast_fp16")];
            tensor<int32, [2]> var_6646_split_sizes_0 = const()[name = string("op_6646_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6646_axis_0 = const()[name = string("op_6646_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_6646_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_6646_cast_fp16_1 = split(axis = var_6646_axis_0, split_sizes = var_6646_split_sizes_0, x = out_71_cast_fp16)[name = string("op_6646_cast_fp16")];
            tensor<int32, [2]> input_35_strides_0 = const()[name = string("input_35_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_35_pad_type_0 = const()[name = string("input_35_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_35_pad_0 = const()[name = string("input_35_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_35_dilations_0 = const()[name = string("input_35_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_35_groups_0 = const()[name = string("input_35_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_35_cast_fp16 = conv(dilations = input_35_dilations_0, groups = input_35_groups_0, pad = input_35_pad_0, pad_type = input_35_pad_type_0, strides = input_35_strides_0, weight = layers_17_mlp_gate_proj_weight_cast_fp16, x = var_6646_cast_fp16_0)[name = string("input_35_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_6663_cast_fp16 = silu(x = input_35_cast_fp16)[name = string("op_6663_cast_fp16")];
            tensor<int32, [2]> var_6669_strides_0 = const()[name = string("op_6669_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6669_pad_type_0 = const()[name = string("op_6669_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6669_pad_0 = const()[name = string("op_6669_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6669_dilations_0 = const()[name = string("op_6669_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6669_groups_0 = const()[name = string("op_6669_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_6669_cast_fp16 = conv(dilations = var_6669_dilations_0, groups = var_6669_groups_0, pad = var_6669_pad_0, pad_type = var_6669_pad_type_0, strides = var_6669_strides_0, weight = layers_17_mlp_up_proj_weight_cast_fp16, x = var_6646_cast_fp16_0)[name = string("op_6669_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_179_cast_fp16 = mul(x = var_6663_cast_fp16, y = var_6669_cast_fp16)[name = string("x_179_cast_fp16")];
            tensor<int32, [2]> hidden_states_177_strides_0 = const()[name = string("hidden_states_177_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_177_pad_type_0 = const()[name = string("hidden_states_177_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_177_pad_0 = const()[name = string("hidden_states_177_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_177_dilations_0 = const()[name = string("hidden_states_177_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_177_groups_0 = const()[name = string("hidden_states_177_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_177_cast_fp16 = conv(dilations = hidden_states_177_dilations_0, groups = hidden_states_177_groups_0, pad = hidden_states_177_pad_0, pad_type = hidden_states_177_pad_type_0, strides = hidden_states_177_strides_0, weight = layers_17_mlp_down_proj_weight_cast_fp16, x = x_179_cast_fp16)[name = string("hidden_states_177_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_179_cast_fp16 = add(x = hidden_states_175_cast_fp16, y = hidden_states_177_cast_fp16)[name = string("hidden_states_179_cast_fp16")];
            fp16 const_182_promoted_to_fp16 = const()[name = string("const_182_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_6687_cast_fp16 = mul(x = hidden_states_179_cast_fp16, y = const_182_promoted_to_fp16)[name = string("op_6687_cast_fp16")];
            int32 var_6685 = const()[name = string("op_6685"), val = int32(1)];
            bool doubled_145_interleave_0 = const()[name = string("doubled_145_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_145_cast_fp16 = concat(axis = var_6685, interleave = doubled_145_interleave_0, values = (hidden_states_179_cast_fp16, var_6687_cast_fp16))[name = string("doubled_145_cast_fp16")];
            tensor<int32, [1]> out_73_axes_0 = const()[name = string("out_73_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_73_gamma_0_to_fp16 = const()[name = string("out_73_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483639616)))];
            fp16 var_6697_to_fp16 = const()[name = string("op_6697_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_73_cast_fp16 = layer_norm(axes = out_73_axes_0, epsilon = var_6697_to_fp16, gamma = out_73_gamma_0_to_fp16, x = doubled_145_cast_fp16)[name = string("out_73_cast_fp16")];
            tensor<int32, [2]> var_6708_split_sizes_0 = const()[name = string("op_6708_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6708_axis_0 = const()[name = string("op_6708_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_6708_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_6708_cast_fp16_1 = split(axis = var_6708_axis_0, split_sizes = var_6708_split_sizes_0, x = out_73_cast_fp16)[name = string("op_6708_cast_fp16")];
            tensor<int32, [2]> query_states_109_strides_0 = const()[name = string("query_states_109_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_109_pad_type_0 = const()[name = string("query_states_109_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_109_pad_0 = const()[name = string("query_states_109_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_109_dilations_0 = const()[name = string("query_states_109_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_109_groups_0 = const()[name = string("query_states_109_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_109_cast_fp16 = conv(dilations = query_states_109_dilations_0, groups = query_states_109_groups_0, pad = query_states_109_pad_0, pad_type = query_states_109_pad_type_0, strides = query_states_109_strides_0, weight = layers_18_self_attn_q_proj_weight_cast_fp16, x = var_6708_cast_fp16_0)[name = string("query_states_109_cast_fp16")];
            tensor<int32, [2]> key_states_181_strides_0 = const()[name = string("key_states_181_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_181_pad_type_0 = const()[name = string("key_states_181_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_181_pad_0 = const()[name = string("key_states_181_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_181_dilations_0 = const()[name = string("key_states_181_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_181_groups_0 = const()[name = string("key_states_181_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_181_cast_fp16 = conv(dilations = key_states_181_dilations_0, groups = key_states_181_groups_0, pad = key_states_181_pad_0, pad_type = key_states_181_pad_type_0, strides = key_states_181_strides_0, weight = layers_18_self_attn_k_proj_weight_cast_fp16, x = var_6708_cast_fp16_0)[name = string("key_states_181_cast_fp16")];
            tensor<int32, [2]> value_states_109_strides_0 = const()[name = string("value_states_109_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_109_pad_type_0 = const()[name = string("value_states_109_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_109_pad_0 = const()[name = string("value_states_109_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_109_dilations_0 = const()[name = string("value_states_109_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_109_groups_0 = const()[name = string("value_states_109_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_109_cast_fp16 = conv(dilations = value_states_109_dilations_0, groups = value_states_109_groups_0, pad = value_states_109_pad_0, pad_type = value_states_109_pad_type_0, strides = value_states_109_strides_0, weight = layers_18_self_attn_v_proj_weight_cast_fp16, x = var_6708_cast_fp16_0)[name = string("value_states_109_cast_fp16")];
            tensor<int32, [4]> concat_216x = const()[name = string("concat_216x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_181_cast_fp16 = reshape(shape = concat_216x, x = query_states_109_cast_fp16)[name = string("x_181_cast_fp16")];
            tensor<int32, [4]> concat_217x = const()[name = string("concat_217x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_6765_cast_fp16 = reshape(shape = concat_217x, x = key_states_181_cast_fp16)[name = string("op_6765_cast_fp16")];
            tensor<int32, [4]> concat_218x = const()[name = string("concat_218x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_6772_cast_fp16 = reshape(shape = concat_218x, x = value_states_109_cast_fp16)[name = string("op_6772_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_6776_cast_fp16 = mul(x = x_181_cast_fp16, y = var_859_cast_fp16)[name = string("op_6776_cast_fp16")];
            tensor<int32, [2]> var_6777_split_sizes_0 = const()[name = string("op_6777_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6777_axis_0 = const()[name = string("op_6777_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_6777_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_6777_cast_fp16_1 = split(axis = var_6777_axis_0, split_sizes = var_6777_split_sizes_0, x = x_181_cast_fp16)[name = string("op_6777_cast_fp16")];
            fp16 const_184_promoted_to_fp16 = const()[name = string("const_184_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_6779_cast_fp16 = mul(x = var_6777_cast_fp16_1, y = const_184_promoted_to_fp16)[name = string("op_6779_cast_fp16")];
            int32 var_6781 = const()[name = string("op_6781"), val = int32(-2)];
            bool var_6782_interleave_0 = const()[name = string("op_6782_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_6782_cast_fp16 = concat(axis = var_6781, interleave = var_6782_interleave_0, values = (var_6779_cast_fp16, var_6777_cast_fp16_0))[name = string("op_6782_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_6783_cast_fp16 = mul(x = var_6782_cast_fp16, y = var_866_cast_fp16)[name = string("op_6783_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_111_cast_fp16 = add(x = var_6776_cast_fp16, y = var_6783_cast_fp16)[name = string("query_states_111_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_6789_cast_fp16 = mul(x = var_6765_cast_fp16, y = var_859_cast_fp16)[name = string("op_6789_cast_fp16")];
            tensor<int32, [2]> var_6790_split_sizes_0 = const()[name = string("op_6790_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6790_axis_0 = const()[name = string("op_6790_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_6790_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_6790_cast_fp16_1 = split(axis = var_6790_axis_0, split_sizes = var_6790_split_sizes_0, x = var_6765_cast_fp16)[name = string("op_6790_cast_fp16")];
            fp16 const_185_promoted_to_fp16 = const()[name = string("const_185_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_6792_cast_fp16 = mul(x = var_6790_cast_fp16_1, y = const_185_promoted_to_fp16)[name = string("op_6792_cast_fp16")];
            int32 var_6794 = const()[name = string("op_6794"), val = int32(-2)];
            bool var_6795_interleave_0 = const()[name = string("op_6795_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_6795_cast_fp16 = concat(axis = var_6794, interleave = var_6795_interleave_0, values = (var_6792_cast_fp16, var_6790_cast_fp16_0))[name = string("op_6795_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_6796_cast_fp16 = mul(x = var_6795_cast_fp16, y = var_866_cast_fp16)[name = string("op_6796_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_185_cast_fp16 = add(x = var_6789_cast_fp16, y = var_6796_cast_fp16)[name = string("key_states_185_cast_fp16")];
            tensor<int32, [1]> expand_dims_216 = const()[name = string("expand_dims_216"), val = tensor<int32, [1]>([18])];
            tensor<int32, [1]> expand_dims_217 = const()[name = string("expand_dims_217"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_219 = const()[name = string("expand_dims_219"), val = tensor<int32, [1]>([0])];
            int32 concat_221_axis_0 = const()[name = string("concat_221_axis_0"), val = int32(0)];
            bool concat_221_interleave_0 = const()[name = string("concat_221_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_221 = concat(axis = concat_221_axis_0, interleave = concat_221_interleave_0, values = (expand_dims_216, expand_dims_217, position_id, expand_dims_219))[name = string("concat_221")];
            tensor<int32, [1]> expand_dims_220 = const()[name = string("expand_dims_220"), val = tensor<int32, [1]>([19])];
            tensor<int32, [1]> concat_222_values1_0 = const()[name = string("concat_222_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_222_values3_0 = const()[name = string("concat_222_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_222_axis_0 = const()[name = string("concat_222_axis_0"), val = int32(0)];
            bool concat_222_interleave_0 = const()[name = string("concat_222_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_222 = concat(axis = concat_222_axis_0, interleave = concat_222_interleave_0, values = (expand_dims_220, concat_222_values1_0, cache_position_end, concat_222_values3_0))[name = string("concat_222")];
            tensor<int32, [4]> key_states_187_perm_0 = const()[name = string("key_states_187_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_19_stride_0 = const()[name = string("key_cache_internal_tensor_assign_19_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_19_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_19_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_19_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_19_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_19_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_19_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_187_cast_fp16 = transpose(perm = key_states_187_perm_0, x = key_states_185_cast_fp16)[name = string("transpose_116")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_19_cast_fp16 = slice_update(begin = concat_221, begin_mask = key_cache_internal_tensor_assign_19_begin_mask_0, end = concat_222, end_mask = key_cache_internal_tensor_assign_19_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_19_squeeze_mask_0, stride = key_cache_internal_tensor_assign_19_stride_0, update = key_states_187_cast_fp16, x = coreml_update_state_90)[name = string("key_cache_internal_tensor_assign_19_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_19_cast_fp16, input = key_cache)[name = string("coreml_update_state_92_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_92 = read_state(input = key_cache)[name = string("coreml_update_state_92")];
            tensor<int32, [4]> value_states_111_perm_0 = const()[name = string("value_states_111_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_19_stride_0 = const()[name = string("value_cache_internal_tensor_assign_19_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_19_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_19_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_19_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_19_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_19_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_19_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_111_cast_fp16 = transpose(perm = value_states_111_perm_0, x = var_6772_cast_fp16)[name = string("transpose_115")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_19_cast_fp16 = slice_update(begin = concat_221, begin_mask = value_cache_internal_tensor_assign_19_begin_mask_0, end = concat_222, end_mask = value_cache_internal_tensor_assign_19_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_19_squeeze_mask_0, stride = value_cache_internal_tensor_assign_19_stride_0, update = value_states_111_cast_fp16, x = coreml_update_state_91)[name = string("value_cache_internal_tensor_assign_19_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_19_cast_fp16, input = value_cache)[name = string("coreml_update_state_93_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_93 = read_state(input = value_cache)[name = string("coreml_update_state_93")];
            tensor<int32, [4]> var_6866_begin_0 = const()[name = string("op_6866_begin_0"), val = tensor<int32, [4]>([18, 0, 0, 0])];
            tensor<int32, [4]> var_6866_end_0 = const()[name = string("op_6866_end_0"), val = tensor<int32, [4]>([19, 2, 2048, 128])];
            tensor<bool, [4]> var_6866_end_mask_0 = const()[name = string("op_6866_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6866_cast_fp16 = slice_by_index(begin = var_6866_begin_0, end = var_6866_end_0, end_mask = var_6866_end_mask_0, x = coreml_update_state_92)[name = string("op_6866_cast_fp16")];
            tensor<int32, [2]> tile_36 = const()[name = string("tile_36"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6869_axis_0 = const()[name = string("op_6869_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6869_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6869_cast_fp16_1 = split(axis = var_6869_axis_0, split_sizes = tile_36, x = var_6866_cast_fp16)[name = string("op_6869_cast_fp16")];
            tensor<int32, [4]> var_6876_begin_0 = const()[name = string("op_6876_begin_0"), val = tensor<int32, [4]>([18, 0, 0, 0])];
            tensor<int32, [4]> var_6876_end_0 = const()[name = string("op_6876_end_0"), val = tensor<int32, [4]>([19, 2, 2048, 128])];
            tensor<bool, [4]> var_6876_end_mask_0 = const()[name = string("op_6876_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6876_cast_fp16 = slice_by_index(begin = var_6876_begin_0, end = var_6876_end_0, end_mask = var_6876_end_mask_0, x = coreml_update_state_93)[name = string("op_6876_cast_fp16")];
            tensor<int32, [2]> tile_37 = const()[name = string("tile_37"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6879_axis_0 = const()[name = string("op_6879_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6879_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6879_cast_fp16_1 = split(axis = var_6879_axis_0, split_sizes = tile_37, x = var_6876_cast_fp16)[name = string("op_6879_cast_fp16")];
            tensor<int32, [2]> var_6882_split_sizes_0 = const()[name = string("op_6882_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_6882_axis_0 = const()[name = string("op_6882_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_6882_0, tensor<fp16, [1, 8, 128, 1]> var_6882_1 = split(axis = var_6882_axis_0, split_sizes = var_6882_split_sizes_0, x = query_states_111_cast_fp16)[name = string("op_6882")];
            bool attn_weights_289_transpose_x_0 = const()[name = string("attn_weights_289_transpose_x_0"), val = bool(false)];
            bool attn_weights_289_transpose_y_0 = const()[name = string("attn_weights_289_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_289_cast_fp16 = matmul(transpose_x = attn_weights_289_transpose_x_0, transpose_y = attn_weights_289_transpose_y_0, x = var_6869_cast_fp16_0, y = var_6882_0)[name = string("attn_weights_289_cast_fp16")];
            fp16 var_6885_to_fp16 = const()[name = string("op_6885_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_291_cast_fp16 = mul(x = attn_weights_289_cast_fp16, y = var_6885_to_fp16)[name = string("attn_weights_291_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_293_cast_fp16 = add(x = attn_weights_291_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_293_cast_fp16")];
            int32 var_6889 = const()[name = string("op_6889"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_295_cast_fp16 = softmax(axis = var_6889, x = attn_weights_293_cast_fp16)[name = string("attn_weights_295_cast_fp16")];
            bool var_6895_transpose_x_1 = const()[name = string("op_6895_transpose_x_1"), val = bool(true)];
            bool var_6895_transpose_y_1 = const()[name = string("op_6895_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_6895_cast_fp16 = matmul(transpose_x = var_6895_transpose_x_1, transpose_y = var_6895_transpose_y_1, x = attn_weights_295_cast_fp16, y = var_6879_cast_fp16_0)[name = string("op_6895_cast_fp16")];
            bool attn_weights_297_transpose_x_0 = const()[name = string("attn_weights_297_transpose_x_0"), val = bool(false)];
            bool attn_weights_297_transpose_y_0 = const()[name = string("attn_weights_297_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_297_cast_fp16 = matmul(transpose_x = attn_weights_297_transpose_x_0, transpose_y = attn_weights_297_transpose_y_0, x = var_6869_cast_fp16_1, y = var_6882_1)[name = string("attn_weights_297_cast_fp16")];
            fp16 var_6897_to_fp16 = const()[name = string("op_6897_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_299_cast_fp16 = mul(x = attn_weights_297_cast_fp16, y = var_6897_to_fp16)[name = string("attn_weights_299_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_301_cast_fp16 = add(x = attn_weights_299_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_301_cast_fp16")];
            int32 var_6901 = const()[name = string("op_6901"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_303_cast_fp16 = softmax(axis = var_6901, x = attn_weights_301_cast_fp16)[name = string("attn_weights_303_cast_fp16")];
            bool attn_output_145_transpose_x_1 = const()[name = string("attn_output_145_transpose_x_1"), val = bool(true)];
            bool attn_output_145_transpose_y_1 = const()[name = string("attn_output_145_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_145_cast_fp16 = matmul(transpose_x = attn_output_145_transpose_x_1, transpose_y = attn_output_145_transpose_y_1, x = attn_weights_303_cast_fp16, y = var_6879_cast_fp16_1)[name = string("attn_output_145_cast_fp16")];
            int32 var_6909 = const()[name = string("op_6909"), val = int32(1)];
            bool attn_output_147_interleave_0 = const()[name = string("attn_output_147_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_147_cast_fp16 = concat(axis = var_6909, interleave = attn_output_147_interleave_0, values = (var_6895_cast_fp16, attn_output_145_cast_fp16))[name = string("attn_output_147_cast_fp16")];
            tensor<int32, [4]> var_6913_perm_0 = const()[name = string("op_6913_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_227x = const()[name = string("concat_227x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_6913_cast_fp16 = transpose(perm = var_6913_perm_0, x = attn_output_147_cast_fp16)[name = string("transpose_114")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_151_cast_fp16 = reshape(shape = concat_227x, x = var_6913_cast_fp16)[name = string("attn_output_151_cast_fp16")];
            tensor<int32, [2]> hidden_states_183_strides_0 = const()[name = string("hidden_states_183_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_183_pad_type_0 = const()[name = string("hidden_states_183_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_183_pad_0 = const()[name = string("hidden_states_183_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_183_dilations_0 = const()[name = string("hidden_states_183_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_183_groups_0 = const()[name = string("hidden_states_183_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_183_cast_fp16 = conv(dilations = hidden_states_183_dilations_0, groups = hidden_states_183_groups_0, pad = hidden_states_183_pad_0, pad_type = hidden_states_183_pad_type_0, strides = hidden_states_183_strides_0, weight = layers_18_self_attn_o_proj_weight_cast_fp16, x = attn_output_151_cast_fp16)[name = string("hidden_states_183_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_185_cast_fp16 = add(x = hidden_states_179_cast_fp16, y = hidden_states_183_cast_fp16)[name = string("hidden_states_185_cast_fp16")];
            fp16 const_190_promoted_to_fp16 = const()[name = string("const_190_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_6946_cast_fp16 = mul(x = hidden_states_185_cast_fp16, y = const_190_promoted_to_fp16)[name = string("op_6946_cast_fp16")];
            int32 var_6944 = const()[name = string("op_6944"), val = int32(1)];
            bool doubled_149_interleave_0 = const()[name = string("doubled_149_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_149_cast_fp16 = concat(axis = var_6944, interleave = doubled_149_interleave_0, values = (hidden_states_185_cast_fp16, var_6946_cast_fp16))[name = string("doubled_149_cast_fp16")];
            tensor<int32, [1]> out_75_axes_0 = const()[name = string("out_75_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_75_gamma_0_to_fp16 = const()[name = string("out_75_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483647872)))];
            fp16 var_6956_to_fp16 = const()[name = string("op_6956_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_75_cast_fp16 = layer_norm(axes = out_75_axes_0, epsilon = var_6956_to_fp16, gamma = out_75_gamma_0_to_fp16, x = doubled_149_cast_fp16)[name = string("out_75_cast_fp16")];
            tensor<int32, [2]> var_6967_split_sizes_0 = const()[name = string("op_6967_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6967_axis_0 = const()[name = string("op_6967_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_6967_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_6967_cast_fp16_1 = split(axis = var_6967_axis_0, split_sizes = var_6967_split_sizes_0, x = out_75_cast_fp16)[name = string("op_6967_cast_fp16")];
            tensor<int32, [2]> input_37_strides_0 = const()[name = string("input_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_37_pad_type_0 = const()[name = string("input_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_37_pad_0 = const()[name = string("input_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_37_dilations_0 = const()[name = string("input_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_37_groups_0 = const()[name = string("input_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_37_cast_fp16 = conv(dilations = input_37_dilations_0, groups = input_37_groups_0, pad = input_37_pad_0, pad_type = input_37_pad_type_0, strides = input_37_strides_0, weight = layers_18_mlp_gate_proj_weight_cast_fp16, x = var_6967_cast_fp16_0)[name = string("input_37_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_6984_cast_fp16 = silu(x = input_37_cast_fp16)[name = string("op_6984_cast_fp16")];
            tensor<int32, [2]> var_6990_strides_0 = const()[name = string("op_6990_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6990_pad_type_0 = const()[name = string("op_6990_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6990_pad_0 = const()[name = string("op_6990_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6990_dilations_0 = const()[name = string("op_6990_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6990_groups_0 = const()[name = string("op_6990_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_6990_cast_fp16 = conv(dilations = var_6990_dilations_0, groups = var_6990_groups_0, pad = var_6990_pad_0, pad_type = var_6990_pad_type_0, strides = var_6990_strides_0, weight = layers_18_mlp_up_proj_weight_cast_fp16, x = var_6967_cast_fp16_0)[name = string("op_6990_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_189_cast_fp16 = mul(x = var_6984_cast_fp16, y = var_6990_cast_fp16)[name = string("x_189_cast_fp16")];
            tensor<int32, [2]> hidden_states_187_strides_0 = const()[name = string("hidden_states_187_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_187_pad_type_0 = const()[name = string("hidden_states_187_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_187_pad_0 = const()[name = string("hidden_states_187_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_187_dilations_0 = const()[name = string("hidden_states_187_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_187_groups_0 = const()[name = string("hidden_states_187_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_187_cast_fp16 = conv(dilations = hidden_states_187_dilations_0, groups = hidden_states_187_groups_0, pad = hidden_states_187_pad_0, pad_type = hidden_states_187_pad_type_0, strides = hidden_states_187_strides_0, weight = layers_18_mlp_down_proj_weight_cast_fp16, x = x_189_cast_fp16)[name = string("hidden_states_187_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_189_cast_fp16 = add(x = hidden_states_185_cast_fp16, y = hidden_states_187_cast_fp16)[name = string("hidden_states_189_cast_fp16")];
            fp16 const_192_promoted_to_fp16 = const()[name = string("const_192_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_7008_cast_fp16 = mul(x = hidden_states_189_cast_fp16, y = const_192_promoted_to_fp16)[name = string("op_7008_cast_fp16")];
            int32 var_7006 = const()[name = string("op_7006"), val = int32(1)];
            bool doubled_153_interleave_0 = const()[name = string("doubled_153_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_153_cast_fp16 = concat(axis = var_7006, interleave = doubled_153_interleave_0, values = (hidden_states_189_cast_fp16, var_7008_cast_fp16))[name = string("doubled_153_cast_fp16")];
            tensor<int32, [1]> out_77_axes_0 = const()[name = string("out_77_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_77_gamma_0_to_fp16 = const()[name = string("out_77_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483656128)))];
            fp16 var_7018_to_fp16 = const()[name = string("op_7018_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_77_cast_fp16 = layer_norm(axes = out_77_axes_0, epsilon = var_7018_to_fp16, gamma = out_77_gamma_0_to_fp16, x = doubled_153_cast_fp16)[name = string("out_77_cast_fp16")];
            tensor<int32, [2]> var_7029_split_sizes_0 = const()[name = string("op_7029_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7029_axis_0 = const()[name = string("op_7029_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_7029_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_7029_cast_fp16_1 = split(axis = var_7029_axis_0, split_sizes = var_7029_split_sizes_0, x = out_77_cast_fp16)[name = string("op_7029_cast_fp16")];
            tensor<int32, [2]> query_states_115_strides_0 = const()[name = string("query_states_115_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_115_pad_type_0 = const()[name = string("query_states_115_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_115_pad_0 = const()[name = string("query_states_115_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_115_dilations_0 = const()[name = string("query_states_115_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_115_groups_0 = const()[name = string("query_states_115_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_115_cast_fp16 = conv(dilations = query_states_115_dilations_0, groups = query_states_115_groups_0, pad = query_states_115_pad_0, pad_type = query_states_115_pad_type_0, strides = query_states_115_strides_0, weight = layers_19_self_attn_q_proj_weight_cast_fp16, x = var_7029_cast_fp16_0)[name = string("query_states_115_cast_fp16")];
            tensor<int32, [2]> key_states_191_strides_0 = const()[name = string("key_states_191_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_191_pad_type_0 = const()[name = string("key_states_191_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_191_pad_0 = const()[name = string("key_states_191_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_191_dilations_0 = const()[name = string("key_states_191_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_191_groups_0 = const()[name = string("key_states_191_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_191_cast_fp16 = conv(dilations = key_states_191_dilations_0, groups = key_states_191_groups_0, pad = key_states_191_pad_0, pad_type = key_states_191_pad_type_0, strides = key_states_191_strides_0, weight = layers_19_self_attn_k_proj_weight_cast_fp16, x = var_7029_cast_fp16_0)[name = string("key_states_191_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_19_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_19_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483664384)))];
            tensor<int32, [2]> value_states_115_strides_0 = const()[name = string("value_states_115_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_115_pad_type_0 = const()[name = string("value_states_115_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_115_pad_0 = const()[name = string("value_states_115_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_115_dilations_0 = const()[name = string("value_states_115_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_115_groups_0 = const()[name = string("value_states_115_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_115_cast_fp16 = conv(dilations = value_states_115_dilations_0, groups = value_states_115_groups_0, pad = value_states_115_pad_0, pad_type = value_states_115_pad_type_0, strides = value_states_115_strides_0, weight = layers_19_self_attn_v_proj_weight_to_fp16, x = var_7029_cast_fp16_0)[name = string("value_states_115_cast_fp16")];
            tensor<int32, [4]> concat_228x = const()[name = string("concat_228x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_191_cast_fp16 = reshape(shape = concat_228x, x = query_states_115_cast_fp16)[name = string("x_191_cast_fp16")];
            tensor<int32, [4]> concat_229x = const()[name = string("concat_229x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_7086_cast_fp16 = reshape(shape = concat_229x, x = key_states_191_cast_fp16)[name = string("op_7086_cast_fp16")];
            tensor<int32, [4]> concat_230x = const()[name = string("concat_230x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_7093_cast_fp16 = reshape(shape = concat_230x, x = value_states_115_cast_fp16)[name = string("op_7093_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_7097_cast_fp16 = mul(x = x_191_cast_fp16, y = var_859_cast_fp16)[name = string("op_7097_cast_fp16")];
            tensor<int32, [2]> var_7098_split_sizes_0 = const()[name = string("op_7098_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7098_axis_0 = const()[name = string("op_7098_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_7098_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_7098_cast_fp16_1 = split(axis = var_7098_axis_0, split_sizes = var_7098_split_sizes_0, x = x_191_cast_fp16)[name = string("op_7098_cast_fp16")];
            fp16 const_194_promoted_to_fp16 = const()[name = string("const_194_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_7100_cast_fp16 = mul(x = var_7098_cast_fp16_1, y = const_194_promoted_to_fp16)[name = string("op_7100_cast_fp16")];
            int32 var_7102 = const()[name = string("op_7102"), val = int32(-2)];
            bool var_7103_interleave_0 = const()[name = string("op_7103_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_7103_cast_fp16 = concat(axis = var_7102, interleave = var_7103_interleave_0, values = (var_7100_cast_fp16, var_7098_cast_fp16_0))[name = string("op_7103_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_7104_cast_fp16 = mul(x = var_7103_cast_fp16, y = var_866_cast_fp16)[name = string("op_7104_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_117_cast_fp16 = add(x = var_7097_cast_fp16, y = var_7104_cast_fp16)[name = string("query_states_117_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_7110_cast_fp16 = mul(x = var_7086_cast_fp16, y = var_859_cast_fp16)[name = string("op_7110_cast_fp16")];
            tensor<int32, [2]> var_7111_split_sizes_0 = const()[name = string("op_7111_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7111_axis_0 = const()[name = string("op_7111_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_7111_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_7111_cast_fp16_1 = split(axis = var_7111_axis_0, split_sizes = var_7111_split_sizes_0, x = var_7086_cast_fp16)[name = string("op_7111_cast_fp16")];
            fp16 const_195_promoted_to_fp16 = const()[name = string("const_195_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_7113_cast_fp16 = mul(x = var_7111_cast_fp16_1, y = const_195_promoted_to_fp16)[name = string("op_7113_cast_fp16")];
            int32 var_7115 = const()[name = string("op_7115"), val = int32(-2)];
            bool var_7116_interleave_0 = const()[name = string("op_7116_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_7116_cast_fp16 = concat(axis = var_7115, interleave = var_7116_interleave_0, values = (var_7113_cast_fp16, var_7111_cast_fp16_0))[name = string("op_7116_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_7117_cast_fp16 = mul(x = var_7116_cast_fp16, y = var_866_cast_fp16)[name = string("op_7117_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_195_cast_fp16 = add(x = var_7110_cast_fp16, y = var_7117_cast_fp16)[name = string("key_states_195_cast_fp16")];
            tensor<int32, [1]> expand_dims_228 = const()[name = string("expand_dims_228"), val = tensor<int32, [1]>([19])];
            tensor<int32, [1]> expand_dims_229 = const()[name = string("expand_dims_229"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_231 = const()[name = string("expand_dims_231"), val = tensor<int32, [1]>([0])];
            int32 concat_233_axis_0 = const()[name = string("concat_233_axis_0"), val = int32(0)];
            bool concat_233_interleave_0 = const()[name = string("concat_233_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_233 = concat(axis = concat_233_axis_0, interleave = concat_233_interleave_0, values = (expand_dims_228, expand_dims_229, position_id, expand_dims_231))[name = string("concat_233")];
            tensor<int32, [1]> expand_dims_232 = const()[name = string("expand_dims_232"), val = tensor<int32, [1]>([20])];
            tensor<int32, [1]> concat_234_values1_0 = const()[name = string("concat_234_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_234_values3_0 = const()[name = string("concat_234_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_234_axis_0 = const()[name = string("concat_234_axis_0"), val = int32(0)];
            bool concat_234_interleave_0 = const()[name = string("concat_234_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_234 = concat(axis = concat_234_axis_0, interleave = concat_234_interleave_0, values = (expand_dims_232, concat_234_values1_0, cache_position_end, concat_234_values3_0))[name = string("concat_234")];
            tensor<int32, [4]> key_states_197_perm_0 = const()[name = string("key_states_197_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_20_stride_0 = const()[name = string("key_cache_internal_tensor_assign_20_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_20_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_20_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_20_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_20_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_20_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_20_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_197_cast_fp16 = transpose(perm = key_states_197_perm_0, x = key_states_195_cast_fp16)[name = string("transpose_113")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_20_cast_fp16 = slice_update(begin = concat_233, begin_mask = key_cache_internal_tensor_assign_20_begin_mask_0, end = concat_234, end_mask = key_cache_internal_tensor_assign_20_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_20_squeeze_mask_0, stride = key_cache_internal_tensor_assign_20_stride_0, update = key_states_197_cast_fp16, x = coreml_update_state_92)[name = string("key_cache_internal_tensor_assign_20_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_20_cast_fp16, input = key_cache)[name = string("coreml_update_state_94_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_94 = read_state(input = key_cache)[name = string("coreml_update_state_94")];
            tensor<int32, [4]> value_states_117_perm_0 = const()[name = string("value_states_117_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_20_stride_0 = const()[name = string("value_cache_internal_tensor_assign_20_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_20_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_20_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_20_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_20_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_20_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_20_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_117_cast_fp16 = transpose(perm = value_states_117_perm_0, x = var_7093_cast_fp16)[name = string("transpose_112")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_20_cast_fp16 = slice_update(begin = concat_233, begin_mask = value_cache_internal_tensor_assign_20_begin_mask_0, end = concat_234, end_mask = value_cache_internal_tensor_assign_20_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_20_squeeze_mask_0, stride = value_cache_internal_tensor_assign_20_stride_0, update = value_states_117_cast_fp16, x = coreml_update_state_93)[name = string("value_cache_internal_tensor_assign_20_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_20_cast_fp16, input = value_cache)[name = string("coreml_update_state_95_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_95 = read_state(input = value_cache)[name = string("coreml_update_state_95")];
            tensor<int32, [4]> var_7187_begin_0 = const()[name = string("op_7187_begin_0"), val = tensor<int32, [4]>([19, 0, 0, 0])];
            tensor<int32, [4]> var_7187_end_0 = const()[name = string("op_7187_end_0"), val = tensor<int32, [4]>([20, 2, 2048, 128])];
            tensor<bool, [4]> var_7187_end_mask_0 = const()[name = string("op_7187_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7187_cast_fp16 = slice_by_index(begin = var_7187_begin_0, end = var_7187_end_0, end_mask = var_7187_end_mask_0, x = coreml_update_state_94)[name = string("op_7187_cast_fp16")];
            tensor<int32, [2]> tile_38 = const()[name = string("tile_38"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7190_axis_0 = const()[name = string("op_7190_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7190_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7190_cast_fp16_1 = split(axis = var_7190_axis_0, split_sizes = tile_38, x = var_7187_cast_fp16)[name = string("op_7190_cast_fp16")];
            tensor<int32, [4]> var_7197_begin_0 = const()[name = string("op_7197_begin_0"), val = tensor<int32, [4]>([19, 0, 0, 0])];
            tensor<int32, [4]> var_7197_end_0 = const()[name = string("op_7197_end_0"), val = tensor<int32, [4]>([20, 2, 2048, 128])];
            tensor<bool, [4]> var_7197_end_mask_0 = const()[name = string("op_7197_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7197_cast_fp16 = slice_by_index(begin = var_7197_begin_0, end = var_7197_end_0, end_mask = var_7197_end_mask_0, x = coreml_update_state_95)[name = string("op_7197_cast_fp16")];
            tensor<int32, [2]> tile_39 = const()[name = string("tile_39"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7200_axis_0 = const()[name = string("op_7200_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7200_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7200_cast_fp16_1 = split(axis = var_7200_axis_0, split_sizes = tile_39, x = var_7197_cast_fp16)[name = string("op_7200_cast_fp16")];
            tensor<int32, [2]> var_7203_split_sizes_0 = const()[name = string("op_7203_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_7203_axis_0 = const()[name = string("op_7203_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_7203_0, tensor<fp16, [1, 8, 128, 1]> var_7203_1 = split(axis = var_7203_axis_0, split_sizes = var_7203_split_sizes_0, x = query_states_117_cast_fp16)[name = string("op_7203")];
            bool attn_weights_305_transpose_x_0 = const()[name = string("attn_weights_305_transpose_x_0"), val = bool(false)];
            bool attn_weights_305_transpose_y_0 = const()[name = string("attn_weights_305_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_305_cast_fp16 = matmul(transpose_x = attn_weights_305_transpose_x_0, transpose_y = attn_weights_305_transpose_y_0, x = var_7190_cast_fp16_0, y = var_7203_0)[name = string("attn_weights_305_cast_fp16")];
            fp16 var_7206_to_fp16 = const()[name = string("op_7206_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_307_cast_fp16 = mul(x = attn_weights_305_cast_fp16, y = var_7206_to_fp16)[name = string("attn_weights_307_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_309_cast_fp16 = add(x = attn_weights_307_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_309_cast_fp16")];
            int32 var_7210 = const()[name = string("op_7210"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_311_cast_fp16 = softmax(axis = var_7210, x = attn_weights_309_cast_fp16)[name = string("attn_weights_311_cast_fp16")];
            bool var_7216_transpose_x_1 = const()[name = string("op_7216_transpose_x_1"), val = bool(true)];
            bool var_7216_transpose_y_1 = const()[name = string("op_7216_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_7216_cast_fp16 = matmul(transpose_x = var_7216_transpose_x_1, transpose_y = var_7216_transpose_y_1, x = attn_weights_311_cast_fp16, y = var_7200_cast_fp16_0)[name = string("op_7216_cast_fp16")];
            bool attn_weights_313_transpose_x_0 = const()[name = string("attn_weights_313_transpose_x_0"), val = bool(false)];
            bool attn_weights_313_transpose_y_0 = const()[name = string("attn_weights_313_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_313_cast_fp16 = matmul(transpose_x = attn_weights_313_transpose_x_0, transpose_y = attn_weights_313_transpose_y_0, x = var_7190_cast_fp16_1, y = var_7203_1)[name = string("attn_weights_313_cast_fp16")];
            fp16 var_7218_to_fp16 = const()[name = string("op_7218_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_315_cast_fp16 = mul(x = attn_weights_313_cast_fp16, y = var_7218_to_fp16)[name = string("attn_weights_315_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_317_cast_fp16 = add(x = attn_weights_315_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_317_cast_fp16")];
            int32 var_7222 = const()[name = string("op_7222"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_319_cast_fp16 = softmax(axis = var_7222, x = attn_weights_317_cast_fp16)[name = string("attn_weights_319_cast_fp16")];
            bool attn_output_153_transpose_x_1 = const()[name = string("attn_output_153_transpose_x_1"), val = bool(true)];
            bool attn_output_153_transpose_y_1 = const()[name = string("attn_output_153_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_153_cast_fp16 = matmul(transpose_x = attn_output_153_transpose_x_1, transpose_y = attn_output_153_transpose_y_1, x = attn_weights_319_cast_fp16, y = var_7200_cast_fp16_1)[name = string("attn_output_153_cast_fp16")];
            int32 var_7230 = const()[name = string("op_7230"), val = int32(1)];
            bool attn_output_155_interleave_0 = const()[name = string("attn_output_155_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_155_cast_fp16 = concat(axis = var_7230, interleave = attn_output_155_interleave_0, values = (var_7216_cast_fp16, attn_output_153_cast_fp16))[name = string("attn_output_155_cast_fp16")];
            tensor<int32, [4]> var_7234_perm_0 = const()[name = string("op_7234_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_239x = const()[name = string("concat_239x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_7234_cast_fp16 = transpose(perm = var_7234_perm_0, x = attn_output_155_cast_fp16)[name = string("transpose_111")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_159_cast_fp16 = reshape(shape = concat_239x, x = var_7234_cast_fp16)[name = string("attn_output_159_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_19_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_19_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1484713024)))];
            tensor<int32, [2]> hidden_states_193_strides_0 = const()[name = string("hidden_states_193_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_193_pad_type_0 = const()[name = string("hidden_states_193_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_193_pad_0 = const()[name = string("hidden_states_193_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_193_dilations_0 = const()[name = string("hidden_states_193_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_193_groups_0 = const()[name = string("hidden_states_193_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_193_cast_fp16 = conv(dilations = hidden_states_193_dilations_0, groups = hidden_states_193_groups_0, pad = hidden_states_193_pad_0, pad_type = hidden_states_193_pad_type_0, strides = hidden_states_193_strides_0, weight = layers_19_self_attn_o_proj_weight_to_fp16, x = attn_output_159_cast_fp16)[name = string("hidden_states_193_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_195_cast_fp16 = add(x = hidden_states_189_cast_fp16, y = hidden_states_193_cast_fp16)[name = string("hidden_states_195_cast_fp16")];
            fp16 const_200_promoted_to_fp16 = const()[name = string("const_200_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_7267_cast_fp16 = mul(x = hidden_states_195_cast_fp16, y = const_200_promoted_to_fp16)[name = string("op_7267_cast_fp16")];
            int32 var_7265 = const()[name = string("op_7265"), val = int32(1)];
            bool doubled_157_interleave_0 = const()[name = string("doubled_157_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_157_cast_fp16 = concat(axis = var_7265, interleave = doubled_157_interleave_0, values = (hidden_states_195_cast_fp16, var_7267_cast_fp16))[name = string("doubled_157_cast_fp16")];
            tensor<int32, [1]> out_79_axes_0 = const()[name = string("out_79_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_79_gamma_0_to_fp16 = const()[name = string("out_79_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1493101696)))];
            fp16 var_7277_to_fp16 = const()[name = string("op_7277_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_79_cast_fp16 = layer_norm(axes = out_79_axes_0, epsilon = var_7277_to_fp16, gamma = out_79_gamma_0_to_fp16, x = doubled_157_cast_fp16)[name = string("out_79_cast_fp16")];
            tensor<int32, [2]> var_7288_split_sizes_0 = const()[name = string("op_7288_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7288_axis_0 = const()[name = string("op_7288_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_7288_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_7288_cast_fp16_1 = split(axis = var_7288_axis_0, split_sizes = var_7288_split_sizes_0, x = out_79_cast_fp16)[name = string("op_7288_cast_fp16")];
            tensor<int32, [2]> input_39_strides_0 = const()[name = string("input_39_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_39_pad_type_0 = const()[name = string("input_39_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_39_pad_0 = const()[name = string("input_39_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_39_dilations_0 = const()[name = string("input_39_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_39_groups_0 = const()[name = string("input_39_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_39_cast_fp16 = conv(dilations = input_39_dilations_0, groups = input_39_groups_0, pad = input_39_pad_0, pad_type = input_39_pad_type_0, strides = input_39_strides_0, weight = layers_19_mlp_gate_proj_weight_cast_fp16, x = var_7288_cast_fp16_0)[name = string("input_39_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_7305_cast_fp16 = silu(x = input_39_cast_fp16)[name = string("op_7305_cast_fp16")];
            tensor<int32, [2]> var_7311_strides_0 = const()[name = string("op_7311_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_7311_pad_type_0 = const()[name = string("op_7311_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_7311_pad_0 = const()[name = string("op_7311_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_7311_dilations_0 = const()[name = string("op_7311_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7311_groups_0 = const()[name = string("op_7311_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_7311_cast_fp16 = conv(dilations = var_7311_dilations_0, groups = var_7311_groups_0, pad = var_7311_pad_0, pad_type = var_7311_pad_type_0, strides = var_7311_strides_0, weight = layers_19_mlp_up_proj_weight_cast_fp16, x = var_7288_cast_fp16_0)[name = string("op_7311_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_199_cast_fp16 = mul(x = var_7305_cast_fp16, y = var_7311_cast_fp16)[name = string("x_199_cast_fp16")];
            tensor<int32, [2]> hidden_states_197_strides_0 = const()[name = string("hidden_states_197_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_197_pad_type_0 = const()[name = string("hidden_states_197_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_197_pad_0 = const()[name = string("hidden_states_197_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_197_dilations_0 = const()[name = string("hidden_states_197_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_197_groups_0 = const()[name = string("hidden_states_197_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_197_cast_fp16 = conv(dilations = hidden_states_197_dilations_0, groups = hidden_states_197_groups_0, pad = hidden_states_197_pad_0, pad_type = hidden_states_197_pad_type_0, strides = hidden_states_197_strides_0, weight = layers_19_mlp_down_proj_weight_cast_fp16, x = x_199_cast_fp16)[name = string("hidden_states_197_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_199_cast_fp16 = add(x = hidden_states_195_cast_fp16, y = hidden_states_197_cast_fp16)[name = string("hidden_states_199_cast_fp16")];
            fp16 const_202_promoted_to_fp16 = const()[name = string("const_202_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_7329_cast_fp16 = mul(x = hidden_states_199_cast_fp16, y = const_202_promoted_to_fp16)[name = string("op_7329_cast_fp16")];
            int32 var_7327 = const()[name = string("op_7327"), val = int32(1)];
            bool doubled_161_interleave_0 = const()[name = string("doubled_161_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_161_cast_fp16 = concat(axis = var_7327, interleave = doubled_161_interleave_0, values = (hidden_states_199_cast_fp16, var_7329_cast_fp16))[name = string("doubled_161_cast_fp16")];
            tensor<int32, [1]> out_81_axes_0 = const()[name = string("out_81_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_81_gamma_0_to_fp16 = const()[name = string("out_81_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1493109952)))];
            fp16 var_7339_to_fp16 = const()[name = string("op_7339_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_81_cast_fp16 = layer_norm(axes = out_81_axes_0, epsilon = var_7339_to_fp16, gamma = out_81_gamma_0_to_fp16, x = doubled_161_cast_fp16)[name = string("out_81_cast_fp16")];
            tensor<int32, [2]> var_7350_split_sizes_0 = const()[name = string("op_7350_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7350_axis_0 = const()[name = string("op_7350_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_7350_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_7350_cast_fp16_1 = split(axis = var_7350_axis_0, split_sizes = var_7350_split_sizes_0, x = out_81_cast_fp16)[name = string("op_7350_cast_fp16")];
            tensor<int32, [2]> query_states_121_strides_0 = const()[name = string("query_states_121_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_121_pad_type_0 = const()[name = string("query_states_121_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_121_pad_0 = const()[name = string("query_states_121_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_121_dilations_0 = const()[name = string("query_states_121_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_121_groups_0 = const()[name = string("query_states_121_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_121_cast_fp16 = conv(dilations = query_states_121_dilations_0, groups = query_states_121_groups_0, pad = query_states_121_pad_0, pad_type = query_states_121_pad_type_0, strides = query_states_121_strides_0, weight = layers_20_self_attn_q_proj_weight_cast_fp16, x = var_7350_cast_fp16_0)[name = string("query_states_121_cast_fp16")];
            tensor<int32, [2]> key_states_201_strides_0 = const()[name = string("key_states_201_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_201_pad_type_0 = const()[name = string("key_states_201_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_201_pad_0 = const()[name = string("key_states_201_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_201_dilations_0 = const()[name = string("key_states_201_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_201_groups_0 = const()[name = string("key_states_201_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_201_cast_fp16 = conv(dilations = key_states_201_dilations_0, groups = key_states_201_groups_0, pad = key_states_201_pad_0, pad_type = key_states_201_pad_type_0, strides = key_states_201_strides_0, weight = layers_20_self_attn_k_proj_weight_cast_fp16, x = var_7350_cast_fp16_0)[name = string("key_states_201_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_20_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_20_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1493118208)))];
            tensor<int32, [2]> value_states_121_strides_0 = const()[name = string("value_states_121_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_121_pad_type_0 = const()[name = string("value_states_121_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_121_pad_0 = const()[name = string("value_states_121_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_121_dilations_0 = const()[name = string("value_states_121_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_121_groups_0 = const()[name = string("value_states_121_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_121_cast_fp16 = conv(dilations = value_states_121_dilations_0, groups = value_states_121_groups_0, pad = value_states_121_pad_0, pad_type = value_states_121_pad_type_0, strides = value_states_121_strides_0, weight = layers_20_self_attn_v_proj_weight_to_fp16, x = var_7350_cast_fp16_0)[name = string("value_states_121_cast_fp16")];
            tensor<int32, [4]> concat_240x = const()[name = string("concat_240x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_201_cast_fp16 = reshape(shape = concat_240x, x = query_states_121_cast_fp16)[name = string("x_201_cast_fp16")];
            tensor<int32, [4]> concat_241x = const()[name = string("concat_241x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_7407_cast_fp16 = reshape(shape = concat_241x, x = key_states_201_cast_fp16)[name = string("op_7407_cast_fp16")];
            tensor<int32, [4]> concat_242x = const()[name = string("concat_242x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_7414_cast_fp16 = reshape(shape = concat_242x, x = value_states_121_cast_fp16)[name = string("op_7414_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_7418_cast_fp16 = mul(x = x_201_cast_fp16, y = var_859_cast_fp16)[name = string("op_7418_cast_fp16")];
            tensor<int32, [2]> var_7419_split_sizes_0 = const()[name = string("op_7419_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7419_axis_0 = const()[name = string("op_7419_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_7419_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_7419_cast_fp16_1 = split(axis = var_7419_axis_0, split_sizes = var_7419_split_sizes_0, x = x_201_cast_fp16)[name = string("op_7419_cast_fp16")];
            fp16 const_204_promoted_to_fp16 = const()[name = string("const_204_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_7421_cast_fp16 = mul(x = var_7419_cast_fp16_1, y = const_204_promoted_to_fp16)[name = string("op_7421_cast_fp16")];
            int32 var_7423 = const()[name = string("op_7423"), val = int32(-2)];
            bool var_7424_interleave_0 = const()[name = string("op_7424_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_7424_cast_fp16 = concat(axis = var_7423, interleave = var_7424_interleave_0, values = (var_7421_cast_fp16, var_7419_cast_fp16_0))[name = string("op_7424_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_7425_cast_fp16 = mul(x = var_7424_cast_fp16, y = var_866_cast_fp16)[name = string("op_7425_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_123_cast_fp16 = add(x = var_7418_cast_fp16, y = var_7425_cast_fp16)[name = string("query_states_123_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_7431_cast_fp16 = mul(x = var_7407_cast_fp16, y = var_859_cast_fp16)[name = string("op_7431_cast_fp16")];
            tensor<int32, [2]> var_7432_split_sizes_0 = const()[name = string("op_7432_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7432_axis_0 = const()[name = string("op_7432_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_7432_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_7432_cast_fp16_1 = split(axis = var_7432_axis_0, split_sizes = var_7432_split_sizes_0, x = var_7407_cast_fp16)[name = string("op_7432_cast_fp16")];
            fp16 const_205_promoted_to_fp16 = const()[name = string("const_205_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_7434_cast_fp16 = mul(x = var_7432_cast_fp16_1, y = const_205_promoted_to_fp16)[name = string("op_7434_cast_fp16")];
            int32 var_7436 = const()[name = string("op_7436"), val = int32(-2)];
            bool var_7437_interleave_0 = const()[name = string("op_7437_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_7437_cast_fp16 = concat(axis = var_7436, interleave = var_7437_interleave_0, values = (var_7434_cast_fp16, var_7432_cast_fp16_0))[name = string("op_7437_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_7438_cast_fp16 = mul(x = var_7437_cast_fp16, y = var_866_cast_fp16)[name = string("op_7438_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_205_cast_fp16 = add(x = var_7431_cast_fp16, y = var_7438_cast_fp16)[name = string("key_states_205_cast_fp16")];
            tensor<int32, [1]> expand_dims_240 = const()[name = string("expand_dims_240"), val = tensor<int32, [1]>([20])];
            tensor<int32, [1]> expand_dims_241 = const()[name = string("expand_dims_241"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_243 = const()[name = string("expand_dims_243"), val = tensor<int32, [1]>([0])];
            int32 concat_245_axis_0 = const()[name = string("concat_245_axis_0"), val = int32(0)];
            bool concat_245_interleave_0 = const()[name = string("concat_245_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_245 = concat(axis = concat_245_axis_0, interleave = concat_245_interleave_0, values = (expand_dims_240, expand_dims_241, position_id, expand_dims_243))[name = string("concat_245")];
            tensor<int32, [1]> expand_dims_244 = const()[name = string("expand_dims_244"), val = tensor<int32, [1]>([21])];
            tensor<int32, [1]> concat_246_values1_0 = const()[name = string("concat_246_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_246_values3_0 = const()[name = string("concat_246_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_246_axis_0 = const()[name = string("concat_246_axis_0"), val = int32(0)];
            bool concat_246_interleave_0 = const()[name = string("concat_246_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_246 = concat(axis = concat_246_axis_0, interleave = concat_246_interleave_0, values = (expand_dims_244, concat_246_values1_0, cache_position_end, concat_246_values3_0))[name = string("concat_246")];
            tensor<int32, [4]> key_states_207_perm_0 = const()[name = string("key_states_207_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_21_stride_0 = const()[name = string("key_cache_internal_tensor_assign_21_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_21_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_21_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_21_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_21_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_21_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_21_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_207_cast_fp16 = transpose(perm = key_states_207_perm_0, x = key_states_205_cast_fp16)[name = string("transpose_110")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_21_cast_fp16 = slice_update(begin = concat_245, begin_mask = key_cache_internal_tensor_assign_21_begin_mask_0, end = concat_246, end_mask = key_cache_internal_tensor_assign_21_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_21_squeeze_mask_0, stride = key_cache_internal_tensor_assign_21_stride_0, update = key_states_207_cast_fp16, x = coreml_update_state_94)[name = string("key_cache_internal_tensor_assign_21_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_21_cast_fp16, input = key_cache)[name = string("coreml_update_state_96_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_96 = read_state(input = key_cache)[name = string("coreml_update_state_96")];
            tensor<int32, [4]> value_states_123_perm_0 = const()[name = string("value_states_123_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_21_stride_0 = const()[name = string("value_cache_internal_tensor_assign_21_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_21_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_21_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_21_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_21_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_21_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_21_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_123_cast_fp16 = transpose(perm = value_states_123_perm_0, x = var_7414_cast_fp16)[name = string("transpose_109")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_21_cast_fp16 = slice_update(begin = concat_245, begin_mask = value_cache_internal_tensor_assign_21_begin_mask_0, end = concat_246, end_mask = value_cache_internal_tensor_assign_21_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_21_squeeze_mask_0, stride = value_cache_internal_tensor_assign_21_stride_0, update = value_states_123_cast_fp16, x = coreml_update_state_95)[name = string("value_cache_internal_tensor_assign_21_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_21_cast_fp16, input = value_cache)[name = string("coreml_update_state_97_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_97 = read_state(input = value_cache)[name = string("coreml_update_state_97")];
            tensor<int32, [4]> var_7508_begin_0 = const()[name = string("op_7508_begin_0"), val = tensor<int32, [4]>([20, 0, 0, 0])];
            tensor<int32, [4]> var_7508_end_0 = const()[name = string("op_7508_end_0"), val = tensor<int32, [4]>([21, 2, 2048, 128])];
            tensor<bool, [4]> var_7508_end_mask_0 = const()[name = string("op_7508_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7508_cast_fp16 = slice_by_index(begin = var_7508_begin_0, end = var_7508_end_0, end_mask = var_7508_end_mask_0, x = coreml_update_state_96)[name = string("op_7508_cast_fp16")];
            tensor<int32, [2]> tile_40 = const()[name = string("tile_40"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7511_axis_0 = const()[name = string("op_7511_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7511_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7511_cast_fp16_1 = split(axis = var_7511_axis_0, split_sizes = tile_40, x = var_7508_cast_fp16)[name = string("op_7511_cast_fp16")];
            tensor<int32, [4]> var_7518_begin_0 = const()[name = string("op_7518_begin_0"), val = tensor<int32, [4]>([20, 0, 0, 0])];
            tensor<int32, [4]> var_7518_end_0 = const()[name = string("op_7518_end_0"), val = tensor<int32, [4]>([21, 2, 2048, 128])];
            tensor<bool, [4]> var_7518_end_mask_0 = const()[name = string("op_7518_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7518_cast_fp16 = slice_by_index(begin = var_7518_begin_0, end = var_7518_end_0, end_mask = var_7518_end_mask_0, x = coreml_update_state_97)[name = string("op_7518_cast_fp16")];
            tensor<int32, [2]> tile_41 = const()[name = string("tile_41"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7521_axis_0 = const()[name = string("op_7521_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7521_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7521_cast_fp16_1 = split(axis = var_7521_axis_0, split_sizes = tile_41, x = var_7518_cast_fp16)[name = string("op_7521_cast_fp16")];
            tensor<int32, [2]> var_7524_split_sizes_0 = const()[name = string("op_7524_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_7524_axis_0 = const()[name = string("op_7524_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_7524_0, tensor<fp16, [1, 8, 128, 1]> var_7524_1 = split(axis = var_7524_axis_0, split_sizes = var_7524_split_sizes_0, x = query_states_123_cast_fp16)[name = string("op_7524")];
            bool attn_weights_321_transpose_x_0 = const()[name = string("attn_weights_321_transpose_x_0"), val = bool(false)];
            bool attn_weights_321_transpose_y_0 = const()[name = string("attn_weights_321_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_321_cast_fp16 = matmul(transpose_x = attn_weights_321_transpose_x_0, transpose_y = attn_weights_321_transpose_y_0, x = var_7511_cast_fp16_0, y = var_7524_0)[name = string("attn_weights_321_cast_fp16")];
            fp16 var_7527_to_fp16 = const()[name = string("op_7527_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_323_cast_fp16 = mul(x = attn_weights_321_cast_fp16, y = var_7527_to_fp16)[name = string("attn_weights_323_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_325_cast_fp16 = add(x = attn_weights_323_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_325_cast_fp16")];
            int32 var_7531 = const()[name = string("op_7531"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_327_cast_fp16 = softmax(axis = var_7531, x = attn_weights_325_cast_fp16)[name = string("attn_weights_327_cast_fp16")];
            bool var_7537_transpose_x_1 = const()[name = string("op_7537_transpose_x_1"), val = bool(true)];
            bool var_7537_transpose_y_1 = const()[name = string("op_7537_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_7537_cast_fp16 = matmul(transpose_x = var_7537_transpose_x_1, transpose_y = var_7537_transpose_y_1, x = attn_weights_327_cast_fp16, y = var_7521_cast_fp16_0)[name = string("op_7537_cast_fp16")];
            bool attn_weights_329_transpose_x_0 = const()[name = string("attn_weights_329_transpose_x_0"), val = bool(false)];
            bool attn_weights_329_transpose_y_0 = const()[name = string("attn_weights_329_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_329_cast_fp16 = matmul(transpose_x = attn_weights_329_transpose_x_0, transpose_y = attn_weights_329_transpose_y_0, x = var_7511_cast_fp16_1, y = var_7524_1)[name = string("attn_weights_329_cast_fp16")];
            fp16 var_7539_to_fp16 = const()[name = string("op_7539_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_331_cast_fp16 = mul(x = attn_weights_329_cast_fp16, y = var_7539_to_fp16)[name = string("attn_weights_331_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_333_cast_fp16 = add(x = attn_weights_331_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_333_cast_fp16")];
            int32 var_7543 = const()[name = string("op_7543"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_335_cast_fp16 = softmax(axis = var_7543, x = attn_weights_333_cast_fp16)[name = string("attn_weights_335_cast_fp16")];
            bool attn_output_161_transpose_x_1 = const()[name = string("attn_output_161_transpose_x_1"), val = bool(true)];
            bool attn_output_161_transpose_y_1 = const()[name = string("attn_output_161_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_161_cast_fp16 = matmul(transpose_x = attn_output_161_transpose_x_1, transpose_y = attn_output_161_transpose_y_1, x = attn_weights_335_cast_fp16, y = var_7521_cast_fp16_1)[name = string("attn_output_161_cast_fp16")];
            int32 var_7551 = const()[name = string("op_7551"), val = int32(1)];
            bool attn_output_163_interleave_0 = const()[name = string("attn_output_163_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_163_cast_fp16 = concat(axis = var_7551, interleave = attn_output_163_interleave_0, values = (var_7537_cast_fp16, attn_output_161_cast_fp16))[name = string("attn_output_163_cast_fp16")];
            tensor<int32, [4]> var_7555_perm_0 = const()[name = string("op_7555_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_251x = const()[name = string("concat_251x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_7555_cast_fp16 = transpose(perm = var_7555_perm_0, x = attn_output_163_cast_fp16)[name = string("transpose_108")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_167_cast_fp16 = reshape(shape = concat_251x, x = var_7555_cast_fp16)[name = string("attn_output_167_cast_fp16")];
            tensor<int32, [2]> hidden_states_203_strides_0 = const()[name = string("hidden_states_203_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_203_pad_type_0 = const()[name = string("hidden_states_203_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_203_pad_0 = const()[name = string("hidden_states_203_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_203_dilations_0 = const()[name = string("hidden_states_203_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_203_groups_0 = const()[name = string("hidden_states_203_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_203_cast_fp16 = conv(dilations = hidden_states_203_dilations_0, groups = hidden_states_203_groups_0, pad = hidden_states_203_pad_0, pad_type = hidden_states_203_pad_type_0, strides = hidden_states_203_strides_0, weight = layers_20_self_attn_o_proj_weight_cast_fp16, x = attn_output_167_cast_fp16)[name = string("hidden_states_203_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_205_cast_fp16 = add(x = hidden_states_199_cast_fp16, y = hidden_states_203_cast_fp16)[name = string("hidden_states_205_cast_fp16")];
            fp16 const_210_promoted_to_fp16 = const()[name = string("const_210_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_7588_cast_fp16 = mul(x = hidden_states_205_cast_fp16, y = const_210_promoted_to_fp16)[name = string("op_7588_cast_fp16")];
            int32 var_7586 = const()[name = string("op_7586"), val = int32(1)];
            bool doubled_165_interleave_0 = const()[name = string("doubled_165_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_165_cast_fp16 = concat(axis = var_7586, interleave = doubled_165_interleave_0, values = (hidden_states_205_cast_fp16, var_7588_cast_fp16))[name = string("doubled_165_cast_fp16")];
            tensor<int32, [1]> out_83_axes_0 = const()[name = string("out_83_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_83_gamma_0_to_fp16 = const()[name = string("out_83_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1494166848)))];
            fp16 var_7598_to_fp16 = const()[name = string("op_7598_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_83_cast_fp16 = layer_norm(axes = out_83_axes_0, epsilon = var_7598_to_fp16, gamma = out_83_gamma_0_to_fp16, x = doubled_165_cast_fp16)[name = string("out_83_cast_fp16")];
            tensor<int32, [2]> var_7609_split_sizes_0 = const()[name = string("op_7609_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7609_axis_0 = const()[name = string("op_7609_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_7609_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_7609_cast_fp16_1 = split(axis = var_7609_axis_0, split_sizes = var_7609_split_sizes_0, x = out_83_cast_fp16)[name = string("op_7609_cast_fp16")];
            tensor<int32, [2]> input_41_strides_0 = const()[name = string("input_41_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_41_pad_type_0 = const()[name = string("input_41_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_41_pad_0 = const()[name = string("input_41_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_41_dilations_0 = const()[name = string("input_41_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_41_groups_0 = const()[name = string("input_41_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_41_cast_fp16 = conv(dilations = input_41_dilations_0, groups = input_41_groups_0, pad = input_41_pad_0, pad_type = input_41_pad_type_0, strides = input_41_strides_0, weight = layers_20_mlp_gate_proj_weight_cast_fp16, x = var_7609_cast_fp16_0)[name = string("input_41_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_7626_cast_fp16 = silu(x = input_41_cast_fp16)[name = string("op_7626_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_20_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_20_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1494175104)))];
            tensor<int32, [2]> var_7632_strides_0 = const()[name = string("op_7632_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_7632_pad_type_0 = const()[name = string("op_7632_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_7632_pad_0 = const()[name = string("op_7632_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_7632_dilations_0 = const()[name = string("op_7632_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7632_groups_0 = const()[name = string("op_7632_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_7632_cast_fp16 = conv(dilations = var_7632_dilations_0, groups = var_7632_groups_0, pad = var_7632_pad_0, pad_type = var_7632_pad_type_0, strides = var_7632_strides_0, weight = layers_20_mlp_up_proj_weight_to_fp16, x = var_7609_cast_fp16_0)[name = string("op_7632_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_209_cast_fp16 = mul(x = var_7626_cast_fp16, y = var_7632_cast_fp16)[name = string("x_209_cast_fp16")];
            tensor<int32, [2]> hidden_states_207_strides_0 = const()[name = string("hidden_states_207_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_207_pad_type_0 = const()[name = string("hidden_states_207_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_207_pad_0 = const()[name = string("hidden_states_207_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_207_dilations_0 = const()[name = string("hidden_states_207_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_207_groups_0 = const()[name = string("hidden_states_207_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_207_cast_fp16 = conv(dilations = hidden_states_207_dilations_0, groups = hidden_states_207_groups_0, pad = hidden_states_207_pad_0, pad_type = hidden_states_207_pad_type_0, strides = hidden_states_207_strides_0, weight = layers_20_mlp_down_proj_weight_cast_fp16, x = x_209_cast_fp16)[name = string("hidden_states_207_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_209_cast_fp16 = add(x = hidden_states_205_cast_fp16, y = hidden_states_207_cast_fp16)[name = string("hidden_states_209_cast_fp16")];
            fp16 const_212_promoted_to_fp16 = const()[name = string("const_212_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_7650_cast_fp16 = mul(x = hidden_states_209_cast_fp16, y = const_212_promoted_to_fp16)[name = string("op_7650_cast_fp16")];
            int32 var_7648 = const()[name = string("op_7648"), val = int32(1)];
            bool doubled_169_interleave_0 = const()[name = string("doubled_169_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_169_cast_fp16 = concat(axis = var_7648, interleave = doubled_169_interleave_0, values = (hidden_states_209_cast_fp16, var_7650_cast_fp16))[name = string("doubled_169_cast_fp16")];
            tensor<int32, [1]> out_85_axes_0 = const()[name = string("out_85_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_85_gamma_0_to_fp16 = const()[name = string("out_85_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1519340992)))];
            fp16 var_7660_to_fp16 = const()[name = string("op_7660_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_85_cast_fp16 = layer_norm(axes = out_85_axes_0, epsilon = var_7660_to_fp16, gamma = out_85_gamma_0_to_fp16, x = doubled_169_cast_fp16)[name = string("out_85_cast_fp16")];
            tensor<int32, [2]> var_7671_split_sizes_0 = const()[name = string("op_7671_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7671_axis_0 = const()[name = string("op_7671_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_7671_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_7671_cast_fp16_1 = split(axis = var_7671_axis_0, split_sizes = var_7671_split_sizes_0, x = out_85_cast_fp16)[name = string("op_7671_cast_fp16")];
            tensor<int32, [2]> query_states_127_strides_0 = const()[name = string("query_states_127_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_127_pad_type_0 = const()[name = string("query_states_127_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_127_pad_0 = const()[name = string("query_states_127_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_127_dilations_0 = const()[name = string("query_states_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_127_groups_0 = const()[name = string("query_states_127_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_127_cast_fp16 = conv(dilations = query_states_127_dilations_0, groups = query_states_127_groups_0, pad = query_states_127_pad_0, pad_type = query_states_127_pad_type_0, strides = query_states_127_strides_0, weight = layers_21_self_attn_q_proj_weight_cast_fp16, x = var_7671_cast_fp16_0)[name = string("query_states_127_cast_fp16")];
            tensor<int32, [2]> key_states_211_strides_0 = const()[name = string("key_states_211_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_211_pad_type_0 = const()[name = string("key_states_211_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_211_pad_0 = const()[name = string("key_states_211_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_211_dilations_0 = const()[name = string("key_states_211_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_211_groups_0 = const()[name = string("key_states_211_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_211_cast_fp16 = conv(dilations = key_states_211_dilations_0, groups = key_states_211_groups_0, pad = key_states_211_pad_0, pad_type = key_states_211_pad_type_0, strides = key_states_211_strides_0, weight = layers_21_self_attn_k_proj_weight_cast_fp16, x = var_7671_cast_fp16_0)[name = string("key_states_211_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_21_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_21_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1519349248)))];
            tensor<int32, [2]> value_states_127_strides_0 = const()[name = string("value_states_127_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_127_pad_type_0 = const()[name = string("value_states_127_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_127_pad_0 = const()[name = string("value_states_127_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_127_dilations_0 = const()[name = string("value_states_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_127_groups_0 = const()[name = string("value_states_127_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_127_cast_fp16 = conv(dilations = value_states_127_dilations_0, groups = value_states_127_groups_0, pad = value_states_127_pad_0, pad_type = value_states_127_pad_type_0, strides = value_states_127_strides_0, weight = layers_21_self_attn_v_proj_weight_to_fp16, x = var_7671_cast_fp16_0)[name = string("value_states_127_cast_fp16")];
            tensor<int32, [4]> concat_252x = const()[name = string("concat_252x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_211_cast_fp16 = reshape(shape = concat_252x, x = query_states_127_cast_fp16)[name = string("x_211_cast_fp16")];
            tensor<int32, [4]> concat_253x = const()[name = string("concat_253x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_7728_cast_fp16 = reshape(shape = concat_253x, x = key_states_211_cast_fp16)[name = string("op_7728_cast_fp16")];
            tensor<int32, [4]> concat_254x = const()[name = string("concat_254x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_7735_cast_fp16 = reshape(shape = concat_254x, x = value_states_127_cast_fp16)[name = string("op_7735_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_7739_cast_fp16 = mul(x = x_211_cast_fp16, y = var_859_cast_fp16)[name = string("op_7739_cast_fp16")];
            tensor<int32, [2]> var_7740_split_sizes_0 = const()[name = string("op_7740_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7740_axis_0 = const()[name = string("op_7740_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_7740_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_7740_cast_fp16_1 = split(axis = var_7740_axis_0, split_sizes = var_7740_split_sizes_0, x = x_211_cast_fp16)[name = string("op_7740_cast_fp16")];
            fp16 const_214_promoted_to_fp16 = const()[name = string("const_214_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_7742_cast_fp16 = mul(x = var_7740_cast_fp16_1, y = const_214_promoted_to_fp16)[name = string("op_7742_cast_fp16")];
            int32 var_7744 = const()[name = string("op_7744"), val = int32(-2)];
            bool var_7745_interleave_0 = const()[name = string("op_7745_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_7745_cast_fp16 = concat(axis = var_7744, interleave = var_7745_interleave_0, values = (var_7742_cast_fp16, var_7740_cast_fp16_0))[name = string("op_7745_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_7746_cast_fp16 = mul(x = var_7745_cast_fp16, y = var_866_cast_fp16)[name = string("op_7746_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_129_cast_fp16 = add(x = var_7739_cast_fp16, y = var_7746_cast_fp16)[name = string("query_states_129_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_7752_cast_fp16 = mul(x = var_7728_cast_fp16, y = var_859_cast_fp16)[name = string("op_7752_cast_fp16")];
            tensor<int32, [2]> var_7753_split_sizes_0 = const()[name = string("op_7753_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7753_axis_0 = const()[name = string("op_7753_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_7753_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_7753_cast_fp16_1 = split(axis = var_7753_axis_0, split_sizes = var_7753_split_sizes_0, x = var_7728_cast_fp16)[name = string("op_7753_cast_fp16")];
            fp16 const_215_promoted_to_fp16 = const()[name = string("const_215_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_7755_cast_fp16 = mul(x = var_7753_cast_fp16_1, y = const_215_promoted_to_fp16)[name = string("op_7755_cast_fp16")];
            int32 var_7757 = const()[name = string("op_7757"), val = int32(-2)];
            bool var_7758_interleave_0 = const()[name = string("op_7758_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_7758_cast_fp16 = concat(axis = var_7757, interleave = var_7758_interleave_0, values = (var_7755_cast_fp16, var_7753_cast_fp16_0))[name = string("op_7758_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_7759_cast_fp16 = mul(x = var_7758_cast_fp16, y = var_866_cast_fp16)[name = string("op_7759_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_215_cast_fp16 = add(x = var_7752_cast_fp16, y = var_7759_cast_fp16)[name = string("key_states_215_cast_fp16")];
            tensor<int32, [1]> expand_dims_252 = const()[name = string("expand_dims_252"), val = tensor<int32, [1]>([21])];
            tensor<int32, [1]> expand_dims_253 = const()[name = string("expand_dims_253"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_255 = const()[name = string("expand_dims_255"), val = tensor<int32, [1]>([0])];
            int32 concat_257_axis_0 = const()[name = string("concat_257_axis_0"), val = int32(0)];
            bool concat_257_interleave_0 = const()[name = string("concat_257_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_257 = concat(axis = concat_257_axis_0, interleave = concat_257_interleave_0, values = (expand_dims_252, expand_dims_253, position_id, expand_dims_255))[name = string("concat_257")];
            tensor<int32, [1]> expand_dims_256 = const()[name = string("expand_dims_256"), val = tensor<int32, [1]>([22])];
            tensor<int32, [1]> concat_258_values1_0 = const()[name = string("concat_258_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_258_values3_0 = const()[name = string("concat_258_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_258_axis_0 = const()[name = string("concat_258_axis_0"), val = int32(0)];
            bool concat_258_interleave_0 = const()[name = string("concat_258_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_258 = concat(axis = concat_258_axis_0, interleave = concat_258_interleave_0, values = (expand_dims_256, concat_258_values1_0, cache_position_end, concat_258_values3_0))[name = string("concat_258")];
            tensor<int32, [4]> key_states_217_perm_0 = const()[name = string("key_states_217_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_22_stride_0 = const()[name = string("key_cache_internal_tensor_assign_22_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_22_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_22_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_22_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_22_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_22_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_22_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_217_cast_fp16 = transpose(perm = key_states_217_perm_0, x = key_states_215_cast_fp16)[name = string("transpose_107")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_22_cast_fp16 = slice_update(begin = concat_257, begin_mask = key_cache_internal_tensor_assign_22_begin_mask_0, end = concat_258, end_mask = key_cache_internal_tensor_assign_22_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_22_squeeze_mask_0, stride = key_cache_internal_tensor_assign_22_stride_0, update = key_states_217_cast_fp16, x = coreml_update_state_96)[name = string("key_cache_internal_tensor_assign_22_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_22_cast_fp16, input = key_cache)[name = string("coreml_update_state_98_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_98 = read_state(input = key_cache)[name = string("coreml_update_state_98")];
            tensor<int32, [4]> value_states_129_perm_0 = const()[name = string("value_states_129_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_22_stride_0 = const()[name = string("value_cache_internal_tensor_assign_22_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_22_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_22_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_22_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_22_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_22_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_22_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_129_cast_fp16 = transpose(perm = value_states_129_perm_0, x = var_7735_cast_fp16)[name = string("transpose_106")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_22_cast_fp16 = slice_update(begin = concat_257, begin_mask = value_cache_internal_tensor_assign_22_begin_mask_0, end = concat_258, end_mask = value_cache_internal_tensor_assign_22_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_22_squeeze_mask_0, stride = value_cache_internal_tensor_assign_22_stride_0, update = value_states_129_cast_fp16, x = coreml_update_state_97)[name = string("value_cache_internal_tensor_assign_22_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_22_cast_fp16, input = value_cache)[name = string("coreml_update_state_99_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_99 = read_state(input = value_cache)[name = string("coreml_update_state_99")];
            tensor<int32, [4]> var_7829_begin_0 = const()[name = string("op_7829_begin_0"), val = tensor<int32, [4]>([21, 0, 0, 0])];
            tensor<int32, [4]> var_7829_end_0 = const()[name = string("op_7829_end_0"), val = tensor<int32, [4]>([22, 2, 2048, 128])];
            tensor<bool, [4]> var_7829_end_mask_0 = const()[name = string("op_7829_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7829_cast_fp16 = slice_by_index(begin = var_7829_begin_0, end = var_7829_end_0, end_mask = var_7829_end_mask_0, x = coreml_update_state_98)[name = string("op_7829_cast_fp16")];
            tensor<int32, [2]> tile_42 = const()[name = string("tile_42"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7832_axis_0 = const()[name = string("op_7832_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7832_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7832_cast_fp16_1 = split(axis = var_7832_axis_0, split_sizes = tile_42, x = var_7829_cast_fp16)[name = string("op_7832_cast_fp16")];
            tensor<int32, [4]> var_7839_begin_0 = const()[name = string("op_7839_begin_0"), val = tensor<int32, [4]>([21, 0, 0, 0])];
            tensor<int32, [4]> var_7839_end_0 = const()[name = string("op_7839_end_0"), val = tensor<int32, [4]>([22, 2, 2048, 128])];
            tensor<bool, [4]> var_7839_end_mask_0 = const()[name = string("op_7839_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7839_cast_fp16 = slice_by_index(begin = var_7839_begin_0, end = var_7839_end_0, end_mask = var_7839_end_mask_0, x = coreml_update_state_99)[name = string("op_7839_cast_fp16")];
            tensor<int32, [2]> tile_43 = const()[name = string("tile_43"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7842_axis_0 = const()[name = string("op_7842_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7842_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7842_cast_fp16_1 = split(axis = var_7842_axis_0, split_sizes = tile_43, x = var_7839_cast_fp16)[name = string("op_7842_cast_fp16")];
            tensor<int32, [2]> var_7845_split_sizes_0 = const()[name = string("op_7845_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_7845_axis_0 = const()[name = string("op_7845_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_7845_0, tensor<fp16, [1, 8, 128, 1]> var_7845_1 = split(axis = var_7845_axis_0, split_sizes = var_7845_split_sizes_0, x = query_states_129_cast_fp16)[name = string("op_7845")];
            bool attn_weights_337_transpose_x_0 = const()[name = string("attn_weights_337_transpose_x_0"), val = bool(false)];
            bool attn_weights_337_transpose_y_0 = const()[name = string("attn_weights_337_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_337_cast_fp16 = matmul(transpose_x = attn_weights_337_transpose_x_0, transpose_y = attn_weights_337_transpose_y_0, x = var_7832_cast_fp16_0, y = var_7845_0)[name = string("attn_weights_337_cast_fp16")];
            fp16 var_7848_to_fp16 = const()[name = string("op_7848_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_339_cast_fp16 = mul(x = attn_weights_337_cast_fp16, y = var_7848_to_fp16)[name = string("attn_weights_339_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_341_cast_fp16 = add(x = attn_weights_339_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_341_cast_fp16")];
            int32 var_7852 = const()[name = string("op_7852"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_343_cast_fp16 = softmax(axis = var_7852, x = attn_weights_341_cast_fp16)[name = string("attn_weights_343_cast_fp16")];
            bool var_7858_transpose_x_1 = const()[name = string("op_7858_transpose_x_1"), val = bool(true)];
            bool var_7858_transpose_y_1 = const()[name = string("op_7858_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_7858_cast_fp16 = matmul(transpose_x = var_7858_transpose_x_1, transpose_y = var_7858_transpose_y_1, x = attn_weights_343_cast_fp16, y = var_7842_cast_fp16_0)[name = string("op_7858_cast_fp16")];
            bool attn_weights_345_transpose_x_0 = const()[name = string("attn_weights_345_transpose_x_0"), val = bool(false)];
            bool attn_weights_345_transpose_y_0 = const()[name = string("attn_weights_345_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_345_cast_fp16 = matmul(transpose_x = attn_weights_345_transpose_x_0, transpose_y = attn_weights_345_transpose_y_0, x = var_7832_cast_fp16_1, y = var_7845_1)[name = string("attn_weights_345_cast_fp16")];
            fp16 var_7860_to_fp16 = const()[name = string("op_7860_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_347_cast_fp16 = mul(x = attn_weights_345_cast_fp16, y = var_7860_to_fp16)[name = string("attn_weights_347_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_349_cast_fp16 = add(x = attn_weights_347_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_349_cast_fp16")];
            int32 var_7864 = const()[name = string("op_7864"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_351_cast_fp16 = softmax(axis = var_7864, x = attn_weights_349_cast_fp16)[name = string("attn_weights_351_cast_fp16")];
            bool attn_output_169_transpose_x_1 = const()[name = string("attn_output_169_transpose_x_1"), val = bool(true)];
            bool attn_output_169_transpose_y_1 = const()[name = string("attn_output_169_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_169_cast_fp16 = matmul(transpose_x = attn_output_169_transpose_x_1, transpose_y = attn_output_169_transpose_y_1, x = attn_weights_351_cast_fp16, y = var_7842_cast_fp16_1)[name = string("attn_output_169_cast_fp16")];
            int32 var_7872 = const()[name = string("op_7872"), val = int32(1)];
            bool attn_output_171_interleave_0 = const()[name = string("attn_output_171_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_171_cast_fp16 = concat(axis = var_7872, interleave = attn_output_171_interleave_0, values = (var_7858_cast_fp16, attn_output_169_cast_fp16))[name = string("attn_output_171_cast_fp16")];
            tensor<int32, [4]> var_7876_perm_0 = const()[name = string("op_7876_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_263x = const()[name = string("concat_263x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_7876_cast_fp16 = transpose(perm = var_7876_perm_0, x = attn_output_171_cast_fp16)[name = string("transpose_105")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_175_cast_fp16 = reshape(shape = concat_263x, x = var_7876_cast_fp16)[name = string("attn_output_175_cast_fp16")];
            tensor<int32, [2]> hidden_states_213_strides_0 = const()[name = string("hidden_states_213_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_213_pad_type_0 = const()[name = string("hidden_states_213_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_213_pad_0 = const()[name = string("hidden_states_213_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_213_dilations_0 = const()[name = string("hidden_states_213_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_213_groups_0 = const()[name = string("hidden_states_213_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_213_cast_fp16 = conv(dilations = hidden_states_213_dilations_0, groups = hidden_states_213_groups_0, pad = hidden_states_213_pad_0, pad_type = hidden_states_213_pad_type_0, strides = hidden_states_213_strides_0, weight = layers_21_self_attn_o_proj_weight_cast_fp16, x = attn_output_175_cast_fp16)[name = string("hidden_states_213_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_215_cast_fp16 = add(x = hidden_states_209_cast_fp16, y = hidden_states_213_cast_fp16)[name = string("hidden_states_215_cast_fp16")];
            fp16 const_220_promoted_to_fp16 = const()[name = string("const_220_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_7909_cast_fp16 = mul(x = hidden_states_215_cast_fp16, y = const_220_promoted_to_fp16)[name = string("op_7909_cast_fp16")];
            int32 var_7907 = const()[name = string("op_7907"), val = int32(1)];
            bool doubled_173_interleave_0 = const()[name = string("doubled_173_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_173_cast_fp16 = concat(axis = var_7907, interleave = doubled_173_interleave_0, values = (hidden_states_215_cast_fp16, var_7909_cast_fp16))[name = string("doubled_173_cast_fp16")];
            tensor<int32, [1]> out_87_axes_0 = const()[name = string("out_87_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_87_gamma_0_to_fp16 = const()[name = string("out_87_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1520397888)))];
            fp16 var_7919_to_fp16 = const()[name = string("op_7919_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_87_cast_fp16 = layer_norm(axes = out_87_axes_0, epsilon = var_7919_to_fp16, gamma = out_87_gamma_0_to_fp16, x = doubled_173_cast_fp16)[name = string("out_87_cast_fp16")];
            tensor<int32, [2]> var_7930_split_sizes_0 = const()[name = string("op_7930_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7930_axis_0 = const()[name = string("op_7930_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_7930_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_7930_cast_fp16_1 = split(axis = var_7930_axis_0, split_sizes = var_7930_split_sizes_0, x = out_87_cast_fp16)[name = string("op_7930_cast_fp16")];
            tensor<int32, [2]> input_43_strides_0 = const()[name = string("input_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_43_pad_type_0 = const()[name = string("input_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_43_pad_0 = const()[name = string("input_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_43_dilations_0 = const()[name = string("input_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_43_groups_0 = const()[name = string("input_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_43_cast_fp16 = conv(dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = layers_21_mlp_gate_proj_weight_cast_fp16, x = var_7930_cast_fp16_0)[name = string("input_43_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_7947_cast_fp16 = silu(x = input_43_cast_fp16)[name = string("op_7947_cast_fp16")];
            tensor<int32, [2]> var_7953_strides_0 = const()[name = string("op_7953_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_7953_pad_type_0 = const()[name = string("op_7953_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_7953_pad_0 = const()[name = string("op_7953_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_7953_dilations_0 = const()[name = string("op_7953_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7953_groups_0 = const()[name = string("op_7953_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_7953_cast_fp16 = conv(dilations = var_7953_dilations_0, groups = var_7953_groups_0, pad = var_7953_pad_0, pad_type = var_7953_pad_type_0, strides = var_7953_strides_0, weight = layers_21_mlp_up_proj_weight_cast_fp16, x = var_7930_cast_fp16_0)[name = string("op_7953_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_219_cast_fp16 = mul(x = var_7947_cast_fp16, y = var_7953_cast_fp16)[name = string("x_219_cast_fp16")];
            tensor<int32, [2]> hidden_states_217_strides_0 = const()[name = string("hidden_states_217_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_217_pad_type_0 = const()[name = string("hidden_states_217_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_217_pad_0 = const()[name = string("hidden_states_217_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_217_dilations_0 = const()[name = string("hidden_states_217_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_217_groups_0 = const()[name = string("hidden_states_217_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_217_cast_fp16 = conv(dilations = hidden_states_217_dilations_0, groups = hidden_states_217_groups_0, pad = hidden_states_217_pad_0, pad_type = hidden_states_217_pad_type_0, strides = hidden_states_217_strides_0, weight = layers_21_mlp_down_proj_weight_cast_fp16, x = x_219_cast_fp16)[name = string("hidden_states_217_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_219_cast_fp16 = add(x = hidden_states_215_cast_fp16, y = hidden_states_217_cast_fp16)[name = string("hidden_states_219_cast_fp16")];
            fp16 const_222_promoted_to_fp16 = const()[name = string("const_222_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_7971_cast_fp16 = mul(x = hidden_states_219_cast_fp16, y = const_222_promoted_to_fp16)[name = string("op_7971_cast_fp16")];
            int32 var_7969 = const()[name = string("op_7969"), val = int32(1)];
            bool doubled_177_interleave_0 = const()[name = string("doubled_177_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_177_cast_fp16 = concat(axis = var_7969, interleave = doubled_177_interleave_0, values = (hidden_states_219_cast_fp16, var_7971_cast_fp16))[name = string("doubled_177_cast_fp16")];
            tensor<int32, [1]> out_89_axes_0 = const()[name = string("out_89_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_89_gamma_0_to_fp16 = const()[name = string("out_89_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1520406144)))];
            fp16 var_7981_to_fp16 = const()[name = string("op_7981_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_89_cast_fp16 = layer_norm(axes = out_89_axes_0, epsilon = var_7981_to_fp16, gamma = out_89_gamma_0_to_fp16, x = doubled_177_cast_fp16)[name = string("out_89_cast_fp16")];
            tensor<int32, [2]> var_7992_split_sizes_0 = const()[name = string("op_7992_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7992_axis_0 = const()[name = string("op_7992_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_7992_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_7992_cast_fp16_1 = split(axis = var_7992_axis_0, split_sizes = var_7992_split_sizes_0, x = out_89_cast_fp16)[name = string("op_7992_cast_fp16")];
            tensor<int32, [2]> query_states_133_strides_0 = const()[name = string("query_states_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_133_pad_type_0 = const()[name = string("query_states_133_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_133_pad_0 = const()[name = string("query_states_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_133_dilations_0 = const()[name = string("query_states_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_133_groups_0 = const()[name = string("query_states_133_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_133_cast_fp16 = conv(dilations = query_states_133_dilations_0, groups = query_states_133_groups_0, pad = query_states_133_pad_0, pad_type = query_states_133_pad_type_0, strides = query_states_133_strides_0, weight = layers_22_self_attn_q_proj_weight_cast_fp16, x = var_7992_cast_fp16_0)[name = string("query_states_133_cast_fp16")];
            tensor<int32, [2]> key_states_221_strides_0 = const()[name = string("key_states_221_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_221_pad_type_0 = const()[name = string("key_states_221_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_221_pad_0 = const()[name = string("key_states_221_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_221_dilations_0 = const()[name = string("key_states_221_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_221_groups_0 = const()[name = string("key_states_221_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_221_cast_fp16 = conv(dilations = key_states_221_dilations_0, groups = key_states_221_groups_0, pad = key_states_221_pad_0, pad_type = key_states_221_pad_type_0, strides = key_states_221_strides_0, weight = layers_22_self_attn_k_proj_weight_cast_fp16, x = var_7992_cast_fp16_0)[name = string("key_states_221_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_22_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_22_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1520414400)))];
            tensor<int32, [2]> value_states_133_strides_0 = const()[name = string("value_states_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_133_pad_type_0 = const()[name = string("value_states_133_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_133_pad_0 = const()[name = string("value_states_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_133_dilations_0 = const()[name = string("value_states_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_133_groups_0 = const()[name = string("value_states_133_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_133_cast_fp16 = conv(dilations = value_states_133_dilations_0, groups = value_states_133_groups_0, pad = value_states_133_pad_0, pad_type = value_states_133_pad_type_0, strides = value_states_133_strides_0, weight = layers_22_self_attn_v_proj_weight_to_fp16, x = var_7992_cast_fp16_0)[name = string("value_states_133_cast_fp16")];
            tensor<int32, [4]> concat_264x = const()[name = string("concat_264x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_221_cast_fp16 = reshape(shape = concat_264x, x = query_states_133_cast_fp16)[name = string("x_221_cast_fp16")];
            tensor<int32, [4]> concat_265x = const()[name = string("concat_265x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_8049_cast_fp16 = reshape(shape = concat_265x, x = key_states_221_cast_fp16)[name = string("op_8049_cast_fp16")];
            tensor<int32, [4]> concat_266x = const()[name = string("concat_266x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_8056_cast_fp16 = reshape(shape = concat_266x, x = value_states_133_cast_fp16)[name = string("op_8056_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_8060_cast_fp16 = mul(x = x_221_cast_fp16, y = var_859_cast_fp16)[name = string("op_8060_cast_fp16")];
            tensor<int32, [2]> var_8061_split_sizes_0 = const()[name = string("op_8061_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8061_axis_0 = const()[name = string("op_8061_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_8061_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_8061_cast_fp16_1 = split(axis = var_8061_axis_0, split_sizes = var_8061_split_sizes_0, x = x_221_cast_fp16)[name = string("op_8061_cast_fp16")];
            fp16 const_224_promoted_to_fp16 = const()[name = string("const_224_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_8063_cast_fp16 = mul(x = var_8061_cast_fp16_1, y = const_224_promoted_to_fp16)[name = string("op_8063_cast_fp16")];
            int32 var_8065 = const()[name = string("op_8065"), val = int32(-2)];
            bool var_8066_interleave_0 = const()[name = string("op_8066_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_8066_cast_fp16 = concat(axis = var_8065, interleave = var_8066_interleave_0, values = (var_8063_cast_fp16, var_8061_cast_fp16_0))[name = string("op_8066_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_8067_cast_fp16 = mul(x = var_8066_cast_fp16, y = var_866_cast_fp16)[name = string("op_8067_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_135_cast_fp16 = add(x = var_8060_cast_fp16, y = var_8067_cast_fp16)[name = string("query_states_135_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_8073_cast_fp16 = mul(x = var_8049_cast_fp16, y = var_859_cast_fp16)[name = string("op_8073_cast_fp16")];
            tensor<int32, [2]> var_8074_split_sizes_0 = const()[name = string("op_8074_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8074_axis_0 = const()[name = string("op_8074_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_8074_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_8074_cast_fp16_1 = split(axis = var_8074_axis_0, split_sizes = var_8074_split_sizes_0, x = var_8049_cast_fp16)[name = string("op_8074_cast_fp16")];
            fp16 const_225_promoted_to_fp16 = const()[name = string("const_225_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_8076_cast_fp16 = mul(x = var_8074_cast_fp16_1, y = const_225_promoted_to_fp16)[name = string("op_8076_cast_fp16")];
            int32 var_8078 = const()[name = string("op_8078"), val = int32(-2)];
            bool var_8079_interleave_0 = const()[name = string("op_8079_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_8079_cast_fp16 = concat(axis = var_8078, interleave = var_8079_interleave_0, values = (var_8076_cast_fp16, var_8074_cast_fp16_0))[name = string("op_8079_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_8080_cast_fp16 = mul(x = var_8079_cast_fp16, y = var_866_cast_fp16)[name = string("op_8080_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_225_cast_fp16 = add(x = var_8073_cast_fp16, y = var_8080_cast_fp16)[name = string("key_states_225_cast_fp16")];
            tensor<int32, [1]> expand_dims_264 = const()[name = string("expand_dims_264"), val = tensor<int32, [1]>([22])];
            tensor<int32, [1]> expand_dims_265 = const()[name = string("expand_dims_265"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_267 = const()[name = string("expand_dims_267"), val = tensor<int32, [1]>([0])];
            int32 concat_269_axis_0 = const()[name = string("concat_269_axis_0"), val = int32(0)];
            bool concat_269_interleave_0 = const()[name = string("concat_269_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_269 = concat(axis = concat_269_axis_0, interleave = concat_269_interleave_0, values = (expand_dims_264, expand_dims_265, position_id, expand_dims_267))[name = string("concat_269")];
            tensor<int32, [1]> expand_dims_268 = const()[name = string("expand_dims_268"), val = tensor<int32, [1]>([23])];
            tensor<int32, [1]> concat_270_values1_0 = const()[name = string("concat_270_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_270_values3_0 = const()[name = string("concat_270_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_270_axis_0 = const()[name = string("concat_270_axis_0"), val = int32(0)];
            bool concat_270_interleave_0 = const()[name = string("concat_270_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_270 = concat(axis = concat_270_axis_0, interleave = concat_270_interleave_0, values = (expand_dims_268, concat_270_values1_0, cache_position_end, concat_270_values3_0))[name = string("concat_270")];
            tensor<int32, [4]> key_states_227_perm_0 = const()[name = string("key_states_227_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_23_stride_0 = const()[name = string("key_cache_internal_tensor_assign_23_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_23_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_23_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_23_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_23_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_23_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_23_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_227_cast_fp16 = transpose(perm = key_states_227_perm_0, x = key_states_225_cast_fp16)[name = string("transpose_104")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_23_cast_fp16 = slice_update(begin = concat_269, begin_mask = key_cache_internal_tensor_assign_23_begin_mask_0, end = concat_270, end_mask = key_cache_internal_tensor_assign_23_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_23_squeeze_mask_0, stride = key_cache_internal_tensor_assign_23_stride_0, update = key_states_227_cast_fp16, x = coreml_update_state_98)[name = string("key_cache_internal_tensor_assign_23_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_23_cast_fp16, input = key_cache)[name = string("coreml_update_state_100_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_100 = read_state(input = key_cache)[name = string("coreml_update_state_100")];
            tensor<int32, [4]> value_states_135_perm_0 = const()[name = string("value_states_135_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_23_stride_0 = const()[name = string("value_cache_internal_tensor_assign_23_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_23_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_23_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_23_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_23_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_23_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_23_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_135_cast_fp16 = transpose(perm = value_states_135_perm_0, x = var_8056_cast_fp16)[name = string("transpose_103")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_23_cast_fp16 = slice_update(begin = concat_269, begin_mask = value_cache_internal_tensor_assign_23_begin_mask_0, end = concat_270, end_mask = value_cache_internal_tensor_assign_23_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_23_squeeze_mask_0, stride = value_cache_internal_tensor_assign_23_stride_0, update = value_states_135_cast_fp16, x = coreml_update_state_99)[name = string("value_cache_internal_tensor_assign_23_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_23_cast_fp16, input = value_cache)[name = string("coreml_update_state_101_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_101 = read_state(input = value_cache)[name = string("coreml_update_state_101")];
            tensor<int32, [4]> var_8150_begin_0 = const()[name = string("op_8150_begin_0"), val = tensor<int32, [4]>([22, 0, 0, 0])];
            tensor<int32, [4]> var_8150_end_0 = const()[name = string("op_8150_end_0"), val = tensor<int32, [4]>([23, 2, 2048, 128])];
            tensor<bool, [4]> var_8150_end_mask_0 = const()[name = string("op_8150_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8150_cast_fp16 = slice_by_index(begin = var_8150_begin_0, end = var_8150_end_0, end_mask = var_8150_end_mask_0, x = coreml_update_state_100)[name = string("op_8150_cast_fp16")];
            tensor<int32, [2]> tile_44 = const()[name = string("tile_44"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8153_axis_0 = const()[name = string("op_8153_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8153_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8153_cast_fp16_1 = split(axis = var_8153_axis_0, split_sizes = tile_44, x = var_8150_cast_fp16)[name = string("op_8153_cast_fp16")];
            tensor<int32, [4]> var_8160_begin_0 = const()[name = string("op_8160_begin_0"), val = tensor<int32, [4]>([22, 0, 0, 0])];
            tensor<int32, [4]> var_8160_end_0 = const()[name = string("op_8160_end_0"), val = tensor<int32, [4]>([23, 2, 2048, 128])];
            tensor<bool, [4]> var_8160_end_mask_0 = const()[name = string("op_8160_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8160_cast_fp16 = slice_by_index(begin = var_8160_begin_0, end = var_8160_end_0, end_mask = var_8160_end_mask_0, x = coreml_update_state_101)[name = string("op_8160_cast_fp16")];
            tensor<int32, [2]> tile_45 = const()[name = string("tile_45"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8163_axis_0 = const()[name = string("op_8163_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8163_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8163_cast_fp16_1 = split(axis = var_8163_axis_0, split_sizes = tile_45, x = var_8160_cast_fp16)[name = string("op_8163_cast_fp16")];
            tensor<int32, [2]> var_8166_split_sizes_0 = const()[name = string("op_8166_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_8166_axis_0 = const()[name = string("op_8166_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_8166_0, tensor<fp16, [1, 8, 128, 1]> var_8166_1 = split(axis = var_8166_axis_0, split_sizes = var_8166_split_sizes_0, x = query_states_135_cast_fp16)[name = string("op_8166")];
            bool attn_weights_353_transpose_x_0 = const()[name = string("attn_weights_353_transpose_x_0"), val = bool(false)];
            bool attn_weights_353_transpose_y_0 = const()[name = string("attn_weights_353_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_353_cast_fp16 = matmul(transpose_x = attn_weights_353_transpose_x_0, transpose_y = attn_weights_353_transpose_y_0, x = var_8153_cast_fp16_0, y = var_8166_0)[name = string("attn_weights_353_cast_fp16")];
            fp16 var_8169_to_fp16 = const()[name = string("op_8169_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_355_cast_fp16 = mul(x = attn_weights_353_cast_fp16, y = var_8169_to_fp16)[name = string("attn_weights_355_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_357_cast_fp16 = add(x = attn_weights_355_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_357_cast_fp16")];
            int32 var_8173 = const()[name = string("op_8173"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_359_cast_fp16 = softmax(axis = var_8173, x = attn_weights_357_cast_fp16)[name = string("attn_weights_359_cast_fp16")];
            bool var_8179_transpose_x_1 = const()[name = string("op_8179_transpose_x_1"), val = bool(true)];
            bool var_8179_transpose_y_1 = const()[name = string("op_8179_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_8179_cast_fp16 = matmul(transpose_x = var_8179_transpose_x_1, transpose_y = var_8179_transpose_y_1, x = attn_weights_359_cast_fp16, y = var_8163_cast_fp16_0)[name = string("op_8179_cast_fp16")];
            bool attn_weights_361_transpose_x_0 = const()[name = string("attn_weights_361_transpose_x_0"), val = bool(false)];
            bool attn_weights_361_transpose_y_0 = const()[name = string("attn_weights_361_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_361_cast_fp16 = matmul(transpose_x = attn_weights_361_transpose_x_0, transpose_y = attn_weights_361_transpose_y_0, x = var_8153_cast_fp16_1, y = var_8166_1)[name = string("attn_weights_361_cast_fp16")];
            fp16 var_8181_to_fp16 = const()[name = string("op_8181_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_363_cast_fp16 = mul(x = attn_weights_361_cast_fp16, y = var_8181_to_fp16)[name = string("attn_weights_363_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_365_cast_fp16 = add(x = attn_weights_363_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_365_cast_fp16")];
            int32 var_8185 = const()[name = string("op_8185"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_367_cast_fp16 = softmax(axis = var_8185, x = attn_weights_365_cast_fp16)[name = string("attn_weights_367_cast_fp16")];
            bool attn_output_177_transpose_x_1 = const()[name = string("attn_output_177_transpose_x_1"), val = bool(true)];
            bool attn_output_177_transpose_y_1 = const()[name = string("attn_output_177_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_177_cast_fp16 = matmul(transpose_x = attn_output_177_transpose_x_1, transpose_y = attn_output_177_transpose_y_1, x = attn_weights_367_cast_fp16, y = var_8163_cast_fp16_1)[name = string("attn_output_177_cast_fp16")];
            int32 var_8193 = const()[name = string("op_8193"), val = int32(1)];
            bool attn_output_179_interleave_0 = const()[name = string("attn_output_179_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_179_cast_fp16 = concat(axis = var_8193, interleave = attn_output_179_interleave_0, values = (var_8179_cast_fp16, attn_output_177_cast_fp16))[name = string("attn_output_179_cast_fp16")];
            tensor<int32, [4]> var_8197_perm_0 = const()[name = string("op_8197_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_275x = const()[name = string("concat_275x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_8197_cast_fp16 = transpose(perm = var_8197_perm_0, x = attn_output_179_cast_fp16)[name = string("transpose_102")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_183_cast_fp16 = reshape(shape = concat_275x, x = var_8197_cast_fp16)[name = string("attn_output_183_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_22_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_22_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1521463040)))];
            tensor<int32, [2]> hidden_states_223_strides_0 = const()[name = string("hidden_states_223_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_223_pad_type_0 = const()[name = string("hidden_states_223_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_223_pad_0 = const()[name = string("hidden_states_223_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_223_dilations_0 = const()[name = string("hidden_states_223_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_223_groups_0 = const()[name = string("hidden_states_223_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_223_cast_fp16 = conv(dilations = hidden_states_223_dilations_0, groups = hidden_states_223_groups_0, pad = hidden_states_223_pad_0, pad_type = hidden_states_223_pad_type_0, strides = hidden_states_223_strides_0, weight = layers_22_self_attn_o_proj_weight_to_fp16, x = attn_output_183_cast_fp16)[name = string("hidden_states_223_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_225_cast_fp16 = add(x = hidden_states_219_cast_fp16, y = hidden_states_223_cast_fp16)[name = string("hidden_states_225_cast_fp16")];
            fp16 const_230_promoted_to_fp16 = const()[name = string("const_230_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_8230_cast_fp16 = mul(x = hidden_states_225_cast_fp16, y = const_230_promoted_to_fp16)[name = string("op_8230_cast_fp16")];
            int32 var_8228 = const()[name = string("op_8228"), val = int32(1)];
            bool doubled_181_interleave_0 = const()[name = string("doubled_181_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_181_cast_fp16 = concat(axis = var_8228, interleave = doubled_181_interleave_0, values = (hidden_states_225_cast_fp16, var_8230_cast_fp16))[name = string("doubled_181_cast_fp16")];
            tensor<int32, [1]> out_91_axes_0 = const()[name = string("out_91_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_91_gamma_0_to_fp16 = const()[name = string("out_91_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1529851712)))];
            fp16 var_8240_to_fp16 = const()[name = string("op_8240_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_91_cast_fp16 = layer_norm(axes = out_91_axes_0, epsilon = var_8240_to_fp16, gamma = out_91_gamma_0_to_fp16, x = doubled_181_cast_fp16)[name = string("out_91_cast_fp16")];
            tensor<int32, [2]> var_8251_split_sizes_0 = const()[name = string("op_8251_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8251_axis_0 = const()[name = string("op_8251_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_8251_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_8251_cast_fp16_1 = split(axis = var_8251_axis_0, split_sizes = var_8251_split_sizes_0, x = out_91_cast_fp16)[name = string("op_8251_cast_fp16")];
            tensor<int32, [2]> input_45_strides_0 = const()[name = string("input_45_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_45_pad_type_0 = const()[name = string("input_45_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_45_pad_0 = const()[name = string("input_45_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_45_dilations_0 = const()[name = string("input_45_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_45_groups_0 = const()[name = string("input_45_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_45_cast_fp16 = conv(dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = layers_22_mlp_gate_proj_weight_cast_fp16, x = var_8251_cast_fp16_0)[name = string("input_45_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_8268_cast_fp16 = silu(x = input_45_cast_fp16)[name = string("op_8268_cast_fp16")];
            tensor<int32, [2]> var_8274_strides_0 = const()[name = string("op_8274_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_8274_pad_type_0 = const()[name = string("op_8274_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_8274_pad_0 = const()[name = string("op_8274_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_8274_dilations_0 = const()[name = string("op_8274_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8274_groups_0 = const()[name = string("op_8274_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_8274_cast_fp16 = conv(dilations = var_8274_dilations_0, groups = var_8274_groups_0, pad = var_8274_pad_0, pad_type = var_8274_pad_type_0, strides = var_8274_strides_0, weight = layers_22_mlp_up_proj_weight_cast_fp16, x = var_8251_cast_fp16_0)[name = string("op_8274_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_229_cast_fp16 = mul(x = var_8268_cast_fp16, y = var_8274_cast_fp16)[name = string("x_229_cast_fp16")];
            tensor<int32, [2]> hidden_states_227_strides_0 = const()[name = string("hidden_states_227_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_227_pad_type_0 = const()[name = string("hidden_states_227_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_227_pad_0 = const()[name = string("hidden_states_227_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_227_dilations_0 = const()[name = string("hidden_states_227_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_227_groups_0 = const()[name = string("hidden_states_227_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_227_cast_fp16 = conv(dilations = hidden_states_227_dilations_0, groups = hidden_states_227_groups_0, pad = hidden_states_227_pad_0, pad_type = hidden_states_227_pad_type_0, strides = hidden_states_227_strides_0, weight = layers_22_mlp_down_proj_weight_cast_fp16, x = x_229_cast_fp16)[name = string("hidden_states_227_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_229_cast_fp16 = add(x = hidden_states_225_cast_fp16, y = hidden_states_227_cast_fp16)[name = string("hidden_states_229_cast_fp16")];
            fp16 const_232_promoted_to_fp16 = const()[name = string("const_232_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_8292_cast_fp16 = mul(x = hidden_states_229_cast_fp16, y = const_232_promoted_to_fp16)[name = string("op_8292_cast_fp16")];
            int32 var_8290 = const()[name = string("op_8290"), val = int32(1)];
            bool doubled_185_interleave_0 = const()[name = string("doubled_185_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_185_cast_fp16 = concat(axis = var_8290, interleave = doubled_185_interleave_0, values = (hidden_states_229_cast_fp16, var_8292_cast_fp16))[name = string("doubled_185_cast_fp16")];
            tensor<int32, [1]> out_93_axes_0 = const()[name = string("out_93_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_93_gamma_0_to_fp16 = const()[name = string("out_93_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1529859968)))];
            fp16 var_8302_to_fp16 = const()[name = string("op_8302_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_93_cast_fp16 = layer_norm(axes = out_93_axes_0, epsilon = var_8302_to_fp16, gamma = out_93_gamma_0_to_fp16, x = doubled_185_cast_fp16)[name = string("out_93_cast_fp16")];
            tensor<int32, [2]> var_8313_split_sizes_0 = const()[name = string("op_8313_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8313_axis_0 = const()[name = string("op_8313_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_8313_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_8313_cast_fp16_1 = split(axis = var_8313_axis_0, split_sizes = var_8313_split_sizes_0, x = out_93_cast_fp16)[name = string("op_8313_cast_fp16")];
            tensor<int32, [2]> query_states_139_strides_0 = const()[name = string("query_states_139_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_139_pad_type_0 = const()[name = string("query_states_139_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_139_pad_0 = const()[name = string("query_states_139_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_139_dilations_0 = const()[name = string("query_states_139_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_139_groups_0 = const()[name = string("query_states_139_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_139_cast_fp16 = conv(dilations = query_states_139_dilations_0, groups = query_states_139_groups_0, pad = query_states_139_pad_0, pad_type = query_states_139_pad_type_0, strides = query_states_139_strides_0, weight = layers_23_self_attn_q_proj_weight_cast_fp16, x = var_8313_cast_fp16_0)[name = string("query_states_139_cast_fp16")];
            tensor<int32, [2]> key_states_231_strides_0 = const()[name = string("key_states_231_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_231_pad_type_0 = const()[name = string("key_states_231_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_231_pad_0 = const()[name = string("key_states_231_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_231_dilations_0 = const()[name = string("key_states_231_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_231_groups_0 = const()[name = string("key_states_231_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_231_cast_fp16 = conv(dilations = key_states_231_dilations_0, groups = key_states_231_groups_0, pad = key_states_231_pad_0, pad_type = key_states_231_pad_type_0, strides = key_states_231_strides_0, weight = layers_23_self_attn_k_proj_weight_cast_fp16, x = var_8313_cast_fp16_0)[name = string("key_states_231_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_23_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_23_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1529868224)))];
            tensor<int32, [2]> value_states_139_strides_0 = const()[name = string("value_states_139_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_139_pad_type_0 = const()[name = string("value_states_139_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_139_pad_0 = const()[name = string("value_states_139_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_139_dilations_0 = const()[name = string("value_states_139_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_139_groups_0 = const()[name = string("value_states_139_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_139_cast_fp16 = conv(dilations = value_states_139_dilations_0, groups = value_states_139_groups_0, pad = value_states_139_pad_0, pad_type = value_states_139_pad_type_0, strides = value_states_139_strides_0, weight = layers_23_self_attn_v_proj_weight_to_fp16, x = var_8313_cast_fp16_0)[name = string("value_states_139_cast_fp16")];
            tensor<int32, [4]> concat_276x = const()[name = string("concat_276x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_231_cast_fp16 = reshape(shape = concat_276x, x = query_states_139_cast_fp16)[name = string("x_231_cast_fp16")];
            tensor<int32, [4]> concat_277x = const()[name = string("concat_277x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_8370_cast_fp16 = reshape(shape = concat_277x, x = key_states_231_cast_fp16)[name = string("op_8370_cast_fp16")];
            tensor<int32, [4]> concat_278x = const()[name = string("concat_278x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_8377_cast_fp16 = reshape(shape = concat_278x, x = value_states_139_cast_fp16)[name = string("op_8377_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_8381_cast_fp16 = mul(x = x_231_cast_fp16, y = var_859_cast_fp16)[name = string("op_8381_cast_fp16")];
            tensor<int32, [2]> var_8382_split_sizes_0 = const()[name = string("op_8382_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8382_axis_0 = const()[name = string("op_8382_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_8382_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_8382_cast_fp16_1 = split(axis = var_8382_axis_0, split_sizes = var_8382_split_sizes_0, x = x_231_cast_fp16)[name = string("op_8382_cast_fp16")];
            fp16 const_234_promoted_to_fp16 = const()[name = string("const_234_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_8384_cast_fp16 = mul(x = var_8382_cast_fp16_1, y = const_234_promoted_to_fp16)[name = string("op_8384_cast_fp16")];
            int32 var_8386 = const()[name = string("op_8386"), val = int32(-2)];
            bool var_8387_interleave_0 = const()[name = string("op_8387_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_8387_cast_fp16 = concat(axis = var_8386, interleave = var_8387_interleave_0, values = (var_8384_cast_fp16, var_8382_cast_fp16_0))[name = string("op_8387_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_8388_cast_fp16 = mul(x = var_8387_cast_fp16, y = var_866_cast_fp16)[name = string("op_8388_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_141_cast_fp16 = add(x = var_8381_cast_fp16, y = var_8388_cast_fp16)[name = string("query_states_141_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_8394_cast_fp16 = mul(x = var_8370_cast_fp16, y = var_859_cast_fp16)[name = string("op_8394_cast_fp16")];
            tensor<int32, [2]> var_8395_split_sizes_0 = const()[name = string("op_8395_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8395_axis_0 = const()[name = string("op_8395_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_8395_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_8395_cast_fp16_1 = split(axis = var_8395_axis_0, split_sizes = var_8395_split_sizes_0, x = var_8370_cast_fp16)[name = string("op_8395_cast_fp16")];
            fp16 const_235_promoted_to_fp16 = const()[name = string("const_235_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_8397_cast_fp16 = mul(x = var_8395_cast_fp16_1, y = const_235_promoted_to_fp16)[name = string("op_8397_cast_fp16")];
            int32 var_8399 = const()[name = string("op_8399"), val = int32(-2)];
            bool var_8400_interleave_0 = const()[name = string("op_8400_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_8400_cast_fp16 = concat(axis = var_8399, interleave = var_8400_interleave_0, values = (var_8397_cast_fp16, var_8395_cast_fp16_0))[name = string("op_8400_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_8401_cast_fp16 = mul(x = var_8400_cast_fp16, y = var_866_cast_fp16)[name = string("op_8401_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_235_cast_fp16 = add(x = var_8394_cast_fp16, y = var_8401_cast_fp16)[name = string("key_states_235_cast_fp16")];
            tensor<int32, [1]> expand_dims_276 = const()[name = string("expand_dims_276"), val = tensor<int32, [1]>([23])];
            tensor<int32, [1]> expand_dims_277 = const()[name = string("expand_dims_277"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_279 = const()[name = string("expand_dims_279"), val = tensor<int32, [1]>([0])];
            int32 concat_281_axis_0 = const()[name = string("concat_281_axis_0"), val = int32(0)];
            bool concat_281_interleave_0 = const()[name = string("concat_281_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_281 = concat(axis = concat_281_axis_0, interleave = concat_281_interleave_0, values = (expand_dims_276, expand_dims_277, position_id, expand_dims_279))[name = string("concat_281")];
            tensor<int32, [1]> expand_dims_280 = const()[name = string("expand_dims_280"), val = tensor<int32, [1]>([24])];
            tensor<int32, [1]> concat_282_values1_0 = const()[name = string("concat_282_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_282_values3_0 = const()[name = string("concat_282_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_282_axis_0 = const()[name = string("concat_282_axis_0"), val = int32(0)];
            bool concat_282_interleave_0 = const()[name = string("concat_282_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_282 = concat(axis = concat_282_axis_0, interleave = concat_282_interleave_0, values = (expand_dims_280, concat_282_values1_0, cache_position_end, concat_282_values3_0))[name = string("concat_282")];
            tensor<int32, [4]> key_states_237_perm_0 = const()[name = string("key_states_237_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_24_stride_0 = const()[name = string("key_cache_internal_tensor_assign_24_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_24_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_24_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_24_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_24_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_24_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_24_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_237_cast_fp16 = transpose(perm = key_states_237_perm_0, x = key_states_235_cast_fp16)[name = string("transpose_101")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_24_cast_fp16 = slice_update(begin = concat_281, begin_mask = key_cache_internal_tensor_assign_24_begin_mask_0, end = concat_282, end_mask = key_cache_internal_tensor_assign_24_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_24_squeeze_mask_0, stride = key_cache_internal_tensor_assign_24_stride_0, update = key_states_237_cast_fp16, x = coreml_update_state_100)[name = string("key_cache_internal_tensor_assign_24_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_24_cast_fp16, input = key_cache)[name = string("coreml_update_state_102_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_102 = read_state(input = key_cache)[name = string("coreml_update_state_102")];
            tensor<int32, [4]> value_states_141_perm_0 = const()[name = string("value_states_141_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_24_stride_0 = const()[name = string("value_cache_internal_tensor_assign_24_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_24_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_24_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_24_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_24_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_24_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_24_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_141_cast_fp16 = transpose(perm = value_states_141_perm_0, x = var_8377_cast_fp16)[name = string("transpose_100")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_24_cast_fp16 = slice_update(begin = concat_281, begin_mask = value_cache_internal_tensor_assign_24_begin_mask_0, end = concat_282, end_mask = value_cache_internal_tensor_assign_24_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_24_squeeze_mask_0, stride = value_cache_internal_tensor_assign_24_stride_0, update = value_states_141_cast_fp16, x = coreml_update_state_101)[name = string("value_cache_internal_tensor_assign_24_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_24_cast_fp16, input = value_cache)[name = string("coreml_update_state_103_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_103 = read_state(input = value_cache)[name = string("coreml_update_state_103")];
            tensor<int32, [4]> var_8471_begin_0 = const()[name = string("op_8471_begin_0"), val = tensor<int32, [4]>([23, 0, 0, 0])];
            tensor<int32, [4]> var_8471_end_0 = const()[name = string("op_8471_end_0"), val = tensor<int32, [4]>([24, 2, 2048, 128])];
            tensor<bool, [4]> var_8471_end_mask_0 = const()[name = string("op_8471_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8471_cast_fp16 = slice_by_index(begin = var_8471_begin_0, end = var_8471_end_0, end_mask = var_8471_end_mask_0, x = coreml_update_state_102)[name = string("op_8471_cast_fp16")];
            tensor<int32, [2]> tile_46 = const()[name = string("tile_46"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8474_axis_0 = const()[name = string("op_8474_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8474_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8474_cast_fp16_1 = split(axis = var_8474_axis_0, split_sizes = tile_46, x = var_8471_cast_fp16)[name = string("op_8474_cast_fp16")];
            tensor<int32, [4]> var_8481_begin_0 = const()[name = string("op_8481_begin_0"), val = tensor<int32, [4]>([23, 0, 0, 0])];
            tensor<int32, [4]> var_8481_end_0 = const()[name = string("op_8481_end_0"), val = tensor<int32, [4]>([24, 2, 2048, 128])];
            tensor<bool, [4]> var_8481_end_mask_0 = const()[name = string("op_8481_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8481_cast_fp16 = slice_by_index(begin = var_8481_begin_0, end = var_8481_end_0, end_mask = var_8481_end_mask_0, x = coreml_update_state_103)[name = string("op_8481_cast_fp16")];
            tensor<int32, [2]> tile_47 = const()[name = string("tile_47"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8484_axis_0 = const()[name = string("op_8484_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8484_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8484_cast_fp16_1 = split(axis = var_8484_axis_0, split_sizes = tile_47, x = var_8481_cast_fp16)[name = string("op_8484_cast_fp16")];
            tensor<int32, [2]> var_8487_split_sizes_0 = const()[name = string("op_8487_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_8487_axis_0 = const()[name = string("op_8487_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_8487_0, tensor<fp16, [1, 8, 128, 1]> var_8487_1 = split(axis = var_8487_axis_0, split_sizes = var_8487_split_sizes_0, x = query_states_141_cast_fp16)[name = string("op_8487")];
            bool attn_weights_369_transpose_x_0 = const()[name = string("attn_weights_369_transpose_x_0"), val = bool(false)];
            bool attn_weights_369_transpose_y_0 = const()[name = string("attn_weights_369_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_369_cast_fp16 = matmul(transpose_x = attn_weights_369_transpose_x_0, transpose_y = attn_weights_369_transpose_y_0, x = var_8474_cast_fp16_0, y = var_8487_0)[name = string("attn_weights_369_cast_fp16")];
            fp16 var_8490_to_fp16 = const()[name = string("op_8490_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_371_cast_fp16 = mul(x = attn_weights_369_cast_fp16, y = var_8490_to_fp16)[name = string("attn_weights_371_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_373_cast_fp16 = add(x = attn_weights_371_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_373_cast_fp16")];
            int32 var_8494 = const()[name = string("op_8494"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_375_cast_fp16 = softmax(axis = var_8494, x = attn_weights_373_cast_fp16)[name = string("attn_weights_375_cast_fp16")];
            bool var_8500_transpose_x_1 = const()[name = string("op_8500_transpose_x_1"), val = bool(true)];
            bool var_8500_transpose_y_1 = const()[name = string("op_8500_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_8500_cast_fp16 = matmul(transpose_x = var_8500_transpose_x_1, transpose_y = var_8500_transpose_y_1, x = attn_weights_375_cast_fp16, y = var_8484_cast_fp16_0)[name = string("op_8500_cast_fp16")];
            bool attn_weights_377_transpose_x_0 = const()[name = string("attn_weights_377_transpose_x_0"), val = bool(false)];
            bool attn_weights_377_transpose_y_0 = const()[name = string("attn_weights_377_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_377_cast_fp16 = matmul(transpose_x = attn_weights_377_transpose_x_0, transpose_y = attn_weights_377_transpose_y_0, x = var_8474_cast_fp16_1, y = var_8487_1)[name = string("attn_weights_377_cast_fp16")];
            fp16 var_8502_to_fp16 = const()[name = string("op_8502_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_379_cast_fp16 = mul(x = attn_weights_377_cast_fp16, y = var_8502_to_fp16)[name = string("attn_weights_379_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_381_cast_fp16 = add(x = attn_weights_379_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_381_cast_fp16")];
            int32 var_8506 = const()[name = string("op_8506"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_383_cast_fp16 = softmax(axis = var_8506, x = attn_weights_381_cast_fp16)[name = string("attn_weights_383_cast_fp16")];
            bool attn_output_185_transpose_x_1 = const()[name = string("attn_output_185_transpose_x_1"), val = bool(true)];
            bool attn_output_185_transpose_y_1 = const()[name = string("attn_output_185_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_185_cast_fp16 = matmul(transpose_x = attn_output_185_transpose_x_1, transpose_y = attn_output_185_transpose_y_1, x = attn_weights_383_cast_fp16, y = var_8484_cast_fp16_1)[name = string("attn_output_185_cast_fp16")];
            int32 var_8514 = const()[name = string("op_8514"), val = int32(1)];
            bool attn_output_187_interleave_0 = const()[name = string("attn_output_187_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_187_cast_fp16 = concat(axis = var_8514, interleave = attn_output_187_interleave_0, values = (var_8500_cast_fp16, attn_output_185_cast_fp16))[name = string("attn_output_187_cast_fp16")];
            tensor<int32, [4]> var_8518_perm_0 = const()[name = string("op_8518_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_287x = const()[name = string("concat_287x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_8518_cast_fp16 = transpose(perm = var_8518_perm_0, x = attn_output_187_cast_fp16)[name = string("transpose_99")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_191_cast_fp16 = reshape(shape = concat_287x, x = var_8518_cast_fp16)[name = string("attn_output_191_cast_fp16")];
            tensor<int32, [2]> hidden_states_233_strides_0 = const()[name = string("hidden_states_233_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_233_pad_type_0 = const()[name = string("hidden_states_233_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_233_pad_0 = const()[name = string("hidden_states_233_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_233_dilations_0 = const()[name = string("hidden_states_233_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_233_groups_0 = const()[name = string("hidden_states_233_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_233_cast_fp16 = conv(dilations = hidden_states_233_dilations_0, groups = hidden_states_233_groups_0, pad = hidden_states_233_pad_0, pad_type = hidden_states_233_pad_type_0, strides = hidden_states_233_strides_0, weight = layers_23_self_attn_o_proj_weight_cast_fp16, x = attn_output_191_cast_fp16)[name = string("hidden_states_233_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_235_cast_fp16 = add(x = hidden_states_229_cast_fp16, y = hidden_states_233_cast_fp16)[name = string("hidden_states_235_cast_fp16")];
            fp16 const_240_promoted_to_fp16 = const()[name = string("const_240_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_8551_cast_fp16 = mul(x = hidden_states_235_cast_fp16, y = const_240_promoted_to_fp16)[name = string("op_8551_cast_fp16")];
            int32 var_8549 = const()[name = string("op_8549"), val = int32(1)];
            bool doubled_189_interleave_0 = const()[name = string("doubled_189_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_189_cast_fp16 = concat(axis = var_8549, interleave = doubled_189_interleave_0, values = (hidden_states_235_cast_fp16, var_8551_cast_fp16))[name = string("doubled_189_cast_fp16")];
            tensor<int32, [1]> out_95_axes_0 = const()[name = string("out_95_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_95_gamma_0_to_fp16 = const()[name = string("out_95_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1530916864)))];
            fp16 var_8561_to_fp16 = const()[name = string("op_8561_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_95_cast_fp16 = layer_norm(axes = out_95_axes_0, epsilon = var_8561_to_fp16, gamma = out_95_gamma_0_to_fp16, x = doubled_189_cast_fp16)[name = string("out_95_cast_fp16")];
            tensor<int32, [2]> var_8572_split_sizes_0 = const()[name = string("op_8572_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8572_axis_0 = const()[name = string("op_8572_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_8572_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_8572_cast_fp16_1 = split(axis = var_8572_axis_0, split_sizes = var_8572_split_sizes_0, x = out_95_cast_fp16)[name = string("op_8572_cast_fp16")];
            tensor<int32, [2]> input_47_strides_0 = const()[name = string("input_47_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_47_pad_type_0 = const()[name = string("input_47_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_47_dilations_0 = const()[name = string("input_47_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_47_groups_0 = const()[name = string("input_47_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_47_cast_fp16 = conv(dilations = input_47_dilations_0, groups = input_47_groups_0, pad = input_47_pad_0, pad_type = input_47_pad_type_0, strides = input_47_strides_0, weight = layers_23_mlp_gate_proj_weight_cast_fp16, x = var_8572_cast_fp16_0)[name = string("input_47_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_8589_cast_fp16 = silu(x = input_47_cast_fp16)[name = string("op_8589_cast_fp16")];
            tensor<int32, [2]> var_8595_strides_0 = const()[name = string("op_8595_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_8595_pad_type_0 = const()[name = string("op_8595_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_8595_pad_0 = const()[name = string("op_8595_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_8595_dilations_0 = const()[name = string("op_8595_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8595_groups_0 = const()[name = string("op_8595_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_8595_cast_fp16 = conv(dilations = var_8595_dilations_0, groups = var_8595_groups_0, pad = var_8595_pad_0, pad_type = var_8595_pad_type_0, strides = var_8595_strides_0, weight = layers_23_mlp_up_proj_weight_cast_fp16, x = var_8572_cast_fp16_0)[name = string("op_8595_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_239_cast_fp16 = mul(x = var_8589_cast_fp16, y = var_8595_cast_fp16)[name = string("x_239_cast_fp16")];
            tensor<int32, [2]> hidden_states_237_strides_0 = const()[name = string("hidden_states_237_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_237_pad_type_0 = const()[name = string("hidden_states_237_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_237_pad_0 = const()[name = string("hidden_states_237_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_237_dilations_0 = const()[name = string("hidden_states_237_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_237_groups_0 = const()[name = string("hidden_states_237_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_237_cast_fp16 = conv(dilations = hidden_states_237_dilations_0, groups = hidden_states_237_groups_0, pad = hidden_states_237_pad_0, pad_type = hidden_states_237_pad_type_0, strides = hidden_states_237_strides_0, weight = layers_23_mlp_down_proj_weight_cast_fp16, x = x_239_cast_fp16)[name = string("hidden_states_237_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_239_cast_fp16 = add(x = hidden_states_235_cast_fp16, y = hidden_states_237_cast_fp16)[name = string("hidden_states_239_cast_fp16")];
            fp16 const_242_promoted_to_fp16 = const()[name = string("const_242_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_8613_cast_fp16 = mul(x = hidden_states_239_cast_fp16, y = const_242_promoted_to_fp16)[name = string("op_8613_cast_fp16")];
            int32 var_8611 = const()[name = string("op_8611"), val = int32(1)];
            bool doubled_193_interleave_0 = const()[name = string("doubled_193_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_193_cast_fp16 = concat(axis = var_8611, interleave = doubled_193_interleave_0, values = (hidden_states_239_cast_fp16, var_8613_cast_fp16))[name = string("doubled_193_cast_fp16")];
            tensor<int32, [1]> out_97_axes_0 = const()[name = string("out_97_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_97_gamma_0_to_fp16 = const()[name = string("out_97_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1530925120)))];
            fp16 var_8623_to_fp16 = const()[name = string("op_8623_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_97_cast_fp16 = layer_norm(axes = out_97_axes_0, epsilon = var_8623_to_fp16, gamma = out_97_gamma_0_to_fp16, x = doubled_193_cast_fp16)[name = string("out_97_cast_fp16")];
            tensor<int32, [2]> var_8634_split_sizes_0 = const()[name = string("op_8634_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8634_axis_0 = const()[name = string("op_8634_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_8634_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_8634_cast_fp16_1 = split(axis = var_8634_axis_0, split_sizes = var_8634_split_sizes_0, x = out_97_cast_fp16)[name = string("op_8634_cast_fp16")];
            tensor<int32, [2]> query_states_145_strides_0 = const()[name = string("query_states_145_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_145_pad_type_0 = const()[name = string("query_states_145_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_145_pad_0 = const()[name = string("query_states_145_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_145_dilations_0 = const()[name = string("query_states_145_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_145_groups_0 = const()[name = string("query_states_145_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_145_cast_fp16 = conv(dilations = query_states_145_dilations_0, groups = query_states_145_groups_0, pad = query_states_145_pad_0, pad_type = query_states_145_pad_type_0, strides = query_states_145_strides_0, weight = layers_24_self_attn_q_proj_weight_cast_fp16, x = var_8634_cast_fp16_0)[name = string("query_states_145_cast_fp16")];
            tensor<int32, [2]> key_states_241_strides_0 = const()[name = string("key_states_241_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_241_pad_type_0 = const()[name = string("key_states_241_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_241_pad_0 = const()[name = string("key_states_241_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_241_dilations_0 = const()[name = string("key_states_241_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_241_groups_0 = const()[name = string("key_states_241_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_241_cast_fp16 = conv(dilations = key_states_241_dilations_0, groups = key_states_241_groups_0, pad = key_states_241_pad_0, pad_type = key_states_241_pad_type_0, strides = key_states_241_strides_0, weight = layers_24_self_attn_k_proj_weight_cast_fp16, x = var_8634_cast_fp16_0)[name = string("key_states_241_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_24_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_24_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1530933376)))];
            tensor<int32, [2]> value_states_145_strides_0 = const()[name = string("value_states_145_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_145_pad_type_0 = const()[name = string("value_states_145_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_145_pad_0 = const()[name = string("value_states_145_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_145_dilations_0 = const()[name = string("value_states_145_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_145_groups_0 = const()[name = string("value_states_145_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_145_cast_fp16 = conv(dilations = value_states_145_dilations_0, groups = value_states_145_groups_0, pad = value_states_145_pad_0, pad_type = value_states_145_pad_type_0, strides = value_states_145_strides_0, weight = layers_24_self_attn_v_proj_weight_to_fp16, x = var_8634_cast_fp16_0)[name = string("value_states_145_cast_fp16")];
            tensor<int32, [4]> concat_288x = const()[name = string("concat_288x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_241_cast_fp16 = reshape(shape = concat_288x, x = query_states_145_cast_fp16)[name = string("x_241_cast_fp16")];
            tensor<int32, [4]> concat_289x = const()[name = string("concat_289x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_8691_cast_fp16 = reshape(shape = concat_289x, x = key_states_241_cast_fp16)[name = string("op_8691_cast_fp16")];
            tensor<int32, [4]> concat_290x = const()[name = string("concat_290x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_8698_cast_fp16 = reshape(shape = concat_290x, x = value_states_145_cast_fp16)[name = string("op_8698_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_8702_cast_fp16 = mul(x = x_241_cast_fp16, y = var_859_cast_fp16)[name = string("op_8702_cast_fp16")];
            tensor<int32, [2]> var_8703_split_sizes_0 = const()[name = string("op_8703_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8703_axis_0 = const()[name = string("op_8703_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_8703_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_8703_cast_fp16_1 = split(axis = var_8703_axis_0, split_sizes = var_8703_split_sizes_0, x = x_241_cast_fp16)[name = string("op_8703_cast_fp16")];
            fp16 const_244_promoted_to_fp16 = const()[name = string("const_244_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_8705_cast_fp16 = mul(x = var_8703_cast_fp16_1, y = const_244_promoted_to_fp16)[name = string("op_8705_cast_fp16")];
            int32 var_8707 = const()[name = string("op_8707"), val = int32(-2)];
            bool var_8708_interleave_0 = const()[name = string("op_8708_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_8708_cast_fp16 = concat(axis = var_8707, interleave = var_8708_interleave_0, values = (var_8705_cast_fp16, var_8703_cast_fp16_0))[name = string("op_8708_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_8709_cast_fp16 = mul(x = var_8708_cast_fp16, y = var_866_cast_fp16)[name = string("op_8709_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_147_cast_fp16 = add(x = var_8702_cast_fp16, y = var_8709_cast_fp16)[name = string("query_states_147_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_8715_cast_fp16 = mul(x = var_8691_cast_fp16, y = var_859_cast_fp16)[name = string("op_8715_cast_fp16")];
            tensor<int32, [2]> var_8716_split_sizes_0 = const()[name = string("op_8716_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8716_axis_0 = const()[name = string("op_8716_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_8716_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_8716_cast_fp16_1 = split(axis = var_8716_axis_0, split_sizes = var_8716_split_sizes_0, x = var_8691_cast_fp16)[name = string("op_8716_cast_fp16")];
            fp16 const_245_promoted_to_fp16 = const()[name = string("const_245_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_8718_cast_fp16 = mul(x = var_8716_cast_fp16_1, y = const_245_promoted_to_fp16)[name = string("op_8718_cast_fp16")];
            int32 var_8720 = const()[name = string("op_8720"), val = int32(-2)];
            bool var_8721_interleave_0 = const()[name = string("op_8721_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_8721_cast_fp16 = concat(axis = var_8720, interleave = var_8721_interleave_0, values = (var_8718_cast_fp16, var_8716_cast_fp16_0))[name = string("op_8721_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_8722_cast_fp16 = mul(x = var_8721_cast_fp16, y = var_866_cast_fp16)[name = string("op_8722_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_245_cast_fp16 = add(x = var_8715_cast_fp16, y = var_8722_cast_fp16)[name = string("key_states_245_cast_fp16")];
            tensor<int32, [1]> expand_dims_288 = const()[name = string("expand_dims_288"), val = tensor<int32, [1]>([24])];
            tensor<int32, [1]> expand_dims_289 = const()[name = string("expand_dims_289"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_291 = const()[name = string("expand_dims_291"), val = tensor<int32, [1]>([0])];
            int32 concat_293_axis_0 = const()[name = string("concat_293_axis_0"), val = int32(0)];
            bool concat_293_interleave_0 = const()[name = string("concat_293_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_293 = concat(axis = concat_293_axis_0, interleave = concat_293_interleave_0, values = (expand_dims_288, expand_dims_289, position_id, expand_dims_291))[name = string("concat_293")];
            tensor<int32, [1]> expand_dims_292 = const()[name = string("expand_dims_292"), val = tensor<int32, [1]>([25])];
            tensor<int32, [1]> concat_294_values1_0 = const()[name = string("concat_294_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_294_values3_0 = const()[name = string("concat_294_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_294_axis_0 = const()[name = string("concat_294_axis_0"), val = int32(0)];
            bool concat_294_interleave_0 = const()[name = string("concat_294_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_294 = concat(axis = concat_294_axis_0, interleave = concat_294_interleave_0, values = (expand_dims_292, concat_294_values1_0, cache_position_end, concat_294_values3_0))[name = string("concat_294")];
            tensor<int32, [4]> key_states_247_perm_0 = const()[name = string("key_states_247_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_25_stride_0 = const()[name = string("key_cache_internal_tensor_assign_25_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_25_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_25_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_25_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_25_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_25_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_25_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_247_cast_fp16 = transpose(perm = key_states_247_perm_0, x = key_states_245_cast_fp16)[name = string("transpose_98")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_25_cast_fp16 = slice_update(begin = concat_293, begin_mask = key_cache_internal_tensor_assign_25_begin_mask_0, end = concat_294, end_mask = key_cache_internal_tensor_assign_25_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_25_squeeze_mask_0, stride = key_cache_internal_tensor_assign_25_stride_0, update = key_states_247_cast_fp16, x = coreml_update_state_102)[name = string("key_cache_internal_tensor_assign_25_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_25_cast_fp16, input = key_cache)[name = string("coreml_update_state_104_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_104 = read_state(input = key_cache)[name = string("coreml_update_state_104")];
            tensor<int32, [4]> value_states_147_perm_0 = const()[name = string("value_states_147_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_25_stride_0 = const()[name = string("value_cache_internal_tensor_assign_25_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_25_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_25_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_25_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_25_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_25_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_25_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_147_cast_fp16 = transpose(perm = value_states_147_perm_0, x = var_8698_cast_fp16)[name = string("transpose_97")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_25_cast_fp16 = slice_update(begin = concat_293, begin_mask = value_cache_internal_tensor_assign_25_begin_mask_0, end = concat_294, end_mask = value_cache_internal_tensor_assign_25_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_25_squeeze_mask_0, stride = value_cache_internal_tensor_assign_25_stride_0, update = value_states_147_cast_fp16, x = coreml_update_state_103)[name = string("value_cache_internal_tensor_assign_25_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_25_cast_fp16, input = value_cache)[name = string("coreml_update_state_105_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_105 = read_state(input = value_cache)[name = string("coreml_update_state_105")];
            tensor<int32, [4]> var_8792_begin_0 = const()[name = string("op_8792_begin_0"), val = tensor<int32, [4]>([24, 0, 0, 0])];
            tensor<int32, [4]> var_8792_end_0 = const()[name = string("op_8792_end_0"), val = tensor<int32, [4]>([25, 2, 2048, 128])];
            tensor<bool, [4]> var_8792_end_mask_0 = const()[name = string("op_8792_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8792_cast_fp16 = slice_by_index(begin = var_8792_begin_0, end = var_8792_end_0, end_mask = var_8792_end_mask_0, x = coreml_update_state_104)[name = string("op_8792_cast_fp16")];
            tensor<int32, [2]> tile_48 = const()[name = string("tile_48"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8795_axis_0 = const()[name = string("op_8795_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8795_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8795_cast_fp16_1 = split(axis = var_8795_axis_0, split_sizes = tile_48, x = var_8792_cast_fp16)[name = string("op_8795_cast_fp16")];
            tensor<int32, [4]> var_8802_begin_0 = const()[name = string("op_8802_begin_0"), val = tensor<int32, [4]>([24, 0, 0, 0])];
            tensor<int32, [4]> var_8802_end_0 = const()[name = string("op_8802_end_0"), val = tensor<int32, [4]>([25, 2, 2048, 128])];
            tensor<bool, [4]> var_8802_end_mask_0 = const()[name = string("op_8802_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8802_cast_fp16 = slice_by_index(begin = var_8802_begin_0, end = var_8802_end_0, end_mask = var_8802_end_mask_0, x = coreml_update_state_105)[name = string("op_8802_cast_fp16")];
            tensor<int32, [2]> tile_49 = const()[name = string("tile_49"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8805_axis_0 = const()[name = string("op_8805_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8805_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8805_cast_fp16_1 = split(axis = var_8805_axis_0, split_sizes = tile_49, x = var_8802_cast_fp16)[name = string("op_8805_cast_fp16")];
            tensor<int32, [2]> var_8808_split_sizes_0 = const()[name = string("op_8808_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_8808_axis_0 = const()[name = string("op_8808_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_8808_0, tensor<fp16, [1, 8, 128, 1]> var_8808_1 = split(axis = var_8808_axis_0, split_sizes = var_8808_split_sizes_0, x = query_states_147_cast_fp16)[name = string("op_8808")];
            bool attn_weights_385_transpose_x_0 = const()[name = string("attn_weights_385_transpose_x_0"), val = bool(false)];
            bool attn_weights_385_transpose_y_0 = const()[name = string("attn_weights_385_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_385_cast_fp16 = matmul(transpose_x = attn_weights_385_transpose_x_0, transpose_y = attn_weights_385_transpose_y_0, x = var_8795_cast_fp16_0, y = var_8808_0)[name = string("attn_weights_385_cast_fp16")];
            fp16 var_8811_to_fp16 = const()[name = string("op_8811_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_387_cast_fp16 = mul(x = attn_weights_385_cast_fp16, y = var_8811_to_fp16)[name = string("attn_weights_387_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_389_cast_fp16 = add(x = attn_weights_387_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_389_cast_fp16")];
            int32 var_8815 = const()[name = string("op_8815"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_391_cast_fp16 = softmax(axis = var_8815, x = attn_weights_389_cast_fp16)[name = string("attn_weights_391_cast_fp16")];
            bool var_8821_transpose_x_1 = const()[name = string("op_8821_transpose_x_1"), val = bool(true)];
            bool var_8821_transpose_y_1 = const()[name = string("op_8821_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_8821_cast_fp16 = matmul(transpose_x = var_8821_transpose_x_1, transpose_y = var_8821_transpose_y_1, x = attn_weights_391_cast_fp16, y = var_8805_cast_fp16_0)[name = string("op_8821_cast_fp16")];
            bool attn_weights_393_transpose_x_0 = const()[name = string("attn_weights_393_transpose_x_0"), val = bool(false)];
            bool attn_weights_393_transpose_y_0 = const()[name = string("attn_weights_393_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_393_cast_fp16 = matmul(transpose_x = attn_weights_393_transpose_x_0, transpose_y = attn_weights_393_transpose_y_0, x = var_8795_cast_fp16_1, y = var_8808_1)[name = string("attn_weights_393_cast_fp16")];
            fp16 var_8823_to_fp16 = const()[name = string("op_8823_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_395_cast_fp16 = mul(x = attn_weights_393_cast_fp16, y = var_8823_to_fp16)[name = string("attn_weights_395_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_397_cast_fp16 = add(x = attn_weights_395_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_397_cast_fp16")];
            int32 var_8827 = const()[name = string("op_8827"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_399_cast_fp16 = softmax(axis = var_8827, x = attn_weights_397_cast_fp16)[name = string("attn_weights_399_cast_fp16")];
            bool attn_output_193_transpose_x_1 = const()[name = string("attn_output_193_transpose_x_1"), val = bool(true)];
            bool attn_output_193_transpose_y_1 = const()[name = string("attn_output_193_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_193_cast_fp16 = matmul(transpose_x = attn_output_193_transpose_x_1, transpose_y = attn_output_193_transpose_y_1, x = attn_weights_399_cast_fp16, y = var_8805_cast_fp16_1)[name = string("attn_output_193_cast_fp16")];
            int32 var_8835 = const()[name = string("op_8835"), val = int32(1)];
            bool attn_output_195_interleave_0 = const()[name = string("attn_output_195_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_195_cast_fp16 = concat(axis = var_8835, interleave = attn_output_195_interleave_0, values = (var_8821_cast_fp16, attn_output_193_cast_fp16))[name = string("attn_output_195_cast_fp16")];
            tensor<int32, [4]> var_8839_perm_0 = const()[name = string("op_8839_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_299x = const()[name = string("concat_299x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_8839_cast_fp16 = transpose(perm = var_8839_perm_0, x = attn_output_195_cast_fp16)[name = string("transpose_96")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_199_cast_fp16 = reshape(shape = concat_299x, x = var_8839_cast_fp16)[name = string("attn_output_199_cast_fp16")];
            tensor<int32, [2]> hidden_states_243_strides_0 = const()[name = string("hidden_states_243_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_243_pad_type_0 = const()[name = string("hidden_states_243_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_243_pad_0 = const()[name = string("hidden_states_243_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_243_dilations_0 = const()[name = string("hidden_states_243_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_243_groups_0 = const()[name = string("hidden_states_243_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_243_cast_fp16 = conv(dilations = hidden_states_243_dilations_0, groups = hidden_states_243_groups_0, pad = hidden_states_243_pad_0, pad_type = hidden_states_243_pad_type_0, strides = hidden_states_243_strides_0, weight = layers_24_self_attn_o_proj_weight_cast_fp16, x = attn_output_199_cast_fp16)[name = string("hidden_states_243_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_245_cast_fp16 = add(x = hidden_states_239_cast_fp16, y = hidden_states_243_cast_fp16)[name = string("hidden_states_245_cast_fp16")];
            fp16 const_250_promoted_to_fp16 = const()[name = string("const_250_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_8872_cast_fp16 = mul(x = hidden_states_245_cast_fp16, y = const_250_promoted_to_fp16)[name = string("op_8872_cast_fp16")];
            int32 var_8870 = const()[name = string("op_8870"), val = int32(1)];
            bool doubled_197_interleave_0 = const()[name = string("doubled_197_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_197_cast_fp16 = concat(axis = var_8870, interleave = doubled_197_interleave_0, values = (hidden_states_245_cast_fp16, var_8872_cast_fp16))[name = string("doubled_197_cast_fp16")];
            tensor<int32, [1]> out_99_axes_0 = const()[name = string("out_99_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_99_gamma_0_to_fp16 = const()[name = string("out_99_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1531982016)))];
            fp16 var_8882_to_fp16 = const()[name = string("op_8882_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_99_cast_fp16 = layer_norm(axes = out_99_axes_0, epsilon = var_8882_to_fp16, gamma = out_99_gamma_0_to_fp16, x = doubled_197_cast_fp16)[name = string("out_99_cast_fp16")];
            tensor<int32, [2]> var_8893_split_sizes_0 = const()[name = string("op_8893_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8893_axis_0 = const()[name = string("op_8893_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_8893_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_8893_cast_fp16_1 = split(axis = var_8893_axis_0, split_sizes = var_8893_split_sizes_0, x = out_99_cast_fp16)[name = string("op_8893_cast_fp16")];
            tensor<int32, [2]> input_49_strides_0 = const()[name = string("input_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_49_pad_type_0 = const()[name = string("input_49_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_49_pad_0 = const()[name = string("input_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_49_dilations_0 = const()[name = string("input_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_49_groups_0 = const()[name = string("input_49_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_49_cast_fp16 = conv(dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = layers_24_mlp_gate_proj_weight_cast_fp16, x = var_8893_cast_fp16_0)[name = string("input_49_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_8910_cast_fp16 = silu(x = input_49_cast_fp16)[name = string("op_8910_cast_fp16")];
            tensor<int32, [2]> var_8916_strides_0 = const()[name = string("op_8916_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_8916_pad_type_0 = const()[name = string("op_8916_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_8916_pad_0 = const()[name = string("op_8916_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_8916_dilations_0 = const()[name = string("op_8916_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8916_groups_0 = const()[name = string("op_8916_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_8916_cast_fp16 = conv(dilations = var_8916_dilations_0, groups = var_8916_groups_0, pad = var_8916_pad_0, pad_type = var_8916_pad_type_0, strides = var_8916_strides_0, weight = layers_24_mlp_up_proj_weight_cast_fp16, x = var_8893_cast_fp16_0)[name = string("op_8916_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_249_cast_fp16 = mul(x = var_8910_cast_fp16, y = var_8916_cast_fp16)[name = string("x_249_cast_fp16")];
            tensor<int32, [2]> hidden_states_247_strides_0 = const()[name = string("hidden_states_247_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_247_pad_type_0 = const()[name = string("hidden_states_247_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_247_pad_0 = const()[name = string("hidden_states_247_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_247_dilations_0 = const()[name = string("hidden_states_247_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_247_groups_0 = const()[name = string("hidden_states_247_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_247_cast_fp16 = conv(dilations = hidden_states_247_dilations_0, groups = hidden_states_247_groups_0, pad = hidden_states_247_pad_0, pad_type = hidden_states_247_pad_type_0, strides = hidden_states_247_strides_0, weight = layers_24_mlp_down_proj_weight_cast_fp16, x = x_249_cast_fp16)[name = string("hidden_states_247_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_249_cast_fp16 = add(x = hidden_states_245_cast_fp16, y = hidden_states_247_cast_fp16)[name = string("hidden_states_249_cast_fp16")];
            fp16 const_252_promoted_to_fp16 = const()[name = string("const_252_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_8934_cast_fp16 = mul(x = hidden_states_249_cast_fp16, y = const_252_promoted_to_fp16)[name = string("op_8934_cast_fp16")];
            int32 var_8932 = const()[name = string("op_8932"), val = int32(1)];
            bool doubled_201_interleave_0 = const()[name = string("doubled_201_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_201_cast_fp16 = concat(axis = var_8932, interleave = doubled_201_interleave_0, values = (hidden_states_249_cast_fp16, var_8934_cast_fp16))[name = string("doubled_201_cast_fp16")];
            tensor<int32, [1]> out_101_axes_0 = const()[name = string("out_101_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_101_gamma_0_to_fp16 = const()[name = string("out_101_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1531990272)))];
            fp16 var_8944_to_fp16 = const()[name = string("op_8944_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_101_cast_fp16 = layer_norm(axes = out_101_axes_0, epsilon = var_8944_to_fp16, gamma = out_101_gamma_0_to_fp16, x = doubled_201_cast_fp16)[name = string("out_101_cast_fp16")];
            tensor<int32, [2]> var_8955_split_sizes_0 = const()[name = string("op_8955_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8955_axis_0 = const()[name = string("op_8955_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_8955_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_8955_cast_fp16_1 = split(axis = var_8955_axis_0, split_sizes = var_8955_split_sizes_0, x = out_101_cast_fp16)[name = string("op_8955_cast_fp16")];
            tensor<int32, [2]> query_states_151_strides_0 = const()[name = string("query_states_151_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_151_pad_type_0 = const()[name = string("query_states_151_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_151_pad_0 = const()[name = string("query_states_151_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_151_dilations_0 = const()[name = string("query_states_151_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_151_groups_0 = const()[name = string("query_states_151_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_151_cast_fp16 = conv(dilations = query_states_151_dilations_0, groups = query_states_151_groups_0, pad = query_states_151_pad_0, pad_type = query_states_151_pad_type_0, strides = query_states_151_strides_0, weight = layers_25_self_attn_q_proj_weight_cast_fp16, x = var_8955_cast_fp16_0)[name = string("query_states_151_cast_fp16")];
            tensor<int32, [2]> key_states_251_strides_0 = const()[name = string("key_states_251_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_251_pad_type_0 = const()[name = string("key_states_251_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_251_pad_0 = const()[name = string("key_states_251_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_251_dilations_0 = const()[name = string("key_states_251_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_251_groups_0 = const()[name = string("key_states_251_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_251_cast_fp16 = conv(dilations = key_states_251_dilations_0, groups = key_states_251_groups_0, pad = key_states_251_pad_0, pad_type = key_states_251_pad_type_0, strides = key_states_251_strides_0, weight = layers_25_self_attn_k_proj_weight_cast_fp16, x = var_8955_cast_fp16_0)[name = string("key_states_251_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_25_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_25_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1531998528)))];
            tensor<int32, [2]> value_states_151_strides_0 = const()[name = string("value_states_151_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_151_pad_type_0 = const()[name = string("value_states_151_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_151_pad_0 = const()[name = string("value_states_151_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_151_dilations_0 = const()[name = string("value_states_151_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_151_groups_0 = const()[name = string("value_states_151_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_151_cast_fp16 = conv(dilations = value_states_151_dilations_0, groups = value_states_151_groups_0, pad = value_states_151_pad_0, pad_type = value_states_151_pad_type_0, strides = value_states_151_strides_0, weight = layers_25_self_attn_v_proj_weight_to_fp16, x = var_8955_cast_fp16_0)[name = string("value_states_151_cast_fp16")];
            tensor<int32, [4]> concat_300x = const()[name = string("concat_300x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_251_cast_fp16 = reshape(shape = concat_300x, x = query_states_151_cast_fp16)[name = string("x_251_cast_fp16")];
            tensor<int32, [4]> concat_301x = const()[name = string("concat_301x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_9012_cast_fp16 = reshape(shape = concat_301x, x = key_states_251_cast_fp16)[name = string("op_9012_cast_fp16")];
            tensor<int32, [4]> concat_302x = const()[name = string("concat_302x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_9019_cast_fp16 = reshape(shape = concat_302x, x = value_states_151_cast_fp16)[name = string("op_9019_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_9023_cast_fp16 = mul(x = x_251_cast_fp16, y = var_859_cast_fp16)[name = string("op_9023_cast_fp16")];
            tensor<int32, [2]> var_9024_split_sizes_0 = const()[name = string("op_9024_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9024_axis_0 = const()[name = string("op_9024_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_9024_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_9024_cast_fp16_1 = split(axis = var_9024_axis_0, split_sizes = var_9024_split_sizes_0, x = x_251_cast_fp16)[name = string("op_9024_cast_fp16")];
            fp16 const_254_promoted_to_fp16 = const()[name = string("const_254_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_9026_cast_fp16 = mul(x = var_9024_cast_fp16_1, y = const_254_promoted_to_fp16)[name = string("op_9026_cast_fp16")];
            int32 var_9028 = const()[name = string("op_9028"), val = int32(-2)];
            bool var_9029_interleave_0 = const()[name = string("op_9029_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_9029_cast_fp16 = concat(axis = var_9028, interleave = var_9029_interleave_0, values = (var_9026_cast_fp16, var_9024_cast_fp16_0))[name = string("op_9029_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_9030_cast_fp16 = mul(x = var_9029_cast_fp16, y = var_866_cast_fp16)[name = string("op_9030_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_153_cast_fp16 = add(x = var_9023_cast_fp16, y = var_9030_cast_fp16)[name = string("query_states_153_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_9036_cast_fp16 = mul(x = var_9012_cast_fp16, y = var_859_cast_fp16)[name = string("op_9036_cast_fp16")];
            tensor<int32, [2]> var_9037_split_sizes_0 = const()[name = string("op_9037_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9037_axis_0 = const()[name = string("op_9037_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_9037_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_9037_cast_fp16_1 = split(axis = var_9037_axis_0, split_sizes = var_9037_split_sizes_0, x = var_9012_cast_fp16)[name = string("op_9037_cast_fp16")];
            fp16 const_255_promoted_to_fp16 = const()[name = string("const_255_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_9039_cast_fp16 = mul(x = var_9037_cast_fp16_1, y = const_255_promoted_to_fp16)[name = string("op_9039_cast_fp16")];
            int32 var_9041 = const()[name = string("op_9041"), val = int32(-2)];
            bool var_9042_interleave_0 = const()[name = string("op_9042_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_9042_cast_fp16 = concat(axis = var_9041, interleave = var_9042_interleave_0, values = (var_9039_cast_fp16, var_9037_cast_fp16_0))[name = string("op_9042_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_9043_cast_fp16 = mul(x = var_9042_cast_fp16, y = var_866_cast_fp16)[name = string("op_9043_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_255_cast_fp16 = add(x = var_9036_cast_fp16, y = var_9043_cast_fp16)[name = string("key_states_255_cast_fp16")];
            tensor<int32, [1]> expand_dims_300 = const()[name = string("expand_dims_300"), val = tensor<int32, [1]>([25])];
            tensor<int32, [1]> expand_dims_301 = const()[name = string("expand_dims_301"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_303 = const()[name = string("expand_dims_303"), val = tensor<int32, [1]>([0])];
            int32 concat_305_axis_0 = const()[name = string("concat_305_axis_0"), val = int32(0)];
            bool concat_305_interleave_0 = const()[name = string("concat_305_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_305 = concat(axis = concat_305_axis_0, interleave = concat_305_interleave_0, values = (expand_dims_300, expand_dims_301, position_id, expand_dims_303))[name = string("concat_305")];
            tensor<int32, [1]> expand_dims_304 = const()[name = string("expand_dims_304"), val = tensor<int32, [1]>([26])];
            tensor<int32, [1]> concat_306_values1_0 = const()[name = string("concat_306_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_306_values3_0 = const()[name = string("concat_306_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_306_axis_0 = const()[name = string("concat_306_axis_0"), val = int32(0)];
            bool concat_306_interleave_0 = const()[name = string("concat_306_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_306 = concat(axis = concat_306_axis_0, interleave = concat_306_interleave_0, values = (expand_dims_304, concat_306_values1_0, cache_position_end, concat_306_values3_0))[name = string("concat_306")];
            tensor<int32, [4]> key_states_257_perm_0 = const()[name = string("key_states_257_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_26_stride_0 = const()[name = string("key_cache_internal_tensor_assign_26_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_26_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_26_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_26_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_26_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_26_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_26_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_257_cast_fp16 = transpose(perm = key_states_257_perm_0, x = key_states_255_cast_fp16)[name = string("transpose_95")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_26_cast_fp16 = slice_update(begin = concat_305, begin_mask = key_cache_internal_tensor_assign_26_begin_mask_0, end = concat_306, end_mask = key_cache_internal_tensor_assign_26_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_26_squeeze_mask_0, stride = key_cache_internal_tensor_assign_26_stride_0, update = key_states_257_cast_fp16, x = coreml_update_state_104)[name = string("key_cache_internal_tensor_assign_26_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_26_cast_fp16, input = key_cache)[name = string("coreml_update_state_106_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_106 = read_state(input = key_cache)[name = string("coreml_update_state_106")];
            tensor<int32, [4]> value_states_153_perm_0 = const()[name = string("value_states_153_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_26_stride_0 = const()[name = string("value_cache_internal_tensor_assign_26_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_26_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_26_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_26_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_26_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_26_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_26_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_153_cast_fp16 = transpose(perm = value_states_153_perm_0, x = var_9019_cast_fp16)[name = string("transpose_94")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_26_cast_fp16 = slice_update(begin = concat_305, begin_mask = value_cache_internal_tensor_assign_26_begin_mask_0, end = concat_306, end_mask = value_cache_internal_tensor_assign_26_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_26_squeeze_mask_0, stride = value_cache_internal_tensor_assign_26_stride_0, update = value_states_153_cast_fp16, x = coreml_update_state_105)[name = string("value_cache_internal_tensor_assign_26_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_26_cast_fp16, input = value_cache)[name = string("coreml_update_state_107_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_107 = read_state(input = value_cache)[name = string("coreml_update_state_107")];
            tensor<int32, [4]> var_9113_begin_0 = const()[name = string("op_9113_begin_0"), val = tensor<int32, [4]>([25, 0, 0, 0])];
            tensor<int32, [4]> var_9113_end_0 = const()[name = string("op_9113_end_0"), val = tensor<int32, [4]>([26, 2, 2048, 128])];
            tensor<bool, [4]> var_9113_end_mask_0 = const()[name = string("op_9113_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9113_cast_fp16 = slice_by_index(begin = var_9113_begin_0, end = var_9113_end_0, end_mask = var_9113_end_mask_0, x = coreml_update_state_106)[name = string("op_9113_cast_fp16")];
            tensor<int32, [2]> tile_50 = const()[name = string("tile_50"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9116_axis_0 = const()[name = string("op_9116_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9116_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9116_cast_fp16_1 = split(axis = var_9116_axis_0, split_sizes = tile_50, x = var_9113_cast_fp16)[name = string("op_9116_cast_fp16")];
            tensor<int32, [4]> var_9123_begin_0 = const()[name = string("op_9123_begin_0"), val = tensor<int32, [4]>([25, 0, 0, 0])];
            tensor<int32, [4]> var_9123_end_0 = const()[name = string("op_9123_end_0"), val = tensor<int32, [4]>([26, 2, 2048, 128])];
            tensor<bool, [4]> var_9123_end_mask_0 = const()[name = string("op_9123_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9123_cast_fp16 = slice_by_index(begin = var_9123_begin_0, end = var_9123_end_0, end_mask = var_9123_end_mask_0, x = coreml_update_state_107)[name = string("op_9123_cast_fp16")];
            tensor<int32, [2]> tile_51 = const()[name = string("tile_51"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9126_axis_0 = const()[name = string("op_9126_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9126_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9126_cast_fp16_1 = split(axis = var_9126_axis_0, split_sizes = tile_51, x = var_9123_cast_fp16)[name = string("op_9126_cast_fp16")];
            tensor<int32, [2]> var_9129_split_sizes_0 = const()[name = string("op_9129_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_9129_axis_0 = const()[name = string("op_9129_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_9129_0, tensor<fp16, [1, 8, 128, 1]> var_9129_1 = split(axis = var_9129_axis_0, split_sizes = var_9129_split_sizes_0, x = query_states_153_cast_fp16)[name = string("op_9129")];
            bool attn_weights_401_transpose_x_0 = const()[name = string("attn_weights_401_transpose_x_0"), val = bool(false)];
            bool attn_weights_401_transpose_y_0 = const()[name = string("attn_weights_401_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_401_cast_fp16 = matmul(transpose_x = attn_weights_401_transpose_x_0, transpose_y = attn_weights_401_transpose_y_0, x = var_9116_cast_fp16_0, y = var_9129_0)[name = string("attn_weights_401_cast_fp16")];
            fp16 var_9132_to_fp16 = const()[name = string("op_9132_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_403_cast_fp16 = mul(x = attn_weights_401_cast_fp16, y = var_9132_to_fp16)[name = string("attn_weights_403_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_405_cast_fp16 = add(x = attn_weights_403_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_405_cast_fp16")];
            int32 var_9136 = const()[name = string("op_9136"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_407_cast_fp16 = softmax(axis = var_9136, x = attn_weights_405_cast_fp16)[name = string("attn_weights_407_cast_fp16")];
            bool var_9142_transpose_x_1 = const()[name = string("op_9142_transpose_x_1"), val = bool(true)];
            bool var_9142_transpose_y_1 = const()[name = string("op_9142_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_9142_cast_fp16 = matmul(transpose_x = var_9142_transpose_x_1, transpose_y = var_9142_transpose_y_1, x = attn_weights_407_cast_fp16, y = var_9126_cast_fp16_0)[name = string("op_9142_cast_fp16")];
            bool attn_weights_409_transpose_x_0 = const()[name = string("attn_weights_409_transpose_x_0"), val = bool(false)];
            bool attn_weights_409_transpose_y_0 = const()[name = string("attn_weights_409_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_409_cast_fp16 = matmul(transpose_x = attn_weights_409_transpose_x_0, transpose_y = attn_weights_409_transpose_y_0, x = var_9116_cast_fp16_1, y = var_9129_1)[name = string("attn_weights_409_cast_fp16")];
            fp16 var_9144_to_fp16 = const()[name = string("op_9144_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_411_cast_fp16 = mul(x = attn_weights_409_cast_fp16, y = var_9144_to_fp16)[name = string("attn_weights_411_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_413_cast_fp16 = add(x = attn_weights_411_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_413_cast_fp16")];
            int32 var_9148 = const()[name = string("op_9148"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_415_cast_fp16 = softmax(axis = var_9148, x = attn_weights_413_cast_fp16)[name = string("attn_weights_415_cast_fp16")];
            bool attn_output_201_transpose_x_1 = const()[name = string("attn_output_201_transpose_x_1"), val = bool(true)];
            bool attn_output_201_transpose_y_1 = const()[name = string("attn_output_201_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_201_cast_fp16 = matmul(transpose_x = attn_output_201_transpose_x_1, transpose_y = attn_output_201_transpose_y_1, x = attn_weights_415_cast_fp16, y = var_9126_cast_fp16_1)[name = string("attn_output_201_cast_fp16")];
            int32 var_9156 = const()[name = string("op_9156"), val = int32(1)];
            bool attn_output_203_interleave_0 = const()[name = string("attn_output_203_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_203_cast_fp16 = concat(axis = var_9156, interleave = attn_output_203_interleave_0, values = (var_9142_cast_fp16, attn_output_201_cast_fp16))[name = string("attn_output_203_cast_fp16")];
            tensor<int32, [4]> var_9160_perm_0 = const()[name = string("op_9160_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_311x = const()[name = string("concat_311x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_9160_cast_fp16 = transpose(perm = var_9160_perm_0, x = attn_output_203_cast_fp16)[name = string("transpose_93")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_207_cast_fp16 = reshape(shape = concat_311x, x = var_9160_cast_fp16)[name = string("attn_output_207_cast_fp16")];
            tensor<int32, [2]> hidden_states_253_strides_0 = const()[name = string("hidden_states_253_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_253_pad_type_0 = const()[name = string("hidden_states_253_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_253_pad_0 = const()[name = string("hidden_states_253_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_253_dilations_0 = const()[name = string("hidden_states_253_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_253_groups_0 = const()[name = string("hidden_states_253_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_253_cast_fp16 = conv(dilations = hidden_states_253_dilations_0, groups = hidden_states_253_groups_0, pad = hidden_states_253_pad_0, pad_type = hidden_states_253_pad_type_0, strides = hidden_states_253_strides_0, weight = layers_25_self_attn_o_proj_weight_cast_fp16, x = attn_output_207_cast_fp16)[name = string("hidden_states_253_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_255_cast_fp16 = add(x = hidden_states_249_cast_fp16, y = hidden_states_253_cast_fp16)[name = string("hidden_states_255_cast_fp16")];
            fp16 const_260_promoted_to_fp16 = const()[name = string("const_260_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_9193_cast_fp16 = mul(x = hidden_states_255_cast_fp16, y = const_260_promoted_to_fp16)[name = string("op_9193_cast_fp16")];
            int32 var_9191 = const()[name = string("op_9191"), val = int32(1)];
            bool doubled_205_interleave_0 = const()[name = string("doubled_205_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_205_cast_fp16 = concat(axis = var_9191, interleave = doubled_205_interleave_0, values = (hidden_states_255_cast_fp16, var_9193_cast_fp16))[name = string("doubled_205_cast_fp16")];
            tensor<int32, [1]> out_103_axes_0 = const()[name = string("out_103_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_103_gamma_0_to_fp16 = const()[name = string("out_103_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1533047168)))];
            fp16 var_9203_to_fp16 = const()[name = string("op_9203_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_103_cast_fp16 = layer_norm(axes = out_103_axes_0, epsilon = var_9203_to_fp16, gamma = out_103_gamma_0_to_fp16, x = doubled_205_cast_fp16)[name = string("out_103_cast_fp16")];
            tensor<int32, [2]> var_9214_split_sizes_0 = const()[name = string("op_9214_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9214_axis_0 = const()[name = string("op_9214_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_9214_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_9214_cast_fp16_1 = split(axis = var_9214_axis_0, split_sizes = var_9214_split_sizes_0, x = out_103_cast_fp16)[name = string("op_9214_cast_fp16")];
            tensor<int32, [2]> input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_51_cast_fp16 = conv(dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = layers_25_mlp_gate_proj_weight_cast_fp16, x = var_9214_cast_fp16_0)[name = string("input_51_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_9231_cast_fp16 = silu(x = input_51_cast_fp16)[name = string("op_9231_cast_fp16")];
            tensor<int32, [2]> var_9237_strides_0 = const()[name = string("op_9237_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_9237_pad_type_0 = const()[name = string("op_9237_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_9237_pad_0 = const()[name = string("op_9237_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_9237_dilations_0 = const()[name = string("op_9237_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9237_groups_0 = const()[name = string("op_9237_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_9237_cast_fp16 = conv(dilations = var_9237_dilations_0, groups = var_9237_groups_0, pad = var_9237_pad_0, pad_type = var_9237_pad_type_0, strides = var_9237_strides_0, weight = layers_25_mlp_up_proj_weight_cast_fp16, x = var_9214_cast_fp16_0)[name = string("op_9237_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_259_cast_fp16 = mul(x = var_9231_cast_fp16, y = var_9237_cast_fp16)[name = string("x_259_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_25_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_25_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1533055424)))];
            tensor<int32, [2]> hidden_states_257_strides_0 = const()[name = string("hidden_states_257_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_257_pad_type_0 = const()[name = string("hidden_states_257_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_257_pad_0 = const()[name = string("hidden_states_257_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_257_dilations_0 = const()[name = string("hidden_states_257_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_257_groups_0 = const()[name = string("hidden_states_257_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_257_cast_fp16 = conv(dilations = hidden_states_257_dilations_0, groups = hidden_states_257_groups_0, pad = hidden_states_257_pad_0, pad_type = hidden_states_257_pad_type_0, strides = hidden_states_257_strides_0, weight = layers_25_mlp_down_proj_weight_to_fp16, x = x_259_cast_fp16)[name = string("hidden_states_257_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_259_cast_fp16 = add(x = hidden_states_255_cast_fp16, y = hidden_states_257_cast_fp16)[name = string("hidden_states_259_cast_fp16")];
            fp16 const_262_promoted_to_fp16 = const()[name = string("const_262_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_9255_cast_fp16 = mul(x = hidden_states_259_cast_fp16, y = const_262_promoted_to_fp16)[name = string("op_9255_cast_fp16")];
            int32 var_9253 = const()[name = string("op_9253"), val = int32(1)];
            bool doubled_209_interleave_0 = const()[name = string("doubled_209_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_209_cast_fp16 = concat(axis = var_9253, interleave = doubled_209_interleave_0, values = (hidden_states_259_cast_fp16, var_9255_cast_fp16))[name = string("doubled_209_cast_fp16")];
            tensor<int32, [1]> out_105_axes_0 = const()[name = string("out_105_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_105_gamma_0_to_fp16 = const()[name = string("out_105_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1558221312)))];
            fp16 var_9265_to_fp16 = const()[name = string("op_9265_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_105_cast_fp16 = layer_norm(axes = out_105_axes_0, epsilon = var_9265_to_fp16, gamma = out_105_gamma_0_to_fp16, x = doubled_209_cast_fp16)[name = string("out_105_cast_fp16")];
            tensor<int32, [2]> var_9276_split_sizes_0 = const()[name = string("op_9276_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9276_axis_0 = const()[name = string("op_9276_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_9276_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_9276_cast_fp16_1 = split(axis = var_9276_axis_0, split_sizes = var_9276_split_sizes_0, x = out_105_cast_fp16)[name = string("op_9276_cast_fp16")];
            tensor<int32, [2]> query_states_157_strides_0 = const()[name = string("query_states_157_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_157_pad_type_0 = const()[name = string("query_states_157_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_157_pad_0 = const()[name = string("query_states_157_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_157_dilations_0 = const()[name = string("query_states_157_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_157_groups_0 = const()[name = string("query_states_157_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_157_cast_fp16 = conv(dilations = query_states_157_dilations_0, groups = query_states_157_groups_0, pad = query_states_157_pad_0, pad_type = query_states_157_pad_type_0, strides = query_states_157_strides_0, weight = layers_26_self_attn_q_proj_weight_cast_fp16, x = var_9276_cast_fp16_0)[name = string("query_states_157_cast_fp16")];
            tensor<int32, [2]> key_states_261_strides_0 = const()[name = string("key_states_261_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_261_pad_type_0 = const()[name = string("key_states_261_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_261_pad_0 = const()[name = string("key_states_261_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_261_dilations_0 = const()[name = string("key_states_261_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_261_groups_0 = const()[name = string("key_states_261_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_261_cast_fp16 = conv(dilations = key_states_261_dilations_0, groups = key_states_261_groups_0, pad = key_states_261_pad_0, pad_type = key_states_261_pad_type_0, strides = key_states_261_strides_0, weight = layers_26_self_attn_k_proj_weight_cast_fp16, x = var_9276_cast_fp16_0)[name = string("key_states_261_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_26_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_26_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1558229568)))];
            tensor<int32, [2]> value_states_157_strides_0 = const()[name = string("value_states_157_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_157_pad_type_0 = const()[name = string("value_states_157_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_157_pad_0 = const()[name = string("value_states_157_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_157_dilations_0 = const()[name = string("value_states_157_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_157_groups_0 = const()[name = string("value_states_157_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_157_cast_fp16 = conv(dilations = value_states_157_dilations_0, groups = value_states_157_groups_0, pad = value_states_157_pad_0, pad_type = value_states_157_pad_type_0, strides = value_states_157_strides_0, weight = layers_26_self_attn_v_proj_weight_to_fp16, x = var_9276_cast_fp16_0)[name = string("value_states_157_cast_fp16")];
            tensor<int32, [4]> concat_312x = const()[name = string("concat_312x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_261_cast_fp16 = reshape(shape = concat_312x, x = query_states_157_cast_fp16)[name = string("x_261_cast_fp16")];
            tensor<int32, [4]> concat_313x = const()[name = string("concat_313x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_9333_cast_fp16 = reshape(shape = concat_313x, x = key_states_261_cast_fp16)[name = string("op_9333_cast_fp16")];
            tensor<int32, [4]> concat_314x = const()[name = string("concat_314x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_9340_cast_fp16 = reshape(shape = concat_314x, x = value_states_157_cast_fp16)[name = string("op_9340_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_9344_cast_fp16 = mul(x = x_261_cast_fp16, y = var_859_cast_fp16)[name = string("op_9344_cast_fp16")];
            tensor<int32, [2]> var_9345_split_sizes_0 = const()[name = string("op_9345_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9345_axis_0 = const()[name = string("op_9345_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_9345_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_9345_cast_fp16_1 = split(axis = var_9345_axis_0, split_sizes = var_9345_split_sizes_0, x = x_261_cast_fp16)[name = string("op_9345_cast_fp16")];
            fp16 const_264_promoted_to_fp16 = const()[name = string("const_264_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_9347_cast_fp16 = mul(x = var_9345_cast_fp16_1, y = const_264_promoted_to_fp16)[name = string("op_9347_cast_fp16")];
            int32 var_9349 = const()[name = string("op_9349"), val = int32(-2)];
            bool var_9350_interleave_0 = const()[name = string("op_9350_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_9350_cast_fp16 = concat(axis = var_9349, interleave = var_9350_interleave_0, values = (var_9347_cast_fp16, var_9345_cast_fp16_0))[name = string("op_9350_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_9351_cast_fp16 = mul(x = var_9350_cast_fp16, y = var_866_cast_fp16)[name = string("op_9351_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_159_cast_fp16 = add(x = var_9344_cast_fp16, y = var_9351_cast_fp16)[name = string("query_states_159_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_9357_cast_fp16 = mul(x = var_9333_cast_fp16, y = var_859_cast_fp16)[name = string("op_9357_cast_fp16")];
            tensor<int32, [2]> var_9358_split_sizes_0 = const()[name = string("op_9358_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9358_axis_0 = const()[name = string("op_9358_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_9358_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_9358_cast_fp16_1 = split(axis = var_9358_axis_0, split_sizes = var_9358_split_sizes_0, x = var_9333_cast_fp16)[name = string("op_9358_cast_fp16")];
            fp16 const_265_promoted_to_fp16 = const()[name = string("const_265_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_9360_cast_fp16 = mul(x = var_9358_cast_fp16_1, y = const_265_promoted_to_fp16)[name = string("op_9360_cast_fp16")];
            int32 var_9362 = const()[name = string("op_9362"), val = int32(-2)];
            bool var_9363_interleave_0 = const()[name = string("op_9363_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_9363_cast_fp16 = concat(axis = var_9362, interleave = var_9363_interleave_0, values = (var_9360_cast_fp16, var_9358_cast_fp16_0))[name = string("op_9363_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_9364_cast_fp16 = mul(x = var_9363_cast_fp16, y = var_866_cast_fp16)[name = string("op_9364_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_265_cast_fp16 = add(x = var_9357_cast_fp16, y = var_9364_cast_fp16)[name = string("key_states_265_cast_fp16")];
            tensor<int32, [1]> expand_dims_312 = const()[name = string("expand_dims_312"), val = tensor<int32, [1]>([26])];
            tensor<int32, [1]> expand_dims_313 = const()[name = string("expand_dims_313"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_315 = const()[name = string("expand_dims_315"), val = tensor<int32, [1]>([0])];
            int32 concat_317_axis_0 = const()[name = string("concat_317_axis_0"), val = int32(0)];
            bool concat_317_interleave_0 = const()[name = string("concat_317_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_317 = concat(axis = concat_317_axis_0, interleave = concat_317_interleave_0, values = (expand_dims_312, expand_dims_313, position_id, expand_dims_315))[name = string("concat_317")];
            tensor<int32, [1]> expand_dims_316 = const()[name = string("expand_dims_316"), val = tensor<int32, [1]>([27])];
            tensor<int32, [1]> concat_318_values1_0 = const()[name = string("concat_318_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_318_values3_0 = const()[name = string("concat_318_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_318_axis_0 = const()[name = string("concat_318_axis_0"), val = int32(0)];
            bool concat_318_interleave_0 = const()[name = string("concat_318_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_318 = concat(axis = concat_318_axis_0, interleave = concat_318_interleave_0, values = (expand_dims_316, concat_318_values1_0, cache_position_end, concat_318_values3_0))[name = string("concat_318")];
            tensor<int32, [4]> key_states_267_perm_0 = const()[name = string("key_states_267_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_27_stride_0 = const()[name = string("key_cache_internal_tensor_assign_27_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_27_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_27_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_27_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_27_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_27_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_27_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_267_cast_fp16 = transpose(perm = key_states_267_perm_0, x = key_states_265_cast_fp16)[name = string("transpose_92")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_27_cast_fp16 = slice_update(begin = concat_317, begin_mask = key_cache_internal_tensor_assign_27_begin_mask_0, end = concat_318, end_mask = key_cache_internal_tensor_assign_27_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_27_squeeze_mask_0, stride = key_cache_internal_tensor_assign_27_stride_0, update = key_states_267_cast_fp16, x = coreml_update_state_106)[name = string("key_cache_internal_tensor_assign_27_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_27_cast_fp16, input = key_cache)[name = string("coreml_update_state_108_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_108 = read_state(input = key_cache)[name = string("coreml_update_state_108")];
            tensor<int32, [4]> value_states_159_perm_0 = const()[name = string("value_states_159_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_27_stride_0 = const()[name = string("value_cache_internal_tensor_assign_27_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_27_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_27_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_27_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_27_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_27_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_27_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_159_cast_fp16 = transpose(perm = value_states_159_perm_0, x = var_9340_cast_fp16)[name = string("transpose_91")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_27_cast_fp16 = slice_update(begin = concat_317, begin_mask = value_cache_internal_tensor_assign_27_begin_mask_0, end = concat_318, end_mask = value_cache_internal_tensor_assign_27_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_27_squeeze_mask_0, stride = value_cache_internal_tensor_assign_27_stride_0, update = value_states_159_cast_fp16, x = coreml_update_state_107)[name = string("value_cache_internal_tensor_assign_27_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_27_cast_fp16, input = value_cache)[name = string("coreml_update_state_109_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_109 = read_state(input = value_cache)[name = string("coreml_update_state_109")];
            tensor<int32, [4]> var_9434_begin_0 = const()[name = string("op_9434_begin_0"), val = tensor<int32, [4]>([26, 0, 0, 0])];
            tensor<int32, [4]> var_9434_end_0 = const()[name = string("op_9434_end_0"), val = tensor<int32, [4]>([27, 2, 2048, 128])];
            tensor<bool, [4]> var_9434_end_mask_0 = const()[name = string("op_9434_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9434_cast_fp16 = slice_by_index(begin = var_9434_begin_0, end = var_9434_end_0, end_mask = var_9434_end_mask_0, x = coreml_update_state_108)[name = string("op_9434_cast_fp16")];
            tensor<int32, [2]> tile_52 = const()[name = string("tile_52"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9437_axis_0 = const()[name = string("op_9437_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9437_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9437_cast_fp16_1 = split(axis = var_9437_axis_0, split_sizes = tile_52, x = var_9434_cast_fp16)[name = string("op_9437_cast_fp16")];
            tensor<int32, [4]> var_9444_begin_0 = const()[name = string("op_9444_begin_0"), val = tensor<int32, [4]>([26, 0, 0, 0])];
            tensor<int32, [4]> var_9444_end_0 = const()[name = string("op_9444_end_0"), val = tensor<int32, [4]>([27, 2, 2048, 128])];
            tensor<bool, [4]> var_9444_end_mask_0 = const()[name = string("op_9444_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9444_cast_fp16 = slice_by_index(begin = var_9444_begin_0, end = var_9444_end_0, end_mask = var_9444_end_mask_0, x = coreml_update_state_109)[name = string("op_9444_cast_fp16")];
            tensor<int32, [2]> tile_53 = const()[name = string("tile_53"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9447_axis_0 = const()[name = string("op_9447_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9447_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9447_cast_fp16_1 = split(axis = var_9447_axis_0, split_sizes = tile_53, x = var_9444_cast_fp16)[name = string("op_9447_cast_fp16")];
            tensor<int32, [2]> var_9450_split_sizes_0 = const()[name = string("op_9450_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_9450_axis_0 = const()[name = string("op_9450_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_9450_0, tensor<fp16, [1, 8, 128, 1]> var_9450_1 = split(axis = var_9450_axis_0, split_sizes = var_9450_split_sizes_0, x = query_states_159_cast_fp16)[name = string("op_9450")];
            bool attn_weights_417_transpose_x_0 = const()[name = string("attn_weights_417_transpose_x_0"), val = bool(false)];
            bool attn_weights_417_transpose_y_0 = const()[name = string("attn_weights_417_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_417_cast_fp16 = matmul(transpose_x = attn_weights_417_transpose_x_0, transpose_y = attn_weights_417_transpose_y_0, x = var_9437_cast_fp16_0, y = var_9450_0)[name = string("attn_weights_417_cast_fp16")];
            fp16 var_9453_to_fp16 = const()[name = string("op_9453_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_419_cast_fp16 = mul(x = attn_weights_417_cast_fp16, y = var_9453_to_fp16)[name = string("attn_weights_419_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_421_cast_fp16 = add(x = attn_weights_419_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_421_cast_fp16")];
            int32 var_9457 = const()[name = string("op_9457"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_423_cast_fp16 = softmax(axis = var_9457, x = attn_weights_421_cast_fp16)[name = string("attn_weights_423_cast_fp16")];
            bool var_9463_transpose_x_1 = const()[name = string("op_9463_transpose_x_1"), val = bool(true)];
            bool var_9463_transpose_y_1 = const()[name = string("op_9463_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_9463_cast_fp16 = matmul(transpose_x = var_9463_transpose_x_1, transpose_y = var_9463_transpose_y_1, x = attn_weights_423_cast_fp16, y = var_9447_cast_fp16_0)[name = string("op_9463_cast_fp16")];
            bool attn_weights_425_transpose_x_0 = const()[name = string("attn_weights_425_transpose_x_0"), val = bool(false)];
            bool attn_weights_425_transpose_y_0 = const()[name = string("attn_weights_425_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_425_cast_fp16 = matmul(transpose_x = attn_weights_425_transpose_x_0, transpose_y = attn_weights_425_transpose_y_0, x = var_9437_cast_fp16_1, y = var_9450_1)[name = string("attn_weights_425_cast_fp16")];
            fp16 var_9465_to_fp16 = const()[name = string("op_9465_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_427_cast_fp16 = mul(x = attn_weights_425_cast_fp16, y = var_9465_to_fp16)[name = string("attn_weights_427_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_429_cast_fp16 = add(x = attn_weights_427_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_429_cast_fp16")];
            int32 var_9469 = const()[name = string("op_9469"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_431_cast_fp16 = softmax(axis = var_9469, x = attn_weights_429_cast_fp16)[name = string("attn_weights_431_cast_fp16")];
            bool attn_output_209_transpose_x_1 = const()[name = string("attn_output_209_transpose_x_1"), val = bool(true)];
            bool attn_output_209_transpose_y_1 = const()[name = string("attn_output_209_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_209_cast_fp16 = matmul(transpose_x = attn_output_209_transpose_x_1, transpose_y = attn_output_209_transpose_y_1, x = attn_weights_431_cast_fp16, y = var_9447_cast_fp16_1)[name = string("attn_output_209_cast_fp16")];
            int32 var_9477 = const()[name = string("op_9477"), val = int32(1)];
            bool attn_output_211_interleave_0 = const()[name = string("attn_output_211_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_211_cast_fp16 = concat(axis = var_9477, interleave = attn_output_211_interleave_0, values = (var_9463_cast_fp16, attn_output_209_cast_fp16))[name = string("attn_output_211_cast_fp16")];
            tensor<int32, [4]> var_9481_perm_0 = const()[name = string("op_9481_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_323x = const()[name = string("concat_323x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_9481_cast_fp16 = transpose(perm = var_9481_perm_0, x = attn_output_211_cast_fp16)[name = string("transpose_90")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_215_cast_fp16 = reshape(shape = concat_323x, x = var_9481_cast_fp16)[name = string("attn_output_215_cast_fp16")];
            tensor<int32, [2]> hidden_states_263_strides_0 = const()[name = string("hidden_states_263_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_263_pad_type_0 = const()[name = string("hidden_states_263_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_263_pad_0 = const()[name = string("hidden_states_263_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_263_dilations_0 = const()[name = string("hidden_states_263_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_263_groups_0 = const()[name = string("hidden_states_263_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_263_cast_fp16 = conv(dilations = hidden_states_263_dilations_0, groups = hidden_states_263_groups_0, pad = hidden_states_263_pad_0, pad_type = hidden_states_263_pad_type_0, strides = hidden_states_263_strides_0, weight = layers_26_self_attn_o_proj_weight_cast_fp16, x = attn_output_215_cast_fp16)[name = string("hidden_states_263_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_265_cast_fp16 = add(x = hidden_states_259_cast_fp16, y = hidden_states_263_cast_fp16)[name = string("hidden_states_265_cast_fp16")];
            fp16 const_270_promoted_to_fp16 = const()[name = string("const_270_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_9514_cast_fp16 = mul(x = hidden_states_265_cast_fp16, y = const_270_promoted_to_fp16)[name = string("op_9514_cast_fp16")];
            int32 var_9512 = const()[name = string("op_9512"), val = int32(1)];
            bool doubled_213_interleave_0 = const()[name = string("doubled_213_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_213_cast_fp16 = concat(axis = var_9512, interleave = doubled_213_interleave_0, values = (hidden_states_265_cast_fp16, var_9514_cast_fp16))[name = string("doubled_213_cast_fp16")];
            tensor<int32, [1]> out_107_axes_0 = const()[name = string("out_107_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_107_gamma_0_to_fp16 = const()[name = string("out_107_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1559278208)))];
            fp16 var_9524_to_fp16 = const()[name = string("op_9524_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_107_cast_fp16 = layer_norm(axes = out_107_axes_0, epsilon = var_9524_to_fp16, gamma = out_107_gamma_0_to_fp16, x = doubled_213_cast_fp16)[name = string("out_107_cast_fp16")];
            tensor<int32, [2]> var_9535_split_sizes_0 = const()[name = string("op_9535_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9535_axis_0 = const()[name = string("op_9535_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_9535_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_9535_cast_fp16_1 = split(axis = var_9535_axis_0, split_sizes = var_9535_split_sizes_0, x = out_107_cast_fp16)[name = string("op_9535_cast_fp16")];
            tensor<int32, [2]> input_53_strides_0 = const()[name = string("input_53_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_53_pad_type_0 = const()[name = string("input_53_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_53_dilations_0 = const()[name = string("input_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_53_groups_0 = const()[name = string("input_53_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_53_cast_fp16 = conv(dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = layers_26_mlp_gate_proj_weight_cast_fp16, x = var_9535_cast_fp16_0)[name = string("input_53_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_9552_cast_fp16 = silu(x = input_53_cast_fp16)[name = string("op_9552_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_26_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_26_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1559286464)))];
            tensor<int32, [2]> var_9558_strides_0 = const()[name = string("op_9558_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_9558_pad_type_0 = const()[name = string("op_9558_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_9558_pad_0 = const()[name = string("op_9558_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_9558_dilations_0 = const()[name = string("op_9558_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9558_groups_0 = const()[name = string("op_9558_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_9558_cast_fp16 = conv(dilations = var_9558_dilations_0, groups = var_9558_groups_0, pad = var_9558_pad_0, pad_type = var_9558_pad_type_0, strides = var_9558_strides_0, weight = layers_26_mlp_up_proj_weight_to_fp16, x = var_9535_cast_fp16_0)[name = string("op_9558_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_269_cast_fp16 = mul(x = var_9552_cast_fp16, y = var_9558_cast_fp16)[name = string("x_269_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_26_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_26_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1584452352)))];
            tensor<int32, [2]> hidden_states_267_strides_0 = const()[name = string("hidden_states_267_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_267_pad_type_0 = const()[name = string("hidden_states_267_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_267_pad_0 = const()[name = string("hidden_states_267_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_267_dilations_0 = const()[name = string("hidden_states_267_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_267_groups_0 = const()[name = string("hidden_states_267_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_267_cast_fp16 = conv(dilations = hidden_states_267_dilations_0, groups = hidden_states_267_groups_0, pad = hidden_states_267_pad_0, pad_type = hidden_states_267_pad_type_0, strides = hidden_states_267_strides_0, weight = layers_26_mlp_down_proj_weight_to_fp16, x = x_269_cast_fp16)[name = string("hidden_states_267_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_269_cast_fp16 = add(x = hidden_states_265_cast_fp16, y = hidden_states_267_cast_fp16)[name = string("hidden_states_269_cast_fp16")];
            fp16 const_272_promoted_to_fp16 = const()[name = string("const_272_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_9576_cast_fp16 = mul(x = hidden_states_269_cast_fp16, y = const_272_promoted_to_fp16)[name = string("op_9576_cast_fp16")];
            int32 var_9574 = const()[name = string("op_9574"), val = int32(1)];
            bool doubled_217_interleave_0 = const()[name = string("doubled_217_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_217_cast_fp16 = concat(axis = var_9574, interleave = doubled_217_interleave_0, values = (hidden_states_269_cast_fp16, var_9576_cast_fp16))[name = string("doubled_217_cast_fp16")];
            tensor<int32, [1]> out_109_axes_0 = const()[name = string("out_109_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_109_gamma_0_to_fp16 = const()[name = string("out_109_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1609618240)))];
            fp16 var_9586_to_fp16 = const()[name = string("op_9586_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_109_cast_fp16 = layer_norm(axes = out_109_axes_0, epsilon = var_9586_to_fp16, gamma = out_109_gamma_0_to_fp16, x = doubled_217_cast_fp16)[name = string("out_109_cast_fp16")];
            tensor<int32, [2]> var_9597_split_sizes_0 = const()[name = string("op_9597_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9597_axis_0 = const()[name = string("op_9597_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_9597_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_9597_cast_fp16_1 = split(axis = var_9597_axis_0, split_sizes = var_9597_split_sizes_0, x = out_109_cast_fp16)[name = string("op_9597_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_27_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1609626496)))];
            tensor<int32, [2]> query_states_163_strides_0 = const()[name = string("query_states_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_163_pad_type_0 = const()[name = string("query_states_163_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_163_pad_0 = const()[name = string("query_states_163_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_163_dilations_0 = const()[name = string("query_states_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_163_groups_0 = const()[name = string("query_states_163_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_163_cast_fp16 = conv(dilations = query_states_163_dilations_0, groups = query_states_163_groups_0, pad = query_states_163_pad_0, pad_type = query_states_163_pad_type_0, strides = query_states_163_strides_0, weight = layers_27_self_attn_q_proj_weight_to_fp16, x = var_9597_cast_fp16_0)[name = string("query_states_163_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_27_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1618015168)))];
            tensor<int32, [2]> key_states_271_strides_0 = const()[name = string("key_states_271_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_271_pad_type_0 = const()[name = string("key_states_271_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_271_pad_0 = const()[name = string("key_states_271_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_271_dilations_0 = const()[name = string("key_states_271_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_271_groups_0 = const()[name = string("key_states_271_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_271_cast_fp16 = conv(dilations = key_states_271_dilations_0, groups = key_states_271_groups_0, pad = key_states_271_pad_0, pad_type = key_states_271_pad_type_0, strides = key_states_271_strides_0, weight = layers_27_self_attn_k_proj_weight_to_fp16, x = var_9597_cast_fp16_0)[name = string("key_states_271_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_27_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1619063808)))];
            tensor<int32, [2]> value_states_163_strides_0 = const()[name = string("value_states_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_163_pad_type_0 = const()[name = string("value_states_163_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_163_pad_0 = const()[name = string("value_states_163_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_163_dilations_0 = const()[name = string("value_states_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_163_groups_0 = const()[name = string("value_states_163_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_163_cast_fp16 = conv(dilations = value_states_163_dilations_0, groups = value_states_163_groups_0, pad = value_states_163_pad_0, pad_type = value_states_163_pad_type_0, strides = value_states_163_strides_0, weight = layers_27_self_attn_v_proj_weight_to_fp16, x = var_9597_cast_fp16_0)[name = string("value_states_163_cast_fp16")];
            tensor<int32, [4]> concat_324x = const()[name = string("concat_324x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_271_cast_fp16 = reshape(shape = concat_324x, x = query_states_163_cast_fp16)[name = string("x_271_cast_fp16")];
            tensor<int32, [4]> concat_325x = const()[name = string("concat_325x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_9654_cast_fp16 = reshape(shape = concat_325x, x = key_states_271_cast_fp16)[name = string("op_9654_cast_fp16")];
            tensor<int32, [4]> concat_326x = const()[name = string("concat_326x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_9661_cast_fp16 = reshape(shape = concat_326x, x = value_states_163_cast_fp16)[name = string("op_9661_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_9665_cast_fp16 = mul(x = x_271_cast_fp16, y = var_859_cast_fp16)[name = string("op_9665_cast_fp16")];
            tensor<int32, [2]> var_9666_split_sizes_0 = const()[name = string("op_9666_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9666_axis_0 = const()[name = string("op_9666_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_9666_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_9666_cast_fp16_1 = split(axis = var_9666_axis_0, split_sizes = var_9666_split_sizes_0, x = x_271_cast_fp16)[name = string("op_9666_cast_fp16")];
            fp16 const_274_promoted_to_fp16 = const()[name = string("const_274_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_9668_cast_fp16 = mul(x = var_9666_cast_fp16_1, y = const_274_promoted_to_fp16)[name = string("op_9668_cast_fp16")];
            int32 var_9670 = const()[name = string("op_9670"), val = int32(-2)];
            bool var_9671_interleave_0 = const()[name = string("op_9671_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_9671_cast_fp16 = concat(axis = var_9670, interleave = var_9671_interleave_0, values = (var_9668_cast_fp16, var_9666_cast_fp16_0))[name = string("op_9671_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_9672_cast_fp16 = mul(x = var_9671_cast_fp16, y = var_866_cast_fp16)[name = string("op_9672_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_165_cast_fp16 = add(x = var_9665_cast_fp16, y = var_9672_cast_fp16)[name = string("query_states_165_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_9678_cast_fp16 = mul(x = var_9654_cast_fp16, y = var_859_cast_fp16)[name = string("op_9678_cast_fp16")];
            tensor<int32, [2]> var_9679_split_sizes_0 = const()[name = string("op_9679_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9679_axis_0 = const()[name = string("op_9679_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_9679_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_9679_cast_fp16_1 = split(axis = var_9679_axis_0, split_sizes = var_9679_split_sizes_0, x = var_9654_cast_fp16)[name = string("op_9679_cast_fp16")];
            fp16 const_275_promoted_to_fp16 = const()[name = string("const_275_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_9681_cast_fp16 = mul(x = var_9679_cast_fp16_1, y = const_275_promoted_to_fp16)[name = string("op_9681_cast_fp16")];
            int32 var_9683 = const()[name = string("op_9683"), val = int32(-2)];
            bool var_9684_interleave_0 = const()[name = string("op_9684_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_9684_cast_fp16 = concat(axis = var_9683, interleave = var_9684_interleave_0, values = (var_9681_cast_fp16, var_9679_cast_fp16_0))[name = string("op_9684_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_9685_cast_fp16 = mul(x = var_9684_cast_fp16, y = var_866_cast_fp16)[name = string("op_9685_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_275_cast_fp16 = add(x = var_9678_cast_fp16, y = var_9685_cast_fp16)[name = string("key_states_275_cast_fp16")];
            tensor<int32, [1]> expand_dims_324 = const()[name = string("expand_dims_324"), val = tensor<int32, [1]>([27])];
            tensor<int32, [1]> expand_dims_325 = const()[name = string("expand_dims_325"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_327 = const()[name = string("expand_dims_327"), val = tensor<int32, [1]>([0])];
            int32 concat_329_axis_0 = const()[name = string("concat_329_axis_0"), val = int32(0)];
            bool concat_329_interleave_0 = const()[name = string("concat_329_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_329 = concat(axis = concat_329_axis_0, interleave = concat_329_interleave_0, values = (expand_dims_324, expand_dims_325, position_id, expand_dims_327))[name = string("concat_329")];
            tensor<int32, [1]> expand_dims_328 = const()[name = string("expand_dims_328"), val = tensor<int32, [1]>([28])];
            tensor<int32, [1]> concat_330_values1_0 = const()[name = string("concat_330_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_330_values3_0 = const()[name = string("concat_330_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_330_axis_0 = const()[name = string("concat_330_axis_0"), val = int32(0)];
            bool concat_330_interleave_0 = const()[name = string("concat_330_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_330 = concat(axis = concat_330_axis_0, interleave = concat_330_interleave_0, values = (expand_dims_328, concat_330_values1_0, cache_position_end, concat_330_values3_0))[name = string("concat_330")];
            tensor<int32, [4]> key_states_277_perm_0 = const()[name = string("key_states_277_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_28_stride_0 = const()[name = string("key_cache_internal_tensor_assign_28_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_28_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_28_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_28_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_28_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_28_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_28_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_277_cast_fp16 = transpose(perm = key_states_277_perm_0, x = key_states_275_cast_fp16)[name = string("transpose_89")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_28_cast_fp16 = slice_update(begin = concat_329, begin_mask = key_cache_internal_tensor_assign_28_begin_mask_0, end = concat_330, end_mask = key_cache_internal_tensor_assign_28_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_28_squeeze_mask_0, stride = key_cache_internal_tensor_assign_28_stride_0, update = key_states_277_cast_fp16, x = coreml_update_state_108)[name = string("key_cache_internal_tensor_assign_28_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_28_cast_fp16, input = key_cache)[name = string("coreml_update_state_110_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_110 = read_state(input = key_cache)[name = string("coreml_update_state_110")];
            tensor<int32, [4]> value_states_165_perm_0 = const()[name = string("value_states_165_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_28_stride_0 = const()[name = string("value_cache_internal_tensor_assign_28_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_28_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_28_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_28_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_28_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_28_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_28_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_165_cast_fp16 = transpose(perm = value_states_165_perm_0, x = var_9661_cast_fp16)[name = string("transpose_88")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_28_cast_fp16 = slice_update(begin = concat_329, begin_mask = value_cache_internal_tensor_assign_28_begin_mask_0, end = concat_330, end_mask = value_cache_internal_tensor_assign_28_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_28_squeeze_mask_0, stride = value_cache_internal_tensor_assign_28_stride_0, update = value_states_165_cast_fp16, x = coreml_update_state_109)[name = string("value_cache_internal_tensor_assign_28_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_28_cast_fp16, input = value_cache)[name = string("coreml_update_state_111_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_111 = read_state(input = value_cache)[name = string("coreml_update_state_111")];
            tensor<int32, [4]> var_9755_begin_0 = const()[name = string("op_9755_begin_0"), val = tensor<int32, [4]>([27, 0, 0, 0])];
            tensor<int32, [4]> var_9755_end_0 = const()[name = string("op_9755_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_9755_end_mask_0 = const()[name = string("op_9755_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9755_cast_fp16 = slice_by_index(begin = var_9755_begin_0, end = var_9755_end_0, end_mask = var_9755_end_mask_0, x = coreml_update_state_110)[name = string("op_9755_cast_fp16")];
            tensor<int32, [2]> tile_54 = const()[name = string("tile_54"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9758_axis_0 = const()[name = string("op_9758_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9758_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9758_cast_fp16_1 = split(axis = var_9758_axis_0, split_sizes = tile_54, x = var_9755_cast_fp16)[name = string("op_9758_cast_fp16")];
            tensor<int32, [4]> var_9765_begin_0 = const()[name = string("op_9765_begin_0"), val = tensor<int32, [4]>([27, 0, 0, 0])];
            tensor<int32, [4]> var_9765_end_0 = const()[name = string("op_9765_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_9765_end_mask_0 = const()[name = string("op_9765_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9765_cast_fp16 = slice_by_index(begin = var_9765_begin_0, end = var_9765_end_0, end_mask = var_9765_end_mask_0, x = coreml_update_state_111)[name = string("op_9765_cast_fp16")];
            tensor<int32, [2]> tile_55 = const()[name = string("tile_55"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9768_axis_0 = const()[name = string("op_9768_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9768_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9768_cast_fp16_1 = split(axis = var_9768_axis_0, split_sizes = tile_55, x = var_9765_cast_fp16)[name = string("op_9768_cast_fp16")];
            tensor<int32, [2]> var_9771_split_sizes_0 = const()[name = string("op_9771_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_9771_axis_0 = const()[name = string("op_9771_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_9771_0, tensor<fp16, [1, 8, 128, 1]> var_9771_1 = split(axis = var_9771_axis_0, split_sizes = var_9771_split_sizes_0, x = query_states_165_cast_fp16)[name = string("op_9771")];
            bool attn_weights_433_transpose_x_0 = const()[name = string("attn_weights_433_transpose_x_0"), val = bool(false)];
            bool attn_weights_433_transpose_y_0 = const()[name = string("attn_weights_433_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_433_cast_fp16 = matmul(transpose_x = attn_weights_433_transpose_x_0, transpose_y = attn_weights_433_transpose_y_0, x = var_9758_cast_fp16_0, y = var_9771_0)[name = string("attn_weights_433_cast_fp16")];
            fp16 var_9774_to_fp16 = const()[name = string("op_9774_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_435_cast_fp16 = mul(x = attn_weights_433_cast_fp16, y = var_9774_to_fp16)[name = string("attn_weights_435_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_437_cast_fp16 = add(x = attn_weights_435_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_437_cast_fp16")];
            int32 var_9778 = const()[name = string("op_9778"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_439_cast_fp16 = softmax(axis = var_9778, x = attn_weights_437_cast_fp16)[name = string("attn_weights_439_cast_fp16")];
            bool var_9784_transpose_x_1 = const()[name = string("op_9784_transpose_x_1"), val = bool(true)];
            bool var_9784_transpose_y_1 = const()[name = string("op_9784_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_9784_cast_fp16 = matmul(transpose_x = var_9784_transpose_x_1, transpose_y = var_9784_transpose_y_1, x = attn_weights_439_cast_fp16, y = var_9768_cast_fp16_0)[name = string("op_9784_cast_fp16")];
            bool attn_weights_441_transpose_x_0 = const()[name = string("attn_weights_441_transpose_x_0"), val = bool(false)];
            bool attn_weights_441_transpose_y_0 = const()[name = string("attn_weights_441_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_441_cast_fp16 = matmul(transpose_x = attn_weights_441_transpose_x_0, transpose_y = attn_weights_441_transpose_y_0, x = var_9758_cast_fp16_1, y = var_9771_1)[name = string("attn_weights_441_cast_fp16")];
            fp16 var_9786_to_fp16 = const()[name = string("op_9786_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_443_cast_fp16 = mul(x = attn_weights_441_cast_fp16, y = var_9786_to_fp16)[name = string("attn_weights_443_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_445_cast_fp16 = add(x = attn_weights_443_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_445_cast_fp16")];
            int32 var_9790 = const()[name = string("op_9790"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_cast_fp16 = softmax(axis = var_9790, x = attn_weights_445_cast_fp16)[name = string("attn_weights_cast_fp16")];
            bool attn_output_217_transpose_x_1 = const()[name = string("attn_output_217_transpose_x_1"), val = bool(true)];
            bool attn_output_217_transpose_y_1 = const()[name = string("attn_output_217_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_217_cast_fp16 = matmul(transpose_x = attn_output_217_transpose_x_1, transpose_y = attn_output_217_transpose_y_1, x = attn_weights_cast_fp16, y = var_9768_cast_fp16_1)[name = string("attn_output_217_cast_fp16")];
            int32 var_9798 = const()[name = string("op_9798"), val = int32(1)];
            bool attn_output_219_interleave_0 = const()[name = string("attn_output_219_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_219_cast_fp16 = concat(axis = var_9798, interleave = attn_output_219_interleave_0, values = (var_9784_cast_fp16, attn_output_217_cast_fp16))[name = string("attn_output_219_cast_fp16")];
            tensor<int32, [4]> var_9802_perm_0 = const()[name = string("op_9802_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_335x = const()[name = string("concat_335x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_9802_cast_fp16 = transpose(perm = var_9802_perm_0, x = attn_output_219_cast_fp16)[name = string("transpose_87")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_cast_fp16 = reshape(shape = concat_335x, x = var_9802_cast_fp16)[name = string("attn_output_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_27_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1620112448)))];
            tensor<int32, [2]> hidden_states_273_strides_0 = const()[name = string("hidden_states_273_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_273_pad_type_0 = const()[name = string("hidden_states_273_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_273_pad_0 = const()[name = string("hidden_states_273_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_273_dilations_0 = const()[name = string("hidden_states_273_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_273_groups_0 = const()[name = string("hidden_states_273_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_273_cast_fp16 = conv(dilations = hidden_states_273_dilations_0, groups = hidden_states_273_groups_0, pad = hidden_states_273_pad_0, pad_type = hidden_states_273_pad_type_0, strides = hidden_states_273_strides_0, weight = layers_27_self_attn_o_proj_weight_to_fp16, x = attn_output_cast_fp16)[name = string("hidden_states_273_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_275_cast_fp16 = add(x = hidden_states_269_cast_fp16, y = hidden_states_273_cast_fp16)[name = string("hidden_states_275_cast_fp16")];
            fp16 const_280_promoted_to_fp16 = const()[name = string("const_280_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_9835_cast_fp16 = mul(x = hidden_states_275_cast_fp16, y = const_280_promoted_to_fp16)[name = string("op_9835_cast_fp16")];
            int32 var_9833 = const()[name = string("op_9833"), val = int32(1)];
            bool doubled_221_interleave_0 = const()[name = string("doubled_221_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_221_cast_fp16 = concat(axis = var_9833, interleave = doubled_221_interleave_0, values = (hidden_states_275_cast_fp16, var_9835_cast_fp16))[name = string("doubled_221_cast_fp16")];
            tensor<int32, [1]> out_111_axes_0 = const()[name = string("out_111_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_111_gamma_0_to_fp16 = const()[name = string("out_111_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1628501120)))];
            fp16 var_9845_to_fp16 = const()[name = string("op_9845_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_111_cast_fp16 = layer_norm(axes = out_111_axes_0, epsilon = var_9845_to_fp16, gamma = out_111_gamma_0_to_fp16, x = doubled_221_cast_fp16)[name = string("out_111_cast_fp16")];
            tensor<int32, [2]> var_9856_split_sizes_0 = const()[name = string("op_9856_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9856_axis_0 = const()[name = string("op_9856_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_9856_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_9856_cast_fp16_1 = split(axis = var_9856_axis_0, split_sizes = var_9856_split_sizes_0, x = out_111_cast_fp16)[name = string("op_9856_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_27_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_27_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1628509376)))];
            tensor<int32, [2]> input_strides_0 = const()[name = string("input_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_pad_type_0 = const()[name = string("input_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_pad_0 = const()[name = string("input_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_dilations_0 = const()[name = string("input_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_groups_0 = const()[name = string("input_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_cast_fp16 = conv(dilations = input_dilations_0, groups = input_groups_0, pad = input_pad_0, pad_type = input_pad_type_0, strides = input_strides_0, weight = layers_27_mlp_gate_proj_weight_to_fp16, x = var_9856_cast_fp16_0)[name = string("input_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_9873_cast_fp16 = silu(x = input_cast_fp16)[name = string("op_9873_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_27_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_27_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1653675264)))];
            tensor<int32, [2]> var_9879_strides_0 = const()[name = string("op_9879_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_9879_pad_type_0 = const()[name = string("op_9879_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_9879_pad_0 = const()[name = string("op_9879_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_9879_dilations_0 = const()[name = string("op_9879_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9879_groups_0 = const()[name = string("op_9879_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_9879_cast_fp16 = conv(dilations = var_9879_dilations_0, groups = var_9879_groups_0, pad = var_9879_pad_0, pad_type = var_9879_pad_type_0, strides = var_9879_strides_0, weight = layers_27_mlp_up_proj_weight_to_fp16, x = var_9856_cast_fp16_0)[name = string("op_9879_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_cast_fp16 = mul(x = var_9873_cast_fp16, y = var_9879_cast_fp16)[name = string("x_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_27_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_27_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1678841152)))];
            tensor<int32, [2]> hidden_states_277_strides_0 = const()[name = string("hidden_states_277_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_277_pad_type_0 = const()[name = string("hidden_states_277_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_277_pad_0 = const()[name = string("hidden_states_277_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_277_dilations_0 = const()[name = string("hidden_states_277_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_277_groups_0 = const()[name = string("hidden_states_277_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_277_cast_fp16 = conv(dilations = hidden_states_277_dilations_0, groups = hidden_states_277_groups_0, pad = hidden_states_277_pad_0, pad_type = hidden_states_277_pad_type_0, strides = hidden_states_277_strides_0, weight = layers_27_mlp_down_proj_weight_to_fp16, x = x_cast_fp16)[name = string("hidden_states_277_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_cast_fp16 = add(x = hidden_states_275_cast_fp16, y = hidden_states_277_cast_fp16)[name = string("hidden_states_cast_fp16")];
            fp16 const_282_promoted_to_fp16 = const()[name = string("const_282_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_9897_cast_fp16 = mul(x = hidden_states_cast_fp16, y = const_282_promoted_to_fp16)[name = string("op_9897_cast_fp16")];
            int32 var_9895 = const()[name = string("op_9895"), val = int32(1)];
            bool doubled_225_interleave_0 = const()[name = string("doubled_225_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_225_cast_fp16 = concat(axis = var_9895, interleave = doubled_225_interleave_0, values = (hidden_states_cast_fp16, var_9897_cast_fp16))[name = string("doubled_225_cast_fp16")];
            tensor<int32, [1]> out_axes_0 = const()[name = string("out_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_gamma_0_to_fp16 = const()[name = string("out_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1704007040)))];
            fp16 var_9907_to_fp16 = const()[name = string("op_9907_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_cast_fp16 = layer_norm(axes = out_axes_0, epsilon = var_9907_to_fp16, gamma = out_gamma_0_to_fp16, x = doubled_225_cast_fp16)[name = string("out_cast_fp16")];
            tensor<int32, [2]> var_9918_split_sizes_0 = const()[name = string("op_9918_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9918_axis_0 = const()[name = string("op_9918_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states, tensor<fp16, [1, 2048, 1, 1]> var_9918_cast_fp16_1 = split(axis = var_9918_axis_0, split_sizes = var_9918_split_sizes_0, x = out_cast_fp16)[name = string("op_9918_cast_fp16")];
        } -> (hidden_states);
    func length_128<ios18>(tensor<fp32, [1, 2048, 1, 128]> inputs_embeds, state<tensor<fp16, [28, 2, 2048, 128]>> key_cache, tensor<int32, [1]> position_id, tensor<int32, [128]> position_index_seed, state<tensor<fp16, [28, 2, 2048, 128]>> value_cache) {
            tensor<fp16, [256, 2048, 1, 1]> layers_1_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524992))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524416))))[name = string("layers_1_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_1_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(525312))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13120640))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13108288))))[name = string("layers_1_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_2_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13126848))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13651776))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13651200))))[name = string("layers_2_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_2_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13652096))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26247424))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26235072))))[name = string("layers_2_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_3_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26253632))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26778560))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26777984))))[name = string("layers_3_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_3_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26778880))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30977408))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30973248))))[name = string("layers_3_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_3_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30979520))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43566656))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43562496))))[name = string("layers_3_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_4_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43568768))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44093696))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44093120))))[name = string("layers_4_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_4_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44094016))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48292544))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48288384))))[name = string("layers_4_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_4_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48294656))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60889984))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60877632))))[name = string("layers_4_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_4_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60896192))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73491520))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73479168))))[name = string("layers_4_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_4_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73497728))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86084864))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86080704))))[name = string("layers_4_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_5_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86086976))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86611904))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86611328))))[name = string("layers_5_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_5_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86612224))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90810752))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90806592))))[name = string("layers_5_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_5_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90812864))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103408192))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103395840))))[name = string("layers_5_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_5_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103414400))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116001536))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115997376))))[name = string("layers_5_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_6_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116003648))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116528576))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116528000))))[name = string("layers_6_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_6_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116528896))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120727424))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120723264))))[name = string("layers_6_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_6_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120729536))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133324864))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133312512))))[name = string("layers_6_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_6_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133331072))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145926400))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145914048))))[name = string("layers_6_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_6_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145932608))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158519744))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158515584))))[name = string("layers_6_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_7_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158521856))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159046784))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159046208))))[name = string("layers_7_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_7_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159047104))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163245632))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163241472))))[name = string("layers_7_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_7_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163247744))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175843072))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175830720))))[name = string("layers_7_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_8_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175849280))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176374208))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176373632))))[name = string("layers_8_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_8_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176374528))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180573056))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180568896))))[name = string("layers_8_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_8_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180575168))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193170496))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193158144))))[name = string("layers_8_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_8_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193176704))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205772032))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205759680))))[name = string("layers_8_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_8_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205778240))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218365376))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218361216))))[name = string("layers_8_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_9_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218367488))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218892416))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218891840))))[name = string("layers_9_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_9_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218892736))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223091264))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223087104))))[name = string("layers_9_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_9_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223093376))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235688704))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235676352))))[name = string("layers_9_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_9_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235694912))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248290240))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248277888))))[name = string("layers_9_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_9_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248296448))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260883584))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260879424))))[name = string("layers_9_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_10_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260885696))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261410624))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261410048))))[name = string("layers_10_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_10_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261410944))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265609472))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265605312))))[name = string("layers_10_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_10_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265611584))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278206912))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278194560))))[name = string("layers_10_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_10_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278213120))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290808448))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290796096))))[name = string("layers_10_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_10_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290814656))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303401792))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303397632))))[name = string("layers_10_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_11_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303403904))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307602432))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307598272))))[name = string("layers_11_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_11_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307604544))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308129472))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308128896))))[name = string("layers_11_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_11_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308129792))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308654720))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308654144))))[name = string("layers_11_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_11_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308655040))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312853568))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312849408))))[name = string("layers_11_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_11_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312855680))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325451008))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325438656))))[name = string("layers_11_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_11_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325457216))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338052544))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338040192))))[name = string("layers_11_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_11_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338058752))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350645888))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350641728))))[name = string("layers_11_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_12_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350648000))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354846528))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354842368))))[name = string("layers_12_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_12_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354848640))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355373568))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355372992))))[name = string("layers_12_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_12_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355373888))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355898816))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355898240))))[name = string("layers_12_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_12_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355899136))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360097664))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360093504))))[name = string("layers_12_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_12_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360099776))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372695104))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372682752))))[name = string("layers_12_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_12_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372701312))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385296640))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385284288))))[name = string("layers_12_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_12_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385302848))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397889984))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397885824))))[name = string("layers_12_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_13_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397892096))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402090624))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402086464))))[name = string("layers_13_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_13_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402092736))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402617664))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402617088))))[name = string("layers_13_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_13_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402617984))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403142912))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403142336))))[name = string("layers_13_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_13_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403143232))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407341760))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407337600))))[name = string("layers_13_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_13_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407343872))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419939200))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419926848))))[name = string("layers_13_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_13_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419945408))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432532544))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432528384))))[name = string("layers_13_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_14_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432534656))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436733184))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436729024))))[name = string("layers_14_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_14_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436735296))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437260224))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437259648))))[name = string("layers_14_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_14_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437260544))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441459072))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441454912))))[name = string("layers_14_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_14_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441461184))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454056512))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454044160))))[name = string("layers_14_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_14_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454062720))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(466658048))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(466645696))))[name = string("layers_14_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_14_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(466664256))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(479251392))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(479247232))))[name = string("layers_14_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_15_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(479253504))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483452032))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483447872))))[name = string("layers_15_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_15_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483454144))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483979072))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483978496))))[name = string("layers_15_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_15_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483979392))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484504320))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484503744))))[name = string("layers_15_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_15_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484504640))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488703168))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488699008))))[name = string("layers_15_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_15_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488705280))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(501300608))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(501288256))))[name = string("layers_15_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_15_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(501306816))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513902144))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513889792))))[name = string("layers_15_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_15_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513908352))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526495488))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526491328))))[name = string("layers_15_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_16_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526497600))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530696128))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530691968))))[name = string("layers_16_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_16_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530698240))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531223168))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531222592))))[name = string("layers_16_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_16_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531223488))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531748416))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531747840))))[name = string("layers_16_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_16_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531748736))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535947264))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535943104))))[name = string("layers_16_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_16_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535949376))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(548536512))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(548532352))))[name = string("layers_16_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_17_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(548538624))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552737152))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552732992))))[name = string("layers_17_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_17_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552739264))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553264192))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553263616))))[name = string("layers_17_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_17_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553264512))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553789440))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553788864))))[name = string("layers_17_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_17_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553789760))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557988288))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557984128))))[name = string("layers_17_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_17_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557990400))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570585728))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570573376))))[name = string("layers_17_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_17_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570591936))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583187264))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583174912))))[name = string("layers_17_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_17_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583193472))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(595780608))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(595776448))))[name = string("layers_17_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_18_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(595782720))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(599981248))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(599977088))))[name = string("layers_18_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_18_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(599983360))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(600508288))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(600507712))))[name = string("layers_18_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_18_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(600508608))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(601033536))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(601032960))))[name = string("layers_18_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_18_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(601033856))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(605232384))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(605228224))))[name = string("layers_18_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_18_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(605234496))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(617829824))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(617817472))))[name = string("layers_18_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_18_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(617836032))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(630431360))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(630419008))))[name = string("layers_18_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_18_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(630437568))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(643024704))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(643020544))))[name = string("layers_18_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_19_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(643026816))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647225344))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647221184))))[name = string("layers_19_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_19_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647227456))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647752384))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647751808))))[name = string("layers_19_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_19_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647752704))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(660348032))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(660335680))))[name = string("layers_19_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_19_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(660354240))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(672949568))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(672937216))))[name = string("layers_19_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_19_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(672955776))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(685542912))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(685538752))))[name = string("layers_19_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_20_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(685545024))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(689743552))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(689739392))))[name = string("layers_20_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_20_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(689745664))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(690270592))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(690270016))))[name = string("layers_20_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_20_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(690270912))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(694469440))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(694465280))))[name = string("layers_20_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_20_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(694471552))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(707066880))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(707054528))))[name = string("layers_20_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_20_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(707073088))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(719660224))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(719656064))))[name = string("layers_20_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_21_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(719662336))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(723860864))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(723856704))))[name = string("layers_21_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_21_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(723862976))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724387904))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724387328))))[name = string("layers_21_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_21_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724388224))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(728586752))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(728582592))))[name = string("layers_21_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_21_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(728588864))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(741184192))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(741171840))))[name = string("layers_21_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_21_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(741190400))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(753785728))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(753773376))))[name = string("layers_21_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_21_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(753791936))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(766379072))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(766374912))))[name = string("layers_21_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_22_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(766381184))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(770579712))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(770575552))))[name = string("layers_22_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_22_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(770581824))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(771106752))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(771106176))))[name = string("layers_22_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_22_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(771107072))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783702400))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783690048))))[name = string("layers_22_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_22_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783708608))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(796303936))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(796291584))))[name = string("layers_22_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_22_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(796310144))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808897280))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808893120))))[name = string("layers_22_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_23_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808899392))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813097920))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813093760))))[name = string("layers_23_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_23_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813100032))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813624960))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813624384))))[name = string("layers_23_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_23_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813625280))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(817823808))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(817819648))))[name = string("layers_23_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_23_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(817825920))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(830421248))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(830408896))))[name = string("layers_23_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_23_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(830427456))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(843022784))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(843010432))))[name = string("layers_23_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_23_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(843028992))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855616128))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855611968))))[name = string("layers_23_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_24_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855618240))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(859816768))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(859812608))))[name = string("layers_24_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_24_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(859818880))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(860343808))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(860343232))))[name = string("layers_24_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_24_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(860344128))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(864542656))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(864538496))))[name = string("layers_24_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_24_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(864544768))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(877140096))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(877127744))))[name = string("layers_24_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_24_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(877146304))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889741632))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889729280))))[name = string("layers_24_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_24_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889747840))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(902334976))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(902330816))))[name = string("layers_24_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_25_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(902337088))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(906535616))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(906531456))))[name = string("layers_25_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_25_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(906537728))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(907062656))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(907062080))))[name = string("layers_25_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_25_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(907062976))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(911261504))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(911257344))))[name = string("layers_25_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_25_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(911263616))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(923858944))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(923846592))))[name = string("layers_25_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_25_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(923865152))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936460480))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936448128))))[name = string("layers_25_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_26_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936466688))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(940665216))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(940661056))))[name = string("layers_26_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_26_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(940667328))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(941192256))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(941191680))))[name = string("layers_26_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_26_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(941192576))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(945391104))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(945386944))))[name = string("layers_26_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_26_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(945393216))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(957988544))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(957976192))))[name = string("layers_26_mlp_gate_proj_weight_cast_fp16")];
            int32 gather_0_cast_uint16_to_int32 = const()[name = string("gather_0_cast_uint16_to_int32"), val = int32(128)];
            tensor<int32, [1]> cache_position_end = add(x = position_id, y = gather_0_cast_uint16_to_int32)[name = string("cache_position_end")];
            fp16 fill_like_0_value_0_to_fp16 = const()[name = string("fill_like_0_value_0_to_fp16"), val = fp16(0x1p+0)];
            tensor<fp16, [128]> fill_like_0_cast_fp16 = fill_like(ref_tensor = position_index_seed, value = fill_like_0_value_0_to_fp16)[name = string("fill_like_0_cast_fp16")];
            int32 var_831 = const()[name = string("op_831"), val = int32(0)];
            bool var_833_exclusive_0 = const()[name = string("op_833_exclusive_0"), val = bool(false)];
            bool var_833_reverse_0 = const()[name = string("op_833_reverse_0"), val = bool(false)];
            tensor<fp16, [128]> var_833_cast_fp16 = cumsum(axis = var_831, exclusive = var_833_exclusive_0, reverse = var_833_reverse_0, x = fill_like_0_cast_fp16)[name = string("op_833_cast_fp16")];
            fp16 var_835_promoted_to_fp16 = const()[name = string("op_835_promoted_to_fp16"), val = fp16(0x1p+0)];
            tensor<fp16, [128]> position_offsets_cast_fp16 = sub(x = var_833_cast_fp16, y = var_835_promoted_to_fp16)[name = string("position_offsets_cast_fp16")];
            tensor<int32, [1]> var_838_axes_0 = const()[name = string("op_838_axes_0"), val = tensor<int32, [1]>([0])];
            tensor<fp16, [1, 128]> var_838_cast_fp16 = expand_dims(axes = var_838_axes_0, x = position_offsets_cast_fp16)[name = string("op_838_cast_fp16")];
            string position_id_promoted_to_fp16_dtype_0 = const()[name = string("position_id_promoted_to_fp16_dtype_0"), val = string("fp16")];
            tensor<fp16, [1]> position_id_to_fp16 = cast(dtype = position_id_promoted_to_fp16_dtype_0, x = position_id)[name = string("cast_27")];
            tensor<fp16, [1, 128]> position_ids_1_cast_fp16 = add(x = var_838_cast_fp16, y = position_id_to_fp16)[name = string("position_ids_1_cast_fp16")];
            string position_ids_dtype_0 = const()[name = string("position_ids_dtype_0"), val = string("int32")];
            int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)];
            tensor<int32, [1, 128]> position_ids_1_cast_fp16_to_int32 = cast(dtype = position_ids_dtype_0, x = position_ids_1_cast_fp16)[name = string("cast_26")];
            tensor<bool, [1, 128]> greater_equal_0 = greater_equal(x = position_ids_1_cast_fp16_to_int32, y = greater_equal_0_y_0)[name = string("greater_equal_0")];
            int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(32768)];
            tensor<int32, [1, 128]> add_0 = add(x = position_ids_1_cast_fp16_to_int32, y = slice_by_index_0)[name = string("add_0")];
            tensor<int32, [1, 128]> select_0 = select(a = position_ids_1_cast_fp16_to_int32, b = add_0, cond = greater_equal_0)[name = string("select_0")];
            tensor<fp16, [32768, 128]> rope_emb_cos_cached_to_fp16 = const()[name = string("rope_emb_cos_cached_to_fp16"), val = tensor<fp16, [32768, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(957994752)))];
            int32 cos_1_batch_dims_0 = const()[name = string("cos_1_batch_dims_0"), val = int32(0)];
            bool cos_1_validate_indices_0 = const()[name = string("cos_1_validate_indices_0"), val = bool(false)];
            int32 greater_equal_12_y_0 = const()[name = string("greater_equal_12_y_0"), val = int32(0)];
            tensor<bool, [1, 128]> greater_equal_12 = greater_equal(x = select_0, y = greater_equal_12_y_0)[name = string("greater_equal_12")];
            int32 slice_by_index_12 = const()[name = string("slice_by_index_12"), val = int32(32768)];
            tensor<int32, [1, 128]> add_12 = add(x = select_0, y = slice_by_index_12)[name = string("add_12")];
            tensor<int32, [1, 128]> select_12 = select(a = select_0, b = add_12, cond = greater_equal_12)[name = string("select_12")];
            int32 cos_1_cast_fp16_axis_6 = const()[name = string("cos_1_cast_fp16_axis_6"), val = int32(0)];
            tensor<fp16, [1, 128, 128]> cos_1_cast_fp16 = gather(axis = cos_1_cast_fp16_axis_6, batch_dims = cos_1_batch_dims_0, indices = select_12, validate_indices = cos_1_validate_indices_0, x = rope_emb_cos_cached_to_fp16)[name = string("cos_1_cast_fp16")];
            tensor<fp16, [32768, 128]> rope_emb_sin_cached_to_fp16 = const()[name = string("rope_emb_sin_cached_to_fp16"), val = tensor<fp16, [32768, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(966383424)))];
            int32 sin_1_batch_dims_0 = const()[name = string("sin_1_batch_dims_0"), val = int32(0)];
            bool sin_1_validate_indices_0 = const()[name = string("sin_1_validate_indices_0"), val = bool(false)];
            int32 sin_1_cast_fp16_axis_6 = const()[name = string("sin_1_cast_fp16_axis_6"), val = int32(0)];
            tensor<fp16, [1, 128, 128]> sin_1_cast_fp16 = gather(axis = sin_1_cast_fp16_axis_6, batch_dims = sin_1_batch_dims_0, indices = select_12, validate_indices = sin_1_validate_indices_0, x = rope_emb_sin_cached_to_fp16)[name = string("sin_1_cast_fp16")];
            tensor<int32, [3]> var_857_perm_0 = const()[name = string("op_857_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<int32, [1]> var_859_axes_0 = const()[name = string("op_859_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 128, 128]> var_857_cast_fp16 = transpose(perm = var_857_perm_0, x = cos_1_cast_fp16)[name = string("transpose_608")];
            tensor<fp16, [1, 1, 128, 128]> var_859_cast_fp16 = expand_dims(axes = var_859_axes_0, x = var_857_cast_fp16)[name = string("op_859_cast_fp16")];
            tensor<int32, [3]> var_864_perm_0 = const()[name = string("op_864_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<int32, [1]> var_866_axes_0 = const()[name = string("op_866_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 128, 128]> var_864_cast_fp16 = transpose(perm = var_864_perm_0, x = sin_1_cast_fp16)[name = string("transpose_607")];
            tensor<fp16, [1, 1, 128, 128]> var_866_cast_fp16 = expand_dims(axes = var_866_axes_0, x = var_864_cast_fp16)[name = string("op_866_cast_fp16")];
            tensor<int32, [1]> var_885_axes_0 = const()[name = string("op_885_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1, 128, 1]> var_885 = expand_dims(axes = var_885_axes_0, x = position_ids_1_cast_fp16_to_int32)[name = string("op_885")];
            tensor<int32, [1, 1, 2048]> var_878 = const()[name = string("op_878"), val = tensor<int32, [1, 1, 2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(974772096)))];
            tensor<bool, [1, 128, 2048]> var_886 = greater(x = var_878, y = var_885)[name = string("op_886")];
            string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")];
            tensor<int32, [1]> var_893_axes_0 = const()[name = string("op_893_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 128, 2048]> var_886_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = var_886)[name = string("cast_25")];
            tensor<fp16, [1, 1, 128, 2048]> var_893_cast_fp16 = expand_dims(axes = var_893_axes_0, x = var_886_to_fp16)[name = string("op_893_cast_fp16")];
            tensor<int32, [4]> mask_perm_0 = const()[name = string("mask_perm_0"), val = tensor<int32, [4]>([0, 1, -1, -2])];
            fp16 var_897_promoted_to_fp16 = const()[name = string("op_897_promoted_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1, 2048, 128]> mask_cast_fp16 = transpose(perm = mask_perm_0, x = var_893_cast_fp16)[name = string("transpose_606")];
            tensor<bool, [1, 1, 2048, 128]> var_898_cast_fp16 = equal(x = mask_cast_fp16, y = var_897_promoted_to_fp16)[name = string("op_898_cast_fp16")];
            fp16 var_899_to_fp16 = const()[name = string("op_899_to_fp16"), val = fp16(-inf)];
            tensor<fp16, [1, 1, 2048, 128]> attn_mask_1_cast_fp16 = select(a = mask_cast_fp16, b = var_899_to_fp16, cond = var_898_cast_fp16)[name = string("attn_mask_1_cast_fp16")];
            string inputs_embeds_to_fp16_dtype_0 = const()[name = string("inputs_embeds_to_fp16_dtype_0"), val = string("fp16")];
            fp16 const_2_promoted_to_fp16 = const()[name = string("const_2_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> inputs_embeds_to_fp16 = cast(dtype = inputs_embeds_to_fp16_dtype_0, x = inputs_embeds)[name = string("cast_24")];
            tensor<fp16, [1, 2048, 1, 128]> var_909_cast_fp16 = mul(x = inputs_embeds_to_fp16, y = const_2_promoted_to_fp16)[name = string("op_909_cast_fp16")];
            int32 var_907 = const()[name = string("op_907"), val = int32(1)];
            bool doubled_1_interleave_0 = const()[name = string("doubled_1_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_1_cast_fp16 = concat(axis = var_907, interleave = doubled_1_interleave_0, values = (inputs_embeds_to_fp16, var_909_cast_fp16))[name = string("doubled_1_cast_fp16")];
            tensor<int32, [1]> out_1_axes_0 = const()[name = string("out_1_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_1_gamma_0_to_fp16 = const()[name = string("out_1_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(974780352)))];
            fp16 var_919_to_fp16 = const()[name = string("op_919_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_1_cast_fp16 = layer_norm(axes = out_1_axes_0, epsilon = var_919_to_fp16, gamma = out_1_gamma_0_to_fp16, x = doubled_1_cast_fp16)[name = string("out_1_cast_fp16")];
            tensor<int32, [2]> var_930_split_sizes_0 = const()[name = string("op_930_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_930_axis_0 = const()[name = string("op_930_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_930_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_930_cast_fp16_1 = split(axis = var_930_axis_0, split_sizes = var_930_split_sizes_0, x = out_1_cast_fp16)[name = string("op_930_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_0_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(974788608)))];
            tensor<int32, [2]> query_states_1_strides_0 = const()[name = string("query_states_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_1_pad_type_0 = const()[name = string("query_states_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_1_pad_0 = const()[name = string("query_states_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_1_dilations_0 = const()[name = string("query_states_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_1_groups_0 = const()[name = string("query_states_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_1_cast_fp16 = conv(dilations = query_states_1_dilations_0, groups = query_states_1_groups_0, pad = query_states_1_pad_0, pad_type = query_states_1_pad_type_0, strides = query_states_1_strides_0, weight = layers_0_self_attn_q_proj_weight_to_fp16, x = var_930_cast_fp16_0)[name = string("query_states_1_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_0_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(983177280)))];
            tensor<int32, [2]> key_states_1_strides_0 = const()[name = string("key_states_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_1_pad_type_0 = const()[name = string("key_states_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_1_pad_0 = const()[name = string("key_states_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_1_dilations_0 = const()[name = string("key_states_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_1_groups_0 = const()[name = string("key_states_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_1_cast_fp16 = conv(dilations = key_states_1_dilations_0, groups = key_states_1_groups_0, pad = key_states_1_pad_0, pad_type = key_states_1_pad_type_0, strides = key_states_1_strides_0, weight = layers_0_self_attn_k_proj_weight_to_fp16, x = var_930_cast_fp16_0)[name = string("key_states_1_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_0_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(984225920)))];
            tensor<int32, [2]> value_states_1_strides_0 = const()[name = string("value_states_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_1_pad_type_0 = const()[name = string("value_states_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_1_pad_0 = const()[name = string("value_states_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_1_dilations_0 = const()[name = string("value_states_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_1_groups_0 = const()[name = string("value_states_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_1_cast_fp16 = conv(dilations = value_states_1_dilations_0, groups = value_states_1_groups_0, pad = value_states_1_pad_0, pad_type = value_states_1_pad_type_0, strides = value_states_1_strides_0, weight = layers_0_self_attn_v_proj_weight_to_fp16, x = var_930_cast_fp16_0)[name = string("value_states_1_cast_fp16")];
            tensor<int32, [4]> concat_0x = const()[name = string("concat_0x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_1_cast_fp16 = reshape(shape = concat_0x, x = query_states_1_cast_fp16)[name = string("x_1_cast_fp16")];
            tensor<int32, [4]> concat_1x = const()[name = string("concat_1x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_987_cast_fp16 = reshape(shape = concat_1x, x = key_states_1_cast_fp16)[name = string("op_987_cast_fp16")];
            tensor<int32, [4]> concat_2x = const()[name = string("concat_2x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_994_cast_fp16 = reshape(shape = concat_2x, x = value_states_1_cast_fp16)[name = string("op_994_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_998_cast_fp16 = mul(x = x_1_cast_fp16, y = var_859_cast_fp16)[name = string("op_998_cast_fp16")];
            tensor<int32, [2]> var_999_split_sizes_0 = const()[name = string("op_999_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_999_axis_0 = const()[name = string("op_999_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_999_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_999_cast_fp16_1 = split(axis = var_999_axis_0, split_sizes = var_999_split_sizes_0, x = x_1_cast_fp16)[name = string("op_999_cast_fp16")];
            fp16 const_4_promoted_to_fp16 = const()[name = string("const_4_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_1001_cast_fp16 = mul(x = var_999_cast_fp16_1, y = const_4_promoted_to_fp16)[name = string("op_1001_cast_fp16")];
            int32 var_1003 = const()[name = string("op_1003"), val = int32(-2)];
            bool var_1004_interleave_0 = const()[name = string("op_1004_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_1004_cast_fp16 = concat(axis = var_1003, interleave = var_1004_interleave_0, values = (var_1001_cast_fp16, var_999_cast_fp16_0))[name = string("op_1004_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_1005_cast_fp16 = mul(x = var_1004_cast_fp16, y = var_866_cast_fp16)[name = string("op_1005_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_3_cast_fp16 = add(x = var_998_cast_fp16, y = var_1005_cast_fp16)[name = string("query_states_3_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_1011_cast_fp16 = mul(x = var_987_cast_fp16, y = var_859_cast_fp16)[name = string("op_1011_cast_fp16")];
            tensor<int32, [2]> var_1012_split_sizes_0 = const()[name = string("op_1012_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1012_axis_0 = const()[name = string("op_1012_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_1012_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_1012_cast_fp16_1 = split(axis = var_1012_axis_0, split_sizes = var_1012_split_sizes_0, x = var_987_cast_fp16)[name = string("op_1012_cast_fp16")];
            fp16 const_5_promoted_to_fp16 = const()[name = string("const_5_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_1014_cast_fp16 = mul(x = var_1012_cast_fp16_1, y = const_5_promoted_to_fp16)[name = string("op_1014_cast_fp16")];
            int32 var_1016 = const()[name = string("op_1016"), val = int32(-2)];
            bool var_1017_interleave_0 = const()[name = string("op_1017_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_1017_cast_fp16 = concat(axis = var_1016, interleave = var_1017_interleave_0, values = (var_1014_cast_fp16, var_1012_cast_fp16_0))[name = string("op_1017_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_1018_cast_fp16 = mul(x = var_1017_cast_fp16, y = var_866_cast_fp16)[name = string("op_1018_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_5_cast_fp16 = add(x = var_1011_cast_fp16, y = var_1018_cast_fp16)[name = string("key_states_5_cast_fp16")];
            tensor<fp16, [28, 2, 2048, 128]> read_state_0 = read_state(input = key_cache)[name = string("read_state_0")];
            tensor<int32, [1]> expand_dims_0 = const()[name = string("expand_dims_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_1 = const()[name = string("expand_dims_1"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_3 = const()[name = string("expand_dims_3"), val = tensor<int32, [1]>([0])];
            int32 concat_5_axis_0 = const()[name = string("concat_5_axis_0"), val = int32(0)];
            bool concat_5_interleave_0 = const()[name = string("concat_5_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_5 = concat(axis = concat_5_axis_0, interleave = concat_5_interleave_0, values = (expand_dims_0, expand_dims_1, position_id, expand_dims_3))[name = string("concat_5")];
            tensor<int32, [1]> expand_dims_4 = const()[name = string("expand_dims_4"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> concat_6_values1_0 = const()[name = string("concat_6_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_6_values3_0 = const()[name = string("concat_6_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_6_axis_0 = const()[name = string("concat_6_axis_0"), val = int32(0)];
            bool concat_6_interleave_0 = const()[name = string("concat_6_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_6 = concat(axis = concat_6_axis_0, interleave = concat_6_interleave_0, values = (expand_dims_4, concat_6_values1_0, cache_position_end, concat_6_values3_0))[name = string("concat_6")];
            tensor<int32, [4]> key_states_7_perm_0 = const()[name = string("key_states_7_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_1_stride_0 = const()[name = string("key_cache_internal_tensor_assign_1_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_1_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_1_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_1_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_1_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_1_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_1_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_7_cast_fp16 = transpose(perm = key_states_7_perm_0, x = key_states_5_cast_fp16)[name = string("transpose_605")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_1_cast_fp16 = slice_update(begin = concat_5, begin_mask = key_cache_internal_tensor_assign_1_begin_mask_0, end = concat_6, end_mask = key_cache_internal_tensor_assign_1_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_1_squeeze_mask_0, stride = key_cache_internal_tensor_assign_1_stride_0, update = key_states_7_cast_fp16, x = read_state_0)[name = string("key_cache_internal_tensor_assign_1_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_1_cast_fp16, input = key_cache)[name = string("coreml_update_state_336_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_336 = read_state(input = key_cache)[name = string("coreml_update_state_336")];
            tensor<fp16, [28, 2, 2048, 128]> read_state_1 = read_state(input = value_cache)[name = string("read_state_1")];
            tensor<int32, [4]> value_states_3_perm_0 = const()[name = string("value_states_3_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_1_stride_0 = const()[name = string("value_cache_internal_tensor_assign_1_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_1_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_1_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_1_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_1_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_1_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_1_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_3_cast_fp16 = transpose(perm = value_states_3_perm_0, x = var_994_cast_fp16)[name = string("transpose_604")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_1_cast_fp16 = slice_update(begin = concat_5, begin_mask = value_cache_internal_tensor_assign_1_begin_mask_0, end = concat_6, end_mask = value_cache_internal_tensor_assign_1_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_1_squeeze_mask_0, stride = value_cache_internal_tensor_assign_1_stride_0, update = value_states_3_cast_fp16, x = read_state_1)[name = string("value_cache_internal_tensor_assign_1_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_1_cast_fp16, input = value_cache)[name = string("coreml_update_state_337_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_337 = read_state(input = value_cache)[name = string("coreml_update_state_337")];
            tensor<int32, [4]> var_1088_begin_0 = const()[name = string("op_1088_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> var_1088_end_0 = const()[name = string("op_1088_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_1088_end_mask_0 = const()[name = string("op_1088_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1088_cast_fp16 = slice_by_index(begin = var_1088_begin_0, end = var_1088_end_0, end_mask = var_1088_end_mask_0, x = coreml_update_state_336)[name = string("op_1088_cast_fp16")];
            tensor<int32, [2]> tile_0 = const()[name = string("tile_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1091_axis_0 = const()[name = string("op_1091_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1091_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1091_cast_fp16_1 = split(axis = var_1091_axis_0, split_sizes = tile_0, x = var_1088_cast_fp16)[name = string("op_1091_cast_fp16")];
            tensor<int32, [4]> var_1098_begin_0 = const()[name = string("op_1098_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> var_1098_end_0 = const()[name = string("op_1098_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_1098_end_mask_0 = const()[name = string("op_1098_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1098_cast_fp16 = slice_by_index(begin = var_1098_begin_0, end = var_1098_end_0, end_mask = var_1098_end_mask_0, x = coreml_update_state_337)[name = string("op_1098_cast_fp16")];
            tensor<int32, [2]> tile_1 = const()[name = string("tile_1"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1101_axis_0 = const()[name = string("op_1101_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1101_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1101_cast_fp16_1 = split(axis = var_1101_axis_0, split_sizes = tile_1, x = var_1098_cast_fp16)[name = string("op_1101_cast_fp16")];
            tensor<int32, [2]> var_1104_split_sizes_0 = const()[name = string("op_1104_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_1104_axis_0 = const()[name = string("op_1104_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_1104_0, tensor<fp16, [1, 8, 128, 128]> var_1104_1 = split(axis = var_1104_axis_0, split_sizes = var_1104_split_sizes_0, x = query_states_3_cast_fp16)[name = string("op_1104")];
            bool attn_weights_1_transpose_x_0 = const()[name = string("attn_weights_1_transpose_x_0"), val = bool(false)];
            bool attn_weights_1_transpose_y_0 = const()[name = string("attn_weights_1_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_1_cast_fp16 = matmul(transpose_x = attn_weights_1_transpose_x_0, transpose_y = attn_weights_1_transpose_y_0, x = var_1091_cast_fp16_0, y = var_1104_0)[name = string("attn_weights_1_cast_fp16")];
            fp16 var_1107_to_fp16 = const()[name = string("op_1107_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_3_cast_fp16 = mul(x = attn_weights_1_cast_fp16, y = var_1107_to_fp16)[name = string("attn_weights_3_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_5_cast_fp16 = add(x = attn_weights_3_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_5_cast_fp16")];
            int32 var_1111 = const()[name = string("op_1111"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_7_cast_fp16 = softmax(axis = var_1111, x = attn_weights_5_cast_fp16)[name = string("attn_weights_7_cast_fp16")];
            bool var_1117_transpose_x_1 = const()[name = string("op_1117_transpose_x_1"), val = bool(true)];
            bool var_1117_transpose_y_1 = const()[name = string("op_1117_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_1117_cast_fp16 = matmul(transpose_x = var_1117_transpose_x_1, transpose_y = var_1117_transpose_y_1, x = attn_weights_7_cast_fp16, y = var_1101_cast_fp16_0)[name = string("op_1117_cast_fp16")];
            bool attn_weights_9_transpose_x_0 = const()[name = string("attn_weights_9_transpose_x_0"), val = bool(false)];
            bool attn_weights_9_transpose_y_0 = const()[name = string("attn_weights_9_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_9_cast_fp16 = matmul(transpose_x = attn_weights_9_transpose_x_0, transpose_y = attn_weights_9_transpose_y_0, x = var_1091_cast_fp16_1, y = var_1104_1)[name = string("attn_weights_9_cast_fp16")];
            fp16 var_1119_to_fp16 = const()[name = string("op_1119_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_11_cast_fp16 = mul(x = attn_weights_9_cast_fp16, y = var_1119_to_fp16)[name = string("attn_weights_11_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_13_cast_fp16 = add(x = attn_weights_11_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_13_cast_fp16")];
            int32 var_1123 = const()[name = string("op_1123"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_15_cast_fp16 = softmax(axis = var_1123, x = attn_weights_13_cast_fp16)[name = string("attn_weights_15_cast_fp16")];
            bool attn_output_1_transpose_x_1 = const()[name = string("attn_output_1_transpose_x_1"), val = bool(true)];
            bool attn_output_1_transpose_y_1 = const()[name = string("attn_output_1_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_1_cast_fp16 = matmul(transpose_x = attn_output_1_transpose_x_1, transpose_y = attn_output_1_transpose_y_1, x = attn_weights_15_cast_fp16, y = var_1101_cast_fp16_1)[name = string("attn_output_1_cast_fp16")];
            int32 var_1131 = const()[name = string("op_1131"), val = int32(1)];
            bool attn_output_3_interleave_0 = const()[name = string("attn_output_3_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_3_cast_fp16 = concat(axis = var_1131, interleave = attn_output_3_interleave_0, values = (var_1117_cast_fp16, attn_output_1_cast_fp16))[name = string("attn_output_3_cast_fp16")];
            tensor<int32, [4]> var_1135_perm_0 = const()[name = string("op_1135_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_11x = const()[name = string("concat_11x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_1135_cast_fp16 = transpose(perm = var_1135_perm_0, x = attn_output_3_cast_fp16)[name = string("transpose_603")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_7_cast_fp16 = reshape(shape = concat_11x, x = var_1135_cast_fp16)[name = string("attn_output_7_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_0_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(985274560)))];
            tensor<int32, [2]> hidden_states_3_strides_0 = const()[name = string("hidden_states_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_3_pad_type_0 = const()[name = string("hidden_states_3_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_3_pad_0 = const()[name = string("hidden_states_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_3_dilations_0 = const()[name = string("hidden_states_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_3_groups_0 = const()[name = string("hidden_states_3_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_3_cast_fp16 = conv(dilations = hidden_states_3_dilations_0, groups = hidden_states_3_groups_0, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = hidden_states_3_strides_0, weight = layers_0_self_attn_o_proj_weight_to_fp16, x = attn_output_7_cast_fp16)[name = string("hidden_states_3_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_5_cast_fp16 = add(x = inputs_embeds_to_fp16, y = hidden_states_3_cast_fp16)[name = string("hidden_states_5_cast_fp16")];
            fp16 const_10_promoted_to_fp16 = const()[name = string("const_10_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_1168_cast_fp16 = mul(x = hidden_states_5_cast_fp16, y = const_10_promoted_to_fp16)[name = string("op_1168_cast_fp16")];
            int32 var_1166 = const()[name = string("op_1166"), val = int32(1)];
            bool doubled_5_interleave_0 = const()[name = string("doubled_5_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_5_cast_fp16 = concat(axis = var_1166, interleave = doubled_5_interleave_0, values = (hidden_states_5_cast_fp16, var_1168_cast_fp16))[name = string("doubled_5_cast_fp16")];
            tensor<int32, [1]> out_3_axes_0 = const()[name = string("out_3_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_3_gamma_0_to_fp16 = const()[name = string("out_3_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(993663232)))];
            fp16 var_1178_to_fp16 = const()[name = string("op_1178_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_3_cast_fp16 = layer_norm(axes = out_3_axes_0, epsilon = var_1178_to_fp16, gamma = out_3_gamma_0_to_fp16, x = doubled_5_cast_fp16)[name = string("out_3_cast_fp16")];
            tensor<int32, [2]> var_1189_split_sizes_0 = const()[name = string("op_1189_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1189_axis_0 = const()[name = string("op_1189_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_1189_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_1189_cast_fp16_1 = split(axis = var_1189_axis_0, split_sizes = var_1189_split_sizes_0, x = out_3_cast_fp16)[name = string("op_1189_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_0_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_0_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(993671488)))];
            tensor<int32, [2]> input_1_strides_0 = const()[name = string("input_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_1_pad_type_0 = const()[name = string("input_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_1_pad_0 = const()[name = string("input_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_1_dilations_0 = const()[name = string("input_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_1_groups_0 = const()[name = string("input_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_1_cast_fp16 = conv(dilations = input_1_dilations_0, groups = input_1_groups_0, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = input_1_strides_0, weight = layers_0_mlp_gate_proj_weight_to_fp16, x = var_1189_cast_fp16_0)[name = string("input_1_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_1206_cast_fp16 = silu(x = input_1_cast_fp16)[name = string("op_1206_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_0_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_0_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1018837376)))];
            tensor<int32, [2]> var_1212_strides_0 = const()[name = string("op_1212_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_1212_pad_type_0 = const()[name = string("op_1212_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_1212_pad_0 = const()[name = string("op_1212_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_1212_dilations_0 = const()[name = string("op_1212_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1212_groups_0 = const()[name = string("op_1212_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_1212_cast_fp16 = conv(dilations = var_1212_dilations_0, groups = var_1212_groups_0, pad = var_1212_pad_0, pad_type = var_1212_pad_type_0, strides = var_1212_strides_0, weight = layers_0_mlp_up_proj_weight_to_fp16, x = var_1189_cast_fp16_0)[name = string("op_1212_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_9_cast_fp16 = mul(x = var_1206_cast_fp16, y = var_1212_cast_fp16)[name = string("x_9_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_0_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_0_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1044003264)))];
            tensor<int32, [2]> hidden_states_7_strides_0 = const()[name = string("hidden_states_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_7_pad_type_0 = const()[name = string("hidden_states_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_7_pad_0 = const()[name = string("hidden_states_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_7_dilations_0 = const()[name = string("hidden_states_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_7_groups_0 = const()[name = string("hidden_states_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_7_cast_fp16 = conv(dilations = hidden_states_7_dilations_0, groups = hidden_states_7_groups_0, pad = hidden_states_7_pad_0, pad_type = hidden_states_7_pad_type_0, strides = hidden_states_7_strides_0, weight = layers_0_mlp_down_proj_weight_to_fp16, x = x_9_cast_fp16)[name = string("hidden_states_7_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_9_cast_fp16 = add(x = hidden_states_5_cast_fp16, y = hidden_states_7_cast_fp16)[name = string("hidden_states_9_cast_fp16")];
            fp16 const_12_promoted_to_fp16 = const()[name = string("const_12_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_1230_cast_fp16 = mul(x = hidden_states_9_cast_fp16, y = const_12_promoted_to_fp16)[name = string("op_1230_cast_fp16")];
            int32 var_1228 = const()[name = string("op_1228"), val = int32(1)];
            bool doubled_9_interleave_0 = const()[name = string("doubled_9_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_9_cast_fp16 = concat(axis = var_1228, interleave = doubled_9_interleave_0, values = (hidden_states_9_cast_fp16, var_1230_cast_fp16))[name = string("doubled_9_cast_fp16")];
            tensor<int32, [1]> out_5_axes_0 = const()[name = string("out_5_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_5_gamma_0_to_fp16 = const()[name = string("out_5_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1069169152)))];
            fp16 var_1240_to_fp16 = const()[name = string("op_1240_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_5_cast_fp16 = layer_norm(axes = out_5_axes_0, epsilon = var_1240_to_fp16, gamma = out_5_gamma_0_to_fp16, x = doubled_9_cast_fp16)[name = string("out_5_cast_fp16")];
            tensor<int32, [2]> var_1251_split_sizes_0 = const()[name = string("op_1251_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1251_axis_0 = const()[name = string("op_1251_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_1251_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_1251_cast_fp16_1 = split(axis = var_1251_axis_0, split_sizes = var_1251_split_sizes_0, x = out_5_cast_fp16)[name = string("op_1251_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_1_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_1_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1069177408)))];
            tensor<int32, [2]> query_states_7_strides_0 = const()[name = string("query_states_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_7_pad_type_0 = const()[name = string("query_states_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_7_pad_0 = const()[name = string("query_states_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_7_dilations_0 = const()[name = string("query_states_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_7_groups_0 = const()[name = string("query_states_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_7_cast_fp16 = conv(dilations = query_states_7_dilations_0, groups = query_states_7_groups_0, pad = query_states_7_pad_0, pad_type = query_states_7_pad_type_0, strides = query_states_7_strides_0, weight = layers_1_self_attn_q_proj_weight_to_fp16, x = var_1251_cast_fp16_0)[name = string("query_states_7_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_1_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_1_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1077566080)))];
            tensor<int32, [2]> key_states_11_strides_0 = const()[name = string("key_states_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_11_pad_type_0 = const()[name = string("key_states_11_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_11_pad_0 = const()[name = string("key_states_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_11_dilations_0 = const()[name = string("key_states_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_11_groups_0 = const()[name = string("key_states_11_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_11_cast_fp16 = conv(dilations = key_states_11_dilations_0, groups = key_states_11_groups_0, pad = key_states_11_pad_0, pad_type = key_states_11_pad_type_0, strides = key_states_11_strides_0, weight = layers_1_self_attn_k_proj_weight_to_fp16, x = var_1251_cast_fp16_0)[name = string("key_states_11_cast_fp16")];
            tensor<int32, [2]> value_states_7_strides_0 = const()[name = string("value_states_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_7_pad_type_0 = const()[name = string("value_states_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_7_pad_0 = const()[name = string("value_states_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_7_dilations_0 = const()[name = string("value_states_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_7_groups_0 = const()[name = string("value_states_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_7_cast_fp16 = conv(dilations = value_states_7_dilations_0, groups = value_states_7_groups_0, pad = value_states_7_pad_0, pad_type = value_states_7_pad_type_0, strides = value_states_7_strides_0, weight = layers_1_self_attn_v_proj_weight_cast_fp16, x = var_1251_cast_fp16_0)[name = string("value_states_7_cast_fp16")];
            tensor<int32, [4]> concat_12x = const()[name = string("concat_12x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_11_cast_fp16 = reshape(shape = concat_12x, x = query_states_7_cast_fp16)[name = string("x_11_cast_fp16")];
            tensor<int32, [4]> concat_13x = const()[name = string("concat_13x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_1308_cast_fp16 = reshape(shape = concat_13x, x = key_states_11_cast_fp16)[name = string("op_1308_cast_fp16")];
            tensor<int32, [4]> concat_14x = const()[name = string("concat_14x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_1315_cast_fp16 = reshape(shape = concat_14x, x = value_states_7_cast_fp16)[name = string("op_1315_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_1319_cast_fp16 = mul(x = x_11_cast_fp16, y = var_859_cast_fp16)[name = string("op_1319_cast_fp16")];
            tensor<int32, [2]> var_1320_split_sizes_0 = const()[name = string("op_1320_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1320_axis_0 = const()[name = string("op_1320_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_1320_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_1320_cast_fp16_1 = split(axis = var_1320_axis_0, split_sizes = var_1320_split_sizes_0, x = x_11_cast_fp16)[name = string("op_1320_cast_fp16")];
            fp16 const_14_promoted_to_fp16 = const()[name = string("const_14_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_1322_cast_fp16 = mul(x = var_1320_cast_fp16_1, y = const_14_promoted_to_fp16)[name = string("op_1322_cast_fp16")];
            int32 var_1324 = const()[name = string("op_1324"), val = int32(-2)];
            bool var_1325_interleave_0 = const()[name = string("op_1325_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_1325_cast_fp16 = concat(axis = var_1324, interleave = var_1325_interleave_0, values = (var_1322_cast_fp16, var_1320_cast_fp16_0))[name = string("op_1325_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_1326_cast_fp16 = mul(x = var_1325_cast_fp16, y = var_866_cast_fp16)[name = string("op_1326_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_9_cast_fp16 = add(x = var_1319_cast_fp16, y = var_1326_cast_fp16)[name = string("query_states_9_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_1332_cast_fp16 = mul(x = var_1308_cast_fp16, y = var_859_cast_fp16)[name = string("op_1332_cast_fp16")];
            tensor<int32, [2]> var_1333_split_sizes_0 = const()[name = string("op_1333_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1333_axis_0 = const()[name = string("op_1333_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_1333_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_1333_cast_fp16_1 = split(axis = var_1333_axis_0, split_sizes = var_1333_split_sizes_0, x = var_1308_cast_fp16)[name = string("op_1333_cast_fp16")];
            fp16 const_15_promoted_to_fp16 = const()[name = string("const_15_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_1335_cast_fp16 = mul(x = var_1333_cast_fp16_1, y = const_15_promoted_to_fp16)[name = string("op_1335_cast_fp16")];
            int32 var_1337 = const()[name = string("op_1337"), val = int32(-2)];
            bool var_1338_interleave_0 = const()[name = string("op_1338_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_1338_cast_fp16 = concat(axis = var_1337, interleave = var_1338_interleave_0, values = (var_1335_cast_fp16, var_1333_cast_fp16_0))[name = string("op_1338_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_1339_cast_fp16 = mul(x = var_1338_cast_fp16, y = var_866_cast_fp16)[name = string("op_1339_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_15_cast_fp16 = add(x = var_1332_cast_fp16, y = var_1339_cast_fp16)[name = string("key_states_15_cast_fp16")];
            tensor<int32, [1]> expand_dims_12 = const()[name = string("expand_dims_12"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> expand_dims_13 = const()[name = string("expand_dims_13"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_15 = const()[name = string("expand_dims_15"), val = tensor<int32, [1]>([0])];
            int32 concat_17_axis_0 = const()[name = string("concat_17_axis_0"), val = int32(0)];
            bool concat_17_interleave_0 = const()[name = string("concat_17_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_17 = concat(axis = concat_17_axis_0, interleave = concat_17_interleave_0, values = (expand_dims_12, expand_dims_13, position_id, expand_dims_15))[name = string("concat_17")];
            tensor<int32, [1]> expand_dims_16 = const()[name = string("expand_dims_16"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> concat_18_values1_0 = const()[name = string("concat_18_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_18_values3_0 = const()[name = string("concat_18_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_18_axis_0 = const()[name = string("concat_18_axis_0"), val = int32(0)];
            bool concat_18_interleave_0 = const()[name = string("concat_18_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_18 = concat(axis = concat_18_axis_0, interleave = concat_18_interleave_0, values = (expand_dims_16, concat_18_values1_0, cache_position_end, concat_18_values3_0))[name = string("concat_18")];
            tensor<int32, [4]> key_states_17_perm_0 = const()[name = string("key_states_17_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_2_stride_0 = const()[name = string("key_cache_internal_tensor_assign_2_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_2_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_2_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_2_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_2_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_2_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_2_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_17_cast_fp16 = transpose(perm = key_states_17_perm_0, x = key_states_15_cast_fp16)[name = string("transpose_602")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_2_cast_fp16 = slice_update(begin = concat_17, begin_mask = key_cache_internal_tensor_assign_2_begin_mask_0, end = concat_18, end_mask = key_cache_internal_tensor_assign_2_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_2_squeeze_mask_0, stride = key_cache_internal_tensor_assign_2_stride_0, update = key_states_17_cast_fp16, x = coreml_update_state_336)[name = string("key_cache_internal_tensor_assign_2_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_2_cast_fp16, input = key_cache)[name = string("coreml_update_state_338_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_338 = read_state(input = key_cache)[name = string("coreml_update_state_338")];
            tensor<int32, [4]> value_states_9_perm_0 = const()[name = string("value_states_9_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_2_stride_0 = const()[name = string("value_cache_internal_tensor_assign_2_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_2_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_2_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_2_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_2_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_2_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_2_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_9_cast_fp16 = transpose(perm = value_states_9_perm_0, x = var_1315_cast_fp16)[name = string("transpose_601")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_2_cast_fp16 = slice_update(begin = concat_17, begin_mask = value_cache_internal_tensor_assign_2_begin_mask_0, end = concat_18, end_mask = value_cache_internal_tensor_assign_2_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_2_squeeze_mask_0, stride = value_cache_internal_tensor_assign_2_stride_0, update = value_states_9_cast_fp16, x = coreml_update_state_337)[name = string("value_cache_internal_tensor_assign_2_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_2_cast_fp16, input = value_cache)[name = string("coreml_update_state_339_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_339 = read_state(input = value_cache)[name = string("coreml_update_state_339")];
            tensor<int32, [4]> var_1409_begin_0 = const()[name = string("op_1409_begin_0"), val = tensor<int32, [4]>([1, 0, 0, 0])];
            tensor<int32, [4]> var_1409_end_0 = const()[name = string("op_1409_end_0"), val = tensor<int32, [4]>([2, 2, 2048, 128])];
            tensor<bool, [4]> var_1409_end_mask_0 = const()[name = string("op_1409_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1409_cast_fp16 = slice_by_index(begin = var_1409_begin_0, end = var_1409_end_0, end_mask = var_1409_end_mask_0, x = coreml_update_state_338)[name = string("op_1409_cast_fp16")];
            tensor<int32, [2]> tile_2 = const()[name = string("tile_2"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1412_axis_0 = const()[name = string("op_1412_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1412_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1412_cast_fp16_1 = split(axis = var_1412_axis_0, split_sizes = tile_2, x = var_1409_cast_fp16)[name = string("op_1412_cast_fp16")];
            tensor<int32, [4]> var_1419_begin_0 = const()[name = string("op_1419_begin_0"), val = tensor<int32, [4]>([1, 0, 0, 0])];
            tensor<int32, [4]> var_1419_end_0 = const()[name = string("op_1419_end_0"), val = tensor<int32, [4]>([2, 2, 2048, 128])];
            tensor<bool, [4]> var_1419_end_mask_0 = const()[name = string("op_1419_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1419_cast_fp16 = slice_by_index(begin = var_1419_begin_0, end = var_1419_end_0, end_mask = var_1419_end_mask_0, x = coreml_update_state_339)[name = string("op_1419_cast_fp16")];
            tensor<int32, [2]> tile_3 = const()[name = string("tile_3"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1422_axis_0 = const()[name = string("op_1422_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1422_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1422_cast_fp16_1 = split(axis = var_1422_axis_0, split_sizes = tile_3, x = var_1419_cast_fp16)[name = string("op_1422_cast_fp16")];
            tensor<int32, [2]> var_1425_split_sizes_0 = const()[name = string("op_1425_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_1425_axis_0 = const()[name = string("op_1425_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_1425_0, tensor<fp16, [1, 8, 128, 128]> var_1425_1 = split(axis = var_1425_axis_0, split_sizes = var_1425_split_sizes_0, x = query_states_9_cast_fp16)[name = string("op_1425")];
            bool attn_weights_17_transpose_x_0 = const()[name = string("attn_weights_17_transpose_x_0"), val = bool(false)];
            bool attn_weights_17_transpose_y_0 = const()[name = string("attn_weights_17_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_17_cast_fp16 = matmul(transpose_x = attn_weights_17_transpose_x_0, transpose_y = attn_weights_17_transpose_y_0, x = var_1412_cast_fp16_0, y = var_1425_0)[name = string("attn_weights_17_cast_fp16")];
            fp16 var_1428_to_fp16 = const()[name = string("op_1428_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_19_cast_fp16 = mul(x = attn_weights_17_cast_fp16, y = var_1428_to_fp16)[name = string("attn_weights_19_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_21_cast_fp16 = add(x = attn_weights_19_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_21_cast_fp16")];
            int32 var_1432 = const()[name = string("op_1432"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_23_cast_fp16 = softmax(axis = var_1432, x = attn_weights_21_cast_fp16)[name = string("attn_weights_23_cast_fp16")];
            bool var_1438_transpose_x_1 = const()[name = string("op_1438_transpose_x_1"), val = bool(true)];
            bool var_1438_transpose_y_1 = const()[name = string("op_1438_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_1438_cast_fp16 = matmul(transpose_x = var_1438_transpose_x_1, transpose_y = var_1438_transpose_y_1, x = attn_weights_23_cast_fp16, y = var_1422_cast_fp16_0)[name = string("op_1438_cast_fp16")];
            bool attn_weights_25_transpose_x_0 = const()[name = string("attn_weights_25_transpose_x_0"), val = bool(false)];
            bool attn_weights_25_transpose_y_0 = const()[name = string("attn_weights_25_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_25_cast_fp16 = matmul(transpose_x = attn_weights_25_transpose_x_0, transpose_y = attn_weights_25_transpose_y_0, x = var_1412_cast_fp16_1, y = var_1425_1)[name = string("attn_weights_25_cast_fp16")];
            fp16 var_1440_to_fp16 = const()[name = string("op_1440_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_27_cast_fp16 = mul(x = attn_weights_25_cast_fp16, y = var_1440_to_fp16)[name = string("attn_weights_27_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_29_cast_fp16 = add(x = attn_weights_27_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_29_cast_fp16")];
            int32 var_1444 = const()[name = string("op_1444"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_31_cast_fp16 = softmax(axis = var_1444, x = attn_weights_29_cast_fp16)[name = string("attn_weights_31_cast_fp16")];
            bool attn_output_9_transpose_x_1 = const()[name = string("attn_output_9_transpose_x_1"), val = bool(true)];
            bool attn_output_9_transpose_y_1 = const()[name = string("attn_output_9_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_9_cast_fp16 = matmul(transpose_x = attn_output_9_transpose_x_1, transpose_y = attn_output_9_transpose_y_1, x = attn_weights_31_cast_fp16, y = var_1422_cast_fp16_1)[name = string("attn_output_9_cast_fp16")];
            int32 var_1452 = const()[name = string("op_1452"), val = int32(1)];
            bool attn_output_11_interleave_0 = const()[name = string("attn_output_11_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_11_cast_fp16 = concat(axis = var_1452, interleave = attn_output_11_interleave_0, values = (var_1438_cast_fp16, attn_output_9_cast_fp16))[name = string("attn_output_11_cast_fp16")];
            tensor<int32, [4]> var_1456_perm_0 = const()[name = string("op_1456_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_23x = const()[name = string("concat_23x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_1456_cast_fp16 = transpose(perm = var_1456_perm_0, x = attn_output_11_cast_fp16)[name = string("transpose_600")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_15_cast_fp16 = reshape(shape = concat_23x, x = var_1456_cast_fp16)[name = string("attn_output_15_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_1_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_1_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1078614720)))];
            tensor<int32, [2]> hidden_states_13_strides_0 = const()[name = string("hidden_states_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_13_pad_type_0 = const()[name = string("hidden_states_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_13_pad_0 = const()[name = string("hidden_states_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_13_dilations_0 = const()[name = string("hidden_states_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_13_groups_0 = const()[name = string("hidden_states_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_13_cast_fp16 = conv(dilations = hidden_states_13_dilations_0, groups = hidden_states_13_groups_0, pad = hidden_states_13_pad_0, pad_type = hidden_states_13_pad_type_0, strides = hidden_states_13_strides_0, weight = layers_1_self_attn_o_proj_weight_to_fp16, x = attn_output_15_cast_fp16)[name = string("hidden_states_13_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_15_cast_fp16 = add(x = hidden_states_9_cast_fp16, y = hidden_states_13_cast_fp16)[name = string("hidden_states_15_cast_fp16")];
            fp16 const_20_promoted_to_fp16 = const()[name = string("const_20_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_1489_cast_fp16 = mul(x = hidden_states_15_cast_fp16, y = const_20_promoted_to_fp16)[name = string("op_1489_cast_fp16")];
            int32 var_1487 = const()[name = string("op_1487"), val = int32(1)];
            bool doubled_13_interleave_0 = const()[name = string("doubled_13_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_13_cast_fp16 = concat(axis = var_1487, interleave = doubled_13_interleave_0, values = (hidden_states_15_cast_fp16, var_1489_cast_fp16))[name = string("doubled_13_cast_fp16")];
            tensor<int32, [1]> out_7_axes_0 = const()[name = string("out_7_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_7_gamma_0_to_fp16 = const()[name = string("out_7_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1087003392)))];
            fp16 var_1499_to_fp16 = const()[name = string("op_1499_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_7_cast_fp16 = layer_norm(axes = out_7_axes_0, epsilon = var_1499_to_fp16, gamma = out_7_gamma_0_to_fp16, x = doubled_13_cast_fp16)[name = string("out_7_cast_fp16")];
            tensor<int32, [2]> var_1510_split_sizes_0 = const()[name = string("op_1510_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1510_axis_0 = const()[name = string("op_1510_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_1510_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_1510_cast_fp16_1 = split(axis = var_1510_axis_0, split_sizes = var_1510_split_sizes_0, x = out_7_cast_fp16)[name = string("op_1510_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_1_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_1_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1087011648)))];
            tensor<int32, [2]> input_3_strides_0 = const()[name = string("input_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_3_dilations_0 = const()[name = string("input_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_3_groups_0 = const()[name = string("input_3_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_3_cast_fp16 = conv(dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = layers_1_mlp_gate_proj_weight_to_fp16, x = var_1510_cast_fp16_0)[name = string("input_3_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_1527_cast_fp16 = silu(x = input_3_cast_fp16)[name = string("op_1527_cast_fp16")];
            tensor<int32, [2]> var_1533_strides_0 = const()[name = string("op_1533_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_1533_pad_type_0 = const()[name = string("op_1533_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_1533_pad_0 = const()[name = string("op_1533_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_1533_dilations_0 = const()[name = string("op_1533_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1533_groups_0 = const()[name = string("op_1533_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_1533_cast_fp16 = conv(dilations = var_1533_dilations_0, groups = var_1533_groups_0, pad = var_1533_pad_0, pad_type = var_1533_pad_type_0, strides = var_1533_strides_0, weight = layers_1_mlp_up_proj_weight_cast_fp16, x = var_1510_cast_fp16_0)[name = string("op_1533_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_19_cast_fp16 = mul(x = var_1527_cast_fp16, y = var_1533_cast_fp16)[name = string("x_19_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_1_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_1_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1112177536)))];
            tensor<int32, [2]> hidden_states_17_strides_0 = const()[name = string("hidden_states_17_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_17_pad_type_0 = const()[name = string("hidden_states_17_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_17_pad_0 = const()[name = string("hidden_states_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_17_dilations_0 = const()[name = string("hidden_states_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_17_groups_0 = const()[name = string("hidden_states_17_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_17_cast_fp16 = conv(dilations = hidden_states_17_dilations_0, groups = hidden_states_17_groups_0, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = hidden_states_17_strides_0, weight = layers_1_mlp_down_proj_weight_to_fp16, x = x_19_cast_fp16)[name = string("hidden_states_17_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_19_cast_fp16 = add(x = hidden_states_15_cast_fp16, y = hidden_states_17_cast_fp16)[name = string("hidden_states_19_cast_fp16")];
            fp16 const_22_promoted_to_fp16 = const()[name = string("const_22_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_1551_cast_fp16 = mul(x = hidden_states_19_cast_fp16, y = const_22_promoted_to_fp16)[name = string("op_1551_cast_fp16")];
            int32 var_1549 = const()[name = string("op_1549"), val = int32(1)];
            bool doubled_17_interleave_0 = const()[name = string("doubled_17_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_17_cast_fp16 = concat(axis = var_1549, interleave = doubled_17_interleave_0, values = (hidden_states_19_cast_fp16, var_1551_cast_fp16))[name = string("doubled_17_cast_fp16")];
            tensor<int32, [1]> out_9_axes_0 = const()[name = string("out_9_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_9_gamma_0_to_fp16 = const()[name = string("out_9_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1137343424)))];
            fp16 var_1561_to_fp16 = const()[name = string("op_1561_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_9_cast_fp16 = layer_norm(axes = out_9_axes_0, epsilon = var_1561_to_fp16, gamma = out_9_gamma_0_to_fp16, x = doubled_17_cast_fp16)[name = string("out_9_cast_fp16")];
            tensor<int32, [2]> var_1572_split_sizes_0 = const()[name = string("op_1572_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1572_axis_0 = const()[name = string("op_1572_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_1572_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_1572_cast_fp16_1 = split(axis = var_1572_axis_0, split_sizes = var_1572_split_sizes_0, x = out_9_cast_fp16)[name = string("op_1572_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_2_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_2_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1137351680)))];
            tensor<int32, [2]> query_states_13_strides_0 = const()[name = string("query_states_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_13_pad_type_0 = const()[name = string("query_states_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_13_pad_0 = const()[name = string("query_states_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_13_dilations_0 = const()[name = string("query_states_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_13_groups_0 = const()[name = string("query_states_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_13_cast_fp16 = conv(dilations = query_states_13_dilations_0, groups = query_states_13_groups_0, pad = query_states_13_pad_0, pad_type = query_states_13_pad_type_0, strides = query_states_13_strides_0, weight = layers_2_self_attn_q_proj_weight_to_fp16, x = var_1572_cast_fp16_0)[name = string("query_states_13_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_2_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_2_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1145740352)))];
            tensor<int32, [2]> key_states_21_strides_0 = const()[name = string("key_states_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_21_pad_type_0 = const()[name = string("key_states_21_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_21_pad_0 = const()[name = string("key_states_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_21_dilations_0 = const()[name = string("key_states_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_21_groups_0 = const()[name = string("key_states_21_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_21_cast_fp16 = conv(dilations = key_states_21_dilations_0, groups = key_states_21_groups_0, pad = key_states_21_pad_0, pad_type = key_states_21_pad_type_0, strides = key_states_21_strides_0, weight = layers_2_self_attn_k_proj_weight_to_fp16, x = var_1572_cast_fp16_0)[name = string("key_states_21_cast_fp16")];
            tensor<int32, [2]> value_states_13_strides_0 = const()[name = string("value_states_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_13_pad_type_0 = const()[name = string("value_states_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_13_pad_0 = const()[name = string("value_states_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_13_dilations_0 = const()[name = string("value_states_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_13_groups_0 = const()[name = string("value_states_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_13_cast_fp16 = conv(dilations = value_states_13_dilations_0, groups = value_states_13_groups_0, pad = value_states_13_pad_0, pad_type = value_states_13_pad_type_0, strides = value_states_13_strides_0, weight = layers_2_self_attn_v_proj_weight_cast_fp16, x = var_1572_cast_fp16_0)[name = string("value_states_13_cast_fp16")];
            tensor<int32, [4]> concat_24x = const()[name = string("concat_24x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_21_cast_fp16 = reshape(shape = concat_24x, x = query_states_13_cast_fp16)[name = string("x_21_cast_fp16")];
            tensor<int32, [4]> concat_25x = const()[name = string("concat_25x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_1629_cast_fp16 = reshape(shape = concat_25x, x = key_states_21_cast_fp16)[name = string("op_1629_cast_fp16")];
            tensor<int32, [4]> concat_26x = const()[name = string("concat_26x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_1636_cast_fp16 = reshape(shape = concat_26x, x = value_states_13_cast_fp16)[name = string("op_1636_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_1640_cast_fp16 = mul(x = x_21_cast_fp16, y = var_859_cast_fp16)[name = string("op_1640_cast_fp16")];
            tensor<int32, [2]> var_1641_split_sizes_0 = const()[name = string("op_1641_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1641_axis_0 = const()[name = string("op_1641_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_1641_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_1641_cast_fp16_1 = split(axis = var_1641_axis_0, split_sizes = var_1641_split_sizes_0, x = x_21_cast_fp16)[name = string("op_1641_cast_fp16")];
            fp16 const_24_promoted_to_fp16 = const()[name = string("const_24_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_1643_cast_fp16 = mul(x = var_1641_cast_fp16_1, y = const_24_promoted_to_fp16)[name = string("op_1643_cast_fp16")];
            int32 var_1645 = const()[name = string("op_1645"), val = int32(-2)];
            bool var_1646_interleave_0 = const()[name = string("op_1646_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_1646_cast_fp16 = concat(axis = var_1645, interleave = var_1646_interleave_0, values = (var_1643_cast_fp16, var_1641_cast_fp16_0))[name = string("op_1646_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_1647_cast_fp16 = mul(x = var_1646_cast_fp16, y = var_866_cast_fp16)[name = string("op_1647_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_15_cast_fp16 = add(x = var_1640_cast_fp16, y = var_1647_cast_fp16)[name = string("query_states_15_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_1653_cast_fp16 = mul(x = var_1629_cast_fp16, y = var_859_cast_fp16)[name = string("op_1653_cast_fp16")];
            tensor<int32, [2]> var_1654_split_sizes_0 = const()[name = string("op_1654_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1654_axis_0 = const()[name = string("op_1654_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_1654_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_1654_cast_fp16_1 = split(axis = var_1654_axis_0, split_sizes = var_1654_split_sizes_0, x = var_1629_cast_fp16)[name = string("op_1654_cast_fp16")];
            fp16 const_25_promoted_to_fp16 = const()[name = string("const_25_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_1656_cast_fp16 = mul(x = var_1654_cast_fp16_1, y = const_25_promoted_to_fp16)[name = string("op_1656_cast_fp16")];
            int32 var_1658 = const()[name = string("op_1658"), val = int32(-2)];
            bool var_1659_interleave_0 = const()[name = string("op_1659_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_1659_cast_fp16 = concat(axis = var_1658, interleave = var_1659_interleave_0, values = (var_1656_cast_fp16, var_1654_cast_fp16_0))[name = string("op_1659_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_1660_cast_fp16 = mul(x = var_1659_cast_fp16, y = var_866_cast_fp16)[name = string("op_1660_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_25_cast_fp16 = add(x = var_1653_cast_fp16, y = var_1660_cast_fp16)[name = string("key_states_25_cast_fp16")];
            tensor<int32, [1]> expand_dims_24 = const()[name = string("expand_dims_24"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> expand_dims_25 = const()[name = string("expand_dims_25"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_27 = const()[name = string("expand_dims_27"), val = tensor<int32, [1]>([0])];
            int32 concat_29_axis_0 = const()[name = string("concat_29_axis_0"), val = int32(0)];
            bool concat_29_interleave_0 = const()[name = string("concat_29_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_29 = concat(axis = concat_29_axis_0, interleave = concat_29_interleave_0, values = (expand_dims_24, expand_dims_25, position_id, expand_dims_27))[name = string("concat_29")];
            tensor<int32, [1]> expand_dims_28 = const()[name = string("expand_dims_28"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> concat_30_values1_0 = const()[name = string("concat_30_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_30_values3_0 = const()[name = string("concat_30_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_30_axis_0 = const()[name = string("concat_30_axis_0"), val = int32(0)];
            bool concat_30_interleave_0 = const()[name = string("concat_30_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_30 = concat(axis = concat_30_axis_0, interleave = concat_30_interleave_0, values = (expand_dims_28, concat_30_values1_0, cache_position_end, concat_30_values3_0))[name = string("concat_30")];
            tensor<int32, [4]> key_states_27_perm_0 = const()[name = string("key_states_27_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_3_stride_0 = const()[name = string("key_cache_internal_tensor_assign_3_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_3_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_3_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_3_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_3_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_3_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_3_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_27_cast_fp16 = transpose(perm = key_states_27_perm_0, x = key_states_25_cast_fp16)[name = string("transpose_599")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_3_cast_fp16 = slice_update(begin = concat_29, begin_mask = key_cache_internal_tensor_assign_3_begin_mask_0, end = concat_30, end_mask = key_cache_internal_tensor_assign_3_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_3_squeeze_mask_0, stride = key_cache_internal_tensor_assign_3_stride_0, update = key_states_27_cast_fp16, x = coreml_update_state_338)[name = string("key_cache_internal_tensor_assign_3_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_3_cast_fp16, input = key_cache)[name = string("coreml_update_state_340_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_340 = read_state(input = key_cache)[name = string("coreml_update_state_340")];
            tensor<int32, [4]> value_states_15_perm_0 = const()[name = string("value_states_15_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_3_stride_0 = const()[name = string("value_cache_internal_tensor_assign_3_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_3_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_3_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_3_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_3_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_3_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_3_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_15_cast_fp16 = transpose(perm = value_states_15_perm_0, x = var_1636_cast_fp16)[name = string("transpose_598")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_3_cast_fp16 = slice_update(begin = concat_29, begin_mask = value_cache_internal_tensor_assign_3_begin_mask_0, end = concat_30, end_mask = value_cache_internal_tensor_assign_3_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_3_squeeze_mask_0, stride = value_cache_internal_tensor_assign_3_stride_0, update = value_states_15_cast_fp16, x = coreml_update_state_339)[name = string("value_cache_internal_tensor_assign_3_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_3_cast_fp16, input = value_cache)[name = string("coreml_update_state_341_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_341 = read_state(input = value_cache)[name = string("coreml_update_state_341")];
            tensor<int32, [4]> var_1730_begin_0 = const()[name = string("op_1730_begin_0"), val = tensor<int32, [4]>([2, 0, 0, 0])];
            tensor<int32, [4]> var_1730_end_0 = const()[name = string("op_1730_end_0"), val = tensor<int32, [4]>([3, 2, 2048, 128])];
            tensor<bool, [4]> var_1730_end_mask_0 = const()[name = string("op_1730_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1730_cast_fp16 = slice_by_index(begin = var_1730_begin_0, end = var_1730_end_0, end_mask = var_1730_end_mask_0, x = coreml_update_state_340)[name = string("op_1730_cast_fp16")];
            tensor<int32, [2]> tile_4 = const()[name = string("tile_4"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1733_axis_0 = const()[name = string("op_1733_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1733_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1733_cast_fp16_1 = split(axis = var_1733_axis_0, split_sizes = tile_4, x = var_1730_cast_fp16)[name = string("op_1733_cast_fp16")];
            tensor<int32, [4]> var_1740_begin_0 = const()[name = string("op_1740_begin_0"), val = tensor<int32, [4]>([2, 0, 0, 0])];
            tensor<int32, [4]> var_1740_end_0 = const()[name = string("op_1740_end_0"), val = tensor<int32, [4]>([3, 2, 2048, 128])];
            tensor<bool, [4]> var_1740_end_mask_0 = const()[name = string("op_1740_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1740_cast_fp16 = slice_by_index(begin = var_1740_begin_0, end = var_1740_end_0, end_mask = var_1740_end_mask_0, x = coreml_update_state_341)[name = string("op_1740_cast_fp16")];
            tensor<int32, [2]> tile_5 = const()[name = string("tile_5"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1743_axis_0 = const()[name = string("op_1743_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1743_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1743_cast_fp16_1 = split(axis = var_1743_axis_0, split_sizes = tile_5, x = var_1740_cast_fp16)[name = string("op_1743_cast_fp16")];
            tensor<int32, [2]> var_1746_split_sizes_0 = const()[name = string("op_1746_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_1746_axis_0 = const()[name = string("op_1746_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_1746_0, tensor<fp16, [1, 8, 128, 128]> var_1746_1 = split(axis = var_1746_axis_0, split_sizes = var_1746_split_sizes_0, x = query_states_15_cast_fp16)[name = string("op_1746")];
            bool attn_weights_33_transpose_x_0 = const()[name = string("attn_weights_33_transpose_x_0"), val = bool(false)];
            bool attn_weights_33_transpose_y_0 = const()[name = string("attn_weights_33_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_33_cast_fp16 = matmul(transpose_x = attn_weights_33_transpose_x_0, transpose_y = attn_weights_33_transpose_y_0, x = var_1733_cast_fp16_0, y = var_1746_0)[name = string("attn_weights_33_cast_fp16")];
            fp16 var_1749_to_fp16 = const()[name = string("op_1749_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_35_cast_fp16 = mul(x = attn_weights_33_cast_fp16, y = var_1749_to_fp16)[name = string("attn_weights_35_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_37_cast_fp16 = add(x = attn_weights_35_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_37_cast_fp16")];
            int32 var_1753 = const()[name = string("op_1753"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_39_cast_fp16 = softmax(axis = var_1753, x = attn_weights_37_cast_fp16)[name = string("attn_weights_39_cast_fp16")];
            bool var_1759_transpose_x_1 = const()[name = string("op_1759_transpose_x_1"), val = bool(true)];
            bool var_1759_transpose_y_1 = const()[name = string("op_1759_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_1759_cast_fp16 = matmul(transpose_x = var_1759_transpose_x_1, transpose_y = var_1759_transpose_y_1, x = attn_weights_39_cast_fp16, y = var_1743_cast_fp16_0)[name = string("op_1759_cast_fp16")];
            bool attn_weights_41_transpose_x_0 = const()[name = string("attn_weights_41_transpose_x_0"), val = bool(false)];
            bool attn_weights_41_transpose_y_0 = const()[name = string("attn_weights_41_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_41_cast_fp16 = matmul(transpose_x = attn_weights_41_transpose_x_0, transpose_y = attn_weights_41_transpose_y_0, x = var_1733_cast_fp16_1, y = var_1746_1)[name = string("attn_weights_41_cast_fp16")];
            fp16 var_1761_to_fp16 = const()[name = string("op_1761_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_43_cast_fp16 = mul(x = attn_weights_41_cast_fp16, y = var_1761_to_fp16)[name = string("attn_weights_43_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_45_cast_fp16 = add(x = attn_weights_43_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_45_cast_fp16")];
            int32 var_1765 = const()[name = string("op_1765"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_47_cast_fp16 = softmax(axis = var_1765, x = attn_weights_45_cast_fp16)[name = string("attn_weights_47_cast_fp16")];
            bool attn_output_17_transpose_x_1 = const()[name = string("attn_output_17_transpose_x_1"), val = bool(true)];
            bool attn_output_17_transpose_y_1 = const()[name = string("attn_output_17_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_17_cast_fp16 = matmul(transpose_x = attn_output_17_transpose_x_1, transpose_y = attn_output_17_transpose_y_1, x = attn_weights_47_cast_fp16, y = var_1743_cast_fp16_1)[name = string("attn_output_17_cast_fp16")];
            int32 var_1773 = const()[name = string("op_1773"), val = int32(1)];
            bool attn_output_19_interleave_0 = const()[name = string("attn_output_19_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_19_cast_fp16 = concat(axis = var_1773, interleave = attn_output_19_interleave_0, values = (var_1759_cast_fp16, attn_output_17_cast_fp16))[name = string("attn_output_19_cast_fp16")];
            tensor<int32, [4]> var_1777_perm_0 = const()[name = string("op_1777_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_35x = const()[name = string("concat_35x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_1777_cast_fp16 = transpose(perm = var_1777_perm_0, x = attn_output_19_cast_fp16)[name = string("transpose_597")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_23_cast_fp16 = reshape(shape = concat_35x, x = var_1777_cast_fp16)[name = string("attn_output_23_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_2_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_2_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1146788992)))];
            tensor<int32, [2]> hidden_states_23_strides_0 = const()[name = string("hidden_states_23_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_23_pad_type_0 = const()[name = string("hidden_states_23_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_23_pad_0 = const()[name = string("hidden_states_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_23_dilations_0 = const()[name = string("hidden_states_23_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_23_groups_0 = const()[name = string("hidden_states_23_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_23_cast_fp16 = conv(dilations = hidden_states_23_dilations_0, groups = hidden_states_23_groups_0, pad = hidden_states_23_pad_0, pad_type = hidden_states_23_pad_type_0, strides = hidden_states_23_strides_0, weight = layers_2_self_attn_o_proj_weight_to_fp16, x = attn_output_23_cast_fp16)[name = string("hidden_states_23_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_25_cast_fp16 = add(x = hidden_states_19_cast_fp16, y = hidden_states_23_cast_fp16)[name = string("hidden_states_25_cast_fp16")];
            fp16 const_30_promoted_to_fp16 = const()[name = string("const_30_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_1810_cast_fp16 = mul(x = hidden_states_25_cast_fp16, y = const_30_promoted_to_fp16)[name = string("op_1810_cast_fp16")];
            int32 var_1808 = const()[name = string("op_1808"), val = int32(1)];
            bool doubled_21_interleave_0 = const()[name = string("doubled_21_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_21_cast_fp16 = concat(axis = var_1808, interleave = doubled_21_interleave_0, values = (hidden_states_25_cast_fp16, var_1810_cast_fp16))[name = string("doubled_21_cast_fp16")];
            tensor<int32, [1]> out_11_axes_0 = const()[name = string("out_11_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_11_gamma_0_to_fp16 = const()[name = string("out_11_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1155177664)))];
            fp16 var_1820_to_fp16 = const()[name = string("op_1820_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_11_cast_fp16 = layer_norm(axes = out_11_axes_0, epsilon = var_1820_to_fp16, gamma = out_11_gamma_0_to_fp16, x = doubled_21_cast_fp16)[name = string("out_11_cast_fp16")];
            tensor<int32, [2]> var_1831_split_sizes_0 = const()[name = string("op_1831_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1831_axis_0 = const()[name = string("op_1831_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_1831_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_1831_cast_fp16_1 = split(axis = var_1831_axis_0, split_sizes = var_1831_split_sizes_0, x = out_11_cast_fp16)[name = string("op_1831_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_2_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_2_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1155185920)))];
            tensor<int32, [2]> input_5_strides_0 = const()[name = string("input_5_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_5_pad_type_0 = const()[name = string("input_5_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_5_dilations_0 = const()[name = string("input_5_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_5_groups_0 = const()[name = string("input_5_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_5_cast_fp16 = conv(dilations = input_5_dilations_0, groups = input_5_groups_0, pad = input_5_pad_0, pad_type = input_5_pad_type_0, strides = input_5_strides_0, weight = layers_2_mlp_gate_proj_weight_to_fp16, x = var_1831_cast_fp16_0)[name = string("input_5_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_1848_cast_fp16 = silu(x = input_5_cast_fp16)[name = string("op_1848_cast_fp16")];
            tensor<int32, [2]> var_1854_strides_0 = const()[name = string("op_1854_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_1854_pad_type_0 = const()[name = string("op_1854_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_1854_pad_0 = const()[name = string("op_1854_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_1854_dilations_0 = const()[name = string("op_1854_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1854_groups_0 = const()[name = string("op_1854_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_1854_cast_fp16 = conv(dilations = var_1854_dilations_0, groups = var_1854_groups_0, pad = var_1854_pad_0, pad_type = var_1854_pad_type_0, strides = var_1854_strides_0, weight = layers_2_mlp_up_proj_weight_cast_fp16, x = var_1831_cast_fp16_0)[name = string("op_1854_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_29_cast_fp16 = mul(x = var_1848_cast_fp16, y = var_1854_cast_fp16)[name = string("x_29_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_2_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_2_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1180351808)))];
            tensor<int32, [2]> hidden_states_27_strides_0 = const()[name = string("hidden_states_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_27_pad_type_0 = const()[name = string("hidden_states_27_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_27_pad_0 = const()[name = string("hidden_states_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_27_dilations_0 = const()[name = string("hidden_states_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_27_groups_0 = const()[name = string("hidden_states_27_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_27_cast_fp16 = conv(dilations = hidden_states_27_dilations_0, groups = hidden_states_27_groups_0, pad = hidden_states_27_pad_0, pad_type = hidden_states_27_pad_type_0, strides = hidden_states_27_strides_0, weight = layers_2_mlp_down_proj_weight_to_fp16, x = x_29_cast_fp16)[name = string("hidden_states_27_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_29_cast_fp16 = add(x = hidden_states_25_cast_fp16, y = hidden_states_27_cast_fp16)[name = string("hidden_states_29_cast_fp16")];
            fp16 const_32_promoted_to_fp16 = const()[name = string("const_32_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_1872_cast_fp16 = mul(x = hidden_states_29_cast_fp16, y = const_32_promoted_to_fp16)[name = string("op_1872_cast_fp16")];
            int32 var_1870 = const()[name = string("op_1870"), val = int32(1)];
            bool doubled_25_interleave_0 = const()[name = string("doubled_25_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_25_cast_fp16 = concat(axis = var_1870, interleave = doubled_25_interleave_0, values = (hidden_states_29_cast_fp16, var_1872_cast_fp16))[name = string("doubled_25_cast_fp16")];
            tensor<int32, [1]> out_13_axes_0 = const()[name = string("out_13_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_13_gamma_0_to_fp16 = const()[name = string("out_13_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1205517696)))];
            fp16 var_1882_to_fp16 = const()[name = string("op_1882_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_13_cast_fp16 = layer_norm(axes = out_13_axes_0, epsilon = var_1882_to_fp16, gamma = out_13_gamma_0_to_fp16, x = doubled_25_cast_fp16)[name = string("out_13_cast_fp16")];
            tensor<int32, [2]> var_1893_split_sizes_0 = const()[name = string("op_1893_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1893_axis_0 = const()[name = string("op_1893_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_1893_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_1893_cast_fp16_1 = split(axis = var_1893_axis_0, split_sizes = var_1893_split_sizes_0, x = out_13_cast_fp16)[name = string("op_1893_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_3_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_3_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1205525952)))];
            tensor<int32, [2]> query_states_19_strides_0 = const()[name = string("query_states_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_19_pad_type_0 = const()[name = string("query_states_19_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_19_pad_0 = const()[name = string("query_states_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_19_dilations_0 = const()[name = string("query_states_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_19_groups_0 = const()[name = string("query_states_19_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_19_cast_fp16 = conv(dilations = query_states_19_dilations_0, groups = query_states_19_groups_0, pad = query_states_19_pad_0, pad_type = query_states_19_pad_type_0, strides = query_states_19_strides_0, weight = layers_3_self_attn_q_proj_weight_to_fp16, x = var_1893_cast_fp16_0)[name = string("query_states_19_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_3_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_3_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1213914624)))];
            tensor<int32, [2]> key_states_31_strides_0 = const()[name = string("key_states_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_31_pad_type_0 = const()[name = string("key_states_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_31_pad_0 = const()[name = string("key_states_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_31_dilations_0 = const()[name = string("key_states_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_31_groups_0 = const()[name = string("key_states_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_31_cast_fp16 = conv(dilations = key_states_31_dilations_0, groups = key_states_31_groups_0, pad = key_states_31_pad_0, pad_type = key_states_31_pad_type_0, strides = key_states_31_strides_0, weight = layers_3_self_attn_k_proj_weight_to_fp16, x = var_1893_cast_fp16_0)[name = string("key_states_31_cast_fp16")];
            tensor<int32, [2]> value_states_19_strides_0 = const()[name = string("value_states_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_19_pad_type_0 = const()[name = string("value_states_19_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_19_pad_0 = const()[name = string("value_states_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_19_dilations_0 = const()[name = string("value_states_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_19_groups_0 = const()[name = string("value_states_19_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_19_cast_fp16 = conv(dilations = value_states_19_dilations_0, groups = value_states_19_groups_0, pad = value_states_19_pad_0, pad_type = value_states_19_pad_type_0, strides = value_states_19_strides_0, weight = layers_3_self_attn_v_proj_weight_cast_fp16, x = var_1893_cast_fp16_0)[name = string("value_states_19_cast_fp16")];
            tensor<int32, [4]> concat_36x = const()[name = string("concat_36x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_31_cast_fp16 = reshape(shape = concat_36x, x = query_states_19_cast_fp16)[name = string("x_31_cast_fp16")];
            tensor<int32, [4]> concat_37x = const()[name = string("concat_37x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_1950_cast_fp16 = reshape(shape = concat_37x, x = key_states_31_cast_fp16)[name = string("op_1950_cast_fp16")];
            tensor<int32, [4]> concat_38x = const()[name = string("concat_38x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_1957_cast_fp16 = reshape(shape = concat_38x, x = value_states_19_cast_fp16)[name = string("op_1957_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_1961_cast_fp16 = mul(x = x_31_cast_fp16, y = var_859_cast_fp16)[name = string("op_1961_cast_fp16")];
            tensor<int32, [2]> var_1962_split_sizes_0 = const()[name = string("op_1962_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1962_axis_0 = const()[name = string("op_1962_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_1962_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_1962_cast_fp16_1 = split(axis = var_1962_axis_0, split_sizes = var_1962_split_sizes_0, x = x_31_cast_fp16)[name = string("op_1962_cast_fp16")];
            fp16 const_34_promoted_to_fp16 = const()[name = string("const_34_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_1964_cast_fp16 = mul(x = var_1962_cast_fp16_1, y = const_34_promoted_to_fp16)[name = string("op_1964_cast_fp16")];
            int32 var_1966 = const()[name = string("op_1966"), val = int32(-2)];
            bool var_1967_interleave_0 = const()[name = string("op_1967_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_1967_cast_fp16 = concat(axis = var_1966, interleave = var_1967_interleave_0, values = (var_1964_cast_fp16, var_1962_cast_fp16_0))[name = string("op_1967_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_1968_cast_fp16 = mul(x = var_1967_cast_fp16, y = var_866_cast_fp16)[name = string("op_1968_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_21_cast_fp16 = add(x = var_1961_cast_fp16, y = var_1968_cast_fp16)[name = string("query_states_21_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_1974_cast_fp16 = mul(x = var_1950_cast_fp16, y = var_859_cast_fp16)[name = string("op_1974_cast_fp16")];
            tensor<int32, [2]> var_1975_split_sizes_0 = const()[name = string("op_1975_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1975_axis_0 = const()[name = string("op_1975_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_1975_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_1975_cast_fp16_1 = split(axis = var_1975_axis_0, split_sizes = var_1975_split_sizes_0, x = var_1950_cast_fp16)[name = string("op_1975_cast_fp16")];
            fp16 const_35_promoted_to_fp16 = const()[name = string("const_35_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_1977_cast_fp16 = mul(x = var_1975_cast_fp16_1, y = const_35_promoted_to_fp16)[name = string("op_1977_cast_fp16")];
            int32 var_1979 = const()[name = string("op_1979"), val = int32(-2)];
            bool var_1980_interleave_0 = const()[name = string("op_1980_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_1980_cast_fp16 = concat(axis = var_1979, interleave = var_1980_interleave_0, values = (var_1977_cast_fp16, var_1975_cast_fp16_0))[name = string("op_1980_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_1981_cast_fp16 = mul(x = var_1980_cast_fp16, y = var_866_cast_fp16)[name = string("op_1981_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_35_cast_fp16 = add(x = var_1974_cast_fp16, y = var_1981_cast_fp16)[name = string("key_states_35_cast_fp16")];
            tensor<int32, [1]> expand_dims_36 = const()[name = string("expand_dims_36"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> expand_dims_37 = const()[name = string("expand_dims_37"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_39 = const()[name = string("expand_dims_39"), val = tensor<int32, [1]>([0])];
            int32 concat_41_axis_0 = const()[name = string("concat_41_axis_0"), val = int32(0)];
            bool concat_41_interleave_0 = const()[name = string("concat_41_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_41 = concat(axis = concat_41_axis_0, interleave = concat_41_interleave_0, values = (expand_dims_36, expand_dims_37, position_id, expand_dims_39))[name = string("concat_41")];
            tensor<int32, [1]> expand_dims_40 = const()[name = string("expand_dims_40"), val = tensor<int32, [1]>([4])];
            tensor<int32, [1]> concat_42_values1_0 = const()[name = string("concat_42_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_42_values3_0 = const()[name = string("concat_42_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_42_axis_0 = const()[name = string("concat_42_axis_0"), val = int32(0)];
            bool concat_42_interleave_0 = const()[name = string("concat_42_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_42 = concat(axis = concat_42_axis_0, interleave = concat_42_interleave_0, values = (expand_dims_40, concat_42_values1_0, cache_position_end, concat_42_values3_0))[name = string("concat_42")];
            tensor<int32, [4]> key_states_37_perm_0 = const()[name = string("key_states_37_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_4_stride_0 = const()[name = string("key_cache_internal_tensor_assign_4_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_4_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_4_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_4_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_4_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_4_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_4_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_37_cast_fp16 = transpose(perm = key_states_37_perm_0, x = key_states_35_cast_fp16)[name = string("transpose_596")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_4_cast_fp16 = slice_update(begin = concat_41, begin_mask = key_cache_internal_tensor_assign_4_begin_mask_0, end = concat_42, end_mask = key_cache_internal_tensor_assign_4_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_4_squeeze_mask_0, stride = key_cache_internal_tensor_assign_4_stride_0, update = key_states_37_cast_fp16, x = coreml_update_state_340)[name = string("key_cache_internal_tensor_assign_4_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_4_cast_fp16, input = key_cache)[name = string("coreml_update_state_342_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_342 = read_state(input = key_cache)[name = string("coreml_update_state_342")];
            tensor<int32, [4]> value_states_21_perm_0 = const()[name = string("value_states_21_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_4_stride_0 = const()[name = string("value_cache_internal_tensor_assign_4_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_4_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_4_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_4_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_4_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_4_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_4_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_21_cast_fp16 = transpose(perm = value_states_21_perm_0, x = var_1957_cast_fp16)[name = string("transpose_595")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_4_cast_fp16 = slice_update(begin = concat_41, begin_mask = value_cache_internal_tensor_assign_4_begin_mask_0, end = concat_42, end_mask = value_cache_internal_tensor_assign_4_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_4_squeeze_mask_0, stride = value_cache_internal_tensor_assign_4_stride_0, update = value_states_21_cast_fp16, x = coreml_update_state_341)[name = string("value_cache_internal_tensor_assign_4_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_4_cast_fp16, input = value_cache)[name = string("coreml_update_state_343_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_343 = read_state(input = value_cache)[name = string("coreml_update_state_343")];
            tensor<int32, [4]> var_2051_begin_0 = const()[name = string("op_2051_begin_0"), val = tensor<int32, [4]>([3, 0, 0, 0])];
            tensor<int32, [4]> var_2051_end_0 = const()[name = string("op_2051_end_0"), val = tensor<int32, [4]>([4, 2, 2048, 128])];
            tensor<bool, [4]> var_2051_end_mask_0 = const()[name = string("op_2051_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2051_cast_fp16 = slice_by_index(begin = var_2051_begin_0, end = var_2051_end_0, end_mask = var_2051_end_mask_0, x = coreml_update_state_342)[name = string("op_2051_cast_fp16")];
            tensor<int32, [2]> tile_6 = const()[name = string("tile_6"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2054_axis_0 = const()[name = string("op_2054_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2054_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2054_cast_fp16_1 = split(axis = var_2054_axis_0, split_sizes = tile_6, x = var_2051_cast_fp16)[name = string("op_2054_cast_fp16")];
            tensor<int32, [4]> var_2061_begin_0 = const()[name = string("op_2061_begin_0"), val = tensor<int32, [4]>([3, 0, 0, 0])];
            tensor<int32, [4]> var_2061_end_0 = const()[name = string("op_2061_end_0"), val = tensor<int32, [4]>([4, 2, 2048, 128])];
            tensor<bool, [4]> var_2061_end_mask_0 = const()[name = string("op_2061_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2061_cast_fp16 = slice_by_index(begin = var_2061_begin_0, end = var_2061_end_0, end_mask = var_2061_end_mask_0, x = coreml_update_state_343)[name = string("op_2061_cast_fp16")];
            tensor<int32, [2]> tile_7 = const()[name = string("tile_7"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2064_axis_0 = const()[name = string("op_2064_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2064_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2064_cast_fp16_1 = split(axis = var_2064_axis_0, split_sizes = tile_7, x = var_2061_cast_fp16)[name = string("op_2064_cast_fp16")];
            tensor<int32, [2]> var_2067_split_sizes_0 = const()[name = string("op_2067_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_2067_axis_0 = const()[name = string("op_2067_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_2067_0, tensor<fp16, [1, 8, 128, 128]> var_2067_1 = split(axis = var_2067_axis_0, split_sizes = var_2067_split_sizes_0, x = query_states_21_cast_fp16)[name = string("op_2067")];
            bool attn_weights_49_transpose_x_0 = const()[name = string("attn_weights_49_transpose_x_0"), val = bool(false)];
            bool attn_weights_49_transpose_y_0 = const()[name = string("attn_weights_49_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_49_cast_fp16 = matmul(transpose_x = attn_weights_49_transpose_x_0, transpose_y = attn_weights_49_transpose_y_0, x = var_2054_cast_fp16_0, y = var_2067_0)[name = string("attn_weights_49_cast_fp16")];
            fp16 var_2070_to_fp16 = const()[name = string("op_2070_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_51_cast_fp16 = mul(x = attn_weights_49_cast_fp16, y = var_2070_to_fp16)[name = string("attn_weights_51_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_53_cast_fp16 = add(x = attn_weights_51_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_53_cast_fp16")];
            int32 var_2074 = const()[name = string("op_2074"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_55_cast_fp16 = softmax(axis = var_2074, x = attn_weights_53_cast_fp16)[name = string("attn_weights_55_cast_fp16")];
            bool var_2080_transpose_x_1 = const()[name = string("op_2080_transpose_x_1"), val = bool(true)];
            bool var_2080_transpose_y_1 = const()[name = string("op_2080_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_2080_cast_fp16 = matmul(transpose_x = var_2080_transpose_x_1, transpose_y = var_2080_transpose_y_1, x = attn_weights_55_cast_fp16, y = var_2064_cast_fp16_0)[name = string("op_2080_cast_fp16")];
            bool attn_weights_57_transpose_x_0 = const()[name = string("attn_weights_57_transpose_x_0"), val = bool(false)];
            bool attn_weights_57_transpose_y_0 = const()[name = string("attn_weights_57_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_57_cast_fp16 = matmul(transpose_x = attn_weights_57_transpose_x_0, transpose_y = attn_weights_57_transpose_y_0, x = var_2054_cast_fp16_1, y = var_2067_1)[name = string("attn_weights_57_cast_fp16")];
            fp16 var_2082_to_fp16 = const()[name = string("op_2082_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_59_cast_fp16 = mul(x = attn_weights_57_cast_fp16, y = var_2082_to_fp16)[name = string("attn_weights_59_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_61_cast_fp16 = add(x = attn_weights_59_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_61_cast_fp16")];
            int32 var_2086 = const()[name = string("op_2086"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_63_cast_fp16 = softmax(axis = var_2086, x = attn_weights_61_cast_fp16)[name = string("attn_weights_63_cast_fp16")];
            bool attn_output_25_transpose_x_1 = const()[name = string("attn_output_25_transpose_x_1"), val = bool(true)];
            bool attn_output_25_transpose_y_1 = const()[name = string("attn_output_25_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_25_cast_fp16 = matmul(transpose_x = attn_output_25_transpose_x_1, transpose_y = attn_output_25_transpose_y_1, x = attn_weights_63_cast_fp16, y = var_2064_cast_fp16_1)[name = string("attn_output_25_cast_fp16")];
            int32 var_2094 = const()[name = string("op_2094"), val = int32(1)];
            bool attn_output_27_interleave_0 = const()[name = string("attn_output_27_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_27_cast_fp16 = concat(axis = var_2094, interleave = attn_output_27_interleave_0, values = (var_2080_cast_fp16, attn_output_25_cast_fp16))[name = string("attn_output_27_cast_fp16")];
            tensor<int32, [4]> var_2098_perm_0 = const()[name = string("op_2098_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_47x = const()[name = string("concat_47x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_2098_cast_fp16 = transpose(perm = var_2098_perm_0, x = attn_output_27_cast_fp16)[name = string("transpose_594")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_31_cast_fp16 = reshape(shape = concat_47x, x = var_2098_cast_fp16)[name = string("attn_output_31_cast_fp16")];
            tensor<int32, [2]> hidden_states_33_strides_0 = const()[name = string("hidden_states_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_33_pad_type_0 = const()[name = string("hidden_states_33_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_33_pad_0 = const()[name = string("hidden_states_33_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_33_dilations_0 = const()[name = string("hidden_states_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_33_groups_0 = const()[name = string("hidden_states_33_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_33_cast_fp16 = conv(dilations = hidden_states_33_dilations_0, groups = hidden_states_33_groups_0, pad = hidden_states_33_pad_0, pad_type = hidden_states_33_pad_type_0, strides = hidden_states_33_strides_0, weight = layers_3_self_attn_o_proj_weight_cast_fp16, x = attn_output_31_cast_fp16)[name = string("hidden_states_33_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_35_cast_fp16 = add(x = hidden_states_29_cast_fp16, y = hidden_states_33_cast_fp16)[name = string("hidden_states_35_cast_fp16")];
            fp16 const_40_promoted_to_fp16 = const()[name = string("const_40_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_2131_cast_fp16 = mul(x = hidden_states_35_cast_fp16, y = const_40_promoted_to_fp16)[name = string("op_2131_cast_fp16")];
            int32 var_2129 = const()[name = string("op_2129"), val = int32(1)];
            bool doubled_29_interleave_0 = const()[name = string("doubled_29_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_29_cast_fp16 = concat(axis = var_2129, interleave = doubled_29_interleave_0, values = (hidden_states_35_cast_fp16, var_2131_cast_fp16))[name = string("doubled_29_cast_fp16")];
            tensor<int32, [1]> out_15_axes_0 = const()[name = string("out_15_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_15_gamma_0_to_fp16 = const()[name = string("out_15_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1214963264)))];
            fp16 var_2141_to_fp16 = const()[name = string("op_2141_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_15_cast_fp16 = layer_norm(axes = out_15_axes_0, epsilon = var_2141_to_fp16, gamma = out_15_gamma_0_to_fp16, x = doubled_29_cast_fp16)[name = string("out_15_cast_fp16")];
            tensor<int32, [2]> var_2152_split_sizes_0 = const()[name = string("op_2152_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2152_axis_0 = const()[name = string("op_2152_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_2152_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_2152_cast_fp16_1 = split(axis = var_2152_axis_0, split_sizes = var_2152_split_sizes_0, x = out_15_cast_fp16)[name = string("op_2152_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_3_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_3_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1214971520)))];
            tensor<int32, [2]> input_7_strides_0 = const()[name = string("input_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_7_pad_type_0 = const()[name = string("input_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_7_pad_0 = const()[name = string("input_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_7_dilations_0 = const()[name = string("input_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_7_groups_0 = const()[name = string("input_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_7_cast_fp16 = conv(dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = layers_3_mlp_gate_proj_weight_to_fp16, x = var_2152_cast_fp16_0)[name = string("input_7_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_2169_cast_fp16 = silu(x = input_7_cast_fp16)[name = string("op_2169_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_3_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_3_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1240137408)))];
            tensor<int32, [2]> var_2175_strides_0 = const()[name = string("op_2175_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_2175_pad_type_0 = const()[name = string("op_2175_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_2175_pad_0 = const()[name = string("op_2175_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_2175_dilations_0 = const()[name = string("op_2175_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2175_groups_0 = const()[name = string("op_2175_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_2175_cast_fp16 = conv(dilations = var_2175_dilations_0, groups = var_2175_groups_0, pad = var_2175_pad_0, pad_type = var_2175_pad_type_0, strides = var_2175_strides_0, weight = layers_3_mlp_up_proj_weight_to_fp16, x = var_2152_cast_fp16_0)[name = string("op_2175_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_39_cast_fp16 = mul(x = var_2169_cast_fp16, y = var_2175_cast_fp16)[name = string("x_39_cast_fp16")];
            tensor<int32, [2]> hidden_states_37_strides_0 = const()[name = string("hidden_states_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_37_pad_type_0 = const()[name = string("hidden_states_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_37_pad_0 = const()[name = string("hidden_states_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_37_dilations_0 = const()[name = string("hidden_states_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_37_groups_0 = const()[name = string("hidden_states_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_37_cast_fp16 = conv(dilations = hidden_states_37_dilations_0, groups = hidden_states_37_groups_0, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = hidden_states_37_strides_0, weight = layers_3_mlp_down_proj_weight_cast_fp16, x = x_39_cast_fp16)[name = string("hidden_states_37_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_39_cast_fp16 = add(x = hidden_states_35_cast_fp16, y = hidden_states_37_cast_fp16)[name = string("hidden_states_39_cast_fp16")];
            fp16 const_42_promoted_to_fp16 = const()[name = string("const_42_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_2193_cast_fp16 = mul(x = hidden_states_39_cast_fp16, y = const_42_promoted_to_fp16)[name = string("op_2193_cast_fp16")];
            int32 var_2191 = const()[name = string("op_2191"), val = int32(1)];
            bool doubled_33_interleave_0 = const()[name = string("doubled_33_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_33_cast_fp16 = concat(axis = var_2191, interleave = doubled_33_interleave_0, values = (hidden_states_39_cast_fp16, var_2193_cast_fp16))[name = string("doubled_33_cast_fp16")];
            tensor<int32, [1]> out_17_axes_0 = const()[name = string("out_17_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_17_gamma_0_to_fp16 = const()[name = string("out_17_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1265303296)))];
            fp16 var_2203_to_fp16 = const()[name = string("op_2203_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_17_cast_fp16 = layer_norm(axes = out_17_axes_0, epsilon = var_2203_to_fp16, gamma = out_17_gamma_0_to_fp16, x = doubled_33_cast_fp16)[name = string("out_17_cast_fp16")];
            tensor<int32, [2]> var_2214_split_sizes_0 = const()[name = string("op_2214_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2214_axis_0 = const()[name = string("op_2214_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_2214_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_2214_cast_fp16_1 = split(axis = var_2214_axis_0, split_sizes = var_2214_split_sizes_0, x = out_17_cast_fp16)[name = string("op_2214_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_4_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_4_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1265311552)))];
            tensor<int32, [2]> query_states_25_strides_0 = const()[name = string("query_states_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_25_pad_type_0 = const()[name = string("query_states_25_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_25_pad_0 = const()[name = string("query_states_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_25_dilations_0 = const()[name = string("query_states_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_25_groups_0 = const()[name = string("query_states_25_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_25_cast_fp16 = conv(dilations = query_states_25_dilations_0, groups = query_states_25_groups_0, pad = query_states_25_pad_0, pad_type = query_states_25_pad_type_0, strides = query_states_25_strides_0, weight = layers_4_self_attn_q_proj_weight_to_fp16, x = var_2214_cast_fp16_0)[name = string("query_states_25_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_4_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_4_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1273700224)))];
            tensor<int32, [2]> key_states_41_strides_0 = const()[name = string("key_states_41_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_41_pad_type_0 = const()[name = string("key_states_41_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_41_pad_0 = const()[name = string("key_states_41_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_41_dilations_0 = const()[name = string("key_states_41_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_41_groups_0 = const()[name = string("key_states_41_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_41_cast_fp16 = conv(dilations = key_states_41_dilations_0, groups = key_states_41_groups_0, pad = key_states_41_pad_0, pad_type = key_states_41_pad_type_0, strides = key_states_41_strides_0, weight = layers_4_self_attn_k_proj_weight_to_fp16, x = var_2214_cast_fp16_0)[name = string("key_states_41_cast_fp16")];
            tensor<int32, [2]> value_states_25_strides_0 = const()[name = string("value_states_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_25_pad_type_0 = const()[name = string("value_states_25_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_25_pad_0 = const()[name = string("value_states_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_25_dilations_0 = const()[name = string("value_states_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_25_groups_0 = const()[name = string("value_states_25_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_25_cast_fp16 = conv(dilations = value_states_25_dilations_0, groups = value_states_25_groups_0, pad = value_states_25_pad_0, pad_type = value_states_25_pad_type_0, strides = value_states_25_strides_0, weight = layers_4_self_attn_v_proj_weight_cast_fp16, x = var_2214_cast_fp16_0)[name = string("value_states_25_cast_fp16")];
            tensor<int32, [4]> concat_48x = const()[name = string("concat_48x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_41_cast_fp16 = reshape(shape = concat_48x, x = query_states_25_cast_fp16)[name = string("x_41_cast_fp16")];
            tensor<int32, [4]> concat_49x = const()[name = string("concat_49x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_2271_cast_fp16 = reshape(shape = concat_49x, x = key_states_41_cast_fp16)[name = string("op_2271_cast_fp16")];
            tensor<int32, [4]> concat_50x = const()[name = string("concat_50x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_2278_cast_fp16 = reshape(shape = concat_50x, x = value_states_25_cast_fp16)[name = string("op_2278_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_2282_cast_fp16 = mul(x = x_41_cast_fp16, y = var_859_cast_fp16)[name = string("op_2282_cast_fp16")];
            tensor<int32, [2]> var_2283_split_sizes_0 = const()[name = string("op_2283_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2283_axis_0 = const()[name = string("op_2283_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_2283_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_2283_cast_fp16_1 = split(axis = var_2283_axis_0, split_sizes = var_2283_split_sizes_0, x = x_41_cast_fp16)[name = string("op_2283_cast_fp16")];
            fp16 const_44_promoted_to_fp16 = const()[name = string("const_44_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_2285_cast_fp16 = mul(x = var_2283_cast_fp16_1, y = const_44_promoted_to_fp16)[name = string("op_2285_cast_fp16")];
            int32 var_2287 = const()[name = string("op_2287"), val = int32(-2)];
            bool var_2288_interleave_0 = const()[name = string("op_2288_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_2288_cast_fp16 = concat(axis = var_2287, interleave = var_2288_interleave_0, values = (var_2285_cast_fp16, var_2283_cast_fp16_0))[name = string("op_2288_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_2289_cast_fp16 = mul(x = var_2288_cast_fp16, y = var_866_cast_fp16)[name = string("op_2289_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_27_cast_fp16 = add(x = var_2282_cast_fp16, y = var_2289_cast_fp16)[name = string("query_states_27_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_2295_cast_fp16 = mul(x = var_2271_cast_fp16, y = var_859_cast_fp16)[name = string("op_2295_cast_fp16")];
            tensor<int32, [2]> var_2296_split_sizes_0 = const()[name = string("op_2296_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2296_axis_0 = const()[name = string("op_2296_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_2296_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_2296_cast_fp16_1 = split(axis = var_2296_axis_0, split_sizes = var_2296_split_sizes_0, x = var_2271_cast_fp16)[name = string("op_2296_cast_fp16")];
            fp16 const_45_promoted_to_fp16 = const()[name = string("const_45_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_2298_cast_fp16 = mul(x = var_2296_cast_fp16_1, y = const_45_promoted_to_fp16)[name = string("op_2298_cast_fp16")];
            int32 var_2300 = const()[name = string("op_2300"), val = int32(-2)];
            bool var_2301_interleave_0 = const()[name = string("op_2301_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_2301_cast_fp16 = concat(axis = var_2300, interleave = var_2301_interleave_0, values = (var_2298_cast_fp16, var_2296_cast_fp16_0))[name = string("op_2301_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_2302_cast_fp16 = mul(x = var_2301_cast_fp16, y = var_866_cast_fp16)[name = string("op_2302_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_45_cast_fp16 = add(x = var_2295_cast_fp16, y = var_2302_cast_fp16)[name = string("key_states_45_cast_fp16")];
            tensor<int32, [1]> expand_dims_48 = const()[name = string("expand_dims_48"), val = tensor<int32, [1]>([4])];
            tensor<int32, [1]> expand_dims_49 = const()[name = string("expand_dims_49"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_51 = const()[name = string("expand_dims_51"), val = tensor<int32, [1]>([0])];
            int32 concat_53_axis_0 = const()[name = string("concat_53_axis_0"), val = int32(0)];
            bool concat_53_interleave_0 = const()[name = string("concat_53_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_53 = concat(axis = concat_53_axis_0, interleave = concat_53_interleave_0, values = (expand_dims_48, expand_dims_49, position_id, expand_dims_51))[name = string("concat_53")];
            tensor<int32, [1]> expand_dims_52 = const()[name = string("expand_dims_52"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> concat_54_values1_0 = const()[name = string("concat_54_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_54_values3_0 = const()[name = string("concat_54_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_54_axis_0 = const()[name = string("concat_54_axis_0"), val = int32(0)];
            bool concat_54_interleave_0 = const()[name = string("concat_54_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_54 = concat(axis = concat_54_axis_0, interleave = concat_54_interleave_0, values = (expand_dims_52, concat_54_values1_0, cache_position_end, concat_54_values3_0))[name = string("concat_54")];
            tensor<int32, [4]> key_states_47_perm_0 = const()[name = string("key_states_47_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_5_stride_0 = const()[name = string("key_cache_internal_tensor_assign_5_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_5_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_5_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_5_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_5_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_5_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_5_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_47_cast_fp16 = transpose(perm = key_states_47_perm_0, x = key_states_45_cast_fp16)[name = string("transpose_593")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_5_cast_fp16 = slice_update(begin = concat_53, begin_mask = key_cache_internal_tensor_assign_5_begin_mask_0, end = concat_54, end_mask = key_cache_internal_tensor_assign_5_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_5_squeeze_mask_0, stride = key_cache_internal_tensor_assign_5_stride_0, update = key_states_47_cast_fp16, x = coreml_update_state_342)[name = string("key_cache_internal_tensor_assign_5_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_5_cast_fp16, input = key_cache)[name = string("coreml_update_state_344_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_344 = read_state(input = key_cache)[name = string("coreml_update_state_344")];
            tensor<int32, [4]> value_states_27_perm_0 = const()[name = string("value_states_27_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_5_stride_0 = const()[name = string("value_cache_internal_tensor_assign_5_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_5_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_5_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_5_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_5_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_5_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_5_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_27_cast_fp16 = transpose(perm = value_states_27_perm_0, x = var_2278_cast_fp16)[name = string("transpose_592")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_5_cast_fp16 = slice_update(begin = concat_53, begin_mask = value_cache_internal_tensor_assign_5_begin_mask_0, end = concat_54, end_mask = value_cache_internal_tensor_assign_5_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_5_squeeze_mask_0, stride = value_cache_internal_tensor_assign_5_stride_0, update = value_states_27_cast_fp16, x = coreml_update_state_343)[name = string("value_cache_internal_tensor_assign_5_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_5_cast_fp16, input = value_cache)[name = string("coreml_update_state_345_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_345 = read_state(input = value_cache)[name = string("coreml_update_state_345")];
            tensor<int32, [4]> var_2372_begin_0 = const()[name = string("op_2372_begin_0"), val = tensor<int32, [4]>([4, 0, 0, 0])];
            tensor<int32, [4]> var_2372_end_0 = const()[name = string("op_2372_end_0"), val = tensor<int32, [4]>([5, 2, 2048, 128])];
            tensor<bool, [4]> var_2372_end_mask_0 = const()[name = string("op_2372_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2372_cast_fp16 = slice_by_index(begin = var_2372_begin_0, end = var_2372_end_0, end_mask = var_2372_end_mask_0, x = coreml_update_state_344)[name = string("op_2372_cast_fp16")];
            tensor<int32, [2]> tile_8 = const()[name = string("tile_8"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2375_axis_0 = const()[name = string("op_2375_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2375_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2375_cast_fp16_1 = split(axis = var_2375_axis_0, split_sizes = tile_8, x = var_2372_cast_fp16)[name = string("op_2375_cast_fp16")];
            tensor<int32, [4]> var_2382_begin_0 = const()[name = string("op_2382_begin_0"), val = tensor<int32, [4]>([4, 0, 0, 0])];
            tensor<int32, [4]> var_2382_end_0 = const()[name = string("op_2382_end_0"), val = tensor<int32, [4]>([5, 2, 2048, 128])];
            tensor<bool, [4]> var_2382_end_mask_0 = const()[name = string("op_2382_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2382_cast_fp16 = slice_by_index(begin = var_2382_begin_0, end = var_2382_end_0, end_mask = var_2382_end_mask_0, x = coreml_update_state_345)[name = string("op_2382_cast_fp16")];
            tensor<int32, [2]> tile_9 = const()[name = string("tile_9"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2385_axis_0 = const()[name = string("op_2385_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2385_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2385_cast_fp16_1 = split(axis = var_2385_axis_0, split_sizes = tile_9, x = var_2382_cast_fp16)[name = string("op_2385_cast_fp16")];
            tensor<int32, [2]> var_2388_split_sizes_0 = const()[name = string("op_2388_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_2388_axis_0 = const()[name = string("op_2388_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_2388_0, tensor<fp16, [1, 8, 128, 128]> var_2388_1 = split(axis = var_2388_axis_0, split_sizes = var_2388_split_sizes_0, x = query_states_27_cast_fp16)[name = string("op_2388")];
            bool attn_weights_65_transpose_x_0 = const()[name = string("attn_weights_65_transpose_x_0"), val = bool(false)];
            bool attn_weights_65_transpose_y_0 = const()[name = string("attn_weights_65_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_65_cast_fp16 = matmul(transpose_x = attn_weights_65_transpose_x_0, transpose_y = attn_weights_65_transpose_y_0, x = var_2375_cast_fp16_0, y = var_2388_0)[name = string("attn_weights_65_cast_fp16")];
            fp16 var_2391_to_fp16 = const()[name = string("op_2391_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_67_cast_fp16 = mul(x = attn_weights_65_cast_fp16, y = var_2391_to_fp16)[name = string("attn_weights_67_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_69_cast_fp16 = add(x = attn_weights_67_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_69_cast_fp16")];
            int32 var_2395 = const()[name = string("op_2395"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_71_cast_fp16 = softmax(axis = var_2395, x = attn_weights_69_cast_fp16)[name = string("attn_weights_71_cast_fp16")];
            bool var_2401_transpose_x_1 = const()[name = string("op_2401_transpose_x_1"), val = bool(true)];
            bool var_2401_transpose_y_1 = const()[name = string("op_2401_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_2401_cast_fp16 = matmul(transpose_x = var_2401_transpose_x_1, transpose_y = var_2401_transpose_y_1, x = attn_weights_71_cast_fp16, y = var_2385_cast_fp16_0)[name = string("op_2401_cast_fp16")];
            bool attn_weights_73_transpose_x_0 = const()[name = string("attn_weights_73_transpose_x_0"), val = bool(false)];
            bool attn_weights_73_transpose_y_0 = const()[name = string("attn_weights_73_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_73_cast_fp16 = matmul(transpose_x = attn_weights_73_transpose_x_0, transpose_y = attn_weights_73_transpose_y_0, x = var_2375_cast_fp16_1, y = var_2388_1)[name = string("attn_weights_73_cast_fp16")];
            fp16 var_2403_to_fp16 = const()[name = string("op_2403_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_75_cast_fp16 = mul(x = attn_weights_73_cast_fp16, y = var_2403_to_fp16)[name = string("attn_weights_75_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_77_cast_fp16 = add(x = attn_weights_75_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_77_cast_fp16")];
            int32 var_2407 = const()[name = string("op_2407"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_79_cast_fp16 = softmax(axis = var_2407, x = attn_weights_77_cast_fp16)[name = string("attn_weights_79_cast_fp16")];
            bool attn_output_33_transpose_x_1 = const()[name = string("attn_output_33_transpose_x_1"), val = bool(true)];
            bool attn_output_33_transpose_y_1 = const()[name = string("attn_output_33_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_33_cast_fp16 = matmul(transpose_x = attn_output_33_transpose_x_1, transpose_y = attn_output_33_transpose_y_1, x = attn_weights_79_cast_fp16, y = var_2385_cast_fp16_1)[name = string("attn_output_33_cast_fp16")];
            int32 var_2415 = const()[name = string("op_2415"), val = int32(1)];
            bool attn_output_35_interleave_0 = const()[name = string("attn_output_35_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_35_cast_fp16 = concat(axis = var_2415, interleave = attn_output_35_interleave_0, values = (var_2401_cast_fp16, attn_output_33_cast_fp16))[name = string("attn_output_35_cast_fp16")];
            tensor<int32, [4]> var_2419_perm_0 = const()[name = string("op_2419_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_59x = const()[name = string("concat_59x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_2419_cast_fp16 = transpose(perm = var_2419_perm_0, x = attn_output_35_cast_fp16)[name = string("transpose_591")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_39_cast_fp16 = reshape(shape = concat_59x, x = var_2419_cast_fp16)[name = string("attn_output_39_cast_fp16")];
            tensor<int32, [2]> hidden_states_43_strides_0 = const()[name = string("hidden_states_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_43_pad_type_0 = const()[name = string("hidden_states_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_43_pad_0 = const()[name = string("hidden_states_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_43_dilations_0 = const()[name = string("hidden_states_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_43_groups_0 = const()[name = string("hidden_states_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_43_cast_fp16 = conv(dilations = hidden_states_43_dilations_0, groups = hidden_states_43_groups_0, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = hidden_states_43_strides_0, weight = layers_4_self_attn_o_proj_weight_cast_fp16, x = attn_output_39_cast_fp16)[name = string("hidden_states_43_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_45_cast_fp16 = add(x = hidden_states_39_cast_fp16, y = hidden_states_43_cast_fp16)[name = string("hidden_states_45_cast_fp16")];
            fp16 const_50_promoted_to_fp16 = const()[name = string("const_50_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_2452_cast_fp16 = mul(x = hidden_states_45_cast_fp16, y = const_50_promoted_to_fp16)[name = string("op_2452_cast_fp16")];
            int32 var_2450 = const()[name = string("op_2450"), val = int32(1)];
            bool doubled_37_interleave_0 = const()[name = string("doubled_37_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_37_cast_fp16 = concat(axis = var_2450, interleave = doubled_37_interleave_0, values = (hidden_states_45_cast_fp16, var_2452_cast_fp16))[name = string("doubled_37_cast_fp16")];
            tensor<int32, [1]> out_19_axes_0 = const()[name = string("out_19_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_19_gamma_0_to_fp16 = const()[name = string("out_19_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1274748864)))];
            fp16 var_2462_to_fp16 = const()[name = string("op_2462_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_19_cast_fp16 = layer_norm(axes = out_19_axes_0, epsilon = var_2462_to_fp16, gamma = out_19_gamma_0_to_fp16, x = doubled_37_cast_fp16)[name = string("out_19_cast_fp16")];
            tensor<int32, [2]> var_2473_split_sizes_0 = const()[name = string("op_2473_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2473_axis_0 = const()[name = string("op_2473_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_2473_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_2473_cast_fp16_1 = split(axis = var_2473_axis_0, split_sizes = var_2473_split_sizes_0, x = out_19_cast_fp16)[name = string("op_2473_cast_fp16")];
            tensor<int32, [2]> input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_9_cast_fp16 = conv(dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = layers_4_mlp_gate_proj_weight_cast_fp16, x = var_2473_cast_fp16_0)[name = string("input_9_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_2490_cast_fp16 = silu(x = input_9_cast_fp16)[name = string("op_2490_cast_fp16")];
            tensor<int32, [2]> var_2496_strides_0 = const()[name = string("op_2496_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_2496_pad_type_0 = const()[name = string("op_2496_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_2496_pad_0 = const()[name = string("op_2496_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_2496_dilations_0 = const()[name = string("op_2496_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2496_groups_0 = const()[name = string("op_2496_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_2496_cast_fp16 = conv(dilations = var_2496_dilations_0, groups = var_2496_groups_0, pad = var_2496_pad_0, pad_type = var_2496_pad_type_0, strides = var_2496_strides_0, weight = layers_4_mlp_up_proj_weight_cast_fp16, x = var_2473_cast_fp16_0)[name = string("op_2496_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_49_cast_fp16 = mul(x = var_2490_cast_fp16, y = var_2496_cast_fp16)[name = string("x_49_cast_fp16")];
            tensor<int32, [2]> hidden_states_47_strides_0 = const()[name = string("hidden_states_47_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_47_pad_type_0 = const()[name = string("hidden_states_47_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_47_pad_0 = const()[name = string("hidden_states_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_47_dilations_0 = const()[name = string("hidden_states_47_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_47_groups_0 = const()[name = string("hidden_states_47_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_47_cast_fp16 = conv(dilations = hidden_states_47_dilations_0, groups = hidden_states_47_groups_0, pad = hidden_states_47_pad_0, pad_type = hidden_states_47_pad_type_0, strides = hidden_states_47_strides_0, weight = layers_4_mlp_down_proj_weight_cast_fp16, x = x_49_cast_fp16)[name = string("hidden_states_47_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_49_cast_fp16 = add(x = hidden_states_45_cast_fp16, y = hidden_states_47_cast_fp16)[name = string("hidden_states_49_cast_fp16")];
            fp16 const_52_promoted_to_fp16 = const()[name = string("const_52_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_2514_cast_fp16 = mul(x = hidden_states_49_cast_fp16, y = const_52_promoted_to_fp16)[name = string("op_2514_cast_fp16")];
            int32 var_2512 = const()[name = string("op_2512"), val = int32(1)];
            bool doubled_41_interleave_0 = const()[name = string("doubled_41_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_41_cast_fp16 = concat(axis = var_2512, interleave = doubled_41_interleave_0, values = (hidden_states_49_cast_fp16, var_2514_cast_fp16))[name = string("doubled_41_cast_fp16")];
            tensor<int32, [1]> out_21_axes_0 = const()[name = string("out_21_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_21_gamma_0_to_fp16 = const()[name = string("out_21_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1274757120)))];
            fp16 var_2524_to_fp16 = const()[name = string("op_2524_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_21_cast_fp16 = layer_norm(axes = out_21_axes_0, epsilon = var_2524_to_fp16, gamma = out_21_gamma_0_to_fp16, x = doubled_41_cast_fp16)[name = string("out_21_cast_fp16")];
            tensor<int32, [2]> var_2535_split_sizes_0 = const()[name = string("op_2535_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2535_axis_0 = const()[name = string("op_2535_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_2535_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_2535_cast_fp16_1 = split(axis = var_2535_axis_0, split_sizes = var_2535_split_sizes_0, x = out_21_cast_fp16)[name = string("op_2535_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_5_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_5_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1274765376)))];
            tensor<int32, [2]> query_states_31_strides_0 = const()[name = string("query_states_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_31_pad_type_0 = const()[name = string("query_states_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_31_pad_0 = const()[name = string("query_states_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_31_dilations_0 = const()[name = string("query_states_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_31_groups_0 = const()[name = string("query_states_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_31_cast_fp16 = conv(dilations = query_states_31_dilations_0, groups = query_states_31_groups_0, pad = query_states_31_pad_0, pad_type = query_states_31_pad_type_0, strides = query_states_31_strides_0, weight = layers_5_self_attn_q_proj_weight_to_fp16, x = var_2535_cast_fp16_0)[name = string("query_states_31_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_5_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_5_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1283154048)))];
            tensor<int32, [2]> key_states_51_strides_0 = const()[name = string("key_states_51_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_51_pad_type_0 = const()[name = string("key_states_51_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_51_pad_0 = const()[name = string("key_states_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_51_dilations_0 = const()[name = string("key_states_51_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_51_groups_0 = const()[name = string("key_states_51_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_51_cast_fp16 = conv(dilations = key_states_51_dilations_0, groups = key_states_51_groups_0, pad = key_states_51_pad_0, pad_type = key_states_51_pad_type_0, strides = key_states_51_strides_0, weight = layers_5_self_attn_k_proj_weight_to_fp16, x = var_2535_cast_fp16_0)[name = string("key_states_51_cast_fp16")];
            tensor<int32, [2]> value_states_31_strides_0 = const()[name = string("value_states_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_31_pad_type_0 = const()[name = string("value_states_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_31_pad_0 = const()[name = string("value_states_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_31_dilations_0 = const()[name = string("value_states_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_31_groups_0 = const()[name = string("value_states_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_31_cast_fp16 = conv(dilations = value_states_31_dilations_0, groups = value_states_31_groups_0, pad = value_states_31_pad_0, pad_type = value_states_31_pad_type_0, strides = value_states_31_strides_0, weight = layers_5_self_attn_v_proj_weight_cast_fp16, x = var_2535_cast_fp16_0)[name = string("value_states_31_cast_fp16")];
            tensor<int32, [4]> concat_60x = const()[name = string("concat_60x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_51_cast_fp16 = reshape(shape = concat_60x, x = query_states_31_cast_fp16)[name = string("x_51_cast_fp16")];
            tensor<int32, [4]> concat_61x = const()[name = string("concat_61x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_2592_cast_fp16 = reshape(shape = concat_61x, x = key_states_51_cast_fp16)[name = string("op_2592_cast_fp16")];
            tensor<int32, [4]> concat_62x = const()[name = string("concat_62x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_2599_cast_fp16 = reshape(shape = concat_62x, x = value_states_31_cast_fp16)[name = string("op_2599_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_2603_cast_fp16 = mul(x = x_51_cast_fp16, y = var_859_cast_fp16)[name = string("op_2603_cast_fp16")];
            tensor<int32, [2]> var_2604_split_sizes_0 = const()[name = string("op_2604_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2604_axis_0 = const()[name = string("op_2604_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_2604_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_2604_cast_fp16_1 = split(axis = var_2604_axis_0, split_sizes = var_2604_split_sizes_0, x = x_51_cast_fp16)[name = string("op_2604_cast_fp16")];
            fp16 const_54_promoted_to_fp16 = const()[name = string("const_54_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_2606_cast_fp16 = mul(x = var_2604_cast_fp16_1, y = const_54_promoted_to_fp16)[name = string("op_2606_cast_fp16")];
            int32 var_2608 = const()[name = string("op_2608"), val = int32(-2)];
            bool var_2609_interleave_0 = const()[name = string("op_2609_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_2609_cast_fp16 = concat(axis = var_2608, interleave = var_2609_interleave_0, values = (var_2606_cast_fp16, var_2604_cast_fp16_0))[name = string("op_2609_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_2610_cast_fp16 = mul(x = var_2609_cast_fp16, y = var_866_cast_fp16)[name = string("op_2610_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_33_cast_fp16 = add(x = var_2603_cast_fp16, y = var_2610_cast_fp16)[name = string("query_states_33_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_2616_cast_fp16 = mul(x = var_2592_cast_fp16, y = var_859_cast_fp16)[name = string("op_2616_cast_fp16")];
            tensor<int32, [2]> var_2617_split_sizes_0 = const()[name = string("op_2617_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2617_axis_0 = const()[name = string("op_2617_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_2617_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_2617_cast_fp16_1 = split(axis = var_2617_axis_0, split_sizes = var_2617_split_sizes_0, x = var_2592_cast_fp16)[name = string("op_2617_cast_fp16")];
            fp16 const_55_promoted_to_fp16 = const()[name = string("const_55_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_2619_cast_fp16 = mul(x = var_2617_cast_fp16_1, y = const_55_promoted_to_fp16)[name = string("op_2619_cast_fp16")];
            int32 var_2621 = const()[name = string("op_2621"), val = int32(-2)];
            bool var_2622_interleave_0 = const()[name = string("op_2622_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_2622_cast_fp16 = concat(axis = var_2621, interleave = var_2622_interleave_0, values = (var_2619_cast_fp16, var_2617_cast_fp16_0))[name = string("op_2622_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_2623_cast_fp16 = mul(x = var_2622_cast_fp16, y = var_866_cast_fp16)[name = string("op_2623_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_55_cast_fp16 = add(x = var_2616_cast_fp16, y = var_2623_cast_fp16)[name = string("key_states_55_cast_fp16")];
            tensor<int32, [1]> expand_dims_60 = const()[name = string("expand_dims_60"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> expand_dims_61 = const()[name = string("expand_dims_61"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_63 = const()[name = string("expand_dims_63"), val = tensor<int32, [1]>([0])];
            int32 concat_65_axis_0 = const()[name = string("concat_65_axis_0"), val = int32(0)];
            bool concat_65_interleave_0 = const()[name = string("concat_65_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_65 = concat(axis = concat_65_axis_0, interleave = concat_65_interleave_0, values = (expand_dims_60, expand_dims_61, position_id, expand_dims_63))[name = string("concat_65")];
            tensor<int32, [1]> expand_dims_64 = const()[name = string("expand_dims_64"), val = tensor<int32, [1]>([6])];
            tensor<int32, [1]> concat_66_values1_0 = const()[name = string("concat_66_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_66_values3_0 = const()[name = string("concat_66_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_66_axis_0 = const()[name = string("concat_66_axis_0"), val = int32(0)];
            bool concat_66_interleave_0 = const()[name = string("concat_66_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_66 = concat(axis = concat_66_axis_0, interleave = concat_66_interleave_0, values = (expand_dims_64, concat_66_values1_0, cache_position_end, concat_66_values3_0))[name = string("concat_66")];
            tensor<int32, [4]> key_states_57_perm_0 = const()[name = string("key_states_57_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_6_stride_0 = const()[name = string("key_cache_internal_tensor_assign_6_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_6_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_6_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_6_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_6_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_6_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_6_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_57_cast_fp16 = transpose(perm = key_states_57_perm_0, x = key_states_55_cast_fp16)[name = string("transpose_590")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_6_cast_fp16 = slice_update(begin = concat_65, begin_mask = key_cache_internal_tensor_assign_6_begin_mask_0, end = concat_66, end_mask = key_cache_internal_tensor_assign_6_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_6_squeeze_mask_0, stride = key_cache_internal_tensor_assign_6_stride_0, update = key_states_57_cast_fp16, x = coreml_update_state_344)[name = string("key_cache_internal_tensor_assign_6_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_6_cast_fp16, input = key_cache)[name = string("coreml_update_state_346_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_346 = read_state(input = key_cache)[name = string("coreml_update_state_346")];
            tensor<int32, [4]> value_states_33_perm_0 = const()[name = string("value_states_33_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_6_stride_0 = const()[name = string("value_cache_internal_tensor_assign_6_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_6_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_6_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_6_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_6_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_6_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_6_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_33_cast_fp16 = transpose(perm = value_states_33_perm_0, x = var_2599_cast_fp16)[name = string("transpose_589")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_6_cast_fp16 = slice_update(begin = concat_65, begin_mask = value_cache_internal_tensor_assign_6_begin_mask_0, end = concat_66, end_mask = value_cache_internal_tensor_assign_6_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_6_squeeze_mask_0, stride = value_cache_internal_tensor_assign_6_stride_0, update = value_states_33_cast_fp16, x = coreml_update_state_345)[name = string("value_cache_internal_tensor_assign_6_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_6_cast_fp16, input = value_cache)[name = string("coreml_update_state_347_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_347 = read_state(input = value_cache)[name = string("coreml_update_state_347")];
            tensor<int32, [4]> var_2693_begin_0 = const()[name = string("op_2693_begin_0"), val = tensor<int32, [4]>([5, 0, 0, 0])];
            tensor<int32, [4]> var_2693_end_0 = const()[name = string("op_2693_end_0"), val = tensor<int32, [4]>([6, 2, 2048, 128])];
            tensor<bool, [4]> var_2693_end_mask_0 = const()[name = string("op_2693_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2693_cast_fp16 = slice_by_index(begin = var_2693_begin_0, end = var_2693_end_0, end_mask = var_2693_end_mask_0, x = coreml_update_state_346)[name = string("op_2693_cast_fp16")];
            tensor<int32, [2]> tile_10 = const()[name = string("tile_10"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2696_axis_0 = const()[name = string("op_2696_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2696_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2696_cast_fp16_1 = split(axis = var_2696_axis_0, split_sizes = tile_10, x = var_2693_cast_fp16)[name = string("op_2696_cast_fp16")];
            tensor<int32, [4]> var_2703_begin_0 = const()[name = string("op_2703_begin_0"), val = tensor<int32, [4]>([5, 0, 0, 0])];
            tensor<int32, [4]> var_2703_end_0 = const()[name = string("op_2703_end_0"), val = tensor<int32, [4]>([6, 2, 2048, 128])];
            tensor<bool, [4]> var_2703_end_mask_0 = const()[name = string("op_2703_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2703_cast_fp16 = slice_by_index(begin = var_2703_begin_0, end = var_2703_end_0, end_mask = var_2703_end_mask_0, x = coreml_update_state_347)[name = string("op_2703_cast_fp16")];
            tensor<int32, [2]> tile_11 = const()[name = string("tile_11"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2706_axis_0 = const()[name = string("op_2706_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2706_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2706_cast_fp16_1 = split(axis = var_2706_axis_0, split_sizes = tile_11, x = var_2703_cast_fp16)[name = string("op_2706_cast_fp16")];
            tensor<int32, [2]> var_2709_split_sizes_0 = const()[name = string("op_2709_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_2709_axis_0 = const()[name = string("op_2709_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_2709_0, tensor<fp16, [1, 8, 128, 128]> var_2709_1 = split(axis = var_2709_axis_0, split_sizes = var_2709_split_sizes_0, x = query_states_33_cast_fp16)[name = string("op_2709")];
            bool attn_weights_81_transpose_x_0 = const()[name = string("attn_weights_81_transpose_x_0"), val = bool(false)];
            bool attn_weights_81_transpose_y_0 = const()[name = string("attn_weights_81_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_81_cast_fp16 = matmul(transpose_x = attn_weights_81_transpose_x_0, transpose_y = attn_weights_81_transpose_y_0, x = var_2696_cast_fp16_0, y = var_2709_0)[name = string("attn_weights_81_cast_fp16")];
            fp16 var_2712_to_fp16 = const()[name = string("op_2712_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_83_cast_fp16 = mul(x = attn_weights_81_cast_fp16, y = var_2712_to_fp16)[name = string("attn_weights_83_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_85_cast_fp16 = add(x = attn_weights_83_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_85_cast_fp16")];
            int32 var_2716 = const()[name = string("op_2716"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_87_cast_fp16 = softmax(axis = var_2716, x = attn_weights_85_cast_fp16)[name = string("attn_weights_87_cast_fp16")];
            bool var_2722_transpose_x_1 = const()[name = string("op_2722_transpose_x_1"), val = bool(true)];
            bool var_2722_transpose_y_1 = const()[name = string("op_2722_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_2722_cast_fp16 = matmul(transpose_x = var_2722_transpose_x_1, transpose_y = var_2722_transpose_y_1, x = attn_weights_87_cast_fp16, y = var_2706_cast_fp16_0)[name = string("op_2722_cast_fp16")];
            bool attn_weights_89_transpose_x_0 = const()[name = string("attn_weights_89_transpose_x_0"), val = bool(false)];
            bool attn_weights_89_transpose_y_0 = const()[name = string("attn_weights_89_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_89_cast_fp16 = matmul(transpose_x = attn_weights_89_transpose_x_0, transpose_y = attn_weights_89_transpose_y_0, x = var_2696_cast_fp16_1, y = var_2709_1)[name = string("attn_weights_89_cast_fp16")];
            fp16 var_2724_to_fp16 = const()[name = string("op_2724_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_91_cast_fp16 = mul(x = attn_weights_89_cast_fp16, y = var_2724_to_fp16)[name = string("attn_weights_91_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_93_cast_fp16 = add(x = attn_weights_91_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_93_cast_fp16")];
            int32 var_2728 = const()[name = string("op_2728"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_95_cast_fp16 = softmax(axis = var_2728, x = attn_weights_93_cast_fp16)[name = string("attn_weights_95_cast_fp16")];
            bool attn_output_41_transpose_x_1 = const()[name = string("attn_output_41_transpose_x_1"), val = bool(true)];
            bool attn_output_41_transpose_y_1 = const()[name = string("attn_output_41_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_41_cast_fp16 = matmul(transpose_x = attn_output_41_transpose_x_1, transpose_y = attn_output_41_transpose_y_1, x = attn_weights_95_cast_fp16, y = var_2706_cast_fp16_1)[name = string("attn_output_41_cast_fp16")];
            int32 var_2736 = const()[name = string("op_2736"), val = int32(1)];
            bool attn_output_43_interleave_0 = const()[name = string("attn_output_43_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_43_cast_fp16 = concat(axis = var_2736, interleave = attn_output_43_interleave_0, values = (var_2722_cast_fp16, attn_output_41_cast_fp16))[name = string("attn_output_43_cast_fp16")];
            tensor<int32, [4]> var_2740_perm_0 = const()[name = string("op_2740_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_71x = const()[name = string("concat_71x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_2740_cast_fp16 = transpose(perm = var_2740_perm_0, x = attn_output_43_cast_fp16)[name = string("transpose_588")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_47_cast_fp16 = reshape(shape = concat_71x, x = var_2740_cast_fp16)[name = string("attn_output_47_cast_fp16")];
            tensor<int32, [2]> hidden_states_53_strides_0 = const()[name = string("hidden_states_53_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_53_pad_type_0 = const()[name = string("hidden_states_53_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_53_pad_0 = const()[name = string("hidden_states_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_53_dilations_0 = const()[name = string("hidden_states_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_53_groups_0 = const()[name = string("hidden_states_53_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_53_cast_fp16 = conv(dilations = hidden_states_53_dilations_0, groups = hidden_states_53_groups_0, pad = hidden_states_53_pad_0, pad_type = hidden_states_53_pad_type_0, strides = hidden_states_53_strides_0, weight = layers_5_self_attn_o_proj_weight_cast_fp16, x = attn_output_47_cast_fp16)[name = string("hidden_states_53_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_55_cast_fp16 = add(x = hidden_states_49_cast_fp16, y = hidden_states_53_cast_fp16)[name = string("hidden_states_55_cast_fp16")];
            fp16 const_60_promoted_to_fp16 = const()[name = string("const_60_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_2773_cast_fp16 = mul(x = hidden_states_55_cast_fp16, y = const_60_promoted_to_fp16)[name = string("op_2773_cast_fp16")];
            int32 var_2771 = const()[name = string("op_2771"), val = int32(1)];
            bool doubled_45_interleave_0 = const()[name = string("doubled_45_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_45_cast_fp16 = concat(axis = var_2771, interleave = doubled_45_interleave_0, values = (hidden_states_55_cast_fp16, var_2773_cast_fp16))[name = string("doubled_45_cast_fp16")];
            tensor<int32, [1]> out_23_axes_0 = const()[name = string("out_23_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_23_gamma_0_to_fp16 = const()[name = string("out_23_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1284202688)))];
            fp16 var_2783_to_fp16 = const()[name = string("op_2783_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_23_cast_fp16 = layer_norm(axes = out_23_axes_0, epsilon = var_2783_to_fp16, gamma = out_23_gamma_0_to_fp16, x = doubled_45_cast_fp16)[name = string("out_23_cast_fp16")];
            tensor<int32, [2]> var_2794_split_sizes_0 = const()[name = string("op_2794_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2794_axis_0 = const()[name = string("op_2794_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_2794_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_2794_cast_fp16_1 = split(axis = var_2794_axis_0, split_sizes = var_2794_split_sizes_0, x = out_23_cast_fp16)[name = string("op_2794_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_5_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_5_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1284210944)))];
            tensor<int32, [2]> input_11_strides_0 = const()[name = string("input_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_11_pad_type_0 = const()[name = string("input_11_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_11_dilations_0 = const()[name = string("input_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_11_groups_0 = const()[name = string("input_11_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_11_cast_fp16 = conv(dilations = input_11_dilations_0, groups = input_11_groups_0, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = input_11_strides_0, weight = layers_5_mlp_gate_proj_weight_to_fp16, x = var_2794_cast_fp16_0)[name = string("input_11_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_2811_cast_fp16 = silu(x = input_11_cast_fp16)[name = string("op_2811_cast_fp16")];
            tensor<int32, [2]> var_2817_strides_0 = const()[name = string("op_2817_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_2817_pad_type_0 = const()[name = string("op_2817_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_2817_pad_0 = const()[name = string("op_2817_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_2817_dilations_0 = const()[name = string("op_2817_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2817_groups_0 = const()[name = string("op_2817_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_2817_cast_fp16 = conv(dilations = var_2817_dilations_0, groups = var_2817_groups_0, pad = var_2817_pad_0, pad_type = var_2817_pad_type_0, strides = var_2817_strides_0, weight = layers_5_mlp_up_proj_weight_cast_fp16, x = var_2794_cast_fp16_0)[name = string("op_2817_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_59_cast_fp16 = mul(x = var_2811_cast_fp16, y = var_2817_cast_fp16)[name = string("x_59_cast_fp16")];
            tensor<int32, [2]> hidden_states_57_strides_0 = const()[name = string("hidden_states_57_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_57_pad_type_0 = const()[name = string("hidden_states_57_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_57_pad_0 = const()[name = string("hidden_states_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_57_dilations_0 = const()[name = string("hidden_states_57_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_57_groups_0 = const()[name = string("hidden_states_57_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_57_cast_fp16 = conv(dilations = hidden_states_57_dilations_0, groups = hidden_states_57_groups_0, pad = hidden_states_57_pad_0, pad_type = hidden_states_57_pad_type_0, strides = hidden_states_57_strides_0, weight = layers_5_mlp_down_proj_weight_cast_fp16, x = x_59_cast_fp16)[name = string("hidden_states_57_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_59_cast_fp16 = add(x = hidden_states_55_cast_fp16, y = hidden_states_57_cast_fp16)[name = string("hidden_states_59_cast_fp16")];
            fp16 const_62_promoted_to_fp16 = const()[name = string("const_62_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_2835_cast_fp16 = mul(x = hidden_states_59_cast_fp16, y = const_62_promoted_to_fp16)[name = string("op_2835_cast_fp16")];
            int32 var_2833 = const()[name = string("op_2833"), val = int32(1)];
            bool doubled_49_interleave_0 = const()[name = string("doubled_49_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_49_cast_fp16 = concat(axis = var_2833, interleave = doubled_49_interleave_0, values = (hidden_states_59_cast_fp16, var_2835_cast_fp16))[name = string("doubled_49_cast_fp16")];
            tensor<int32, [1]> out_25_axes_0 = const()[name = string("out_25_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_25_gamma_0_to_fp16 = const()[name = string("out_25_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1309376832)))];
            fp16 var_2845_to_fp16 = const()[name = string("op_2845_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_25_cast_fp16 = layer_norm(axes = out_25_axes_0, epsilon = var_2845_to_fp16, gamma = out_25_gamma_0_to_fp16, x = doubled_49_cast_fp16)[name = string("out_25_cast_fp16")];
            tensor<int32, [2]> var_2856_split_sizes_0 = const()[name = string("op_2856_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2856_axis_0 = const()[name = string("op_2856_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_2856_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_2856_cast_fp16_1 = split(axis = var_2856_axis_0, split_sizes = var_2856_split_sizes_0, x = out_25_cast_fp16)[name = string("op_2856_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_6_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_6_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1309385088)))];
            tensor<int32, [2]> query_states_37_strides_0 = const()[name = string("query_states_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_37_pad_type_0 = const()[name = string("query_states_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_37_pad_0 = const()[name = string("query_states_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_37_dilations_0 = const()[name = string("query_states_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_37_groups_0 = const()[name = string("query_states_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_37_cast_fp16 = conv(dilations = query_states_37_dilations_0, groups = query_states_37_groups_0, pad = query_states_37_pad_0, pad_type = query_states_37_pad_type_0, strides = query_states_37_strides_0, weight = layers_6_self_attn_q_proj_weight_to_fp16, x = var_2856_cast_fp16_0)[name = string("query_states_37_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_6_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_6_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1317773760)))];
            tensor<int32, [2]> key_states_61_strides_0 = const()[name = string("key_states_61_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_61_pad_type_0 = const()[name = string("key_states_61_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_61_pad_0 = const()[name = string("key_states_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_61_dilations_0 = const()[name = string("key_states_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_61_groups_0 = const()[name = string("key_states_61_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_61_cast_fp16 = conv(dilations = key_states_61_dilations_0, groups = key_states_61_groups_0, pad = key_states_61_pad_0, pad_type = key_states_61_pad_type_0, strides = key_states_61_strides_0, weight = layers_6_self_attn_k_proj_weight_to_fp16, x = var_2856_cast_fp16_0)[name = string("key_states_61_cast_fp16")];
            tensor<int32, [2]> value_states_37_strides_0 = const()[name = string("value_states_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_37_pad_type_0 = const()[name = string("value_states_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_37_pad_0 = const()[name = string("value_states_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_37_dilations_0 = const()[name = string("value_states_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_37_groups_0 = const()[name = string("value_states_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_37_cast_fp16 = conv(dilations = value_states_37_dilations_0, groups = value_states_37_groups_0, pad = value_states_37_pad_0, pad_type = value_states_37_pad_type_0, strides = value_states_37_strides_0, weight = layers_6_self_attn_v_proj_weight_cast_fp16, x = var_2856_cast_fp16_0)[name = string("value_states_37_cast_fp16")];
            tensor<int32, [4]> concat_72x = const()[name = string("concat_72x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_61_cast_fp16 = reshape(shape = concat_72x, x = query_states_37_cast_fp16)[name = string("x_61_cast_fp16")];
            tensor<int32, [4]> concat_73x = const()[name = string("concat_73x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_2913_cast_fp16 = reshape(shape = concat_73x, x = key_states_61_cast_fp16)[name = string("op_2913_cast_fp16")];
            tensor<int32, [4]> concat_74x = const()[name = string("concat_74x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_2920_cast_fp16 = reshape(shape = concat_74x, x = value_states_37_cast_fp16)[name = string("op_2920_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_2924_cast_fp16 = mul(x = x_61_cast_fp16, y = var_859_cast_fp16)[name = string("op_2924_cast_fp16")];
            tensor<int32, [2]> var_2925_split_sizes_0 = const()[name = string("op_2925_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2925_axis_0 = const()[name = string("op_2925_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_2925_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_2925_cast_fp16_1 = split(axis = var_2925_axis_0, split_sizes = var_2925_split_sizes_0, x = x_61_cast_fp16)[name = string("op_2925_cast_fp16")];
            fp16 const_64_promoted_to_fp16 = const()[name = string("const_64_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_2927_cast_fp16 = mul(x = var_2925_cast_fp16_1, y = const_64_promoted_to_fp16)[name = string("op_2927_cast_fp16")];
            int32 var_2929 = const()[name = string("op_2929"), val = int32(-2)];
            bool var_2930_interleave_0 = const()[name = string("op_2930_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_2930_cast_fp16 = concat(axis = var_2929, interleave = var_2930_interleave_0, values = (var_2927_cast_fp16, var_2925_cast_fp16_0))[name = string("op_2930_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_2931_cast_fp16 = mul(x = var_2930_cast_fp16, y = var_866_cast_fp16)[name = string("op_2931_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_39_cast_fp16 = add(x = var_2924_cast_fp16, y = var_2931_cast_fp16)[name = string("query_states_39_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_2937_cast_fp16 = mul(x = var_2913_cast_fp16, y = var_859_cast_fp16)[name = string("op_2937_cast_fp16")];
            tensor<int32, [2]> var_2938_split_sizes_0 = const()[name = string("op_2938_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2938_axis_0 = const()[name = string("op_2938_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_2938_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_2938_cast_fp16_1 = split(axis = var_2938_axis_0, split_sizes = var_2938_split_sizes_0, x = var_2913_cast_fp16)[name = string("op_2938_cast_fp16")];
            fp16 const_65_promoted_to_fp16 = const()[name = string("const_65_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_2940_cast_fp16 = mul(x = var_2938_cast_fp16_1, y = const_65_promoted_to_fp16)[name = string("op_2940_cast_fp16")];
            int32 var_2942 = const()[name = string("op_2942"), val = int32(-2)];
            bool var_2943_interleave_0 = const()[name = string("op_2943_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_2943_cast_fp16 = concat(axis = var_2942, interleave = var_2943_interleave_0, values = (var_2940_cast_fp16, var_2938_cast_fp16_0))[name = string("op_2943_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_2944_cast_fp16 = mul(x = var_2943_cast_fp16, y = var_866_cast_fp16)[name = string("op_2944_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_65_cast_fp16 = add(x = var_2937_cast_fp16, y = var_2944_cast_fp16)[name = string("key_states_65_cast_fp16")];
            tensor<int32, [1]> expand_dims_72 = const()[name = string("expand_dims_72"), val = tensor<int32, [1]>([6])];
            tensor<int32, [1]> expand_dims_73 = const()[name = string("expand_dims_73"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_75 = const()[name = string("expand_dims_75"), val = tensor<int32, [1]>([0])];
            int32 concat_77_axis_0 = const()[name = string("concat_77_axis_0"), val = int32(0)];
            bool concat_77_interleave_0 = const()[name = string("concat_77_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_77 = concat(axis = concat_77_axis_0, interleave = concat_77_interleave_0, values = (expand_dims_72, expand_dims_73, position_id, expand_dims_75))[name = string("concat_77")];
            tensor<int32, [1]> expand_dims_76 = const()[name = string("expand_dims_76"), val = tensor<int32, [1]>([7])];
            tensor<int32, [1]> concat_78_values1_0 = const()[name = string("concat_78_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_78_values3_0 = const()[name = string("concat_78_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_78_axis_0 = const()[name = string("concat_78_axis_0"), val = int32(0)];
            bool concat_78_interleave_0 = const()[name = string("concat_78_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_78 = concat(axis = concat_78_axis_0, interleave = concat_78_interleave_0, values = (expand_dims_76, concat_78_values1_0, cache_position_end, concat_78_values3_0))[name = string("concat_78")];
            tensor<int32, [4]> key_states_67_perm_0 = const()[name = string("key_states_67_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_7_stride_0 = const()[name = string("key_cache_internal_tensor_assign_7_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_7_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_7_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_7_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_7_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_7_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_7_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_67_cast_fp16 = transpose(perm = key_states_67_perm_0, x = key_states_65_cast_fp16)[name = string("transpose_587")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_7_cast_fp16 = slice_update(begin = concat_77, begin_mask = key_cache_internal_tensor_assign_7_begin_mask_0, end = concat_78, end_mask = key_cache_internal_tensor_assign_7_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_7_squeeze_mask_0, stride = key_cache_internal_tensor_assign_7_stride_0, update = key_states_67_cast_fp16, x = coreml_update_state_346)[name = string("key_cache_internal_tensor_assign_7_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_7_cast_fp16, input = key_cache)[name = string("coreml_update_state_348_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_348 = read_state(input = key_cache)[name = string("coreml_update_state_348")];
            tensor<int32, [4]> value_states_39_perm_0 = const()[name = string("value_states_39_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_7_stride_0 = const()[name = string("value_cache_internal_tensor_assign_7_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_7_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_7_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_7_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_7_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_7_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_7_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_39_cast_fp16 = transpose(perm = value_states_39_perm_0, x = var_2920_cast_fp16)[name = string("transpose_586")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_7_cast_fp16 = slice_update(begin = concat_77, begin_mask = value_cache_internal_tensor_assign_7_begin_mask_0, end = concat_78, end_mask = value_cache_internal_tensor_assign_7_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_7_squeeze_mask_0, stride = value_cache_internal_tensor_assign_7_stride_0, update = value_states_39_cast_fp16, x = coreml_update_state_347)[name = string("value_cache_internal_tensor_assign_7_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_7_cast_fp16, input = value_cache)[name = string("coreml_update_state_349_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_349 = read_state(input = value_cache)[name = string("coreml_update_state_349")];
            tensor<int32, [4]> var_3014_begin_0 = const()[name = string("op_3014_begin_0"), val = tensor<int32, [4]>([6, 0, 0, 0])];
            tensor<int32, [4]> var_3014_end_0 = const()[name = string("op_3014_end_0"), val = tensor<int32, [4]>([7, 2, 2048, 128])];
            tensor<bool, [4]> var_3014_end_mask_0 = const()[name = string("op_3014_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3014_cast_fp16 = slice_by_index(begin = var_3014_begin_0, end = var_3014_end_0, end_mask = var_3014_end_mask_0, x = coreml_update_state_348)[name = string("op_3014_cast_fp16")];
            tensor<int32, [2]> tile_12 = const()[name = string("tile_12"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3017_axis_0 = const()[name = string("op_3017_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3017_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3017_cast_fp16_1 = split(axis = var_3017_axis_0, split_sizes = tile_12, x = var_3014_cast_fp16)[name = string("op_3017_cast_fp16")];
            tensor<int32, [4]> var_3024_begin_0 = const()[name = string("op_3024_begin_0"), val = tensor<int32, [4]>([6, 0, 0, 0])];
            tensor<int32, [4]> var_3024_end_0 = const()[name = string("op_3024_end_0"), val = tensor<int32, [4]>([7, 2, 2048, 128])];
            tensor<bool, [4]> var_3024_end_mask_0 = const()[name = string("op_3024_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3024_cast_fp16 = slice_by_index(begin = var_3024_begin_0, end = var_3024_end_0, end_mask = var_3024_end_mask_0, x = coreml_update_state_349)[name = string("op_3024_cast_fp16")];
            tensor<int32, [2]> tile_13 = const()[name = string("tile_13"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3027_axis_0 = const()[name = string("op_3027_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3027_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3027_cast_fp16_1 = split(axis = var_3027_axis_0, split_sizes = tile_13, x = var_3024_cast_fp16)[name = string("op_3027_cast_fp16")];
            tensor<int32, [2]> var_3030_split_sizes_0 = const()[name = string("op_3030_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3030_axis_0 = const()[name = string("op_3030_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_3030_0, tensor<fp16, [1, 8, 128, 128]> var_3030_1 = split(axis = var_3030_axis_0, split_sizes = var_3030_split_sizes_0, x = query_states_39_cast_fp16)[name = string("op_3030")];
            bool attn_weights_97_transpose_x_0 = const()[name = string("attn_weights_97_transpose_x_0"), val = bool(false)];
            bool attn_weights_97_transpose_y_0 = const()[name = string("attn_weights_97_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_97_cast_fp16 = matmul(transpose_x = attn_weights_97_transpose_x_0, transpose_y = attn_weights_97_transpose_y_0, x = var_3017_cast_fp16_0, y = var_3030_0)[name = string("attn_weights_97_cast_fp16")];
            fp16 var_3033_to_fp16 = const()[name = string("op_3033_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_99_cast_fp16 = mul(x = attn_weights_97_cast_fp16, y = var_3033_to_fp16)[name = string("attn_weights_99_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_101_cast_fp16 = add(x = attn_weights_99_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_101_cast_fp16")];
            int32 var_3037 = const()[name = string("op_3037"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_103_cast_fp16 = softmax(axis = var_3037, x = attn_weights_101_cast_fp16)[name = string("attn_weights_103_cast_fp16")];
            bool var_3043_transpose_x_1 = const()[name = string("op_3043_transpose_x_1"), val = bool(true)];
            bool var_3043_transpose_y_1 = const()[name = string("op_3043_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_3043_cast_fp16 = matmul(transpose_x = var_3043_transpose_x_1, transpose_y = var_3043_transpose_y_1, x = attn_weights_103_cast_fp16, y = var_3027_cast_fp16_0)[name = string("op_3043_cast_fp16")];
            bool attn_weights_105_transpose_x_0 = const()[name = string("attn_weights_105_transpose_x_0"), val = bool(false)];
            bool attn_weights_105_transpose_y_0 = const()[name = string("attn_weights_105_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_105_cast_fp16 = matmul(transpose_x = attn_weights_105_transpose_x_0, transpose_y = attn_weights_105_transpose_y_0, x = var_3017_cast_fp16_1, y = var_3030_1)[name = string("attn_weights_105_cast_fp16")];
            fp16 var_3045_to_fp16 = const()[name = string("op_3045_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_107_cast_fp16 = mul(x = attn_weights_105_cast_fp16, y = var_3045_to_fp16)[name = string("attn_weights_107_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_109_cast_fp16 = add(x = attn_weights_107_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_109_cast_fp16")];
            int32 var_3049 = const()[name = string("op_3049"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_111_cast_fp16 = softmax(axis = var_3049, x = attn_weights_109_cast_fp16)[name = string("attn_weights_111_cast_fp16")];
            bool attn_output_49_transpose_x_1 = const()[name = string("attn_output_49_transpose_x_1"), val = bool(true)];
            bool attn_output_49_transpose_y_1 = const()[name = string("attn_output_49_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_49_cast_fp16 = matmul(transpose_x = attn_output_49_transpose_x_1, transpose_y = attn_output_49_transpose_y_1, x = attn_weights_111_cast_fp16, y = var_3027_cast_fp16_1)[name = string("attn_output_49_cast_fp16")];
            int32 var_3057 = const()[name = string("op_3057"), val = int32(1)];
            bool attn_output_51_interleave_0 = const()[name = string("attn_output_51_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_51_cast_fp16 = concat(axis = var_3057, interleave = attn_output_51_interleave_0, values = (var_3043_cast_fp16, attn_output_49_cast_fp16))[name = string("attn_output_51_cast_fp16")];
            tensor<int32, [4]> var_3061_perm_0 = const()[name = string("op_3061_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_83x = const()[name = string("concat_83x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_3061_cast_fp16 = transpose(perm = var_3061_perm_0, x = attn_output_51_cast_fp16)[name = string("transpose_585")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_55_cast_fp16 = reshape(shape = concat_83x, x = var_3061_cast_fp16)[name = string("attn_output_55_cast_fp16")];
            tensor<int32, [2]> hidden_states_63_strides_0 = const()[name = string("hidden_states_63_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_63_pad_type_0 = const()[name = string("hidden_states_63_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_63_pad_0 = const()[name = string("hidden_states_63_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_63_dilations_0 = const()[name = string("hidden_states_63_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_63_groups_0 = const()[name = string("hidden_states_63_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_63_cast_fp16 = conv(dilations = hidden_states_63_dilations_0, groups = hidden_states_63_groups_0, pad = hidden_states_63_pad_0, pad_type = hidden_states_63_pad_type_0, strides = hidden_states_63_strides_0, weight = layers_6_self_attn_o_proj_weight_cast_fp16, x = attn_output_55_cast_fp16)[name = string("hidden_states_63_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_65_cast_fp16 = add(x = hidden_states_59_cast_fp16, y = hidden_states_63_cast_fp16)[name = string("hidden_states_65_cast_fp16")];
            fp16 const_70_promoted_to_fp16 = const()[name = string("const_70_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_3094_cast_fp16 = mul(x = hidden_states_65_cast_fp16, y = const_70_promoted_to_fp16)[name = string("op_3094_cast_fp16")];
            int32 var_3092 = const()[name = string("op_3092"), val = int32(1)];
            bool doubled_53_interleave_0 = const()[name = string("doubled_53_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_53_cast_fp16 = concat(axis = var_3092, interleave = doubled_53_interleave_0, values = (hidden_states_65_cast_fp16, var_3094_cast_fp16))[name = string("doubled_53_cast_fp16")];
            tensor<int32, [1]> out_27_axes_0 = const()[name = string("out_27_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_27_gamma_0_to_fp16 = const()[name = string("out_27_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1318822400)))];
            fp16 var_3104_to_fp16 = const()[name = string("op_3104_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_27_cast_fp16 = layer_norm(axes = out_27_axes_0, epsilon = var_3104_to_fp16, gamma = out_27_gamma_0_to_fp16, x = doubled_53_cast_fp16)[name = string("out_27_cast_fp16")];
            tensor<int32, [2]> var_3115_split_sizes_0 = const()[name = string("op_3115_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3115_axis_0 = const()[name = string("op_3115_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_3115_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_3115_cast_fp16_1 = split(axis = var_3115_axis_0, split_sizes = var_3115_split_sizes_0, x = out_27_cast_fp16)[name = string("op_3115_cast_fp16")];
            tensor<int32, [2]> input_13_strides_0 = const()[name = string("input_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_13_pad_type_0 = const()[name = string("input_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_13_pad_0 = const()[name = string("input_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_13_dilations_0 = const()[name = string("input_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_13_groups_0 = const()[name = string("input_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_13_cast_fp16 = conv(dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = layers_6_mlp_gate_proj_weight_cast_fp16, x = var_3115_cast_fp16_0)[name = string("input_13_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_3132_cast_fp16 = silu(x = input_13_cast_fp16)[name = string("op_3132_cast_fp16")];
            tensor<int32, [2]> var_3138_strides_0 = const()[name = string("op_3138_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_3138_pad_type_0 = const()[name = string("op_3138_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_3138_pad_0 = const()[name = string("op_3138_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_3138_dilations_0 = const()[name = string("op_3138_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3138_groups_0 = const()[name = string("op_3138_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_3138_cast_fp16 = conv(dilations = var_3138_dilations_0, groups = var_3138_groups_0, pad = var_3138_pad_0, pad_type = var_3138_pad_type_0, strides = var_3138_strides_0, weight = layers_6_mlp_up_proj_weight_cast_fp16, x = var_3115_cast_fp16_0)[name = string("op_3138_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_69_cast_fp16 = mul(x = var_3132_cast_fp16, y = var_3138_cast_fp16)[name = string("x_69_cast_fp16")];
            tensor<int32, [2]> hidden_states_67_strides_0 = const()[name = string("hidden_states_67_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_67_pad_type_0 = const()[name = string("hidden_states_67_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_67_pad_0 = const()[name = string("hidden_states_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_67_dilations_0 = const()[name = string("hidden_states_67_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_67_groups_0 = const()[name = string("hidden_states_67_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_67_cast_fp16 = conv(dilations = hidden_states_67_dilations_0, groups = hidden_states_67_groups_0, pad = hidden_states_67_pad_0, pad_type = hidden_states_67_pad_type_0, strides = hidden_states_67_strides_0, weight = layers_6_mlp_down_proj_weight_cast_fp16, x = x_69_cast_fp16)[name = string("hidden_states_67_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_69_cast_fp16 = add(x = hidden_states_65_cast_fp16, y = hidden_states_67_cast_fp16)[name = string("hidden_states_69_cast_fp16")];
            fp16 const_72_promoted_to_fp16 = const()[name = string("const_72_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_3156_cast_fp16 = mul(x = hidden_states_69_cast_fp16, y = const_72_promoted_to_fp16)[name = string("op_3156_cast_fp16")];
            int32 var_3154 = const()[name = string("op_3154"), val = int32(1)];
            bool doubled_57_interleave_0 = const()[name = string("doubled_57_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_57_cast_fp16 = concat(axis = var_3154, interleave = doubled_57_interleave_0, values = (hidden_states_69_cast_fp16, var_3156_cast_fp16))[name = string("doubled_57_cast_fp16")];
            tensor<int32, [1]> out_29_axes_0 = const()[name = string("out_29_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_29_gamma_0_to_fp16 = const()[name = string("out_29_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1318830656)))];
            fp16 var_3166_to_fp16 = const()[name = string("op_3166_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_29_cast_fp16 = layer_norm(axes = out_29_axes_0, epsilon = var_3166_to_fp16, gamma = out_29_gamma_0_to_fp16, x = doubled_57_cast_fp16)[name = string("out_29_cast_fp16")];
            tensor<int32, [2]> var_3177_split_sizes_0 = const()[name = string("op_3177_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3177_axis_0 = const()[name = string("op_3177_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_3177_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_3177_cast_fp16_1 = split(axis = var_3177_axis_0, split_sizes = var_3177_split_sizes_0, x = out_29_cast_fp16)[name = string("op_3177_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_7_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_7_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1318838912)))];
            tensor<int32, [2]> query_states_43_strides_0 = const()[name = string("query_states_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_43_pad_type_0 = const()[name = string("query_states_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_43_pad_0 = const()[name = string("query_states_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_43_dilations_0 = const()[name = string("query_states_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_43_groups_0 = const()[name = string("query_states_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_43_cast_fp16 = conv(dilations = query_states_43_dilations_0, groups = query_states_43_groups_0, pad = query_states_43_pad_0, pad_type = query_states_43_pad_type_0, strides = query_states_43_strides_0, weight = layers_7_self_attn_q_proj_weight_to_fp16, x = var_3177_cast_fp16_0)[name = string("query_states_43_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_7_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_7_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1327227584)))];
            tensor<int32, [2]> key_states_71_strides_0 = const()[name = string("key_states_71_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_71_pad_type_0 = const()[name = string("key_states_71_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_71_pad_0 = const()[name = string("key_states_71_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_71_dilations_0 = const()[name = string("key_states_71_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_71_groups_0 = const()[name = string("key_states_71_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_71_cast_fp16 = conv(dilations = key_states_71_dilations_0, groups = key_states_71_groups_0, pad = key_states_71_pad_0, pad_type = key_states_71_pad_type_0, strides = key_states_71_strides_0, weight = layers_7_self_attn_k_proj_weight_to_fp16, x = var_3177_cast_fp16_0)[name = string("key_states_71_cast_fp16")];
            tensor<int32, [2]> value_states_43_strides_0 = const()[name = string("value_states_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_43_pad_type_0 = const()[name = string("value_states_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_43_pad_0 = const()[name = string("value_states_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_43_dilations_0 = const()[name = string("value_states_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_43_groups_0 = const()[name = string("value_states_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_43_cast_fp16 = conv(dilations = value_states_43_dilations_0, groups = value_states_43_groups_0, pad = value_states_43_pad_0, pad_type = value_states_43_pad_type_0, strides = value_states_43_strides_0, weight = layers_7_self_attn_v_proj_weight_cast_fp16, x = var_3177_cast_fp16_0)[name = string("value_states_43_cast_fp16")];
            tensor<int32, [4]> concat_84x = const()[name = string("concat_84x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_71_cast_fp16 = reshape(shape = concat_84x, x = query_states_43_cast_fp16)[name = string("x_71_cast_fp16")];
            tensor<int32, [4]> concat_85x = const()[name = string("concat_85x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_3234_cast_fp16 = reshape(shape = concat_85x, x = key_states_71_cast_fp16)[name = string("op_3234_cast_fp16")];
            tensor<int32, [4]> concat_86x = const()[name = string("concat_86x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_3241_cast_fp16 = reshape(shape = concat_86x, x = value_states_43_cast_fp16)[name = string("op_3241_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_3245_cast_fp16 = mul(x = x_71_cast_fp16, y = var_859_cast_fp16)[name = string("op_3245_cast_fp16")];
            tensor<int32, [2]> var_3246_split_sizes_0 = const()[name = string("op_3246_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3246_axis_0 = const()[name = string("op_3246_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_3246_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_3246_cast_fp16_1 = split(axis = var_3246_axis_0, split_sizes = var_3246_split_sizes_0, x = x_71_cast_fp16)[name = string("op_3246_cast_fp16")];
            fp16 const_74_promoted_to_fp16 = const()[name = string("const_74_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_3248_cast_fp16 = mul(x = var_3246_cast_fp16_1, y = const_74_promoted_to_fp16)[name = string("op_3248_cast_fp16")];
            int32 var_3250 = const()[name = string("op_3250"), val = int32(-2)];
            bool var_3251_interleave_0 = const()[name = string("op_3251_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_3251_cast_fp16 = concat(axis = var_3250, interleave = var_3251_interleave_0, values = (var_3248_cast_fp16, var_3246_cast_fp16_0))[name = string("op_3251_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_3252_cast_fp16 = mul(x = var_3251_cast_fp16, y = var_866_cast_fp16)[name = string("op_3252_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_45_cast_fp16 = add(x = var_3245_cast_fp16, y = var_3252_cast_fp16)[name = string("query_states_45_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_3258_cast_fp16 = mul(x = var_3234_cast_fp16, y = var_859_cast_fp16)[name = string("op_3258_cast_fp16")];
            tensor<int32, [2]> var_3259_split_sizes_0 = const()[name = string("op_3259_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3259_axis_0 = const()[name = string("op_3259_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_3259_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_3259_cast_fp16_1 = split(axis = var_3259_axis_0, split_sizes = var_3259_split_sizes_0, x = var_3234_cast_fp16)[name = string("op_3259_cast_fp16")];
            fp16 const_75_promoted_to_fp16 = const()[name = string("const_75_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_3261_cast_fp16 = mul(x = var_3259_cast_fp16_1, y = const_75_promoted_to_fp16)[name = string("op_3261_cast_fp16")];
            int32 var_3263 = const()[name = string("op_3263"), val = int32(-2)];
            bool var_3264_interleave_0 = const()[name = string("op_3264_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_3264_cast_fp16 = concat(axis = var_3263, interleave = var_3264_interleave_0, values = (var_3261_cast_fp16, var_3259_cast_fp16_0))[name = string("op_3264_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_3265_cast_fp16 = mul(x = var_3264_cast_fp16, y = var_866_cast_fp16)[name = string("op_3265_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_75_cast_fp16 = add(x = var_3258_cast_fp16, y = var_3265_cast_fp16)[name = string("key_states_75_cast_fp16")];
            tensor<int32, [1]> expand_dims_84 = const()[name = string("expand_dims_84"), val = tensor<int32, [1]>([7])];
            tensor<int32, [1]> expand_dims_85 = const()[name = string("expand_dims_85"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_87 = const()[name = string("expand_dims_87"), val = tensor<int32, [1]>([0])];
            int32 concat_89_axis_0 = const()[name = string("concat_89_axis_0"), val = int32(0)];
            bool concat_89_interleave_0 = const()[name = string("concat_89_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_89 = concat(axis = concat_89_axis_0, interleave = concat_89_interleave_0, values = (expand_dims_84, expand_dims_85, position_id, expand_dims_87))[name = string("concat_89")];
            tensor<int32, [1]> expand_dims_88 = const()[name = string("expand_dims_88"), val = tensor<int32, [1]>([8])];
            tensor<int32, [1]> concat_90_values1_0 = const()[name = string("concat_90_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_90_values3_0 = const()[name = string("concat_90_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_90_axis_0 = const()[name = string("concat_90_axis_0"), val = int32(0)];
            bool concat_90_interleave_0 = const()[name = string("concat_90_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_90 = concat(axis = concat_90_axis_0, interleave = concat_90_interleave_0, values = (expand_dims_88, concat_90_values1_0, cache_position_end, concat_90_values3_0))[name = string("concat_90")];
            tensor<int32, [4]> key_states_77_perm_0 = const()[name = string("key_states_77_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_8_stride_0 = const()[name = string("key_cache_internal_tensor_assign_8_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_8_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_8_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_8_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_8_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_8_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_8_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_77_cast_fp16 = transpose(perm = key_states_77_perm_0, x = key_states_75_cast_fp16)[name = string("transpose_584")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_8_cast_fp16 = slice_update(begin = concat_89, begin_mask = key_cache_internal_tensor_assign_8_begin_mask_0, end = concat_90, end_mask = key_cache_internal_tensor_assign_8_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_8_squeeze_mask_0, stride = key_cache_internal_tensor_assign_8_stride_0, update = key_states_77_cast_fp16, x = coreml_update_state_348)[name = string("key_cache_internal_tensor_assign_8_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_8_cast_fp16, input = key_cache)[name = string("coreml_update_state_350_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_350 = read_state(input = key_cache)[name = string("coreml_update_state_350")];
            tensor<int32, [4]> value_states_45_perm_0 = const()[name = string("value_states_45_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_8_stride_0 = const()[name = string("value_cache_internal_tensor_assign_8_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_8_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_8_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_8_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_8_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_8_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_8_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_45_cast_fp16 = transpose(perm = value_states_45_perm_0, x = var_3241_cast_fp16)[name = string("transpose_583")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_8_cast_fp16 = slice_update(begin = concat_89, begin_mask = value_cache_internal_tensor_assign_8_begin_mask_0, end = concat_90, end_mask = value_cache_internal_tensor_assign_8_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_8_squeeze_mask_0, stride = value_cache_internal_tensor_assign_8_stride_0, update = value_states_45_cast_fp16, x = coreml_update_state_349)[name = string("value_cache_internal_tensor_assign_8_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_8_cast_fp16, input = value_cache)[name = string("coreml_update_state_351_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_351 = read_state(input = value_cache)[name = string("coreml_update_state_351")];
            tensor<int32, [4]> var_3335_begin_0 = const()[name = string("op_3335_begin_0"), val = tensor<int32, [4]>([7, 0, 0, 0])];
            tensor<int32, [4]> var_3335_end_0 = const()[name = string("op_3335_end_0"), val = tensor<int32, [4]>([8, 2, 2048, 128])];
            tensor<bool, [4]> var_3335_end_mask_0 = const()[name = string("op_3335_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3335_cast_fp16 = slice_by_index(begin = var_3335_begin_0, end = var_3335_end_0, end_mask = var_3335_end_mask_0, x = coreml_update_state_350)[name = string("op_3335_cast_fp16")];
            tensor<int32, [2]> tile_14 = const()[name = string("tile_14"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3338_axis_0 = const()[name = string("op_3338_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3338_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3338_cast_fp16_1 = split(axis = var_3338_axis_0, split_sizes = tile_14, x = var_3335_cast_fp16)[name = string("op_3338_cast_fp16")];
            tensor<int32, [4]> var_3345_begin_0 = const()[name = string("op_3345_begin_0"), val = tensor<int32, [4]>([7, 0, 0, 0])];
            tensor<int32, [4]> var_3345_end_0 = const()[name = string("op_3345_end_0"), val = tensor<int32, [4]>([8, 2, 2048, 128])];
            tensor<bool, [4]> var_3345_end_mask_0 = const()[name = string("op_3345_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3345_cast_fp16 = slice_by_index(begin = var_3345_begin_0, end = var_3345_end_0, end_mask = var_3345_end_mask_0, x = coreml_update_state_351)[name = string("op_3345_cast_fp16")];
            tensor<int32, [2]> tile_15 = const()[name = string("tile_15"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3348_axis_0 = const()[name = string("op_3348_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3348_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3348_cast_fp16_1 = split(axis = var_3348_axis_0, split_sizes = tile_15, x = var_3345_cast_fp16)[name = string("op_3348_cast_fp16")];
            tensor<int32, [2]> var_3351_split_sizes_0 = const()[name = string("op_3351_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3351_axis_0 = const()[name = string("op_3351_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_3351_0, tensor<fp16, [1, 8, 128, 128]> var_3351_1 = split(axis = var_3351_axis_0, split_sizes = var_3351_split_sizes_0, x = query_states_45_cast_fp16)[name = string("op_3351")];
            bool attn_weights_113_transpose_x_0 = const()[name = string("attn_weights_113_transpose_x_0"), val = bool(false)];
            bool attn_weights_113_transpose_y_0 = const()[name = string("attn_weights_113_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_113_cast_fp16 = matmul(transpose_x = attn_weights_113_transpose_x_0, transpose_y = attn_weights_113_transpose_y_0, x = var_3338_cast_fp16_0, y = var_3351_0)[name = string("attn_weights_113_cast_fp16")];
            fp16 var_3354_to_fp16 = const()[name = string("op_3354_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_115_cast_fp16 = mul(x = attn_weights_113_cast_fp16, y = var_3354_to_fp16)[name = string("attn_weights_115_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_117_cast_fp16 = add(x = attn_weights_115_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_117_cast_fp16")];
            int32 var_3358 = const()[name = string("op_3358"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_119_cast_fp16 = softmax(axis = var_3358, x = attn_weights_117_cast_fp16)[name = string("attn_weights_119_cast_fp16")];
            bool var_3364_transpose_x_1 = const()[name = string("op_3364_transpose_x_1"), val = bool(true)];
            bool var_3364_transpose_y_1 = const()[name = string("op_3364_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_3364_cast_fp16 = matmul(transpose_x = var_3364_transpose_x_1, transpose_y = var_3364_transpose_y_1, x = attn_weights_119_cast_fp16, y = var_3348_cast_fp16_0)[name = string("op_3364_cast_fp16")];
            bool attn_weights_121_transpose_x_0 = const()[name = string("attn_weights_121_transpose_x_0"), val = bool(false)];
            bool attn_weights_121_transpose_y_0 = const()[name = string("attn_weights_121_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_121_cast_fp16 = matmul(transpose_x = attn_weights_121_transpose_x_0, transpose_y = attn_weights_121_transpose_y_0, x = var_3338_cast_fp16_1, y = var_3351_1)[name = string("attn_weights_121_cast_fp16")];
            fp16 var_3366_to_fp16 = const()[name = string("op_3366_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_123_cast_fp16 = mul(x = attn_weights_121_cast_fp16, y = var_3366_to_fp16)[name = string("attn_weights_123_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_125_cast_fp16 = add(x = attn_weights_123_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_125_cast_fp16")];
            int32 var_3370 = const()[name = string("op_3370"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_127_cast_fp16 = softmax(axis = var_3370, x = attn_weights_125_cast_fp16)[name = string("attn_weights_127_cast_fp16")];
            bool attn_output_57_transpose_x_1 = const()[name = string("attn_output_57_transpose_x_1"), val = bool(true)];
            bool attn_output_57_transpose_y_1 = const()[name = string("attn_output_57_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_57_cast_fp16 = matmul(transpose_x = attn_output_57_transpose_x_1, transpose_y = attn_output_57_transpose_y_1, x = attn_weights_127_cast_fp16, y = var_3348_cast_fp16_1)[name = string("attn_output_57_cast_fp16")];
            int32 var_3378 = const()[name = string("op_3378"), val = int32(1)];
            bool attn_output_59_interleave_0 = const()[name = string("attn_output_59_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_59_cast_fp16 = concat(axis = var_3378, interleave = attn_output_59_interleave_0, values = (var_3364_cast_fp16, attn_output_57_cast_fp16))[name = string("attn_output_59_cast_fp16")];
            tensor<int32, [4]> var_3382_perm_0 = const()[name = string("op_3382_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_95x = const()[name = string("concat_95x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_3382_cast_fp16 = transpose(perm = var_3382_perm_0, x = attn_output_59_cast_fp16)[name = string("transpose_582")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_63_cast_fp16 = reshape(shape = concat_95x, x = var_3382_cast_fp16)[name = string("attn_output_63_cast_fp16")];
            tensor<int32, [2]> hidden_states_73_strides_0 = const()[name = string("hidden_states_73_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_73_pad_type_0 = const()[name = string("hidden_states_73_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_73_pad_0 = const()[name = string("hidden_states_73_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_73_dilations_0 = const()[name = string("hidden_states_73_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_73_groups_0 = const()[name = string("hidden_states_73_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_73_cast_fp16 = conv(dilations = hidden_states_73_dilations_0, groups = hidden_states_73_groups_0, pad = hidden_states_73_pad_0, pad_type = hidden_states_73_pad_type_0, strides = hidden_states_73_strides_0, weight = layers_7_self_attn_o_proj_weight_cast_fp16, x = attn_output_63_cast_fp16)[name = string("hidden_states_73_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_75_cast_fp16 = add(x = hidden_states_69_cast_fp16, y = hidden_states_73_cast_fp16)[name = string("hidden_states_75_cast_fp16")];
            fp16 const_80_promoted_to_fp16 = const()[name = string("const_80_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_3415_cast_fp16 = mul(x = hidden_states_75_cast_fp16, y = const_80_promoted_to_fp16)[name = string("op_3415_cast_fp16")];
            int32 var_3413 = const()[name = string("op_3413"), val = int32(1)];
            bool doubled_61_interleave_0 = const()[name = string("doubled_61_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_61_cast_fp16 = concat(axis = var_3413, interleave = doubled_61_interleave_0, values = (hidden_states_75_cast_fp16, var_3415_cast_fp16))[name = string("doubled_61_cast_fp16")];
            tensor<int32, [1]> out_31_axes_0 = const()[name = string("out_31_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_31_gamma_0_to_fp16 = const()[name = string("out_31_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1328276224)))];
            fp16 var_3425_to_fp16 = const()[name = string("op_3425_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_31_cast_fp16 = layer_norm(axes = out_31_axes_0, epsilon = var_3425_to_fp16, gamma = out_31_gamma_0_to_fp16, x = doubled_61_cast_fp16)[name = string("out_31_cast_fp16")];
            tensor<int32, [2]> var_3436_split_sizes_0 = const()[name = string("op_3436_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3436_axis_0 = const()[name = string("op_3436_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_3436_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_3436_cast_fp16_1 = split(axis = var_3436_axis_0, split_sizes = var_3436_split_sizes_0, x = out_31_cast_fp16)[name = string("op_3436_cast_fp16")];
            tensor<int32, [2]> input_15_strides_0 = const()[name = string("input_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_15_pad_type_0 = const()[name = string("input_15_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_15_dilations_0 = const()[name = string("input_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_15_groups_0 = const()[name = string("input_15_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_15_cast_fp16 = conv(dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = layers_7_mlp_gate_proj_weight_cast_fp16, x = var_3436_cast_fp16_0)[name = string("input_15_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_3453_cast_fp16 = silu(x = input_15_cast_fp16)[name = string("op_3453_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_7_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_7_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1328284480)))];
            tensor<int32, [2]> var_3459_strides_0 = const()[name = string("op_3459_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_3459_pad_type_0 = const()[name = string("op_3459_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_3459_pad_0 = const()[name = string("op_3459_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_3459_dilations_0 = const()[name = string("op_3459_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3459_groups_0 = const()[name = string("op_3459_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_3459_cast_fp16 = conv(dilations = var_3459_dilations_0, groups = var_3459_groups_0, pad = var_3459_pad_0, pad_type = var_3459_pad_type_0, strides = var_3459_strides_0, weight = layers_7_mlp_up_proj_weight_to_fp16, x = var_3436_cast_fp16_0)[name = string("op_3459_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_79_cast_fp16 = mul(x = var_3453_cast_fp16, y = var_3459_cast_fp16)[name = string("x_79_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_7_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_7_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353450368)))];
            tensor<int32, [2]> hidden_states_77_strides_0 = const()[name = string("hidden_states_77_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_77_pad_type_0 = const()[name = string("hidden_states_77_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_77_pad_0 = const()[name = string("hidden_states_77_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_77_dilations_0 = const()[name = string("hidden_states_77_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_77_groups_0 = const()[name = string("hidden_states_77_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_77_cast_fp16 = conv(dilations = hidden_states_77_dilations_0, groups = hidden_states_77_groups_0, pad = hidden_states_77_pad_0, pad_type = hidden_states_77_pad_type_0, strides = hidden_states_77_strides_0, weight = layers_7_mlp_down_proj_weight_to_fp16, x = x_79_cast_fp16)[name = string("hidden_states_77_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_79_cast_fp16 = add(x = hidden_states_75_cast_fp16, y = hidden_states_77_cast_fp16)[name = string("hidden_states_79_cast_fp16")];
            fp16 const_82_promoted_to_fp16 = const()[name = string("const_82_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_3477_cast_fp16 = mul(x = hidden_states_79_cast_fp16, y = const_82_promoted_to_fp16)[name = string("op_3477_cast_fp16")];
            int32 var_3475 = const()[name = string("op_3475"), val = int32(1)];
            bool doubled_65_interleave_0 = const()[name = string("doubled_65_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_65_cast_fp16 = concat(axis = var_3475, interleave = doubled_65_interleave_0, values = (hidden_states_79_cast_fp16, var_3477_cast_fp16))[name = string("doubled_65_cast_fp16")];
            tensor<int32, [1]> out_33_axes_0 = const()[name = string("out_33_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_33_gamma_0_to_fp16 = const()[name = string("out_33_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1378616256)))];
            fp16 var_3487_to_fp16 = const()[name = string("op_3487_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_33_cast_fp16 = layer_norm(axes = out_33_axes_0, epsilon = var_3487_to_fp16, gamma = out_33_gamma_0_to_fp16, x = doubled_65_cast_fp16)[name = string("out_33_cast_fp16")];
            tensor<int32, [2]> var_3498_split_sizes_0 = const()[name = string("op_3498_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3498_axis_0 = const()[name = string("op_3498_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_3498_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_3498_cast_fp16_1 = split(axis = var_3498_axis_0, split_sizes = var_3498_split_sizes_0, x = out_33_cast_fp16)[name = string("op_3498_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_8_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_8_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1378624512)))];
            tensor<int32, [2]> query_states_49_strides_0 = const()[name = string("query_states_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_49_pad_type_0 = const()[name = string("query_states_49_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_49_pad_0 = const()[name = string("query_states_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_49_dilations_0 = const()[name = string("query_states_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_49_groups_0 = const()[name = string("query_states_49_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_49_cast_fp16 = conv(dilations = query_states_49_dilations_0, groups = query_states_49_groups_0, pad = query_states_49_pad_0, pad_type = query_states_49_pad_type_0, strides = query_states_49_strides_0, weight = layers_8_self_attn_q_proj_weight_to_fp16, x = var_3498_cast_fp16_0)[name = string("query_states_49_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_8_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_8_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1387013184)))];
            tensor<int32, [2]> key_states_81_strides_0 = const()[name = string("key_states_81_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_81_pad_type_0 = const()[name = string("key_states_81_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_81_pad_0 = const()[name = string("key_states_81_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_81_dilations_0 = const()[name = string("key_states_81_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_81_groups_0 = const()[name = string("key_states_81_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_81_cast_fp16 = conv(dilations = key_states_81_dilations_0, groups = key_states_81_groups_0, pad = key_states_81_pad_0, pad_type = key_states_81_pad_type_0, strides = key_states_81_strides_0, weight = layers_8_self_attn_k_proj_weight_to_fp16, x = var_3498_cast_fp16_0)[name = string("key_states_81_cast_fp16")];
            tensor<int32, [2]> value_states_49_strides_0 = const()[name = string("value_states_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_49_pad_type_0 = const()[name = string("value_states_49_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_49_pad_0 = const()[name = string("value_states_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_49_dilations_0 = const()[name = string("value_states_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_49_groups_0 = const()[name = string("value_states_49_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_49_cast_fp16 = conv(dilations = value_states_49_dilations_0, groups = value_states_49_groups_0, pad = value_states_49_pad_0, pad_type = value_states_49_pad_type_0, strides = value_states_49_strides_0, weight = layers_8_self_attn_v_proj_weight_cast_fp16, x = var_3498_cast_fp16_0)[name = string("value_states_49_cast_fp16")];
            tensor<int32, [4]> concat_96x = const()[name = string("concat_96x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_81_cast_fp16 = reshape(shape = concat_96x, x = query_states_49_cast_fp16)[name = string("x_81_cast_fp16")];
            tensor<int32, [4]> concat_97x = const()[name = string("concat_97x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_3555_cast_fp16 = reshape(shape = concat_97x, x = key_states_81_cast_fp16)[name = string("op_3555_cast_fp16")];
            tensor<int32, [4]> concat_98x = const()[name = string("concat_98x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_3562_cast_fp16 = reshape(shape = concat_98x, x = value_states_49_cast_fp16)[name = string("op_3562_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_3566_cast_fp16 = mul(x = x_81_cast_fp16, y = var_859_cast_fp16)[name = string("op_3566_cast_fp16")];
            tensor<int32, [2]> var_3567_split_sizes_0 = const()[name = string("op_3567_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3567_axis_0 = const()[name = string("op_3567_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_3567_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_3567_cast_fp16_1 = split(axis = var_3567_axis_0, split_sizes = var_3567_split_sizes_0, x = x_81_cast_fp16)[name = string("op_3567_cast_fp16")];
            fp16 const_84_promoted_to_fp16 = const()[name = string("const_84_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_3569_cast_fp16 = mul(x = var_3567_cast_fp16_1, y = const_84_promoted_to_fp16)[name = string("op_3569_cast_fp16")];
            int32 var_3571 = const()[name = string("op_3571"), val = int32(-2)];
            bool var_3572_interleave_0 = const()[name = string("op_3572_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_3572_cast_fp16 = concat(axis = var_3571, interleave = var_3572_interleave_0, values = (var_3569_cast_fp16, var_3567_cast_fp16_0))[name = string("op_3572_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_3573_cast_fp16 = mul(x = var_3572_cast_fp16, y = var_866_cast_fp16)[name = string("op_3573_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_51_cast_fp16 = add(x = var_3566_cast_fp16, y = var_3573_cast_fp16)[name = string("query_states_51_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_3579_cast_fp16 = mul(x = var_3555_cast_fp16, y = var_859_cast_fp16)[name = string("op_3579_cast_fp16")];
            tensor<int32, [2]> var_3580_split_sizes_0 = const()[name = string("op_3580_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3580_axis_0 = const()[name = string("op_3580_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_3580_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_3580_cast_fp16_1 = split(axis = var_3580_axis_0, split_sizes = var_3580_split_sizes_0, x = var_3555_cast_fp16)[name = string("op_3580_cast_fp16")];
            fp16 const_85_promoted_to_fp16 = const()[name = string("const_85_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_3582_cast_fp16 = mul(x = var_3580_cast_fp16_1, y = const_85_promoted_to_fp16)[name = string("op_3582_cast_fp16")];
            int32 var_3584 = const()[name = string("op_3584"), val = int32(-2)];
            bool var_3585_interleave_0 = const()[name = string("op_3585_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_3585_cast_fp16 = concat(axis = var_3584, interleave = var_3585_interleave_0, values = (var_3582_cast_fp16, var_3580_cast_fp16_0))[name = string("op_3585_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_3586_cast_fp16 = mul(x = var_3585_cast_fp16, y = var_866_cast_fp16)[name = string("op_3586_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_85_cast_fp16 = add(x = var_3579_cast_fp16, y = var_3586_cast_fp16)[name = string("key_states_85_cast_fp16")];
            tensor<int32, [1]> expand_dims_96 = const()[name = string("expand_dims_96"), val = tensor<int32, [1]>([8])];
            tensor<int32, [1]> expand_dims_97 = const()[name = string("expand_dims_97"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_99 = const()[name = string("expand_dims_99"), val = tensor<int32, [1]>([0])];
            int32 concat_101_axis_0 = const()[name = string("concat_101_axis_0"), val = int32(0)];
            bool concat_101_interleave_0 = const()[name = string("concat_101_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_101 = concat(axis = concat_101_axis_0, interleave = concat_101_interleave_0, values = (expand_dims_96, expand_dims_97, position_id, expand_dims_99))[name = string("concat_101")];
            tensor<int32, [1]> expand_dims_100 = const()[name = string("expand_dims_100"), val = tensor<int32, [1]>([9])];
            tensor<int32, [1]> concat_102_values1_0 = const()[name = string("concat_102_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_102_values3_0 = const()[name = string("concat_102_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_102_axis_0 = const()[name = string("concat_102_axis_0"), val = int32(0)];
            bool concat_102_interleave_0 = const()[name = string("concat_102_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_102 = concat(axis = concat_102_axis_0, interleave = concat_102_interleave_0, values = (expand_dims_100, concat_102_values1_0, cache_position_end, concat_102_values3_0))[name = string("concat_102")];
            tensor<int32, [4]> key_states_87_perm_0 = const()[name = string("key_states_87_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_9_stride_0 = const()[name = string("key_cache_internal_tensor_assign_9_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_9_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_9_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_9_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_9_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_9_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_9_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_87_cast_fp16 = transpose(perm = key_states_87_perm_0, x = key_states_85_cast_fp16)[name = string("transpose_581")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_9_cast_fp16 = slice_update(begin = concat_101, begin_mask = key_cache_internal_tensor_assign_9_begin_mask_0, end = concat_102, end_mask = key_cache_internal_tensor_assign_9_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_9_squeeze_mask_0, stride = key_cache_internal_tensor_assign_9_stride_0, update = key_states_87_cast_fp16, x = coreml_update_state_350)[name = string("key_cache_internal_tensor_assign_9_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_9_cast_fp16, input = key_cache)[name = string("coreml_update_state_352_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_352 = read_state(input = key_cache)[name = string("coreml_update_state_352")];
            tensor<int32, [4]> value_states_51_perm_0 = const()[name = string("value_states_51_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_9_stride_0 = const()[name = string("value_cache_internal_tensor_assign_9_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_9_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_9_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_9_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_9_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_9_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_9_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_51_cast_fp16 = transpose(perm = value_states_51_perm_0, x = var_3562_cast_fp16)[name = string("transpose_580")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_9_cast_fp16 = slice_update(begin = concat_101, begin_mask = value_cache_internal_tensor_assign_9_begin_mask_0, end = concat_102, end_mask = value_cache_internal_tensor_assign_9_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_9_squeeze_mask_0, stride = value_cache_internal_tensor_assign_9_stride_0, update = value_states_51_cast_fp16, x = coreml_update_state_351)[name = string("value_cache_internal_tensor_assign_9_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_9_cast_fp16, input = value_cache)[name = string("coreml_update_state_353_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_353 = read_state(input = value_cache)[name = string("coreml_update_state_353")];
            tensor<int32, [4]> var_3656_begin_0 = const()[name = string("op_3656_begin_0"), val = tensor<int32, [4]>([8, 0, 0, 0])];
            tensor<int32, [4]> var_3656_end_0 = const()[name = string("op_3656_end_0"), val = tensor<int32, [4]>([9, 2, 2048, 128])];
            tensor<bool, [4]> var_3656_end_mask_0 = const()[name = string("op_3656_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3656_cast_fp16 = slice_by_index(begin = var_3656_begin_0, end = var_3656_end_0, end_mask = var_3656_end_mask_0, x = coreml_update_state_352)[name = string("op_3656_cast_fp16")];
            tensor<int32, [2]> tile_16 = const()[name = string("tile_16"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3659_axis_0 = const()[name = string("op_3659_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3659_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3659_cast_fp16_1 = split(axis = var_3659_axis_0, split_sizes = tile_16, x = var_3656_cast_fp16)[name = string("op_3659_cast_fp16")];
            tensor<int32, [4]> var_3666_begin_0 = const()[name = string("op_3666_begin_0"), val = tensor<int32, [4]>([8, 0, 0, 0])];
            tensor<int32, [4]> var_3666_end_0 = const()[name = string("op_3666_end_0"), val = tensor<int32, [4]>([9, 2, 2048, 128])];
            tensor<bool, [4]> var_3666_end_mask_0 = const()[name = string("op_3666_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3666_cast_fp16 = slice_by_index(begin = var_3666_begin_0, end = var_3666_end_0, end_mask = var_3666_end_mask_0, x = coreml_update_state_353)[name = string("op_3666_cast_fp16")];
            tensor<int32, [2]> tile_17 = const()[name = string("tile_17"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3669_axis_0 = const()[name = string("op_3669_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3669_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3669_cast_fp16_1 = split(axis = var_3669_axis_0, split_sizes = tile_17, x = var_3666_cast_fp16)[name = string("op_3669_cast_fp16")];
            tensor<int32, [2]> var_3672_split_sizes_0 = const()[name = string("op_3672_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3672_axis_0 = const()[name = string("op_3672_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_3672_0, tensor<fp16, [1, 8, 128, 128]> var_3672_1 = split(axis = var_3672_axis_0, split_sizes = var_3672_split_sizes_0, x = query_states_51_cast_fp16)[name = string("op_3672")];
            bool attn_weights_129_transpose_x_0 = const()[name = string("attn_weights_129_transpose_x_0"), val = bool(false)];
            bool attn_weights_129_transpose_y_0 = const()[name = string("attn_weights_129_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_129_cast_fp16 = matmul(transpose_x = attn_weights_129_transpose_x_0, transpose_y = attn_weights_129_transpose_y_0, x = var_3659_cast_fp16_0, y = var_3672_0)[name = string("attn_weights_129_cast_fp16")];
            fp16 var_3675_to_fp16 = const()[name = string("op_3675_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_131_cast_fp16 = mul(x = attn_weights_129_cast_fp16, y = var_3675_to_fp16)[name = string("attn_weights_131_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_133_cast_fp16 = add(x = attn_weights_131_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_133_cast_fp16")];
            int32 var_3679 = const()[name = string("op_3679"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_135_cast_fp16 = softmax(axis = var_3679, x = attn_weights_133_cast_fp16)[name = string("attn_weights_135_cast_fp16")];
            bool var_3685_transpose_x_1 = const()[name = string("op_3685_transpose_x_1"), val = bool(true)];
            bool var_3685_transpose_y_1 = const()[name = string("op_3685_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_3685_cast_fp16 = matmul(transpose_x = var_3685_transpose_x_1, transpose_y = var_3685_transpose_y_1, x = attn_weights_135_cast_fp16, y = var_3669_cast_fp16_0)[name = string("op_3685_cast_fp16")];
            bool attn_weights_137_transpose_x_0 = const()[name = string("attn_weights_137_transpose_x_0"), val = bool(false)];
            bool attn_weights_137_transpose_y_0 = const()[name = string("attn_weights_137_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_137_cast_fp16 = matmul(transpose_x = attn_weights_137_transpose_x_0, transpose_y = attn_weights_137_transpose_y_0, x = var_3659_cast_fp16_1, y = var_3672_1)[name = string("attn_weights_137_cast_fp16")];
            fp16 var_3687_to_fp16 = const()[name = string("op_3687_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_139_cast_fp16 = mul(x = attn_weights_137_cast_fp16, y = var_3687_to_fp16)[name = string("attn_weights_139_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_141_cast_fp16 = add(x = attn_weights_139_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_141_cast_fp16")];
            int32 var_3691 = const()[name = string("op_3691"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_143_cast_fp16 = softmax(axis = var_3691, x = attn_weights_141_cast_fp16)[name = string("attn_weights_143_cast_fp16")];
            bool attn_output_65_transpose_x_1 = const()[name = string("attn_output_65_transpose_x_1"), val = bool(true)];
            bool attn_output_65_transpose_y_1 = const()[name = string("attn_output_65_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_65_cast_fp16 = matmul(transpose_x = attn_output_65_transpose_x_1, transpose_y = attn_output_65_transpose_y_1, x = attn_weights_143_cast_fp16, y = var_3669_cast_fp16_1)[name = string("attn_output_65_cast_fp16")];
            int32 var_3699 = const()[name = string("op_3699"), val = int32(1)];
            bool attn_output_67_interleave_0 = const()[name = string("attn_output_67_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_67_cast_fp16 = concat(axis = var_3699, interleave = attn_output_67_interleave_0, values = (var_3685_cast_fp16, attn_output_65_cast_fp16))[name = string("attn_output_67_cast_fp16")];
            tensor<int32, [4]> var_3703_perm_0 = const()[name = string("op_3703_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_107x = const()[name = string("concat_107x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_3703_cast_fp16 = transpose(perm = var_3703_perm_0, x = attn_output_67_cast_fp16)[name = string("transpose_579")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_71_cast_fp16 = reshape(shape = concat_107x, x = var_3703_cast_fp16)[name = string("attn_output_71_cast_fp16")];
            tensor<int32, [2]> hidden_states_83_strides_0 = const()[name = string("hidden_states_83_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_83_pad_type_0 = const()[name = string("hidden_states_83_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_83_pad_0 = const()[name = string("hidden_states_83_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_83_dilations_0 = const()[name = string("hidden_states_83_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_83_groups_0 = const()[name = string("hidden_states_83_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_83_cast_fp16 = conv(dilations = hidden_states_83_dilations_0, groups = hidden_states_83_groups_0, pad = hidden_states_83_pad_0, pad_type = hidden_states_83_pad_type_0, strides = hidden_states_83_strides_0, weight = layers_8_self_attn_o_proj_weight_cast_fp16, x = attn_output_71_cast_fp16)[name = string("hidden_states_83_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_85_cast_fp16 = add(x = hidden_states_79_cast_fp16, y = hidden_states_83_cast_fp16)[name = string("hidden_states_85_cast_fp16")];
            fp16 const_90_promoted_to_fp16 = const()[name = string("const_90_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_3736_cast_fp16 = mul(x = hidden_states_85_cast_fp16, y = const_90_promoted_to_fp16)[name = string("op_3736_cast_fp16")];
            int32 var_3734 = const()[name = string("op_3734"), val = int32(1)];
            bool doubled_69_interleave_0 = const()[name = string("doubled_69_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_69_cast_fp16 = concat(axis = var_3734, interleave = doubled_69_interleave_0, values = (hidden_states_85_cast_fp16, var_3736_cast_fp16))[name = string("doubled_69_cast_fp16")];
            tensor<int32, [1]> out_35_axes_0 = const()[name = string("out_35_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_35_gamma_0_to_fp16 = const()[name = string("out_35_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1388061824)))];
            fp16 var_3746_to_fp16 = const()[name = string("op_3746_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_35_cast_fp16 = layer_norm(axes = out_35_axes_0, epsilon = var_3746_to_fp16, gamma = out_35_gamma_0_to_fp16, x = doubled_69_cast_fp16)[name = string("out_35_cast_fp16")];
            tensor<int32, [2]> var_3757_split_sizes_0 = const()[name = string("op_3757_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3757_axis_0 = const()[name = string("op_3757_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_3757_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_3757_cast_fp16_1 = split(axis = var_3757_axis_0, split_sizes = var_3757_split_sizes_0, x = out_35_cast_fp16)[name = string("op_3757_cast_fp16")];
            tensor<int32, [2]> input_17_strides_0 = const()[name = string("input_17_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_17_pad_type_0 = const()[name = string("input_17_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_17_pad_0 = const()[name = string("input_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_17_dilations_0 = const()[name = string("input_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_17_groups_0 = const()[name = string("input_17_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_17_cast_fp16 = conv(dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = layers_8_mlp_gate_proj_weight_cast_fp16, x = var_3757_cast_fp16_0)[name = string("input_17_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_3774_cast_fp16 = silu(x = input_17_cast_fp16)[name = string("op_3774_cast_fp16")];
            tensor<int32, [2]> var_3780_strides_0 = const()[name = string("op_3780_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_3780_pad_type_0 = const()[name = string("op_3780_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_3780_pad_0 = const()[name = string("op_3780_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_3780_dilations_0 = const()[name = string("op_3780_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3780_groups_0 = const()[name = string("op_3780_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_3780_cast_fp16 = conv(dilations = var_3780_dilations_0, groups = var_3780_groups_0, pad = var_3780_pad_0, pad_type = var_3780_pad_type_0, strides = var_3780_strides_0, weight = layers_8_mlp_up_proj_weight_cast_fp16, x = var_3757_cast_fp16_0)[name = string("op_3780_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_89_cast_fp16 = mul(x = var_3774_cast_fp16, y = var_3780_cast_fp16)[name = string("x_89_cast_fp16")];
            tensor<int32, [2]> hidden_states_87_strides_0 = const()[name = string("hidden_states_87_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_87_pad_type_0 = const()[name = string("hidden_states_87_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_87_pad_0 = const()[name = string("hidden_states_87_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_87_dilations_0 = const()[name = string("hidden_states_87_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_87_groups_0 = const()[name = string("hidden_states_87_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_87_cast_fp16 = conv(dilations = hidden_states_87_dilations_0, groups = hidden_states_87_groups_0, pad = hidden_states_87_pad_0, pad_type = hidden_states_87_pad_type_0, strides = hidden_states_87_strides_0, weight = layers_8_mlp_down_proj_weight_cast_fp16, x = x_89_cast_fp16)[name = string("hidden_states_87_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_89_cast_fp16 = add(x = hidden_states_85_cast_fp16, y = hidden_states_87_cast_fp16)[name = string("hidden_states_89_cast_fp16")];
            fp16 const_92_promoted_to_fp16 = const()[name = string("const_92_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_3798_cast_fp16 = mul(x = hidden_states_89_cast_fp16, y = const_92_promoted_to_fp16)[name = string("op_3798_cast_fp16")];
            int32 var_3796 = const()[name = string("op_3796"), val = int32(1)];
            bool doubled_73_interleave_0 = const()[name = string("doubled_73_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_73_cast_fp16 = concat(axis = var_3796, interleave = doubled_73_interleave_0, values = (hidden_states_89_cast_fp16, var_3798_cast_fp16))[name = string("doubled_73_cast_fp16")];
            tensor<int32, [1]> out_37_axes_0 = const()[name = string("out_37_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_37_gamma_0_to_fp16 = const()[name = string("out_37_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1388070080)))];
            fp16 var_3808_to_fp16 = const()[name = string("op_3808_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_37_cast_fp16 = layer_norm(axes = out_37_axes_0, epsilon = var_3808_to_fp16, gamma = out_37_gamma_0_to_fp16, x = doubled_73_cast_fp16)[name = string("out_37_cast_fp16")];
            tensor<int32, [2]> var_3819_split_sizes_0 = const()[name = string("op_3819_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3819_axis_0 = const()[name = string("op_3819_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_3819_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_3819_cast_fp16_1 = split(axis = var_3819_axis_0, split_sizes = var_3819_split_sizes_0, x = out_37_cast_fp16)[name = string("op_3819_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_9_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_9_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1388078336)))];
            tensor<int32, [2]> query_states_55_strides_0 = const()[name = string("query_states_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_55_pad_type_0 = const()[name = string("query_states_55_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_55_pad_0 = const()[name = string("query_states_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_55_dilations_0 = const()[name = string("query_states_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_55_groups_0 = const()[name = string("query_states_55_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_55_cast_fp16 = conv(dilations = query_states_55_dilations_0, groups = query_states_55_groups_0, pad = query_states_55_pad_0, pad_type = query_states_55_pad_type_0, strides = query_states_55_strides_0, weight = layers_9_self_attn_q_proj_weight_to_fp16, x = var_3819_cast_fp16_0)[name = string("query_states_55_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_9_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_9_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1396467008)))];
            tensor<int32, [2]> key_states_91_strides_0 = const()[name = string("key_states_91_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_91_pad_type_0 = const()[name = string("key_states_91_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_91_pad_0 = const()[name = string("key_states_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_91_dilations_0 = const()[name = string("key_states_91_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_91_groups_0 = const()[name = string("key_states_91_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_91_cast_fp16 = conv(dilations = key_states_91_dilations_0, groups = key_states_91_groups_0, pad = key_states_91_pad_0, pad_type = key_states_91_pad_type_0, strides = key_states_91_strides_0, weight = layers_9_self_attn_k_proj_weight_to_fp16, x = var_3819_cast_fp16_0)[name = string("key_states_91_cast_fp16")];
            tensor<int32, [2]> value_states_55_strides_0 = const()[name = string("value_states_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_55_pad_type_0 = const()[name = string("value_states_55_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_55_pad_0 = const()[name = string("value_states_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_55_dilations_0 = const()[name = string("value_states_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_55_groups_0 = const()[name = string("value_states_55_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_55_cast_fp16 = conv(dilations = value_states_55_dilations_0, groups = value_states_55_groups_0, pad = value_states_55_pad_0, pad_type = value_states_55_pad_type_0, strides = value_states_55_strides_0, weight = layers_9_self_attn_v_proj_weight_cast_fp16, x = var_3819_cast_fp16_0)[name = string("value_states_55_cast_fp16")];
            tensor<int32, [4]> concat_108x = const()[name = string("concat_108x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_91_cast_fp16 = reshape(shape = concat_108x, x = query_states_55_cast_fp16)[name = string("x_91_cast_fp16")];
            tensor<int32, [4]> concat_109x = const()[name = string("concat_109x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_3876_cast_fp16 = reshape(shape = concat_109x, x = key_states_91_cast_fp16)[name = string("op_3876_cast_fp16")];
            tensor<int32, [4]> concat_110x = const()[name = string("concat_110x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_3883_cast_fp16 = reshape(shape = concat_110x, x = value_states_55_cast_fp16)[name = string("op_3883_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_3887_cast_fp16 = mul(x = x_91_cast_fp16, y = var_859_cast_fp16)[name = string("op_3887_cast_fp16")];
            tensor<int32, [2]> var_3888_split_sizes_0 = const()[name = string("op_3888_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3888_axis_0 = const()[name = string("op_3888_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_3888_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_3888_cast_fp16_1 = split(axis = var_3888_axis_0, split_sizes = var_3888_split_sizes_0, x = x_91_cast_fp16)[name = string("op_3888_cast_fp16")];
            fp16 const_94_promoted_to_fp16 = const()[name = string("const_94_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_3890_cast_fp16 = mul(x = var_3888_cast_fp16_1, y = const_94_promoted_to_fp16)[name = string("op_3890_cast_fp16")];
            int32 var_3892 = const()[name = string("op_3892"), val = int32(-2)];
            bool var_3893_interleave_0 = const()[name = string("op_3893_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_3893_cast_fp16 = concat(axis = var_3892, interleave = var_3893_interleave_0, values = (var_3890_cast_fp16, var_3888_cast_fp16_0))[name = string("op_3893_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_3894_cast_fp16 = mul(x = var_3893_cast_fp16, y = var_866_cast_fp16)[name = string("op_3894_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_57_cast_fp16 = add(x = var_3887_cast_fp16, y = var_3894_cast_fp16)[name = string("query_states_57_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_3900_cast_fp16 = mul(x = var_3876_cast_fp16, y = var_859_cast_fp16)[name = string("op_3900_cast_fp16")];
            tensor<int32, [2]> var_3901_split_sizes_0 = const()[name = string("op_3901_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3901_axis_0 = const()[name = string("op_3901_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_3901_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_3901_cast_fp16_1 = split(axis = var_3901_axis_0, split_sizes = var_3901_split_sizes_0, x = var_3876_cast_fp16)[name = string("op_3901_cast_fp16")];
            fp16 const_95_promoted_to_fp16 = const()[name = string("const_95_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_3903_cast_fp16 = mul(x = var_3901_cast_fp16_1, y = const_95_promoted_to_fp16)[name = string("op_3903_cast_fp16")];
            int32 var_3905 = const()[name = string("op_3905"), val = int32(-2)];
            bool var_3906_interleave_0 = const()[name = string("op_3906_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_3906_cast_fp16 = concat(axis = var_3905, interleave = var_3906_interleave_0, values = (var_3903_cast_fp16, var_3901_cast_fp16_0))[name = string("op_3906_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_3907_cast_fp16 = mul(x = var_3906_cast_fp16, y = var_866_cast_fp16)[name = string("op_3907_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_95_cast_fp16 = add(x = var_3900_cast_fp16, y = var_3907_cast_fp16)[name = string("key_states_95_cast_fp16")];
            tensor<int32, [1]> expand_dims_108 = const()[name = string("expand_dims_108"), val = tensor<int32, [1]>([9])];
            tensor<int32, [1]> expand_dims_109 = const()[name = string("expand_dims_109"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_111 = const()[name = string("expand_dims_111"), val = tensor<int32, [1]>([0])];
            int32 concat_113_axis_0 = const()[name = string("concat_113_axis_0"), val = int32(0)];
            bool concat_113_interleave_0 = const()[name = string("concat_113_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_113 = concat(axis = concat_113_axis_0, interleave = concat_113_interleave_0, values = (expand_dims_108, expand_dims_109, position_id, expand_dims_111))[name = string("concat_113")];
            tensor<int32, [1]> expand_dims_112 = const()[name = string("expand_dims_112"), val = tensor<int32, [1]>([10])];
            tensor<int32, [1]> concat_114_values1_0 = const()[name = string("concat_114_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_114_values3_0 = const()[name = string("concat_114_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_114_axis_0 = const()[name = string("concat_114_axis_0"), val = int32(0)];
            bool concat_114_interleave_0 = const()[name = string("concat_114_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_114 = concat(axis = concat_114_axis_0, interleave = concat_114_interleave_0, values = (expand_dims_112, concat_114_values1_0, cache_position_end, concat_114_values3_0))[name = string("concat_114")];
            tensor<int32, [4]> key_states_97_perm_0 = const()[name = string("key_states_97_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_10_stride_0 = const()[name = string("key_cache_internal_tensor_assign_10_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_10_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_10_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_10_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_10_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_10_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_10_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_97_cast_fp16 = transpose(perm = key_states_97_perm_0, x = key_states_95_cast_fp16)[name = string("transpose_578")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_10_cast_fp16 = slice_update(begin = concat_113, begin_mask = key_cache_internal_tensor_assign_10_begin_mask_0, end = concat_114, end_mask = key_cache_internal_tensor_assign_10_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_10_squeeze_mask_0, stride = key_cache_internal_tensor_assign_10_stride_0, update = key_states_97_cast_fp16, x = coreml_update_state_352)[name = string("key_cache_internal_tensor_assign_10_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_10_cast_fp16, input = key_cache)[name = string("coreml_update_state_354_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_354 = read_state(input = key_cache)[name = string("coreml_update_state_354")];
            tensor<int32, [4]> value_states_57_perm_0 = const()[name = string("value_states_57_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_10_stride_0 = const()[name = string("value_cache_internal_tensor_assign_10_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_10_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_10_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_10_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_10_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_10_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_10_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_57_cast_fp16 = transpose(perm = value_states_57_perm_0, x = var_3883_cast_fp16)[name = string("transpose_577")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_10_cast_fp16 = slice_update(begin = concat_113, begin_mask = value_cache_internal_tensor_assign_10_begin_mask_0, end = concat_114, end_mask = value_cache_internal_tensor_assign_10_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_10_squeeze_mask_0, stride = value_cache_internal_tensor_assign_10_stride_0, update = value_states_57_cast_fp16, x = coreml_update_state_353)[name = string("value_cache_internal_tensor_assign_10_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_10_cast_fp16, input = value_cache)[name = string("coreml_update_state_355_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_355 = read_state(input = value_cache)[name = string("coreml_update_state_355")];
            tensor<int32, [4]> var_3977_begin_0 = const()[name = string("op_3977_begin_0"), val = tensor<int32, [4]>([9, 0, 0, 0])];
            tensor<int32, [4]> var_3977_end_0 = const()[name = string("op_3977_end_0"), val = tensor<int32, [4]>([10, 2, 2048, 128])];
            tensor<bool, [4]> var_3977_end_mask_0 = const()[name = string("op_3977_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3977_cast_fp16 = slice_by_index(begin = var_3977_begin_0, end = var_3977_end_0, end_mask = var_3977_end_mask_0, x = coreml_update_state_354)[name = string("op_3977_cast_fp16")];
            tensor<int32, [2]> tile_18 = const()[name = string("tile_18"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3980_axis_0 = const()[name = string("op_3980_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3980_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3980_cast_fp16_1 = split(axis = var_3980_axis_0, split_sizes = tile_18, x = var_3977_cast_fp16)[name = string("op_3980_cast_fp16")];
            tensor<int32, [4]> var_3987_begin_0 = const()[name = string("op_3987_begin_0"), val = tensor<int32, [4]>([9, 0, 0, 0])];
            tensor<int32, [4]> var_3987_end_0 = const()[name = string("op_3987_end_0"), val = tensor<int32, [4]>([10, 2, 2048, 128])];
            tensor<bool, [4]> var_3987_end_mask_0 = const()[name = string("op_3987_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3987_cast_fp16 = slice_by_index(begin = var_3987_begin_0, end = var_3987_end_0, end_mask = var_3987_end_mask_0, x = coreml_update_state_355)[name = string("op_3987_cast_fp16")];
            tensor<int32, [2]> tile_19 = const()[name = string("tile_19"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3990_axis_0 = const()[name = string("op_3990_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3990_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3990_cast_fp16_1 = split(axis = var_3990_axis_0, split_sizes = tile_19, x = var_3987_cast_fp16)[name = string("op_3990_cast_fp16")];
            tensor<int32, [2]> var_3993_split_sizes_0 = const()[name = string("op_3993_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3993_axis_0 = const()[name = string("op_3993_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_3993_0, tensor<fp16, [1, 8, 128, 128]> var_3993_1 = split(axis = var_3993_axis_0, split_sizes = var_3993_split_sizes_0, x = query_states_57_cast_fp16)[name = string("op_3993")];
            bool attn_weights_145_transpose_x_0 = const()[name = string("attn_weights_145_transpose_x_0"), val = bool(false)];
            bool attn_weights_145_transpose_y_0 = const()[name = string("attn_weights_145_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_145_cast_fp16 = matmul(transpose_x = attn_weights_145_transpose_x_0, transpose_y = attn_weights_145_transpose_y_0, x = var_3980_cast_fp16_0, y = var_3993_0)[name = string("attn_weights_145_cast_fp16")];
            fp16 var_3996_to_fp16 = const()[name = string("op_3996_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_147_cast_fp16 = mul(x = attn_weights_145_cast_fp16, y = var_3996_to_fp16)[name = string("attn_weights_147_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_149_cast_fp16 = add(x = attn_weights_147_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_149_cast_fp16")];
            int32 var_4000 = const()[name = string("op_4000"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_151_cast_fp16 = softmax(axis = var_4000, x = attn_weights_149_cast_fp16)[name = string("attn_weights_151_cast_fp16")];
            bool var_4006_transpose_x_1 = const()[name = string("op_4006_transpose_x_1"), val = bool(true)];
            bool var_4006_transpose_y_1 = const()[name = string("op_4006_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_4006_cast_fp16 = matmul(transpose_x = var_4006_transpose_x_1, transpose_y = var_4006_transpose_y_1, x = attn_weights_151_cast_fp16, y = var_3990_cast_fp16_0)[name = string("op_4006_cast_fp16")];
            bool attn_weights_153_transpose_x_0 = const()[name = string("attn_weights_153_transpose_x_0"), val = bool(false)];
            bool attn_weights_153_transpose_y_0 = const()[name = string("attn_weights_153_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_153_cast_fp16 = matmul(transpose_x = attn_weights_153_transpose_x_0, transpose_y = attn_weights_153_transpose_y_0, x = var_3980_cast_fp16_1, y = var_3993_1)[name = string("attn_weights_153_cast_fp16")];
            fp16 var_4008_to_fp16 = const()[name = string("op_4008_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_155_cast_fp16 = mul(x = attn_weights_153_cast_fp16, y = var_4008_to_fp16)[name = string("attn_weights_155_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_157_cast_fp16 = add(x = attn_weights_155_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_157_cast_fp16")];
            int32 var_4012 = const()[name = string("op_4012"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_159_cast_fp16 = softmax(axis = var_4012, x = attn_weights_157_cast_fp16)[name = string("attn_weights_159_cast_fp16")];
            bool attn_output_73_transpose_x_1 = const()[name = string("attn_output_73_transpose_x_1"), val = bool(true)];
            bool attn_output_73_transpose_y_1 = const()[name = string("attn_output_73_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_73_cast_fp16 = matmul(transpose_x = attn_output_73_transpose_x_1, transpose_y = attn_output_73_transpose_y_1, x = attn_weights_159_cast_fp16, y = var_3990_cast_fp16_1)[name = string("attn_output_73_cast_fp16")];
            int32 var_4020 = const()[name = string("op_4020"), val = int32(1)];
            bool attn_output_75_interleave_0 = const()[name = string("attn_output_75_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_75_cast_fp16 = concat(axis = var_4020, interleave = attn_output_75_interleave_0, values = (var_4006_cast_fp16, attn_output_73_cast_fp16))[name = string("attn_output_75_cast_fp16")];
            tensor<int32, [4]> var_4024_perm_0 = const()[name = string("op_4024_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_119x = const()[name = string("concat_119x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_4024_cast_fp16 = transpose(perm = var_4024_perm_0, x = attn_output_75_cast_fp16)[name = string("transpose_576")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_79_cast_fp16 = reshape(shape = concat_119x, x = var_4024_cast_fp16)[name = string("attn_output_79_cast_fp16")];
            tensor<int32, [2]> hidden_states_93_strides_0 = const()[name = string("hidden_states_93_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_93_pad_type_0 = const()[name = string("hidden_states_93_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_93_pad_0 = const()[name = string("hidden_states_93_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_93_dilations_0 = const()[name = string("hidden_states_93_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_93_groups_0 = const()[name = string("hidden_states_93_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_93_cast_fp16 = conv(dilations = hidden_states_93_dilations_0, groups = hidden_states_93_groups_0, pad = hidden_states_93_pad_0, pad_type = hidden_states_93_pad_type_0, strides = hidden_states_93_strides_0, weight = layers_9_self_attn_o_proj_weight_cast_fp16, x = attn_output_79_cast_fp16)[name = string("hidden_states_93_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_95_cast_fp16 = add(x = hidden_states_89_cast_fp16, y = hidden_states_93_cast_fp16)[name = string("hidden_states_95_cast_fp16")];
            fp16 const_100_promoted_to_fp16 = const()[name = string("const_100_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_4057_cast_fp16 = mul(x = hidden_states_95_cast_fp16, y = const_100_promoted_to_fp16)[name = string("op_4057_cast_fp16")];
            int32 var_4055 = const()[name = string("op_4055"), val = int32(1)];
            bool doubled_77_interleave_0 = const()[name = string("doubled_77_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_77_cast_fp16 = concat(axis = var_4055, interleave = doubled_77_interleave_0, values = (hidden_states_95_cast_fp16, var_4057_cast_fp16))[name = string("doubled_77_cast_fp16")];
            tensor<int32, [1]> out_39_axes_0 = const()[name = string("out_39_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_39_gamma_0_to_fp16 = const()[name = string("out_39_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1397515648)))];
            fp16 var_4067_to_fp16 = const()[name = string("op_4067_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_39_cast_fp16 = layer_norm(axes = out_39_axes_0, epsilon = var_4067_to_fp16, gamma = out_39_gamma_0_to_fp16, x = doubled_77_cast_fp16)[name = string("out_39_cast_fp16")];
            tensor<int32, [2]> var_4078_split_sizes_0 = const()[name = string("op_4078_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4078_axis_0 = const()[name = string("op_4078_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_4078_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_4078_cast_fp16_1 = split(axis = var_4078_axis_0, split_sizes = var_4078_split_sizes_0, x = out_39_cast_fp16)[name = string("op_4078_cast_fp16")];
            tensor<int32, [2]> input_19_strides_0 = const()[name = string("input_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_19_pad_type_0 = const()[name = string("input_19_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_19_pad_0 = const()[name = string("input_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_19_dilations_0 = const()[name = string("input_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_19_groups_0 = const()[name = string("input_19_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_19_cast_fp16 = conv(dilations = input_19_dilations_0, groups = input_19_groups_0, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = input_19_strides_0, weight = layers_9_mlp_gate_proj_weight_cast_fp16, x = var_4078_cast_fp16_0)[name = string("input_19_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_4095_cast_fp16 = silu(x = input_19_cast_fp16)[name = string("op_4095_cast_fp16")];
            tensor<int32, [2]> var_4101_strides_0 = const()[name = string("op_4101_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_4101_pad_type_0 = const()[name = string("op_4101_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_4101_pad_0 = const()[name = string("op_4101_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_4101_dilations_0 = const()[name = string("op_4101_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4101_groups_0 = const()[name = string("op_4101_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_4101_cast_fp16 = conv(dilations = var_4101_dilations_0, groups = var_4101_groups_0, pad = var_4101_pad_0, pad_type = var_4101_pad_type_0, strides = var_4101_strides_0, weight = layers_9_mlp_up_proj_weight_cast_fp16, x = var_4078_cast_fp16_0)[name = string("op_4101_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_99_cast_fp16 = mul(x = var_4095_cast_fp16, y = var_4101_cast_fp16)[name = string("x_99_cast_fp16")];
            tensor<int32, [2]> hidden_states_97_strides_0 = const()[name = string("hidden_states_97_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_97_pad_type_0 = const()[name = string("hidden_states_97_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_97_pad_0 = const()[name = string("hidden_states_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_97_dilations_0 = const()[name = string("hidden_states_97_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_97_groups_0 = const()[name = string("hidden_states_97_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_97_cast_fp16 = conv(dilations = hidden_states_97_dilations_0, groups = hidden_states_97_groups_0, pad = hidden_states_97_pad_0, pad_type = hidden_states_97_pad_type_0, strides = hidden_states_97_strides_0, weight = layers_9_mlp_down_proj_weight_cast_fp16, x = x_99_cast_fp16)[name = string("hidden_states_97_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_99_cast_fp16 = add(x = hidden_states_95_cast_fp16, y = hidden_states_97_cast_fp16)[name = string("hidden_states_99_cast_fp16")];
            fp16 const_102_promoted_to_fp16 = const()[name = string("const_102_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_4119_cast_fp16 = mul(x = hidden_states_99_cast_fp16, y = const_102_promoted_to_fp16)[name = string("op_4119_cast_fp16")];
            int32 var_4117 = const()[name = string("op_4117"), val = int32(1)];
            bool doubled_81_interleave_0 = const()[name = string("doubled_81_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_81_cast_fp16 = concat(axis = var_4117, interleave = doubled_81_interleave_0, values = (hidden_states_99_cast_fp16, var_4119_cast_fp16))[name = string("doubled_81_cast_fp16")];
            tensor<int32, [1]> out_41_axes_0 = const()[name = string("out_41_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_41_gamma_0_to_fp16 = const()[name = string("out_41_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1397523904)))];
            fp16 var_4129_to_fp16 = const()[name = string("op_4129_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_41_cast_fp16 = layer_norm(axes = out_41_axes_0, epsilon = var_4129_to_fp16, gamma = out_41_gamma_0_to_fp16, x = doubled_81_cast_fp16)[name = string("out_41_cast_fp16")];
            tensor<int32, [2]> var_4140_split_sizes_0 = const()[name = string("op_4140_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4140_axis_0 = const()[name = string("op_4140_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_4140_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_4140_cast_fp16_1 = split(axis = var_4140_axis_0, split_sizes = var_4140_split_sizes_0, x = out_41_cast_fp16)[name = string("op_4140_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_10_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_10_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1397532160)))];
            tensor<int32, [2]> query_states_61_strides_0 = const()[name = string("query_states_61_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_61_pad_type_0 = const()[name = string("query_states_61_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_61_pad_0 = const()[name = string("query_states_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_61_dilations_0 = const()[name = string("query_states_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_61_groups_0 = const()[name = string("query_states_61_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_61_cast_fp16 = conv(dilations = query_states_61_dilations_0, groups = query_states_61_groups_0, pad = query_states_61_pad_0, pad_type = query_states_61_pad_type_0, strides = query_states_61_strides_0, weight = layers_10_self_attn_q_proj_weight_to_fp16, x = var_4140_cast_fp16_0)[name = string("query_states_61_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_10_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_10_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1405920832)))];
            tensor<int32, [2]> key_states_101_strides_0 = const()[name = string("key_states_101_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_101_pad_type_0 = const()[name = string("key_states_101_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_101_pad_0 = const()[name = string("key_states_101_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_101_dilations_0 = const()[name = string("key_states_101_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_101_groups_0 = const()[name = string("key_states_101_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_101_cast_fp16 = conv(dilations = key_states_101_dilations_0, groups = key_states_101_groups_0, pad = key_states_101_pad_0, pad_type = key_states_101_pad_type_0, strides = key_states_101_strides_0, weight = layers_10_self_attn_k_proj_weight_to_fp16, x = var_4140_cast_fp16_0)[name = string("key_states_101_cast_fp16")];
            tensor<int32, [2]> value_states_61_strides_0 = const()[name = string("value_states_61_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_61_pad_type_0 = const()[name = string("value_states_61_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_61_pad_0 = const()[name = string("value_states_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_61_dilations_0 = const()[name = string("value_states_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_61_groups_0 = const()[name = string("value_states_61_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_61_cast_fp16 = conv(dilations = value_states_61_dilations_0, groups = value_states_61_groups_0, pad = value_states_61_pad_0, pad_type = value_states_61_pad_type_0, strides = value_states_61_strides_0, weight = layers_10_self_attn_v_proj_weight_cast_fp16, x = var_4140_cast_fp16_0)[name = string("value_states_61_cast_fp16")];
            tensor<int32, [4]> concat_120x = const()[name = string("concat_120x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_101_cast_fp16 = reshape(shape = concat_120x, x = query_states_61_cast_fp16)[name = string("x_101_cast_fp16")];
            tensor<int32, [4]> concat_121x = const()[name = string("concat_121x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_4197_cast_fp16 = reshape(shape = concat_121x, x = key_states_101_cast_fp16)[name = string("op_4197_cast_fp16")];
            tensor<int32, [4]> concat_122x = const()[name = string("concat_122x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_4204_cast_fp16 = reshape(shape = concat_122x, x = value_states_61_cast_fp16)[name = string("op_4204_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_4208_cast_fp16 = mul(x = x_101_cast_fp16, y = var_859_cast_fp16)[name = string("op_4208_cast_fp16")];
            tensor<int32, [2]> var_4209_split_sizes_0 = const()[name = string("op_4209_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4209_axis_0 = const()[name = string("op_4209_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_4209_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_4209_cast_fp16_1 = split(axis = var_4209_axis_0, split_sizes = var_4209_split_sizes_0, x = x_101_cast_fp16)[name = string("op_4209_cast_fp16")];
            fp16 const_104_promoted_to_fp16 = const()[name = string("const_104_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_4211_cast_fp16 = mul(x = var_4209_cast_fp16_1, y = const_104_promoted_to_fp16)[name = string("op_4211_cast_fp16")];
            int32 var_4213 = const()[name = string("op_4213"), val = int32(-2)];
            bool var_4214_interleave_0 = const()[name = string("op_4214_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_4214_cast_fp16 = concat(axis = var_4213, interleave = var_4214_interleave_0, values = (var_4211_cast_fp16, var_4209_cast_fp16_0))[name = string("op_4214_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_4215_cast_fp16 = mul(x = var_4214_cast_fp16, y = var_866_cast_fp16)[name = string("op_4215_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_63_cast_fp16 = add(x = var_4208_cast_fp16, y = var_4215_cast_fp16)[name = string("query_states_63_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_4221_cast_fp16 = mul(x = var_4197_cast_fp16, y = var_859_cast_fp16)[name = string("op_4221_cast_fp16")];
            tensor<int32, [2]> var_4222_split_sizes_0 = const()[name = string("op_4222_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4222_axis_0 = const()[name = string("op_4222_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_4222_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_4222_cast_fp16_1 = split(axis = var_4222_axis_0, split_sizes = var_4222_split_sizes_0, x = var_4197_cast_fp16)[name = string("op_4222_cast_fp16")];
            fp16 const_105_promoted_to_fp16 = const()[name = string("const_105_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_4224_cast_fp16 = mul(x = var_4222_cast_fp16_1, y = const_105_promoted_to_fp16)[name = string("op_4224_cast_fp16")];
            int32 var_4226 = const()[name = string("op_4226"), val = int32(-2)];
            bool var_4227_interleave_0 = const()[name = string("op_4227_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_4227_cast_fp16 = concat(axis = var_4226, interleave = var_4227_interleave_0, values = (var_4224_cast_fp16, var_4222_cast_fp16_0))[name = string("op_4227_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_4228_cast_fp16 = mul(x = var_4227_cast_fp16, y = var_866_cast_fp16)[name = string("op_4228_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_105_cast_fp16 = add(x = var_4221_cast_fp16, y = var_4228_cast_fp16)[name = string("key_states_105_cast_fp16")];
            tensor<int32, [1]> expand_dims_120 = const()[name = string("expand_dims_120"), val = tensor<int32, [1]>([10])];
            tensor<int32, [1]> expand_dims_121 = const()[name = string("expand_dims_121"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_123 = const()[name = string("expand_dims_123"), val = tensor<int32, [1]>([0])];
            int32 concat_125_axis_0 = const()[name = string("concat_125_axis_0"), val = int32(0)];
            bool concat_125_interleave_0 = const()[name = string("concat_125_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_125 = concat(axis = concat_125_axis_0, interleave = concat_125_interleave_0, values = (expand_dims_120, expand_dims_121, position_id, expand_dims_123))[name = string("concat_125")];
            tensor<int32, [1]> expand_dims_124 = const()[name = string("expand_dims_124"), val = tensor<int32, [1]>([11])];
            tensor<int32, [1]> concat_126_values1_0 = const()[name = string("concat_126_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_126_values3_0 = const()[name = string("concat_126_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_126_axis_0 = const()[name = string("concat_126_axis_0"), val = int32(0)];
            bool concat_126_interleave_0 = const()[name = string("concat_126_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_126 = concat(axis = concat_126_axis_0, interleave = concat_126_interleave_0, values = (expand_dims_124, concat_126_values1_0, cache_position_end, concat_126_values3_0))[name = string("concat_126")];
            tensor<int32, [4]> key_states_107_perm_0 = const()[name = string("key_states_107_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_11_stride_0 = const()[name = string("key_cache_internal_tensor_assign_11_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_11_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_11_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_11_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_11_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_11_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_11_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_107_cast_fp16 = transpose(perm = key_states_107_perm_0, x = key_states_105_cast_fp16)[name = string("transpose_575")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_11_cast_fp16 = slice_update(begin = concat_125, begin_mask = key_cache_internal_tensor_assign_11_begin_mask_0, end = concat_126, end_mask = key_cache_internal_tensor_assign_11_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_11_squeeze_mask_0, stride = key_cache_internal_tensor_assign_11_stride_0, update = key_states_107_cast_fp16, x = coreml_update_state_354)[name = string("key_cache_internal_tensor_assign_11_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_11_cast_fp16, input = key_cache)[name = string("coreml_update_state_356_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_356 = read_state(input = key_cache)[name = string("coreml_update_state_356")];
            tensor<int32, [4]> value_states_63_perm_0 = const()[name = string("value_states_63_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_11_stride_0 = const()[name = string("value_cache_internal_tensor_assign_11_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_11_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_11_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_11_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_11_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_11_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_11_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_63_cast_fp16 = transpose(perm = value_states_63_perm_0, x = var_4204_cast_fp16)[name = string("transpose_574")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_11_cast_fp16 = slice_update(begin = concat_125, begin_mask = value_cache_internal_tensor_assign_11_begin_mask_0, end = concat_126, end_mask = value_cache_internal_tensor_assign_11_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_11_squeeze_mask_0, stride = value_cache_internal_tensor_assign_11_stride_0, update = value_states_63_cast_fp16, x = coreml_update_state_355)[name = string("value_cache_internal_tensor_assign_11_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_11_cast_fp16, input = value_cache)[name = string("coreml_update_state_357_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_357 = read_state(input = value_cache)[name = string("coreml_update_state_357")];
            tensor<int32, [4]> var_4298_begin_0 = const()[name = string("op_4298_begin_0"), val = tensor<int32, [4]>([10, 0, 0, 0])];
            tensor<int32, [4]> var_4298_end_0 = const()[name = string("op_4298_end_0"), val = tensor<int32, [4]>([11, 2, 2048, 128])];
            tensor<bool, [4]> var_4298_end_mask_0 = const()[name = string("op_4298_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4298_cast_fp16 = slice_by_index(begin = var_4298_begin_0, end = var_4298_end_0, end_mask = var_4298_end_mask_0, x = coreml_update_state_356)[name = string("op_4298_cast_fp16")];
            tensor<int32, [2]> tile_20 = const()[name = string("tile_20"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4301_axis_0 = const()[name = string("op_4301_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4301_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4301_cast_fp16_1 = split(axis = var_4301_axis_0, split_sizes = tile_20, x = var_4298_cast_fp16)[name = string("op_4301_cast_fp16")];
            tensor<int32, [4]> var_4308_begin_0 = const()[name = string("op_4308_begin_0"), val = tensor<int32, [4]>([10, 0, 0, 0])];
            tensor<int32, [4]> var_4308_end_0 = const()[name = string("op_4308_end_0"), val = tensor<int32, [4]>([11, 2, 2048, 128])];
            tensor<bool, [4]> var_4308_end_mask_0 = const()[name = string("op_4308_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4308_cast_fp16 = slice_by_index(begin = var_4308_begin_0, end = var_4308_end_0, end_mask = var_4308_end_mask_0, x = coreml_update_state_357)[name = string("op_4308_cast_fp16")];
            tensor<int32, [2]> tile_21 = const()[name = string("tile_21"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4311_axis_0 = const()[name = string("op_4311_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4311_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4311_cast_fp16_1 = split(axis = var_4311_axis_0, split_sizes = tile_21, x = var_4308_cast_fp16)[name = string("op_4311_cast_fp16")];
            tensor<int32, [2]> var_4314_split_sizes_0 = const()[name = string("op_4314_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_4314_axis_0 = const()[name = string("op_4314_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_4314_0, tensor<fp16, [1, 8, 128, 128]> var_4314_1 = split(axis = var_4314_axis_0, split_sizes = var_4314_split_sizes_0, x = query_states_63_cast_fp16)[name = string("op_4314")];
            bool attn_weights_161_transpose_x_0 = const()[name = string("attn_weights_161_transpose_x_0"), val = bool(false)];
            bool attn_weights_161_transpose_y_0 = const()[name = string("attn_weights_161_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_161_cast_fp16 = matmul(transpose_x = attn_weights_161_transpose_x_0, transpose_y = attn_weights_161_transpose_y_0, x = var_4301_cast_fp16_0, y = var_4314_0)[name = string("attn_weights_161_cast_fp16")];
            fp16 var_4317_to_fp16 = const()[name = string("op_4317_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_163_cast_fp16 = mul(x = attn_weights_161_cast_fp16, y = var_4317_to_fp16)[name = string("attn_weights_163_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_165_cast_fp16 = add(x = attn_weights_163_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_165_cast_fp16")];
            int32 var_4321 = const()[name = string("op_4321"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_167_cast_fp16 = softmax(axis = var_4321, x = attn_weights_165_cast_fp16)[name = string("attn_weights_167_cast_fp16")];
            bool var_4327_transpose_x_1 = const()[name = string("op_4327_transpose_x_1"), val = bool(true)];
            bool var_4327_transpose_y_1 = const()[name = string("op_4327_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_4327_cast_fp16 = matmul(transpose_x = var_4327_transpose_x_1, transpose_y = var_4327_transpose_y_1, x = attn_weights_167_cast_fp16, y = var_4311_cast_fp16_0)[name = string("op_4327_cast_fp16")];
            bool attn_weights_169_transpose_x_0 = const()[name = string("attn_weights_169_transpose_x_0"), val = bool(false)];
            bool attn_weights_169_transpose_y_0 = const()[name = string("attn_weights_169_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_169_cast_fp16 = matmul(transpose_x = attn_weights_169_transpose_x_0, transpose_y = attn_weights_169_transpose_y_0, x = var_4301_cast_fp16_1, y = var_4314_1)[name = string("attn_weights_169_cast_fp16")];
            fp16 var_4329_to_fp16 = const()[name = string("op_4329_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_171_cast_fp16 = mul(x = attn_weights_169_cast_fp16, y = var_4329_to_fp16)[name = string("attn_weights_171_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_173_cast_fp16 = add(x = attn_weights_171_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_173_cast_fp16")];
            int32 var_4333 = const()[name = string("op_4333"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_175_cast_fp16 = softmax(axis = var_4333, x = attn_weights_173_cast_fp16)[name = string("attn_weights_175_cast_fp16")];
            bool attn_output_81_transpose_x_1 = const()[name = string("attn_output_81_transpose_x_1"), val = bool(true)];
            bool attn_output_81_transpose_y_1 = const()[name = string("attn_output_81_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_81_cast_fp16 = matmul(transpose_x = attn_output_81_transpose_x_1, transpose_y = attn_output_81_transpose_y_1, x = attn_weights_175_cast_fp16, y = var_4311_cast_fp16_1)[name = string("attn_output_81_cast_fp16")];
            int32 var_4341 = const()[name = string("op_4341"), val = int32(1)];
            bool attn_output_83_interleave_0 = const()[name = string("attn_output_83_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_83_cast_fp16 = concat(axis = var_4341, interleave = attn_output_83_interleave_0, values = (var_4327_cast_fp16, attn_output_81_cast_fp16))[name = string("attn_output_83_cast_fp16")];
            tensor<int32, [4]> var_4345_perm_0 = const()[name = string("op_4345_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_131x = const()[name = string("concat_131x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_4345_cast_fp16 = transpose(perm = var_4345_perm_0, x = attn_output_83_cast_fp16)[name = string("transpose_573")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_87_cast_fp16 = reshape(shape = concat_131x, x = var_4345_cast_fp16)[name = string("attn_output_87_cast_fp16")];
            tensor<int32, [2]> hidden_states_103_strides_0 = const()[name = string("hidden_states_103_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_103_pad_type_0 = const()[name = string("hidden_states_103_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_103_pad_0 = const()[name = string("hidden_states_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_103_dilations_0 = const()[name = string("hidden_states_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_103_groups_0 = const()[name = string("hidden_states_103_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_103_cast_fp16 = conv(dilations = hidden_states_103_dilations_0, groups = hidden_states_103_groups_0, pad = hidden_states_103_pad_0, pad_type = hidden_states_103_pad_type_0, strides = hidden_states_103_strides_0, weight = layers_10_self_attn_o_proj_weight_cast_fp16, x = attn_output_87_cast_fp16)[name = string("hidden_states_103_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_105_cast_fp16 = add(x = hidden_states_99_cast_fp16, y = hidden_states_103_cast_fp16)[name = string("hidden_states_105_cast_fp16")];
            fp16 const_110_promoted_to_fp16 = const()[name = string("const_110_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_4378_cast_fp16 = mul(x = hidden_states_105_cast_fp16, y = const_110_promoted_to_fp16)[name = string("op_4378_cast_fp16")];
            int32 var_4376 = const()[name = string("op_4376"), val = int32(1)];
            bool doubled_85_interleave_0 = const()[name = string("doubled_85_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_85_cast_fp16 = concat(axis = var_4376, interleave = doubled_85_interleave_0, values = (hidden_states_105_cast_fp16, var_4378_cast_fp16))[name = string("doubled_85_cast_fp16")];
            tensor<int32, [1]> out_43_axes_0 = const()[name = string("out_43_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_43_gamma_0_to_fp16 = const()[name = string("out_43_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406969472)))];
            fp16 var_4388_to_fp16 = const()[name = string("op_4388_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_43_cast_fp16 = layer_norm(axes = out_43_axes_0, epsilon = var_4388_to_fp16, gamma = out_43_gamma_0_to_fp16, x = doubled_85_cast_fp16)[name = string("out_43_cast_fp16")];
            tensor<int32, [2]> var_4399_split_sizes_0 = const()[name = string("op_4399_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4399_axis_0 = const()[name = string("op_4399_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_4399_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_4399_cast_fp16_1 = split(axis = var_4399_axis_0, split_sizes = var_4399_split_sizes_0, x = out_43_cast_fp16)[name = string("op_4399_cast_fp16")];
            tensor<int32, [2]> input_21_strides_0 = const()[name = string("input_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_21_pad_type_0 = const()[name = string("input_21_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_21_pad_0 = const()[name = string("input_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_21_dilations_0 = const()[name = string("input_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_21_groups_0 = const()[name = string("input_21_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_21_cast_fp16 = conv(dilations = input_21_dilations_0, groups = input_21_groups_0, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = input_21_strides_0, weight = layers_10_mlp_gate_proj_weight_cast_fp16, x = var_4399_cast_fp16_0)[name = string("input_21_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_4416_cast_fp16 = silu(x = input_21_cast_fp16)[name = string("op_4416_cast_fp16")];
            tensor<int32, [2]> var_4422_strides_0 = const()[name = string("op_4422_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_4422_pad_type_0 = const()[name = string("op_4422_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_4422_pad_0 = const()[name = string("op_4422_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_4422_dilations_0 = const()[name = string("op_4422_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4422_groups_0 = const()[name = string("op_4422_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_4422_cast_fp16 = conv(dilations = var_4422_dilations_0, groups = var_4422_groups_0, pad = var_4422_pad_0, pad_type = var_4422_pad_type_0, strides = var_4422_strides_0, weight = layers_10_mlp_up_proj_weight_cast_fp16, x = var_4399_cast_fp16_0)[name = string("op_4422_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_109_cast_fp16 = mul(x = var_4416_cast_fp16, y = var_4422_cast_fp16)[name = string("x_109_cast_fp16")];
            tensor<int32, [2]> hidden_states_107_strides_0 = const()[name = string("hidden_states_107_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_107_pad_type_0 = const()[name = string("hidden_states_107_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_107_pad_0 = const()[name = string("hidden_states_107_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_107_dilations_0 = const()[name = string("hidden_states_107_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_107_groups_0 = const()[name = string("hidden_states_107_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_107_cast_fp16 = conv(dilations = hidden_states_107_dilations_0, groups = hidden_states_107_groups_0, pad = hidden_states_107_pad_0, pad_type = hidden_states_107_pad_type_0, strides = hidden_states_107_strides_0, weight = layers_10_mlp_down_proj_weight_cast_fp16, x = x_109_cast_fp16)[name = string("hidden_states_107_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_109_cast_fp16 = add(x = hidden_states_105_cast_fp16, y = hidden_states_107_cast_fp16)[name = string("hidden_states_109_cast_fp16")];
            fp16 const_112_promoted_to_fp16 = const()[name = string("const_112_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_4440_cast_fp16 = mul(x = hidden_states_109_cast_fp16, y = const_112_promoted_to_fp16)[name = string("op_4440_cast_fp16")];
            int32 var_4438 = const()[name = string("op_4438"), val = int32(1)];
            bool doubled_89_interleave_0 = const()[name = string("doubled_89_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_89_cast_fp16 = concat(axis = var_4438, interleave = doubled_89_interleave_0, values = (hidden_states_109_cast_fp16, var_4440_cast_fp16))[name = string("doubled_89_cast_fp16")];
            tensor<int32, [1]> out_45_axes_0 = const()[name = string("out_45_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_45_gamma_0_to_fp16 = const()[name = string("out_45_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406977728)))];
            fp16 var_4450_to_fp16 = const()[name = string("op_4450_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_45_cast_fp16 = layer_norm(axes = out_45_axes_0, epsilon = var_4450_to_fp16, gamma = out_45_gamma_0_to_fp16, x = doubled_89_cast_fp16)[name = string("out_45_cast_fp16")];
            tensor<int32, [2]> var_4461_split_sizes_0 = const()[name = string("op_4461_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4461_axis_0 = const()[name = string("op_4461_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_4461_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_4461_cast_fp16_1 = split(axis = var_4461_axis_0, split_sizes = var_4461_split_sizes_0, x = out_45_cast_fp16)[name = string("op_4461_cast_fp16")];
            tensor<int32, [2]> query_states_67_strides_0 = const()[name = string("query_states_67_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_67_pad_type_0 = const()[name = string("query_states_67_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_67_pad_0 = const()[name = string("query_states_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_67_dilations_0 = const()[name = string("query_states_67_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_67_groups_0 = const()[name = string("query_states_67_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_67_cast_fp16 = conv(dilations = query_states_67_dilations_0, groups = query_states_67_groups_0, pad = query_states_67_pad_0, pad_type = query_states_67_pad_type_0, strides = query_states_67_strides_0, weight = layers_11_self_attn_q_proj_weight_cast_fp16, x = var_4461_cast_fp16_0)[name = string("query_states_67_cast_fp16")];
            tensor<int32, [2]> key_states_111_strides_0 = const()[name = string("key_states_111_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_111_pad_type_0 = const()[name = string("key_states_111_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_111_pad_0 = const()[name = string("key_states_111_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_111_dilations_0 = const()[name = string("key_states_111_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_111_groups_0 = const()[name = string("key_states_111_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_111_cast_fp16 = conv(dilations = key_states_111_dilations_0, groups = key_states_111_groups_0, pad = key_states_111_pad_0, pad_type = key_states_111_pad_type_0, strides = key_states_111_strides_0, weight = layers_11_self_attn_k_proj_weight_cast_fp16, x = var_4461_cast_fp16_0)[name = string("key_states_111_cast_fp16")];
            tensor<int32, [2]> value_states_67_strides_0 = const()[name = string("value_states_67_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_67_pad_type_0 = const()[name = string("value_states_67_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_67_pad_0 = const()[name = string("value_states_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_67_dilations_0 = const()[name = string("value_states_67_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_67_groups_0 = const()[name = string("value_states_67_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_67_cast_fp16 = conv(dilations = value_states_67_dilations_0, groups = value_states_67_groups_0, pad = value_states_67_pad_0, pad_type = value_states_67_pad_type_0, strides = value_states_67_strides_0, weight = layers_11_self_attn_v_proj_weight_cast_fp16, x = var_4461_cast_fp16_0)[name = string("value_states_67_cast_fp16")];
            tensor<int32, [4]> concat_132x = const()[name = string("concat_132x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_111_cast_fp16 = reshape(shape = concat_132x, x = query_states_67_cast_fp16)[name = string("x_111_cast_fp16")];
            tensor<int32, [4]> concat_133x = const()[name = string("concat_133x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_4518_cast_fp16 = reshape(shape = concat_133x, x = key_states_111_cast_fp16)[name = string("op_4518_cast_fp16")];
            tensor<int32, [4]> concat_134x = const()[name = string("concat_134x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_4525_cast_fp16 = reshape(shape = concat_134x, x = value_states_67_cast_fp16)[name = string("op_4525_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_4529_cast_fp16 = mul(x = x_111_cast_fp16, y = var_859_cast_fp16)[name = string("op_4529_cast_fp16")];
            tensor<int32, [2]> var_4530_split_sizes_0 = const()[name = string("op_4530_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4530_axis_0 = const()[name = string("op_4530_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_4530_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_4530_cast_fp16_1 = split(axis = var_4530_axis_0, split_sizes = var_4530_split_sizes_0, x = x_111_cast_fp16)[name = string("op_4530_cast_fp16")];
            fp16 const_114_promoted_to_fp16 = const()[name = string("const_114_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_4532_cast_fp16 = mul(x = var_4530_cast_fp16_1, y = const_114_promoted_to_fp16)[name = string("op_4532_cast_fp16")];
            int32 var_4534 = const()[name = string("op_4534"), val = int32(-2)];
            bool var_4535_interleave_0 = const()[name = string("op_4535_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_4535_cast_fp16 = concat(axis = var_4534, interleave = var_4535_interleave_0, values = (var_4532_cast_fp16, var_4530_cast_fp16_0))[name = string("op_4535_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_4536_cast_fp16 = mul(x = var_4535_cast_fp16, y = var_866_cast_fp16)[name = string("op_4536_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_69_cast_fp16 = add(x = var_4529_cast_fp16, y = var_4536_cast_fp16)[name = string("query_states_69_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_4542_cast_fp16 = mul(x = var_4518_cast_fp16, y = var_859_cast_fp16)[name = string("op_4542_cast_fp16")];
            tensor<int32, [2]> var_4543_split_sizes_0 = const()[name = string("op_4543_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4543_axis_0 = const()[name = string("op_4543_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_4543_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_4543_cast_fp16_1 = split(axis = var_4543_axis_0, split_sizes = var_4543_split_sizes_0, x = var_4518_cast_fp16)[name = string("op_4543_cast_fp16")];
            fp16 const_115_promoted_to_fp16 = const()[name = string("const_115_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_4545_cast_fp16 = mul(x = var_4543_cast_fp16_1, y = const_115_promoted_to_fp16)[name = string("op_4545_cast_fp16")];
            int32 var_4547 = const()[name = string("op_4547"), val = int32(-2)];
            bool var_4548_interleave_0 = const()[name = string("op_4548_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_4548_cast_fp16 = concat(axis = var_4547, interleave = var_4548_interleave_0, values = (var_4545_cast_fp16, var_4543_cast_fp16_0))[name = string("op_4548_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_4549_cast_fp16 = mul(x = var_4548_cast_fp16, y = var_866_cast_fp16)[name = string("op_4549_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_115_cast_fp16 = add(x = var_4542_cast_fp16, y = var_4549_cast_fp16)[name = string("key_states_115_cast_fp16")];
            tensor<int32, [1]> expand_dims_132 = const()[name = string("expand_dims_132"), val = tensor<int32, [1]>([11])];
            tensor<int32, [1]> expand_dims_133 = const()[name = string("expand_dims_133"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_135 = const()[name = string("expand_dims_135"), val = tensor<int32, [1]>([0])];
            int32 concat_137_axis_0 = const()[name = string("concat_137_axis_0"), val = int32(0)];
            bool concat_137_interleave_0 = const()[name = string("concat_137_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_137 = concat(axis = concat_137_axis_0, interleave = concat_137_interleave_0, values = (expand_dims_132, expand_dims_133, position_id, expand_dims_135))[name = string("concat_137")];
            tensor<int32, [1]> expand_dims_136 = const()[name = string("expand_dims_136"), val = tensor<int32, [1]>([12])];
            tensor<int32, [1]> concat_138_values1_0 = const()[name = string("concat_138_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_138_values3_0 = const()[name = string("concat_138_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_138_axis_0 = const()[name = string("concat_138_axis_0"), val = int32(0)];
            bool concat_138_interleave_0 = const()[name = string("concat_138_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_138 = concat(axis = concat_138_axis_0, interleave = concat_138_interleave_0, values = (expand_dims_136, concat_138_values1_0, cache_position_end, concat_138_values3_0))[name = string("concat_138")];
            tensor<int32, [4]> key_states_117_perm_0 = const()[name = string("key_states_117_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_12_stride_0 = const()[name = string("key_cache_internal_tensor_assign_12_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_12_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_12_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_12_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_12_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_12_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_12_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_117_cast_fp16 = transpose(perm = key_states_117_perm_0, x = key_states_115_cast_fp16)[name = string("transpose_572")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_12_cast_fp16 = slice_update(begin = concat_137, begin_mask = key_cache_internal_tensor_assign_12_begin_mask_0, end = concat_138, end_mask = key_cache_internal_tensor_assign_12_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_12_squeeze_mask_0, stride = key_cache_internal_tensor_assign_12_stride_0, update = key_states_117_cast_fp16, x = coreml_update_state_356)[name = string("key_cache_internal_tensor_assign_12_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_12_cast_fp16, input = key_cache)[name = string("coreml_update_state_358_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_358 = read_state(input = key_cache)[name = string("coreml_update_state_358")];
            tensor<int32, [4]> value_states_69_perm_0 = const()[name = string("value_states_69_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_12_stride_0 = const()[name = string("value_cache_internal_tensor_assign_12_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_12_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_12_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_12_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_12_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_12_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_12_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_69_cast_fp16 = transpose(perm = value_states_69_perm_0, x = var_4525_cast_fp16)[name = string("transpose_571")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_12_cast_fp16 = slice_update(begin = concat_137, begin_mask = value_cache_internal_tensor_assign_12_begin_mask_0, end = concat_138, end_mask = value_cache_internal_tensor_assign_12_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_12_squeeze_mask_0, stride = value_cache_internal_tensor_assign_12_stride_0, update = value_states_69_cast_fp16, x = coreml_update_state_357)[name = string("value_cache_internal_tensor_assign_12_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_12_cast_fp16, input = value_cache)[name = string("coreml_update_state_359_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_359 = read_state(input = value_cache)[name = string("coreml_update_state_359")];
            tensor<int32, [4]> var_4619_begin_0 = const()[name = string("op_4619_begin_0"), val = tensor<int32, [4]>([11, 0, 0, 0])];
            tensor<int32, [4]> var_4619_end_0 = const()[name = string("op_4619_end_0"), val = tensor<int32, [4]>([12, 2, 2048, 128])];
            tensor<bool, [4]> var_4619_end_mask_0 = const()[name = string("op_4619_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4619_cast_fp16 = slice_by_index(begin = var_4619_begin_0, end = var_4619_end_0, end_mask = var_4619_end_mask_0, x = coreml_update_state_358)[name = string("op_4619_cast_fp16")];
            tensor<int32, [2]> tile_22 = const()[name = string("tile_22"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4622_axis_0 = const()[name = string("op_4622_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4622_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4622_cast_fp16_1 = split(axis = var_4622_axis_0, split_sizes = tile_22, x = var_4619_cast_fp16)[name = string("op_4622_cast_fp16")];
            tensor<int32, [4]> var_4629_begin_0 = const()[name = string("op_4629_begin_0"), val = tensor<int32, [4]>([11, 0, 0, 0])];
            tensor<int32, [4]> var_4629_end_0 = const()[name = string("op_4629_end_0"), val = tensor<int32, [4]>([12, 2, 2048, 128])];
            tensor<bool, [4]> var_4629_end_mask_0 = const()[name = string("op_4629_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4629_cast_fp16 = slice_by_index(begin = var_4629_begin_0, end = var_4629_end_0, end_mask = var_4629_end_mask_0, x = coreml_update_state_359)[name = string("op_4629_cast_fp16")];
            tensor<int32, [2]> tile_23 = const()[name = string("tile_23"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4632_axis_0 = const()[name = string("op_4632_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4632_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4632_cast_fp16_1 = split(axis = var_4632_axis_0, split_sizes = tile_23, x = var_4629_cast_fp16)[name = string("op_4632_cast_fp16")];
            tensor<int32, [2]> var_4635_split_sizes_0 = const()[name = string("op_4635_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_4635_axis_0 = const()[name = string("op_4635_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_4635_0, tensor<fp16, [1, 8, 128, 128]> var_4635_1 = split(axis = var_4635_axis_0, split_sizes = var_4635_split_sizes_0, x = query_states_69_cast_fp16)[name = string("op_4635")];
            bool attn_weights_177_transpose_x_0 = const()[name = string("attn_weights_177_transpose_x_0"), val = bool(false)];
            bool attn_weights_177_transpose_y_0 = const()[name = string("attn_weights_177_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_177_cast_fp16 = matmul(transpose_x = attn_weights_177_transpose_x_0, transpose_y = attn_weights_177_transpose_y_0, x = var_4622_cast_fp16_0, y = var_4635_0)[name = string("attn_weights_177_cast_fp16")];
            fp16 var_4638_to_fp16 = const()[name = string("op_4638_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_179_cast_fp16 = mul(x = attn_weights_177_cast_fp16, y = var_4638_to_fp16)[name = string("attn_weights_179_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_181_cast_fp16 = add(x = attn_weights_179_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_181_cast_fp16")];
            int32 var_4642 = const()[name = string("op_4642"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_183_cast_fp16 = softmax(axis = var_4642, x = attn_weights_181_cast_fp16)[name = string("attn_weights_183_cast_fp16")];
            bool var_4648_transpose_x_1 = const()[name = string("op_4648_transpose_x_1"), val = bool(true)];
            bool var_4648_transpose_y_1 = const()[name = string("op_4648_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_4648_cast_fp16 = matmul(transpose_x = var_4648_transpose_x_1, transpose_y = var_4648_transpose_y_1, x = attn_weights_183_cast_fp16, y = var_4632_cast_fp16_0)[name = string("op_4648_cast_fp16")];
            bool attn_weights_185_transpose_x_0 = const()[name = string("attn_weights_185_transpose_x_0"), val = bool(false)];
            bool attn_weights_185_transpose_y_0 = const()[name = string("attn_weights_185_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_185_cast_fp16 = matmul(transpose_x = attn_weights_185_transpose_x_0, transpose_y = attn_weights_185_transpose_y_0, x = var_4622_cast_fp16_1, y = var_4635_1)[name = string("attn_weights_185_cast_fp16")];
            fp16 var_4650_to_fp16 = const()[name = string("op_4650_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_187_cast_fp16 = mul(x = attn_weights_185_cast_fp16, y = var_4650_to_fp16)[name = string("attn_weights_187_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_189_cast_fp16 = add(x = attn_weights_187_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_189_cast_fp16")];
            int32 var_4654 = const()[name = string("op_4654"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_191_cast_fp16 = softmax(axis = var_4654, x = attn_weights_189_cast_fp16)[name = string("attn_weights_191_cast_fp16")];
            bool attn_output_89_transpose_x_1 = const()[name = string("attn_output_89_transpose_x_1"), val = bool(true)];
            bool attn_output_89_transpose_y_1 = const()[name = string("attn_output_89_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_89_cast_fp16 = matmul(transpose_x = attn_output_89_transpose_x_1, transpose_y = attn_output_89_transpose_y_1, x = attn_weights_191_cast_fp16, y = var_4632_cast_fp16_1)[name = string("attn_output_89_cast_fp16")];
            int32 var_4662 = const()[name = string("op_4662"), val = int32(1)];
            bool attn_output_91_interleave_0 = const()[name = string("attn_output_91_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_91_cast_fp16 = concat(axis = var_4662, interleave = attn_output_91_interleave_0, values = (var_4648_cast_fp16, attn_output_89_cast_fp16))[name = string("attn_output_91_cast_fp16")];
            tensor<int32, [4]> var_4666_perm_0 = const()[name = string("op_4666_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_143x = const()[name = string("concat_143x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_4666_cast_fp16 = transpose(perm = var_4666_perm_0, x = attn_output_91_cast_fp16)[name = string("transpose_570")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_95_cast_fp16 = reshape(shape = concat_143x, x = var_4666_cast_fp16)[name = string("attn_output_95_cast_fp16")];
            tensor<int32, [2]> hidden_states_113_strides_0 = const()[name = string("hidden_states_113_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_113_pad_type_0 = const()[name = string("hidden_states_113_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_113_pad_0 = const()[name = string("hidden_states_113_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_113_dilations_0 = const()[name = string("hidden_states_113_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_113_groups_0 = const()[name = string("hidden_states_113_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_113_cast_fp16 = conv(dilations = hidden_states_113_dilations_0, groups = hidden_states_113_groups_0, pad = hidden_states_113_pad_0, pad_type = hidden_states_113_pad_type_0, strides = hidden_states_113_strides_0, weight = layers_11_self_attn_o_proj_weight_cast_fp16, x = attn_output_95_cast_fp16)[name = string("hidden_states_113_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_115_cast_fp16 = add(x = hidden_states_109_cast_fp16, y = hidden_states_113_cast_fp16)[name = string("hidden_states_115_cast_fp16")];
            fp16 const_120_promoted_to_fp16 = const()[name = string("const_120_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_4699_cast_fp16 = mul(x = hidden_states_115_cast_fp16, y = const_120_promoted_to_fp16)[name = string("op_4699_cast_fp16")];
            int32 var_4697 = const()[name = string("op_4697"), val = int32(1)];
            bool doubled_93_interleave_0 = const()[name = string("doubled_93_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_93_cast_fp16 = concat(axis = var_4697, interleave = doubled_93_interleave_0, values = (hidden_states_115_cast_fp16, var_4699_cast_fp16))[name = string("doubled_93_cast_fp16")];
            tensor<int32, [1]> out_47_axes_0 = const()[name = string("out_47_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_47_gamma_0_to_fp16 = const()[name = string("out_47_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406985984)))];
            fp16 var_4709_to_fp16 = const()[name = string("op_4709_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_47_cast_fp16 = layer_norm(axes = out_47_axes_0, epsilon = var_4709_to_fp16, gamma = out_47_gamma_0_to_fp16, x = doubled_93_cast_fp16)[name = string("out_47_cast_fp16")];
            tensor<int32, [2]> var_4720_split_sizes_0 = const()[name = string("op_4720_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4720_axis_0 = const()[name = string("op_4720_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_4720_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_4720_cast_fp16_1 = split(axis = var_4720_axis_0, split_sizes = var_4720_split_sizes_0, x = out_47_cast_fp16)[name = string("op_4720_cast_fp16")];
            tensor<int32, [2]> input_23_strides_0 = const()[name = string("input_23_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_23_pad_type_0 = const()[name = string("input_23_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_23_pad_0 = const()[name = string("input_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_23_dilations_0 = const()[name = string("input_23_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_23_groups_0 = const()[name = string("input_23_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_23_cast_fp16 = conv(dilations = input_23_dilations_0, groups = input_23_groups_0, pad = input_23_pad_0, pad_type = input_23_pad_type_0, strides = input_23_strides_0, weight = layers_11_mlp_gate_proj_weight_cast_fp16, x = var_4720_cast_fp16_0)[name = string("input_23_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_4737_cast_fp16 = silu(x = input_23_cast_fp16)[name = string("op_4737_cast_fp16")];
            tensor<int32, [2]> var_4743_strides_0 = const()[name = string("op_4743_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_4743_pad_type_0 = const()[name = string("op_4743_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_4743_pad_0 = const()[name = string("op_4743_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_4743_dilations_0 = const()[name = string("op_4743_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4743_groups_0 = const()[name = string("op_4743_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_4743_cast_fp16 = conv(dilations = var_4743_dilations_0, groups = var_4743_groups_0, pad = var_4743_pad_0, pad_type = var_4743_pad_type_0, strides = var_4743_strides_0, weight = layers_11_mlp_up_proj_weight_cast_fp16, x = var_4720_cast_fp16_0)[name = string("op_4743_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_119_cast_fp16 = mul(x = var_4737_cast_fp16, y = var_4743_cast_fp16)[name = string("x_119_cast_fp16")];
            tensor<int32, [2]> hidden_states_117_strides_0 = const()[name = string("hidden_states_117_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_117_pad_type_0 = const()[name = string("hidden_states_117_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_117_pad_0 = const()[name = string("hidden_states_117_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_117_dilations_0 = const()[name = string("hidden_states_117_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_117_groups_0 = const()[name = string("hidden_states_117_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_117_cast_fp16 = conv(dilations = hidden_states_117_dilations_0, groups = hidden_states_117_groups_0, pad = hidden_states_117_pad_0, pad_type = hidden_states_117_pad_type_0, strides = hidden_states_117_strides_0, weight = layers_11_mlp_down_proj_weight_cast_fp16, x = x_119_cast_fp16)[name = string("hidden_states_117_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_119_cast_fp16 = add(x = hidden_states_115_cast_fp16, y = hidden_states_117_cast_fp16)[name = string("hidden_states_119_cast_fp16")];
            fp16 const_122_promoted_to_fp16 = const()[name = string("const_122_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_4761_cast_fp16 = mul(x = hidden_states_119_cast_fp16, y = const_122_promoted_to_fp16)[name = string("op_4761_cast_fp16")];
            int32 var_4759 = const()[name = string("op_4759"), val = int32(1)];
            bool doubled_97_interleave_0 = const()[name = string("doubled_97_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_97_cast_fp16 = concat(axis = var_4759, interleave = doubled_97_interleave_0, values = (hidden_states_119_cast_fp16, var_4761_cast_fp16))[name = string("doubled_97_cast_fp16")];
            tensor<int32, [1]> out_49_axes_0 = const()[name = string("out_49_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_49_gamma_0_to_fp16 = const()[name = string("out_49_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406994240)))];
            fp16 var_4771_to_fp16 = const()[name = string("op_4771_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_49_cast_fp16 = layer_norm(axes = out_49_axes_0, epsilon = var_4771_to_fp16, gamma = out_49_gamma_0_to_fp16, x = doubled_97_cast_fp16)[name = string("out_49_cast_fp16")];
            tensor<int32, [2]> var_4782_split_sizes_0 = const()[name = string("op_4782_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4782_axis_0 = const()[name = string("op_4782_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_4782_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_4782_cast_fp16_1 = split(axis = var_4782_axis_0, split_sizes = var_4782_split_sizes_0, x = out_49_cast_fp16)[name = string("op_4782_cast_fp16")];
            tensor<int32, [2]> query_states_73_strides_0 = const()[name = string("query_states_73_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_73_pad_type_0 = const()[name = string("query_states_73_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_73_pad_0 = const()[name = string("query_states_73_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_73_dilations_0 = const()[name = string("query_states_73_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_73_groups_0 = const()[name = string("query_states_73_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_73_cast_fp16 = conv(dilations = query_states_73_dilations_0, groups = query_states_73_groups_0, pad = query_states_73_pad_0, pad_type = query_states_73_pad_type_0, strides = query_states_73_strides_0, weight = layers_12_self_attn_q_proj_weight_cast_fp16, x = var_4782_cast_fp16_0)[name = string("query_states_73_cast_fp16")];
            tensor<int32, [2]> key_states_121_strides_0 = const()[name = string("key_states_121_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_121_pad_type_0 = const()[name = string("key_states_121_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_121_pad_0 = const()[name = string("key_states_121_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_121_dilations_0 = const()[name = string("key_states_121_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_121_groups_0 = const()[name = string("key_states_121_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_121_cast_fp16 = conv(dilations = key_states_121_dilations_0, groups = key_states_121_groups_0, pad = key_states_121_pad_0, pad_type = key_states_121_pad_type_0, strides = key_states_121_strides_0, weight = layers_12_self_attn_k_proj_weight_cast_fp16, x = var_4782_cast_fp16_0)[name = string("key_states_121_cast_fp16")];
            tensor<int32, [2]> value_states_73_strides_0 = const()[name = string("value_states_73_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_73_pad_type_0 = const()[name = string("value_states_73_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_73_pad_0 = const()[name = string("value_states_73_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_73_dilations_0 = const()[name = string("value_states_73_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_73_groups_0 = const()[name = string("value_states_73_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_73_cast_fp16 = conv(dilations = value_states_73_dilations_0, groups = value_states_73_groups_0, pad = value_states_73_pad_0, pad_type = value_states_73_pad_type_0, strides = value_states_73_strides_0, weight = layers_12_self_attn_v_proj_weight_cast_fp16, x = var_4782_cast_fp16_0)[name = string("value_states_73_cast_fp16")];
            tensor<int32, [4]> concat_144x = const()[name = string("concat_144x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_121_cast_fp16 = reshape(shape = concat_144x, x = query_states_73_cast_fp16)[name = string("x_121_cast_fp16")];
            tensor<int32, [4]> concat_145x = const()[name = string("concat_145x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_4839_cast_fp16 = reshape(shape = concat_145x, x = key_states_121_cast_fp16)[name = string("op_4839_cast_fp16")];
            tensor<int32, [4]> concat_146x = const()[name = string("concat_146x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_4846_cast_fp16 = reshape(shape = concat_146x, x = value_states_73_cast_fp16)[name = string("op_4846_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_4850_cast_fp16 = mul(x = x_121_cast_fp16, y = var_859_cast_fp16)[name = string("op_4850_cast_fp16")];
            tensor<int32, [2]> var_4851_split_sizes_0 = const()[name = string("op_4851_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4851_axis_0 = const()[name = string("op_4851_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_4851_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_4851_cast_fp16_1 = split(axis = var_4851_axis_0, split_sizes = var_4851_split_sizes_0, x = x_121_cast_fp16)[name = string("op_4851_cast_fp16")];
            fp16 const_124_promoted_to_fp16 = const()[name = string("const_124_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_4853_cast_fp16 = mul(x = var_4851_cast_fp16_1, y = const_124_promoted_to_fp16)[name = string("op_4853_cast_fp16")];
            int32 var_4855 = const()[name = string("op_4855"), val = int32(-2)];
            bool var_4856_interleave_0 = const()[name = string("op_4856_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_4856_cast_fp16 = concat(axis = var_4855, interleave = var_4856_interleave_0, values = (var_4853_cast_fp16, var_4851_cast_fp16_0))[name = string("op_4856_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_4857_cast_fp16 = mul(x = var_4856_cast_fp16, y = var_866_cast_fp16)[name = string("op_4857_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_75_cast_fp16 = add(x = var_4850_cast_fp16, y = var_4857_cast_fp16)[name = string("query_states_75_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_4863_cast_fp16 = mul(x = var_4839_cast_fp16, y = var_859_cast_fp16)[name = string("op_4863_cast_fp16")];
            tensor<int32, [2]> var_4864_split_sizes_0 = const()[name = string("op_4864_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4864_axis_0 = const()[name = string("op_4864_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_4864_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_4864_cast_fp16_1 = split(axis = var_4864_axis_0, split_sizes = var_4864_split_sizes_0, x = var_4839_cast_fp16)[name = string("op_4864_cast_fp16")];
            fp16 const_125_promoted_to_fp16 = const()[name = string("const_125_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_4866_cast_fp16 = mul(x = var_4864_cast_fp16_1, y = const_125_promoted_to_fp16)[name = string("op_4866_cast_fp16")];
            int32 var_4868 = const()[name = string("op_4868"), val = int32(-2)];
            bool var_4869_interleave_0 = const()[name = string("op_4869_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_4869_cast_fp16 = concat(axis = var_4868, interleave = var_4869_interleave_0, values = (var_4866_cast_fp16, var_4864_cast_fp16_0))[name = string("op_4869_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_4870_cast_fp16 = mul(x = var_4869_cast_fp16, y = var_866_cast_fp16)[name = string("op_4870_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_125_cast_fp16 = add(x = var_4863_cast_fp16, y = var_4870_cast_fp16)[name = string("key_states_125_cast_fp16")];
            tensor<int32, [1]> expand_dims_144 = const()[name = string("expand_dims_144"), val = tensor<int32, [1]>([12])];
            tensor<int32, [1]> expand_dims_145 = const()[name = string("expand_dims_145"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_147 = const()[name = string("expand_dims_147"), val = tensor<int32, [1]>([0])];
            int32 concat_149_axis_0 = const()[name = string("concat_149_axis_0"), val = int32(0)];
            bool concat_149_interleave_0 = const()[name = string("concat_149_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_149 = concat(axis = concat_149_axis_0, interleave = concat_149_interleave_0, values = (expand_dims_144, expand_dims_145, position_id, expand_dims_147))[name = string("concat_149")];
            tensor<int32, [1]> expand_dims_148 = const()[name = string("expand_dims_148"), val = tensor<int32, [1]>([13])];
            tensor<int32, [1]> concat_150_values1_0 = const()[name = string("concat_150_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_150_values3_0 = const()[name = string("concat_150_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_150_axis_0 = const()[name = string("concat_150_axis_0"), val = int32(0)];
            bool concat_150_interleave_0 = const()[name = string("concat_150_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_150 = concat(axis = concat_150_axis_0, interleave = concat_150_interleave_0, values = (expand_dims_148, concat_150_values1_0, cache_position_end, concat_150_values3_0))[name = string("concat_150")];
            tensor<int32, [4]> key_states_127_perm_0 = const()[name = string("key_states_127_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_13_stride_0 = const()[name = string("key_cache_internal_tensor_assign_13_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_13_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_13_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_13_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_13_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_13_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_13_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_127_cast_fp16 = transpose(perm = key_states_127_perm_0, x = key_states_125_cast_fp16)[name = string("transpose_569")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_13_cast_fp16 = slice_update(begin = concat_149, begin_mask = key_cache_internal_tensor_assign_13_begin_mask_0, end = concat_150, end_mask = key_cache_internal_tensor_assign_13_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_13_squeeze_mask_0, stride = key_cache_internal_tensor_assign_13_stride_0, update = key_states_127_cast_fp16, x = coreml_update_state_358)[name = string("key_cache_internal_tensor_assign_13_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_13_cast_fp16, input = key_cache)[name = string("coreml_update_state_360_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_360 = read_state(input = key_cache)[name = string("coreml_update_state_360")];
            tensor<int32, [4]> value_states_75_perm_0 = const()[name = string("value_states_75_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_13_stride_0 = const()[name = string("value_cache_internal_tensor_assign_13_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_13_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_13_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_13_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_13_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_13_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_13_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_75_cast_fp16 = transpose(perm = value_states_75_perm_0, x = var_4846_cast_fp16)[name = string("transpose_568")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_13_cast_fp16 = slice_update(begin = concat_149, begin_mask = value_cache_internal_tensor_assign_13_begin_mask_0, end = concat_150, end_mask = value_cache_internal_tensor_assign_13_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_13_squeeze_mask_0, stride = value_cache_internal_tensor_assign_13_stride_0, update = value_states_75_cast_fp16, x = coreml_update_state_359)[name = string("value_cache_internal_tensor_assign_13_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_13_cast_fp16, input = value_cache)[name = string("coreml_update_state_361_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_361 = read_state(input = value_cache)[name = string("coreml_update_state_361")];
            tensor<int32, [4]> var_4940_begin_0 = const()[name = string("op_4940_begin_0"), val = tensor<int32, [4]>([12, 0, 0, 0])];
            tensor<int32, [4]> var_4940_end_0 = const()[name = string("op_4940_end_0"), val = tensor<int32, [4]>([13, 2, 2048, 128])];
            tensor<bool, [4]> var_4940_end_mask_0 = const()[name = string("op_4940_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4940_cast_fp16 = slice_by_index(begin = var_4940_begin_0, end = var_4940_end_0, end_mask = var_4940_end_mask_0, x = coreml_update_state_360)[name = string("op_4940_cast_fp16")];
            tensor<int32, [2]> tile_24 = const()[name = string("tile_24"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4943_axis_0 = const()[name = string("op_4943_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4943_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4943_cast_fp16_1 = split(axis = var_4943_axis_0, split_sizes = tile_24, x = var_4940_cast_fp16)[name = string("op_4943_cast_fp16")];
            tensor<int32, [4]> var_4950_begin_0 = const()[name = string("op_4950_begin_0"), val = tensor<int32, [4]>([12, 0, 0, 0])];
            tensor<int32, [4]> var_4950_end_0 = const()[name = string("op_4950_end_0"), val = tensor<int32, [4]>([13, 2, 2048, 128])];
            tensor<bool, [4]> var_4950_end_mask_0 = const()[name = string("op_4950_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4950_cast_fp16 = slice_by_index(begin = var_4950_begin_0, end = var_4950_end_0, end_mask = var_4950_end_mask_0, x = coreml_update_state_361)[name = string("op_4950_cast_fp16")];
            tensor<int32, [2]> tile_25 = const()[name = string("tile_25"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4953_axis_0 = const()[name = string("op_4953_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4953_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4953_cast_fp16_1 = split(axis = var_4953_axis_0, split_sizes = tile_25, x = var_4950_cast_fp16)[name = string("op_4953_cast_fp16")];
            tensor<int32, [2]> var_4956_split_sizes_0 = const()[name = string("op_4956_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_4956_axis_0 = const()[name = string("op_4956_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_4956_0, tensor<fp16, [1, 8, 128, 128]> var_4956_1 = split(axis = var_4956_axis_0, split_sizes = var_4956_split_sizes_0, x = query_states_75_cast_fp16)[name = string("op_4956")];
            bool attn_weights_193_transpose_x_0 = const()[name = string("attn_weights_193_transpose_x_0"), val = bool(false)];
            bool attn_weights_193_transpose_y_0 = const()[name = string("attn_weights_193_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_193_cast_fp16 = matmul(transpose_x = attn_weights_193_transpose_x_0, transpose_y = attn_weights_193_transpose_y_0, x = var_4943_cast_fp16_0, y = var_4956_0)[name = string("attn_weights_193_cast_fp16")];
            fp16 var_4959_to_fp16 = const()[name = string("op_4959_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_195_cast_fp16 = mul(x = attn_weights_193_cast_fp16, y = var_4959_to_fp16)[name = string("attn_weights_195_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_197_cast_fp16 = add(x = attn_weights_195_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_197_cast_fp16")];
            int32 var_4963 = const()[name = string("op_4963"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_199_cast_fp16 = softmax(axis = var_4963, x = attn_weights_197_cast_fp16)[name = string("attn_weights_199_cast_fp16")];
            bool var_4969_transpose_x_1 = const()[name = string("op_4969_transpose_x_1"), val = bool(true)];
            bool var_4969_transpose_y_1 = const()[name = string("op_4969_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_4969_cast_fp16 = matmul(transpose_x = var_4969_transpose_x_1, transpose_y = var_4969_transpose_y_1, x = attn_weights_199_cast_fp16, y = var_4953_cast_fp16_0)[name = string("op_4969_cast_fp16")];
            bool attn_weights_201_transpose_x_0 = const()[name = string("attn_weights_201_transpose_x_0"), val = bool(false)];
            bool attn_weights_201_transpose_y_0 = const()[name = string("attn_weights_201_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_201_cast_fp16 = matmul(transpose_x = attn_weights_201_transpose_x_0, transpose_y = attn_weights_201_transpose_y_0, x = var_4943_cast_fp16_1, y = var_4956_1)[name = string("attn_weights_201_cast_fp16")];
            fp16 var_4971_to_fp16 = const()[name = string("op_4971_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_203_cast_fp16 = mul(x = attn_weights_201_cast_fp16, y = var_4971_to_fp16)[name = string("attn_weights_203_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_205_cast_fp16 = add(x = attn_weights_203_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_205_cast_fp16")];
            int32 var_4975 = const()[name = string("op_4975"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_207_cast_fp16 = softmax(axis = var_4975, x = attn_weights_205_cast_fp16)[name = string("attn_weights_207_cast_fp16")];
            bool attn_output_97_transpose_x_1 = const()[name = string("attn_output_97_transpose_x_1"), val = bool(true)];
            bool attn_output_97_transpose_y_1 = const()[name = string("attn_output_97_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_97_cast_fp16 = matmul(transpose_x = attn_output_97_transpose_x_1, transpose_y = attn_output_97_transpose_y_1, x = attn_weights_207_cast_fp16, y = var_4953_cast_fp16_1)[name = string("attn_output_97_cast_fp16")];
            int32 var_4983 = const()[name = string("op_4983"), val = int32(1)];
            bool attn_output_99_interleave_0 = const()[name = string("attn_output_99_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_99_cast_fp16 = concat(axis = var_4983, interleave = attn_output_99_interleave_0, values = (var_4969_cast_fp16, attn_output_97_cast_fp16))[name = string("attn_output_99_cast_fp16")];
            tensor<int32, [4]> var_4987_perm_0 = const()[name = string("op_4987_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_155x = const()[name = string("concat_155x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_4987_cast_fp16 = transpose(perm = var_4987_perm_0, x = attn_output_99_cast_fp16)[name = string("transpose_567")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_103_cast_fp16 = reshape(shape = concat_155x, x = var_4987_cast_fp16)[name = string("attn_output_103_cast_fp16")];
            tensor<int32, [2]> hidden_states_123_strides_0 = const()[name = string("hidden_states_123_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_123_pad_type_0 = const()[name = string("hidden_states_123_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_123_pad_0 = const()[name = string("hidden_states_123_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_123_dilations_0 = const()[name = string("hidden_states_123_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_123_groups_0 = const()[name = string("hidden_states_123_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_123_cast_fp16 = conv(dilations = hidden_states_123_dilations_0, groups = hidden_states_123_groups_0, pad = hidden_states_123_pad_0, pad_type = hidden_states_123_pad_type_0, strides = hidden_states_123_strides_0, weight = layers_12_self_attn_o_proj_weight_cast_fp16, x = attn_output_103_cast_fp16)[name = string("hidden_states_123_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_125_cast_fp16 = add(x = hidden_states_119_cast_fp16, y = hidden_states_123_cast_fp16)[name = string("hidden_states_125_cast_fp16")];
            fp16 const_130_promoted_to_fp16 = const()[name = string("const_130_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_5020_cast_fp16 = mul(x = hidden_states_125_cast_fp16, y = const_130_promoted_to_fp16)[name = string("op_5020_cast_fp16")];
            int32 var_5018 = const()[name = string("op_5018"), val = int32(1)];
            bool doubled_101_interleave_0 = const()[name = string("doubled_101_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_101_cast_fp16 = concat(axis = var_5018, interleave = doubled_101_interleave_0, values = (hidden_states_125_cast_fp16, var_5020_cast_fp16))[name = string("doubled_101_cast_fp16")];
            tensor<int32, [1]> out_51_axes_0 = const()[name = string("out_51_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_51_gamma_0_to_fp16 = const()[name = string("out_51_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407002496)))];
            fp16 var_5030_to_fp16 = const()[name = string("op_5030_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_51_cast_fp16 = layer_norm(axes = out_51_axes_0, epsilon = var_5030_to_fp16, gamma = out_51_gamma_0_to_fp16, x = doubled_101_cast_fp16)[name = string("out_51_cast_fp16")];
            tensor<int32, [2]> var_5041_split_sizes_0 = const()[name = string("op_5041_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5041_axis_0 = const()[name = string("op_5041_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_5041_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_5041_cast_fp16_1 = split(axis = var_5041_axis_0, split_sizes = var_5041_split_sizes_0, x = out_51_cast_fp16)[name = string("op_5041_cast_fp16")];
            tensor<int32, [2]> input_25_strides_0 = const()[name = string("input_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_25_pad_type_0 = const()[name = string("input_25_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_25_pad_0 = const()[name = string("input_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_25_dilations_0 = const()[name = string("input_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_25_groups_0 = const()[name = string("input_25_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_25_cast_fp16 = conv(dilations = input_25_dilations_0, groups = input_25_groups_0, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = input_25_strides_0, weight = layers_12_mlp_gate_proj_weight_cast_fp16, x = var_5041_cast_fp16_0)[name = string("input_25_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_5058_cast_fp16 = silu(x = input_25_cast_fp16)[name = string("op_5058_cast_fp16")];
            tensor<int32, [2]> var_5064_strides_0 = const()[name = string("op_5064_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_5064_pad_type_0 = const()[name = string("op_5064_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_5064_pad_0 = const()[name = string("op_5064_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_5064_dilations_0 = const()[name = string("op_5064_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5064_groups_0 = const()[name = string("op_5064_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_5064_cast_fp16 = conv(dilations = var_5064_dilations_0, groups = var_5064_groups_0, pad = var_5064_pad_0, pad_type = var_5064_pad_type_0, strides = var_5064_strides_0, weight = layers_12_mlp_up_proj_weight_cast_fp16, x = var_5041_cast_fp16_0)[name = string("op_5064_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_129_cast_fp16 = mul(x = var_5058_cast_fp16, y = var_5064_cast_fp16)[name = string("x_129_cast_fp16")];
            tensor<int32, [2]> hidden_states_127_strides_0 = const()[name = string("hidden_states_127_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_127_pad_type_0 = const()[name = string("hidden_states_127_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_127_pad_0 = const()[name = string("hidden_states_127_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_127_dilations_0 = const()[name = string("hidden_states_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_127_groups_0 = const()[name = string("hidden_states_127_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_127_cast_fp16 = conv(dilations = hidden_states_127_dilations_0, groups = hidden_states_127_groups_0, pad = hidden_states_127_pad_0, pad_type = hidden_states_127_pad_type_0, strides = hidden_states_127_strides_0, weight = layers_12_mlp_down_proj_weight_cast_fp16, x = x_129_cast_fp16)[name = string("hidden_states_127_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_129_cast_fp16 = add(x = hidden_states_125_cast_fp16, y = hidden_states_127_cast_fp16)[name = string("hidden_states_129_cast_fp16")];
            fp16 const_132_promoted_to_fp16 = const()[name = string("const_132_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_5082_cast_fp16 = mul(x = hidden_states_129_cast_fp16, y = const_132_promoted_to_fp16)[name = string("op_5082_cast_fp16")];
            int32 var_5080 = const()[name = string("op_5080"), val = int32(1)];
            bool doubled_105_interleave_0 = const()[name = string("doubled_105_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_105_cast_fp16 = concat(axis = var_5080, interleave = doubled_105_interleave_0, values = (hidden_states_129_cast_fp16, var_5082_cast_fp16))[name = string("doubled_105_cast_fp16")];
            tensor<int32, [1]> out_53_axes_0 = const()[name = string("out_53_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_53_gamma_0_to_fp16 = const()[name = string("out_53_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407010752)))];
            fp16 var_5092_to_fp16 = const()[name = string("op_5092_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_53_cast_fp16 = layer_norm(axes = out_53_axes_0, epsilon = var_5092_to_fp16, gamma = out_53_gamma_0_to_fp16, x = doubled_105_cast_fp16)[name = string("out_53_cast_fp16")];
            tensor<int32, [2]> var_5103_split_sizes_0 = const()[name = string("op_5103_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5103_axis_0 = const()[name = string("op_5103_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_5103_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_5103_cast_fp16_1 = split(axis = var_5103_axis_0, split_sizes = var_5103_split_sizes_0, x = out_53_cast_fp16)[name = string("op_5103_cast_fp16")];
            tensor<int32, [2]> query_states_79_strides_0 = const()[name = string("query_states_79_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_79_pad_type_0 = const()[name = string("query_states_79_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_79_pad_0 = const()[name = string("query_states_79_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_79_dilations_0 = const()[name = string("query_states_79_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_79_groups_0 = const()[name = string("query_states_79_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_79_cast_fp16 = conv(dilations = query_states_79_dilations_0, groups = query_states_79_groups_0, pad = query_states_79_pad_0, pad_type = query_states_79_pad_type_0, strides = query_states_79_strides_0, weight = layers_13_self_attn_q_proj_weight_cast_fp16, x = var_5103_cast_fp16_0)[name = string("query_states_79_cast_fp16")];
            tensor<int32, [2]> key_states_131_strides_0 = const()[name = string("key_states_131_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_131_pad_type_0 = const()[name = string("key_states_131_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_131_pad_0 = const()[name = string("key_states_131_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_131_dilations_0 = const()[name = string("key_states_131_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_131_groups_0 = const()[name = string("key_states_131_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_131_cast_fp16 = conv(dilations = key_states_131_dilations_0, groups = key_states_131_groups_0, pad = key_states_131_pad_0, pad_type = key_states_131_pad_type_0, strides = key_states_131_strides_0, weight = layers_13_self_attn_k_proj_weight_cast_fp16, x = var_5103_cast_fp16_0)[name = string("key_states_131_cast_fp16")];
            tensor<int32, [2]> value_states_79_strides_0 = const()[name = string("value_states_79_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_79_pad_type_0 = const()[name = string("value_states_79_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_79_pad_0 = const()[name = string("value_states_79_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_79_dilations_0 = const()[name = string("value_states_79_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_79_groups_0 = const()[name = string("value_states_79_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_79_cast_fp16 = conv(dilations = value_states_79_dilations_0, groups = value_states_79_groups_0, pad = value_states_79_pad_0, pad_type = value_states_79_pad_type_0, strides = value_states_79_strides_0, weight = layers_13_self_attn_v_proj_weight_cast_fp16, x = var_5103_cast_fp16_0)[name = string("value_states_79_cast_fp16")];
            tensor<int32, [4]> concat_156x = const()[name = string("concat_156x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_131_cast_fp16 = reshape(shape = concat_156x, x = query_states_79_cast_fp16)[name = string("x_131_cast_fp16")];
            tensor<int32, [4]> concat_157x = const()[name = string("concat_157x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_5160_cast_fp16 = reshape(shape = concat_157x, x = key_states_131_cast_fp16)[name = string("op_5160_cast_fp16")];
            tensor<int32, [4]> concat_158x = const()[name = string("concat_158x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_5167_cast_fp16 = reshape(shape = concat_158x, x = value_states_79_cast_fp16)[name = string("op_5167_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_5171_cast_fp16 = mul(x = x_131_cast_fp16, y = var_859_cast_fp16)[name = string("op_5171_cast_fp16")];
            tensor<int32, [2]> var_5172_split_sizes_0 = const()[name = string("op_5172_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5172_axis_0 = const()[name = string("op_5172_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_5172_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_5172_cast_fp16_1 = split(axis = var_5172_axis_0, split_sizes = var_5172_split_sizes_0, x = x_131_cast_fp16)[name = string("op_5172_cast_fp16")];
            fp16 const_134_promoted_to_fp16 = const()[name = string("const_134_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_5174_cast_fp16 = mul(x = var_5172_cast_fp16_1, y = const_134_promoted_to_fp16)[name = string("op_5174_cast_fp16")];
            int32 var_5176 = const()[name = string("op_5176"), val = int32(-2)];
            bool var_5177_interleave_0 = const()[name = string("op_5177_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_5177_cast_fp16 = concat(axis = var_5176, interleave = var_5177_interleave_0, values = (var_5174_cast_fp16, var_5172_cast_fp16_0))[name = string("op_5177_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_5178_cast_fp16 = mul(x = var_5177_cast_fp16, y = var_866_cast_fp16)[name = string("op_5178_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_81_cast_fp16 = add(x = var_5171_cast_fp16, y = var_5178_cast_fp16)[name = string("query_states_81_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_5184_cast_fp16 = mul(x = var_5160_cast_fp16, y = var_859_cast_fp16)[name = string("op_5184_cast_fp16")];
            tensor<int32, [2]> var_5185_split_sizes_0 = const()[name = string("op_5185_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5185_axis_0 = const()[name = string("op_5185_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_5185_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_5185_cast_fp16_1 = split(axis = var_5185_axis_0, split_sizes = var_5185_split_sizes_0, x = var_5160_cast_fp16)[name = string("op_5185_cast_fp16")];
            fp16 const_135_promoted_to_fp16 = const()[name = string("const_135_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_5187_cast_fp16 = mul(x = var_5185_cast_fp16_1, y = const_135_promoted_to_fp16)[name = string("op_5187_cast_fp16")];
            int32 var_5189 = const()[name = string("op_5189"), val = int32(-2)];
            bool var_5190_interleave_0 = const()[name = string("op_5190_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_5190_cast_fp16 = concat(axis = var_5189, interleave = var_5190_interleave_0, values = (var_5187_cast_fp16, var_5185_cast_fp16_0))[name = string("op_5190_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_5191_cast_fp16 = mul(x = var_5190_cast_fp16, y = var_866_cast_fp16)[name = string("op_5191_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_135_cast_fp16 = add(x = var_5184_cast_fp16, y = var_5191_cast_fp16)[name = string("key_states_135_cast_fp16")];
            tensor<int32, [1]> expand_dims_156 = const()[name = string("expand_dims_156"), val = tensor<int32, [1]>([13])];
            tensor<int32, [1]> expand_dims_157 = const()[name = string("expand_dims_157"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_159 = const()[name = string("expand_dims_159"), val = tensor<int32, [1]>([0])];
            int32 concat_161_axis_0 = const()[name = string("concat_161_axis_0"), val = int32(0)];
            bool concat_161_interleave_0 = const()[name = string("concat_161_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_161 = concat(axis = concat_161_axis_0, interleave = concat_161_interleave_0, values = (expand_dims_156, expand_dims_157, position_id, expand_dims_159))[name = string("concat_161")];
            tensor<int32, [1]> expand_dims_160 = const()[name = string("expand_dims_160"), val = tensor<int32, [1]>([14])];
            tensor<int32, [1]> concat_162_values1_0 = const()[name = string("concat_162_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_162_values3_0 = const()[name = string("concat_162_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_162_axis_0 = const()[name = string("concat_162_axis_0"), val = int32(0)];
            bool concat_162_interleave_0 = const()[name = string("concat_162_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_162 = concat(axis = concat_162_axis_0, interleave = concat_162_interleave_0, values = (expand_dims_160, concat_162_values1_0, cache_position_end, concat_162_values3_0))[name = string("concat_162")];
            tensor<int32, [4]> key_states_137_perm_0 = const()[name = string("key_states_137_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_14_stride_0 = const()[name = string("key_cache_internal_tensor_assign_14_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_14_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_14_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_14_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_14_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_14_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_14_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_137_cast_fp16 = transpose(perm = key_states_137_perm_0, x = key_states_135_cast_fp16)[name = string("transpose_566")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_14_cast_fp16 = slice_update(begin = concat_161, begin_mask = key_cache_internal_tensor_assign_14_begin_mask_0, end = concat_162, end_mask = key_cache_internal_tensor_assign_14_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_14_squeeze_mask_0, stride = key_cache_internal_tensor_assign_14_stride_0, update = key_states_137_cast_fp16, x = coreml_update_state_360)[name = string("key_cache_internal_tensor_assign_14_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_14_cast_fp16, input = key_cache)[name = string("coreml_update_state_362_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_362 = read_state(input = key_cache)[name = string("coreml_update_state_362")];
            tensor<int32, [4]> value_states_81_perm_0 = const()[name = string("value_states_81_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_14_stride_0 = const()[name = string("value_cache_internal_tensor_assign_14_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_14_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_14_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_14_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_14_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_14_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_14_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_81_cast_fp16 = transpose(perm = value_states_81_perm_0, x = var_5167_cast_fp16)[name = string("transpose_565")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_14_cast_fp16 = slice_update(begin = concat_161, begin_mask = value_cache_internal_tensor_assign_14_begin_mask_0, end = concat_162, end_mask = value_cache_internal_tensor_assign_14_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_14_squeeze_mask_0, stride = value_cache_internal_tensor_assign_14_stride_0, update = value_states_81_cast_fp16, x = coreml_update_state_361)[name = string("value_cache_internal_tensor_assign_14_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_14_cast_fp16, input = value_cache)[name = string("coreml_update_state_363_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_363 = read_state(input = value_cache)[name = string("coreml_update_state_363")];
            tensor<int32, [4]> var_5261_begin_0 = const()[name = string("op_5261_begin_0"), val = tensor<int32, [4]>([13, 0, 0, 0])];
            tensor<int32, [4]> var_5261_end_0 = const()[name = string("op_5261_end_0"), val = tensor<int32, [4]>([14, 2, 2048, 128])];
            tensor<bool, [4]> var_5261_end_mask_0 = const()[name = string("op_5261_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5261_cast_fp16 = slice_by_index(begin = var_5261_begin_0, end = var_5261_end_0, end_mask = var_5261_end_mask_0, x = coreml_update_state_362)[name = string("op_5261_cast_fp16")];
            tensor<int32, [2]> tile_26 = const()[name = string("tile_26"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5264_axis_0 = const()[name = string("op_5264_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5264_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5264_cast_fp16_1 = split(axis = var_5264_axis_0, split_sizes = tile_26, x = var_5261_cast_fp16)[name = string("op_5264_cast_fp16")];
            tensor<int32, [4]> var_5271_begin_0 = const()[name = string("op_5271_begin_0"), val = tensor<int32, [4]>([13, 0, 0, 0])];
            tensor<int32, [4]> var_5271_end_0 = const()[name = string("op_5271_end_0"), val = tensor<int32, [4]>([14, 2, 2048, 128])];
            tensor<bool, [4]> var_5271_end_mask_0 = const()[name = string("op_5271_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5271_cast_fp16 = slice_by_index(begin = var_5271_begin_0, end = var_5271_end_0, end_mask = var_5271_end_mask_0, x = coreml_update_state_363)[name = string("op_5271_cast_fp16")];
            tensor<int32, [2]> tile_27 = const()[name = string("tile_27"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5274_axis_0 = const()[name = string("op_5274_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5274_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5274_cast_fp16_1 = split(axis = var_5274_axis_0, split_sizes = tile_27, x = var_5271_cast_fp16)[name = string("op_5274_cast_fp16")];
            tensor<int32, [2]> var_5277_split_sizes_0 = const()[name = string("op_5277_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_5277_axis_0 = const()[name = string("op_5277_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_5277_0, tensor<fp16, [1, 8, 128, 128]> var_5277_1 = split(axis = var_5277_axis_0, split_sizes = var_5277_split_sizes_0, x = query_states_81_cast_fp16)[name = string("op_5277")];
            bool attn_weights_209_transpose_x_0 = const()[name = string("attn_weights_209_transpose_x_0"), val = bool(false)];
            bool attn_weights_209_transpose_y_0 = const()[name = string("attn_weights_209_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_209_cast_fp16 = matmul(transpose_x = attn_weights_209_transpose_x_0, transpose_y = attn_weights_209_transpose_y_0, x = var_5264_cast_fp16_0, y = var_5277_0)[name = string("attn_weights_209_cast_fp16")];
            fp16 var_5280_to_fp16 = const()[name = string("op_5280_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_211_cast_fp16 = mul(x = attn_weights_209_cast_fp16, y = var_5280_to_fp16)[name = string("attn_weights_211_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_213_cast_fp16 = add(x = attn_weights_211_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_213_cast_fp16")];
            int32 var_5284 = const()[name = string("op_5284"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_215_cast_fp16 = softmax(axis = var_5284, x = attn_weights_213_cast_fp16)[name = string("attn_weights_215_cast_fp16")];
            bool var_5290_transpose_x_1 = const()[name = string("op_5290_transpose_x_1"), val = bool(true)];
            bool var_5290_transpose_y_1 = const()[name = string("op_5290_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_5290_cast_fp16 = matmul(transpose_x = var_5290_transpose_x_1, transpose_y = var_5290_transpose_y_1, x = attn_weights_215_cast_fp16, y = var_5274_cast_fp16_0)[name = string("op_5290_cast_fp16")];
            bool attn_weights_217_transpose_x_0 = const()[name = string("attn_weights_217_transpose_x_0"), val = bool(false)];
            bool attn_weights_217_transpose_y_0 = const()[name = string("attn_weights_217_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_217_cast_fp16 = matmul(transpose_x = attn_weights_217_transpose_x_0, transpose_y = attn_weights_217_transpose_y_0, x = var_5264_cast_fp16_1, y = var_5277_1)[name = string("attn_weights_217_cast_fp16")];
            fp16 var_5292_to_fp16 = const()[name = string("op_5292_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_219_cast_fp16 = mul(x = attn_weights_217_cast_fp16, y = var_5292_to_fp16)[name = string("attn_weights_219_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_221_cast_fp16 = add(x = attn_weights_219_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_221_cast_fp16")];
            int32 var_5296 = const()[name = string("op_5296"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_223_cast_fp16 = softmax(axis = var_5296, x = attn_weights_221_cast_fp16)[name = string("attn_weights_223_cast_fp16")];
            bool attn_output_105_transpose_x_1 = const()[name = string("attn_output_105_transpose_x_1"), val = bool(true)];
            bool attn_output_105_transpose_y_1 = const()[name = string("attn_output_105_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_105_cast_fp16 = matmul(transpose_x = attn_output_105_transpose_x_1, transpose_y = attn_output_105_transpose_y_1, x = attn_weights_223_cast_fp16, y = var_5274_cast_fp16_1)[name = string("attn_output_105_cast_fp16")];
            int32 var_5304 = const()[name = string("op_5304"), val = int32(1)];
            bool attn_output_107_interleave_0 = const()[name = string("attn_output_107_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_107_cast_fp16 = concat(axis = var_5304, interleave = attn_output_107_interleave_0, values = (var_5290_cast_fp16, attn_output_105_cast_fp16))[name = string("attn_output_107_cast_fp16")];
            tensor<int32, [4]> var_5308_perm_0 = const()[name = string("op_5308_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_167x = const()[name = string("concat_167x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_5308_cast_fp16 = transpose(perm = var_5308_perm_0, x = attn_output_107_cast_fp16)[name = string("transpose_564")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_111_cast_fp16 = reshape(shape = concat_167x, x = var_5308_cast_fp16)[name = string("attn_output_111_cast_fp16")];
            tensor<int32, [2]> hidden_states_133_strides_0 = const()[name = string("hidden_states_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_133_pad_type_0 = const()[name = string("hidden_states_133_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_133_pad_0 = const()[name = string("hidden_states_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_133_dilations_0 = const()[name = string("hidden_states_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_133_groups_0 = const()[name = string("hidden_states_133_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_133_cast_fp16 = conv(dilations = hidden_states_133_dilations_0, groups = hidden_states_133_groups_0, pad = hidden_states_133_pad_0, pad_type = hidden_states_133_pad_type_0, strides = hidden_states_133_strides_0, weight = layers_13_self_attn_o_proj_weight_cast_fp16, x = attn_output_111_cast_fp16)[name = string("hidden_states_133_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_135_cast_fp16 = add(x = hidden_states_129_cast_fp16, y = hidden_states_133_cast_fp16)[name = string("hidden_states_135_cast_fp16")];
            fp16 const_140_promoted_to_fp16 = const()[name = string("const_140_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_5341_cast_fp16 = mul(x = hidden_states_135_cast_fp16, y = const_140_promoted_to_fp16)[name = string("op_5341_cast_fp16")];
            int32 var_5339 = const()[name = string("op_5339"), val = int32(1)];
            bool doubled_109_interleave_0 = const()[name = string("doubled_109_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_109_cast_fp16 = concat(axis = var_5339, interleave = doubled_109_interleave_0, values = (hidden_states_135_cast_fp16, var_5341_cast_fp16))[name = string("doubled_109_cast_fp16")];
            tensor<int32, [1]> out_55_axes_0 = const()[name = string("out_55_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_55_gamma_0_to_fp16 = const()[name = string("out_55_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407019008)))];
            fp16 var_5351_to_fp16 = const()[name = string("op_5351_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_55_cast_fp16 = layer_norm(axes = out_55_axes_0, epsilon = var_5351_to_fp16, gamma = out_55_gamma_0_to_fp16, x = doubled_109_cast_fp16)[name = string("out_55_cast_fp16")];
            tensor<int32, [2]> var_5362_split_sizes_0 = const()[name = string("op_5362_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5362_axis_0 = const()[name = string("op_5362_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_5362_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_5362_cast_fp16_1 = split(axis = var_5362_axis_0, split_sizes = var_5362_split_sizes_0, x = out_55_cast_fp16)[name = string("op_5362_cast_fp16")];
            tensor<int32, [2]> input_27_strides_0 = const()[name = string("input_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_27_pad_type_0 = const()[name = string("input_27_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_27_pad_0 = const()[name = string("input_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_27_dilations_0 = const()[name = string("input_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_27_groups_0 = const()[name = string("input_27_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_27_cast_fp16 = conv(dilations = input_27_dilations_0, groups = input_27_groups_0, pad = input_27_pad_0, pad_type = input_27_pad_type_0, strides = input_27_strides_0, weight = layers_13_mlp_gate_proj_weight_cast_fp16, x = var_5362_cast_fp16_0)[name = string("input_27_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_5379_cast_fp16 = silu(x = input_27_cast_fp16)[name = string("op_5379_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_13_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_13_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407027264)))];
            tensor<int32, [2]> var_5385_strides_0 = const()[name = string("op_5385_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_5385_pad_type_0 = const()[name = string("op_5385_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_5385_pad_0 = const()[name = string("op_5385_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_5385_dilations_0 = const()[name = string("op_5385_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5385_groups_0 = const()[name = string("op_5385_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_5385_cast_fp16 = conv(dilations = var_5385_dilations_0, groups = var_5385_groups_0, pad = var_5385_pad_0, pad_type = var_5385_pad_type_0, strides = var_5385_strides_0, weight = layers_13_mlp_up_proj_weight_to_fp16, x = var_5362_cast_fp16_0)[name = string("op_5385_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_139_cast_fp16 = mul(x = var_5379_cast_fp16, y = var_5385_cast_fp16)[name = string("x_139_cast_fp16")];
            tensor<int32, [2]> hidden_states_137_strides_0 = const()[name = string("hidden_states_137_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_137_pad_type_0 = const()[name = string("hidden_states_137_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_137_pad_0 = const()[name = string("hidden_states_137_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_137_dilations_0 = const()[name = string("hidden_states_137_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_137_groups_0 = const()[name = string("hidden_states_137_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_137_cast_fp16 = conv(dilations = hidden_states_137_dilations_0, groups = hidden_states_137_groups_0, pad = hidden_states_137_pad_0, pad_type = hidden_states_137_pad_type_0, strides = hidden_states_137_strides_0, weight = layers_13_mlp_down_proj_weight_cast_fp16, x = x_139_cast_fp16)[name = string("hidden_states_137_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_139_cast_fp16 = add(x = hidden_states_135_cast_fp16, y = hidden_states_137_cast_fp16)[name = string("hidden_states_139_cast_fp16")];
            fp16 const_142_promoted_to_fp16 = const()[name = string("const_142_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_5403_cast_fp16 = mul(x = hidden_states_139_cast_fp16, y = const_142_promoted_to_fp16)[name = string("op_5403_cast_fp16")];
            int32 var_5401 = const()[name = string("op_5401"), val = int32(1)];
            bool doubled_113_interleave_0 = const()[name = string("doubled_113_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_113_cast_fp16 = concat(axis = var_5401, interleave = doubled_113_interleave_0, values = (hidden_states_139_cast_fp16, var_5403_cast_fp16))[name = string("doubled_113_cast_fp16")];
            tensor<int32, [1]> out_57_axes_0 = const()[name = string("out_57_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_57_gamma_0_to_fp16 = const()[name = string("out_57_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1432193152)))];
            fp16 var_5413_to_fp16 = const()[name = string("op_5413_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_57_cast_fp16 = layer_norm(axes = out_57_axes_0, epsilon = var_5413_to_fp16, gamma = out_57_gamma_0_to_fp16, x = doubled_113_cast_fp16)[name = string("out_57_cast_fp16")];
            tensor<int32, [2]> var_5424_split_sizes_0 = const()[name = string("op_5424_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5424_axis_0 = const()[name = string("op_5424_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_5424_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_5424_cast_fp16_1 = split(axis = var_5424_axis_0, split_sizes = var_5424_split_sizes_0, x = out_57_cast_fp16)[name = string("op_5424_cast_fp16")];
            tensor<int32, [2]> query_states_85_strides_0 = const()[name = string("query_states_85_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_85_pad_type_0 = const()[name = string("query_states_85_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_85_pad_0 = const()[name = string("query_states_85_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_85_dilations_0 = const()[name = string("query_states_85_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_85_groups_0 = const()[name = string("query_states_85_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_85_cast_fp16 = conv(dilations = query_states_85_dilations_0, groups = query_states_85_groups_0, pad = query_states_85_pad_0, pad_type = query_states_85_pad_type_0, strides = query_states_85_strides_0, weight = layers_14_self_attn_q_proj_weight_cast_fp16, x = var_5424_cast_fp16_0)[name = string("query_states_85_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_14_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_14_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1432201408)))];
            tensor<int32, [2]> key_states_141_strides_0 = const()[name = string("key_states_141_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_141_pad_type_0 = const()[name = string("key_states_141_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_141_pad_0 = const()[name = string("key_states_141_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_141_dilations_0 = const()[name = string("key_states_141_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_141_groups_0 = const()[name = string("key_states_141_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_141_cast_fp16 = conv(dilations = key_states_141_dilations_0, groups = key_states_141_groups_0, pad = key_states_141_pad_0, pad_type = key_states_141_pad_type_0, strides = key_states_141_strides_0, weight = layers_14_self_attn_k_proj_weight_to_fp16, x = var_5424_cast_fp16_0)[name = string("key_states_141_cast_fp16")];
            tensor<int32, [2]> value_states_85_strides_0 = const()[name = string("value_states_85_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_85_pad_type_0 = const()[name = string("value_states_85_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_85_pad_0 = const()[name = string("value_states_85_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_85_dilations_0 = const()[name = string("value_states_85_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_85_groups_0 = const()[name = string("value_states_85_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_85_cast_fp16 = conv(dilations = value_states_85_dilations_0, groups = value_states_85_groups_0, pad = value_states_85_pad_0, pad_type = value_states_85_pad_type_0, strides = value_states_85_strides_0, weight = layers_14_self_attn_v_proj_weight_cast_fp16, x = var_5424_cast_fp16_0)[name = string("value_states_85_cast_fp16")];
            tensor<int32, [4]> concat_168x = const()[name = string("concat_168x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_141_cast_fp16 = reshape(shape = concat_168x, x = query_states_85_cast_fp16)[name = string("x_141_cast_fp16")];
            tensor<int32, [4]> concat_169x = const()[name = string("concat_169x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_5481_cast_fp16 = reshape(shape = concat_169x, x = key_states_141_cast_fp16)[name = string("op_5481_cast_fp16")];
            tensor<int32, [4]> concat_170x = const()[name = string("concat_170x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_5488_cast_fp16 = reshape(shape = concat_170x, x = value_states_85_cast_fp16)[name = string("op_5488_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_5492_cast_fp16 = mul(x = x_141_cast_fp16, y = var_859_cast_fp16)[name = string("op_5492_cast_fp16")];
            tensor<int32, [2]> var_5493_split_sizes_0 = const()[name = string("op_5493_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5493_axis_0 = const()[name = string("op_5493_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_5493_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_5493_cast_fp16_1 = split(axis = var_5493_axis_0, split_sizes = var_5493_split_sizes_0, x = x_141_cast_fp16)[name = string("op_5493_cast_fp16")];
            fp16 const_144_promoted_to_fp16 = const()[name = string("const_144_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_5495_cast_fp16 = mul(x = var_5493_cast_fp16_1, y = const_144_promoted_to_fp16)[name = string("op_5495_cast_fp16")];
            int32 var_5497 = const()[name = string("op_5497"), val = int32(-2)];
            bool var_5498_interleave_0 = const()[name = string("op_5498_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_5498_cast_fp16 = concat(axis = var_5497, interleave = var_5498_interleave_0, values = (var_5495_cast_fp16, var_5493_cast_fp16_0))[name = string("op_5498_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_5499_cast_fp16 = mul(x = var_5498_cast_fp16, y = var_866_cast_fp16)[name = string("op_5499_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_87_cast_fp16 = add(x = var_5492_cast_fp16, y = var_5499_cast_fp16)[name = string("query_states_87_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_5505_cast_fp16 = mul(x = var_5481_cast_fp16, y = var_859_cast_fp16)[name = string("op_5505_cast_fp16")];
            tensor<int32, [2]> var_5506_split_sizes_0 = const()[name = string("op_5506_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5506_axis_0 = const()[name = string("op_5506_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_5506_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_5506_cast_fp16_1 = split(axis = var_5506_axis_0, split_sizes = var_5506_split_sizes_0, x = var_5481_cast_fp16)[name = string("op_5506_cast_fp16")];
            fp16 const_145_promoted_to_fp16 = const()[name = string("const_145_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_5508_cast_fp16 = mul(x = var_5506_cast_fp16_1, y = const_145_promoted_to_fp16)[name = string("op_5508_cast_fp16")];
            int32 var_5510 = const()[name = string("op_5510"), val = int32(-2)];
            bool var_5511_interleave_0 = const()[name = string("op_5511_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_5511_cast_fp16 = concat(axis = var_5510, interleave = var_5511_interleave_0, values = (var_5508_cast_fp16, var_5506_cast_fp16_0))[name = string("op_5511_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_5512_cast_fp16 = mul(x = var_5511_cast_fp16, y = var_866_cast_fp16)[name = string("op_5512_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_145_cast_fp16 = add(x = var_5505_cast_fp16, y = var_5512_cast_fp16)[name = string("key_states_145_cast_fp16")];
            tensor<int32, [1]> expand_dims_168 = const()[name = string("expand_dims_168"), val = tensor<int32, [1]>([14])];
            tensor<int32, [1]> expand_dims_169 = const()[name = string("expand_dims_169"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_171 = const()[name = string("expand_dims_171"), val = tensor<int32, [1]>([0])];
            int32 concat_173_axis_0 = const()[name = string("concat_173_axis_0"), val = int32(0)];
            bool concat_173_interleave_0 = const()[name = string("concat_173_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_173 = concat(axis = concat_173_axis_0, interleave = concat_173_interleave_0, values = (expand_dims_168, expand_dims_169, position_id, expand_dims_171))[name = string("concat_173")];
            tensor<int32, [1]> expand_dims_172 = const()[name = string("expand_dims_172"), val = tensor<int32, [1]>([15])];
            tensor<int32, [1]> concat_174_values1_0 = const()[name = string("concat_174_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_174_values3_0 = const()[name = string("concat_174_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_174_axis_0 = const()[name = string("concat_174_axis_0"), val = int32(0)];
            bool concat_174_interleave_0 = const()[name = string("concat_174_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_174 = concat(axis = concat_174_axis_0, interleave = concat_174_interleave_0, values = (expand_dims_172, concat_174_values1_0, cache_position_end, concat_174_values3_0))[name = string("concat_174")];
            tensor<int32, [4]> key_states_147_perm_0 = const()[name = string("key_states_147_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_15_stride_0 = const()[name = string("key_cache_internal_tensor_assign_15_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_15_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_15_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_15_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_15_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_15_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_15_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_147_cast_fp16 = transpose(perm = key_states_147_perm_0, x = key_states_145_cast_fp16)[name = string("transpose_563")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_15_cast_fp16 = slice_update(begin = concat_173, begin_mask = key_cache_internal_tensor_assign_15_begin_mask_0, end = concat_174, end_mask = key_cache_internal_tensor_assign_15_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_15_squeeze_mask_0, stride = key_cache_internal_tensor_assign_15_stride_0, update = key_states_147_cast_fp16, x = coreml_update_state_362)[name = string("key_cache_internal_tensor_assign_15_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_15_cast_fp16, input = key_cache)[name = string("coreml_update_state_364_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_364 = read_state(input = key_cache)[name = string("coreml_update_state_364")];
            tensor<int32, [4]> value_states_87_perm_0 = const()[name = string("value_states_87_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_15_stride_0 = const()[name = string("value_cache_internal_tensor_assign_15_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_15_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_15_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_15_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_15_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_15_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_15_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_87_cast_fp16 = transpose(perm = value_states_87_perm_0, x = var_5488_cast_fp16)[name = string("transpose_562")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_15_cast_fp16 = slice_update(begin = concat_173, begin_mask = value_cache_internal_tensor_assign_15_begin_mask_0, end = concat_174, end_mask = value_cache_internal_tensor_assign_15_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_15_squeeze_mask_0, stride = value_cache_internal_tensor_assign_15_stride_0, update = value_states_87_cast_fp16, x = coreml_update_state_363)[name = string("value_cache_internal_tensor_assign_15_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_15_cast_fp16, input = value_cache)[name = string("coreml_update_state_365_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_365 = read_state(input = value_cache)[name = string("coreml_update_state_365")];
            tensor<int32, [4]> var_5582_begin_0 = const()[name = string("op_5582_begin_0"), val = tensor<int32, [4]>([14, 0, 0, 0])];
            tensor<int32, [4]> var_5582_end_0 = const()[name = string("op_5582_end_0"), val = tensor<int32, [4]>([15, 2, 2048, 128])];
            tensor<bool, [4]> var_5582_end_mask_0 = const()[name = string("op_5582_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5582_cast_fp16 = slice_by_index(begin = var_5582_begin_0, end = var_5582_end_0, end_mask = var_5582_end_mask_0, x = coreml_update_state_364)[name = string("op_5582_cast_fp16")];
            tensor<int32, [2]> tile_28 = const()[name = string("tile_28"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5585_axis_0 = const()[name = string("op_5585_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5585_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5585_cast_fp16_1 = split(axis = var_5585_axis_0, split_sizes = tile_28, x = var_5582_cast_fp16)[name = string("op_5585_cast_fp16")];
            tensor<int32, [4]> var_5592_begin_0 = const()[name = string("op_5592_begin_0"), val = tensor<int32, [4]>([14, 0, 0, 0])];
            tensor<int32, [4]> var_5592_end_0 = const()[name = string("op_5592_end_0"), val = tensor<int32, [4]>([15, 2, 2048, 128])];
            tensor<bool, [4]> var_5592_end_mask_0 = const()[name = string("op_5592_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5592_cast_fp16 = slice_by_index(begin = var_5592_begin_0, end = var_5592_end_0, end_mask = var_5592_end_mask_0, x = coreml_update_state_365)[name = string("op_5592_cast_fp16")];
            tensor<int32, [2]> tile_29 = const()[name = string("tile_29"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5595_axis_0 = const()[name = string("op_5595_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5595_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5595_cast_fp16_1 = split(axis = var_5595_axis_0, split_sizes = tile_29, x = var_5592_cast_fp16)[name = string("op_5595_cast_fp16")];
            tensor<int32, [2]> var_5598_split_sizes_0 = const()[name = string("op_5598_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_5598_axis_0 = const()[name = string("op_5598_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_5598_0, tensor<fp16, [1, 8, 128, 128]> var_5598_1 = split(axis = var_5598_axis_0, split_sizes = var_5598_split_sizes_0, x = query_states_87_cast_fp16)[name = string("op_5598")];
            bool attn_weights_225_transpose_x_0 = const()[name = string("attn_weights_225_transpose_x_0"), val = bool(false)];
            bool attn_weights_225_transpose_y_0 = const()[name = string("attn_weights_225_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_225_cast_fp16 = matmul(transpose_x = attn_weights_225_transpose_x_0, transpose_y = attn_weights_225_transpose_y_0, x = var_5585_cast_fp16_0, y = var_5598_0)[name = string("attn_weights_225_cast_fp16")];
            fp16 var_5601_to_fp16 = const()[name = string("op_5601_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_227_cast_fp16 = mul(x = attn_weights_225_cast_fp16, y = var_5601_to_fp16)[name = string("attn_weights_227_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_229_cast_fp16 = add(x = attn_weights_227_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_229_cast_fp16")];
            int32 var_5605 = const()[name = string("op_5605"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_231_cast_fp16 = softmax(axis = var_5605, x = attn_weights_229_cast_fp16)[name = string("attn_weights_231_cast_fp16")];
            bool var_5611_transpose_x_1 = const()[name = string("op_5611_transpose_x_1"), val = bool(true)];
            bool var_5611_transpose_y_1 = const()[name = string("op_5611_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_5611_cast_fp16 = matmul(transpose_x = var_5611_transpose_x_1, transpose_y = var_5611_transpose_y_1, x = attn_weights_231_cast_fp16, y = var_5595_cast_fp16_0)[name = string("op_5611_cast_fp16")];
            bool attn_weights_233_transpose_x_0 = const()[name = string("attn_weights_233_transpose_x_0"), val = bool(false)];
            bool attn_weights_233_transpose_y_0 = const()[name = string("attn_weights_233_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_233_cast_fp16 = matmul(transpose_x = attn_weights_233_transpose_x_0, transpose_y = attn_weights_233_transpose_y_0, x = var_5585_cast_fp16_1, y = var_5598_1)[name = string("attn_weights_233_cast_fp16")];
            fp16 var_5613_to_fp16 = const()[name = string("op_5613_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_235_cast_fp16 = mul(x = attn_weights_233_cast_fp16, y = var_5613_to_fp16)[name = string("attn_weights_235_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_237_cast_fp16 = add(x = attn_weights_235_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_237_cast_fp16")];
            int32 var_5617 = const()[name = string("op_5617"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_239_cast_fp16 = softmax(axis = var_5617, x = attn_weights_237_cast_fp16)[name = string("attn_weights_239_cast_fp16")];
            bool attn_output_113_transpose_x_1 = const()[name = string("attn_output_113_transpose_x_1"), val = bool(true)];
            bool attn_output_113_transpose_y_1 = const()[name = string("attn_output_113_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_113_cast_fp16 = matmul(transpose_x = attn_output_113_transpose_x_1, transpose_y = attn_output_113_transpose_y_1, x = attn_weights_239_cast_fp16, y = var_5595_cast_fp16_1)[name = string("attn_output_113_cast_fp16")];
            int32 var_5625 = const()[name = string("op_5625"), val = int32(1)];
            bool attn_output_115_interleave_0 = const()[name = string("attn_output_115_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_115_cast_fp16 = concat(axis = var_5625, interleave = attn_output_115_interleave_0, values = (var_5611_cast_fp16, attn_output_113_cast_fp16))[name = string("attn_output_115_cast_fp16")];
            tensor<int32, [4]> var_5629_perm_0 = const()[name = string("op_5629_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_179x = const()[name = string("concat_179x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_5629_cast_fp16 = transpose(perm = var_5629_perm_0, x = attn_output_115_cast_fp16)[name = string("transpose_561")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_119_cast_fp16 = reshape(shape = concat_179x, x = var_5629_cast_fp16)[name = string("attn_output_119_cast_fp16")];
            tensor<int32, [2]> hidden_states_143_strides_0 = const()[name = string("hidden_states_143_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_143_pad_type_0 = const()[name = string("hidden_states_143_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_143_pad_0 = const()[name = string("hidden_states_143_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_143_dilations_0 = const()[name = string("hidden_states_143_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_143_groups_0 = const()[name = string("hidden_states_143_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_143_cast_fp16 = conv(dilations = hidden_states_143_dilations_0, groups = hidden_states_143_groups_0, pad = hidden_states_143_pad_0, pad_type = hidden_states_143_pad_type_0, strides = hidden_states_143_strides_0, weight = layers_14_self_attn_o_proj_weight_cast_fp16, x = attn_output_119_cast_fp16)[name = string("hidden_states_143_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_145_cast_fp16 = add(x = hidden_states_139_cast_fp16, y = hidden_states_143_cast_fp16)[name = string("hidden_states_145_cast_fp16")];
            fp16 const_150_promoted_to_fp16 = const()[name = string("const_150_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_5662_cast_fp16 = mul(x = hidden_states_145_cast_fp16, y = const_150_promoted_to_fp16)[name = string("op_5662_cast_fp16")];
            int32 var_5660 = const()[name = string("op_5660"), val = int32(1)];
            bool doubled_117_interleave_0 = const()[name = string("doubled_117_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_117_cast_fp16 = concat(axis = var_5660, interleave = doubled_117_interleave_0, values = (hidden_states_145_cast_fp16, var_5662_cast_fp16))[name = string("doubled_117_cast_fp16")];
            tensor<int32, [1]> out_59_axes_0 = const()[name = string("out_59_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_59_gamma_0_to_fp16 = const()[name = string("out_59_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433250048)))];
            fp16 var_5672_to_fp16 = const()[name = string("op_5672_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_59_cast_fp16 = layer_norm(axes = out_59_axes_0, epsilon = var_5672_to_fp16, gamma = out_59_gamma_0_to_fp16, x = doubled_117_cast_fp16)[name = string("out_59_cast_fp16")];
            tensor<int32, [2]> var_5683_split_sizes_0 = const()[name = string("op_5683_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5683_axis_0 = const()[name = string("op_5683_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_5683_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_5683_cast_fp16_1 = split(axis = var_5683_axis_0, split_sizes = var_5683_split_sizes_0, x = out_59_cast_fp16)[name = string("op_5683_cast_fp16")];
            tensor<int32, [2]> input_29_strides_0 = const()[name = string("input_29_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_29_pad_type_0 = const()[name = string("input_29_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_29_pad_0 = const()[name = string("input_29_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_29_dilations_0 = const()[name = string("input_29_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_29_groups_0 = const()[name = string("input_29_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_29_cast_fp16 = conv(dilations = input_29_dilations_0, groups = input_29_groups_0, pad = input_29_pad_0, pad_type = input_29_pad_type_0, strides = input_29_strides_0, weight = layers_14_mlp_gate_proj_weight_cast_fp16, x = var_5683_cast_fp16_0)[name = string("input_29_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_5700_cast_fp16 = silu(x = input_29_cast_fp16)[name = string("op_5700_cast_fp16")];
            tensor<int32, [2]> var_5706_strides_0 = const()[name = string("op_5706_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_5706_pad_type_0 = const()[name = string("op_5706_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_5706_pad_0 = const()[name = string("op_5706_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_5706_dilations_0 = const()[name = string("op_5706_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5706_groups_0 = const()[name = string("op_5706_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_5706_cast_fp16 = conv(dilations = var_5706_dilations_0, groups = var_5706_groups_0, pad = var_5706_pad_0, pad_type = var_5706_pad_type_0, strides = var_5706_strides_0, weight = layers_14_mlp_up_proj_weight_cast_fp16, x = var_5683_cast_fp16_0)[name = string("op_5706_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_149_cast_fp16 = mul(x = var_5700_cast_fp16, y = var_5706_cast_fp16)[name = string("x_149_cast_fp16")];
            tensor<int32, [2]> hidden_states_147_strides_0 = const()[name = string("hidden_states_147_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_147_pad_type_0 = const()[name = string("hidden_states_147_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_147_pad_0 = const()[name = string("hidden_states_147_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_147_dilations_0 = const()[name = string("hidden_states_147_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_147_groups_0 = const()[name = string("hidden_states_147_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_147_cast_fp16 = conv(dilations = hidden_states_147_dilations_0, groups = hidden_states_147_groups_0, pad = hidden_states_147_pad_0, pad_type = hidden_states_147_pad_type_0, strides = hidden_states_147_strides_0, weight = layers_14_mlp_down_proj_weight_cast_fp16, x = x_149_cast_fp16)[name = string("hidden_states_147_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_149_cast_fp16 = add(x = hidden_states_145_cast_fp16, y = hidden_states_147_cast_fp16)[name = string("hidden_states_149_cast_fp16")];
            fp16 const_152_promoted_to_fp16 = const()[name = string("const_152_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_5724_cast_fp16 = mul(x = hidden_states_149_cast_fp16, y = const_152_promoted_to_fp16)[name = string("op_5724_cast_fp16")];
            int32 var_5722 = const()[name = string("op_5722"), val = int32(1)];
            bool doubled_121_interleave_0 = const()[name = string("doubled_121_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_121_cast_fp16 = concat(axis = var_5722, interleave = doubled_121_interleave_0, values = (hidden_states_149_cast_fp16, var_5724_cast_fp16))[name = string("doubled_121_cast_fp16")];
            tensor<int32, [1]> out_61_axes_0 = const()[name = string("out_61_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_61_gamma_0_to_fp16 = const()[name = string("out_61_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433258304)))];
            fp16 var_5734_to_fp16 = const()[name = string("op_5734_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_61_cast_fp16 = layer_norm(axes = out_61_axes_0, epsilon = var_5734_to_fp16, gamma = out_61_gamma_0_to_fp16, x = doubled_121_cast_fp16)[name = string("out_61_cast_fp16")];
            tensor<int32, [2]> var_5745_split_sizes_0 = const()[name = string("op_5745_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5745_axis_0 = const()[name = string("op_5745_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_5745_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_5745_cast_fp16_1 = split(axis = var_5745_axis_0, split_sizes = var_5745_split_sizes_0, x = out_61_cast_fp16)[name = string("op_5745_cast_fp16")];
            tensor<int32, [2]> query_states_91_strides_0 = const()[name = string("query_states_91_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_91_pad_type_0 = const()[name = string("query_states_91_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_91_pad_0 = const()[name = string("query_states_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_91_dilations_0 = const()[name = string("query_states_91_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_91_groups_0 = const()[name = string("query_states_91_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_91_cast_fp16 = conv(dilations = query_states_91_dilations_0, groups = query_states_91_groups_0, pad = query_states_91_pad_0, pad_type = query_states_91_pad_type_0, strides = query_states_91_strides_0, weight = layers_15_self_attn_q_proj_weight_cast_fp16, x = var_5745_cast_fp16_0)[name = string("query_states_91_cast_fp16")];
            tensor<int32, [2]> key_states_151_strides_0 = const()[name = string("key_states_151_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_151_pad_type_0 = const()[name = string("key_states_151_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_151_pad_0 = const()[name = string("key_states_151_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_151_dilations_0 = const()[name = string("key_states_151_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_151_groups_0 = const()[name = string("key_states_151_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_151_cast_fp16 = conv(dilations = key_states_151_dilations_0, groups = key_states_151_groups_0, pad = key_states_151_pad_0, pad_type = key_states_151_pad_type_0, strides = key_states_151_strides_0, weight = layers_15_self_attn_k_proj_weight_cast_fp16, x = var_5745_cast_fp16_0)[name = string("key_states_151_cast_fp16")];
            tensor<int32, [2]> value_states_91_strides_0 = const()[name = string("value_states_91_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_91_pad_type_0 = const()[name = string("value_states_91_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_91_pad_0 = const()[name = string("value_states_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_91_dilations_0 = const()[name = string("value_states_91_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_91_groups_0 = const()[name = string("value_states_91_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_91_cast_fp16 = conv(dilations = value_states_91_dilations_0, groups = value_states_91_groups_0, pad = value_states_91_pad_0, pad_type = value_states_91_pad_type_0, strides = value_states_91_strides_0, weight = layers_15_self_attn_v_proj_weight_cast_fp16, x = var_5745_cast_fp16_0)[name = string("value_states_91_cast_fp16")];
            tensor<int32, [4]> concat_180x = const()[name = string("concat_180x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_151_cast_fp16 = reshape(shape = concat_180x, x = query_states_91_cast_fp16)[name = string("x_151_cast_fp16")];
            tensor<int32, [4]> concat_181x = const()[name = string("concat_181x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_5802_cast_fp16 = reshape(shape = concat_181x, x = key_states_151_cast_fp16)[name = string("op_5802_cast_fp16")];
            tensor<int32, [4]> concat_182x = const()[name = string("concat_182x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_5809_cast_fp16 = reshape(shape = concat_182x, x = value_states_91_cast_fp16)[name = string("op_5809_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_5813_cast_fp16 = mul(x = x_151_cast_fp16, y = var_859_cast_fp16)[name = string("op_5813_cast_fp16")];
            tensor<int32, [2]> var_5814_split_sizes_0 = const()[name = string("op_5814_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5814_axis_0 = const()[name = string("op_5814_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_5814_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_5814_cast_fp16_1 = split(axis = var_5814_axis_0, split_sizes = var_5814_split_sizes_0, x = x_151_cast_fp16)[name = string("op_5814_cast_fp16")];
            fp16 const_154_promoted_to_fp16 = const()[name = string("const_154_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_5816_cast_fp16 = mul(x = var_5814_cast_fp16_1, y = const_154_promoted_to_fp16)[name = string("op_5816_cast_fp16")];
            int32 var_5818 = const()[name = string("op_5818"), val = int32(-2)];
            bool var_5819_interleave_0 = const()[name = string("op_5819_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_5819_cast_fp16 = concat(axis = var_5818, interleave = var_5819_interleave_0, values = (var_5816_cast_fp16, var_5814_cast_fp16_0))[name = string("op_5819_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_5820_cast_fp16 = mul(x = var_5819_cast_fp16, y = var_866_cast_fp16)[name = string("op_5820_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_93_cast_fp16 = add(x = var_5813_cast_fp16, y = var_5820_cast_fp16)[name = string("query_states_93_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_5826_cast_fp16 = mul(x = var_5802_cast_fp16, y = var_859_cast_fp16)[name = string("op_5826_cast_fp16")];
            tensor<int32, [2]> var_5827_split_sizes_0 = const()[name = string("op_5827_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5827_axis_0 = const()[name = string("op_5827_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_5827_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_5827_cast_fp16_1 = split(axis = var_5827_axis_0, split_sizes = var_5827_split_sizes_0, x = var_5802_cast_fp16)[name = string("op_5827_cast_fp16")];
            fp16 const_155_promoted_to_fp16 = const()[name = string("const_155_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_5829_cast_fp16 = mul(x = var_5827_cast_fp16_1, y = const_155_promoted_to_fp16)[name = string("op_5829_cast_fp16")];
            int32 var_5831 = const()[name = string("op_5831"), val = int32(-2)];
            bool var_5832_interleave_0 = const()[name = string("op_5832_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_5832_cast_fp16 = concat(axis = var_5831, interleave = var_5832_interleave_0, values = (var_5829_cast_fp16, var_5827_cast_fp16_0))[name = string("op_5832_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_5833_cast_fp16 = mul(x = var_5832_cast_fp16, y = var_866_cast_fp16)[name = string("op_5833_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_155_cast_fp16 = add(x = var_5826_cast_fp16, y = var_5833_cast_fp16)[name = string("key_states_155_cast_fp16")];
            tensor<int32, [1]> expand_dims_180 = const()[name = string("expand_dims_180"), val = tensor<int32, [1]>([15])];
            tensor<int32, [1]> expand_dims_181 = const()[name = string("expand_dims_181"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_183 = const()[name = string("expand_dims_183"), val = tensor<int32, [1]>([0])];
            int32 concat_185_axis_0 = const()[name = string("concat_185_axis_0"), val = int32(0)];
            bool concat_185_interleave_0 = const()[name = string("concat_185_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_185 = concat(axis = concat_185_axis_0, interleave = concat_185_interleave_0, values = (expand_dims_180, expand_dims_181, position_id, expand_dims_183))[name = string("concat_185")];
            tensor<int32, [1]> expand_dims_184 = const()[name = string("expand_dims_184"), val = tensor<int32, [1]>([16])];
            tensor<int32, [1]> concat_186_values1_0 = const()[name = string("concat_186_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_186_values3_0 = const()[name = string("concat_186_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_186_axis_0 = const()[name = string("concat_186_axis_0"), val = int32(0)];
            bool concat_186_interleave_0 = const()[name = string("concat_186_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_186 = concat(axis = concat_186_axis_0, interleave = concat_186_interleave_0, values = (expand_dims_184, concat_186_values1_0, cache_position_end, concat_186_values3_0))[name = string("concat_186")];
            tensor<int32, [4]> key_states_157_perm_0 = const()[name = string("key_states_157_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_16_stride_0 = const()[name = string("key_cache_internal_tensor_assign_16_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_16_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_16_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_16_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_16_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_16_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_16_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_157_cast_fp16 = transpose(perm = key_states_157_perm_0, x = key_states_155_cast_fp16)[name = string("transpose_560")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_16_cast_fp16 = slice_update(begin = concat_185, begin_mask = key_cache_internal_tensor_assign_16_begin_mask_0, end = concat_186, end_mask = key_cache_internal_tensor_assign_16_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_16_squeeze_mask_0, stride = key_cache_internal_tensor_assign_16_stride_0, update = key_states_157_cast_fp16, x = coreml_update_state_364)[name = string("key_cache_internal_tensor_assign_16_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_16_cast_fp16, input = key_cache)[name = string("coreml_update_state_366_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_366 = read_state(input = key_cache)[name = string("coreml_update_state_366")];
            tensor<int32, [4]> value_states_93_perm_0 = const()[name = string("value_states_93_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_16_stride_0 = const()[name = string("value_cache_internal_tensor_assign_16_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_16_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_16_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_16_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_16_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_16_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_16_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_93_cast_fp16 = transpose(perm = value_states_93_perm_0, x = var_5809_cast_fp16)[name = string("transpose_559")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_16_cast_fp16 = slice_update(begin = concat_185, begin_mask = value_cache_internal_tensor_assign_16_begin_mask_0, end = concat_186, end_mask = value_cache_internal_tensor_assign_16_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_16_squeeze_mask_0, stride = value_cache_internal_tensor_assign_16_stride_0, update = value_states_93_cast_fp16, x = coreml_update_state_365)[name = string("value_cache_internal_tensor_assign_16_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_16_cast_fp16, input = value_cache)[name = string("coreml_update_state_367_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_367 = read_state(input = value_cache)[name = string("coreml_update_state_367")];
            tensor<int32, [4]> var_5903_begin_0 = const()[name = string("op_5903_begin_0"), val = tensor<int32, [4]>([15, 0, 0, 0])];
            tensor<int32, [4]> var_5903_end_0 = const()[name = string("op_5903_end_0"), val = tensor<int32, [4]>([16, 2, 2048, 128])];
            tensor<bool, [4]> var_5903_end_mask_0 = const()[name = string("op_5903_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5903_cast_fp16 = slice_by_index(begin = var_5903_begin_0, end = var_5903_end_0, end_mask = var_5903_end_mask_0, x = coreml_update_state_366)[name = string("op_5903_cast_fp16")];
            tensor<int32, [2]> tile_30 = const()[name = string("tile_30"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5906_axis_0 = const()[name = string("op_5906_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5906_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5906_cast_fp16_1 = split(axis = var_5906_axis_0, split_sizes = tile_30, x = var_5903_cast_fp16)[name = string("op_5906_cast_fp16")];
            tensor<int32, [4]> var_5913_begin_0 = const()[name = string("op_5913_begin_0"), val = tensor<int32, [4]>([15, 0, 0, 0])];
            tensor<int32, [4]> var_5913_end_0 = const()[name = string("op_5913_end_0"), val = tensor<int32, [4]>([16, 2, 2048, 128])];
            tensor<bool, [4]> var_5913_end_mask_0 = const()[name = string("op_5913_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5913_cast_fp16 = slice_by_index(begin = var_5913_begin_0, end = var_5913_end_0, end_mask = var_5913_end_mask_0, x = coreml_update_state_367)[name = string("op_5913_cast_fp16")];
            tensor<int32, [2]> tile_31 = const()[name = string("tile_31"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5916_axis_0 = const()[name = string("op_5916_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5916_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5916_cast_fp16_1 = split(axis = var_5916_axis_0, split_sizes = tile_31, x = var_5913_cast_fp16)[name = string("op_5916_cast_fp16")];
            tensor<int32, [2]> var_5919_split_sizes_0 = const()[name = string("op_5919_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_5919_axis_0 = const()[name = string("op_5919_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_5919_0, tensor<fp16, [1, 8, 128, 128]> var_5919_1 = split(axis = var_5919_axis_0, split_sizes = var_5919_split_sizes_0, x = query_states_93_cast_fp16)[name = string("op_5919")];
            bool attn_weights_241_transpose_x_0 = const()[name = string("attn_weights_241_transpose_x_0"), val = bool(false)];
            bool attn_weights_241_transpose_y_0 = const()[name = string("attn_weights_241_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_241_cast_fp16 = matmul(transpose_x = attn_weights_241_transpose_x_0, transpose_y = attn_weights_241_transpose_y_0, x = var_5906_cast_fp16_0, y = var_5919_0)[name = string("attn_weights_241_cast_fp16")];
            fp16 var_5922_to_fp16 = const()[name = string("op_5922_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_243_cast_fp16 = mul(x = attn_weights_241_cast_fp16, y = var_5922_to_fp16)[name = string("attn_weights_243_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_245_cast_fp16 = add(x = attn_weights_243_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_245_cast_fp16")];
            int32 var_5926 = const()[name = string("op_5926"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_247_cast_fp16 = softmax(axis = var_5926, x = attn_weights_245_cast_fp16)[name = string("attn_weights_247_cast_fp16")];
            bool var_5932_transpose_x_1 = const()[name = string("op_5932_transpose_x_1"), val = bool(true)];
            bool var_5932_transpose_y_1 = const()[name = string("op_5932_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_5932_cast_fp16 = matmul(transpose_x = var_5932_transpose_x_1, transpose_y = var_5932_transpose_y_1, x = attn_weights_247_cast_fp16, y = var_5916_cast_fp16_0)[name = string("op_5932_cast_fp16")];
            bool attn_weights_249_transpose_x_0 = const()[name = string("attn_weights_249_transpose_x_0"), val = bool(false)];
            bool attn_weights_249_transpose_y_0 = const()[name = string("attn_weights_249_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_249_cast_fp16 = matmul(transpose_x = attn_weights_249_transpose_x_0, transpose_y = attn_weights_249_transpose_y_0, x = var_5906_cast_fp16_1, y = var_5919_1)[name = string("attn_weights_249_cast_fp16")];
            fp16 var_5934_to_fp16 = const()[name = string("op_5934_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_251_cast_fp16 = mul(x = attn_weights_249_cast_fp16, y = var_5934_to_fp16)[name = string("attn_weights_251_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_253_cast_fp16 = add(x = attn_weights_251_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_253_cast_fp16")];
            int32 var_5938 = const()[name = string("op_5938"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_255_cast_fp16 = softmax(axis = var_5938, x = attn_weights_253_cast_fp16)[name = string("attn_weights_255_cast_fp16")];
            bool attn_output_121_transpose_x_1 = const()[name = string("attn_output_121_transpose_x_1"), val = bool(true)];
            bool attn_output_121_transpose_y_1 = const()[name = string("attn_output_121_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_121_cast_fp16 = matmul(transpose_x = attn_output_121_transpose_x_1, transpose_y = attn_output_121_transpose_y_1, x = attn_weights_255_cast_fp16, y = var_5916_cast_fp16_1)[name = string("attn_output_121_cast_fp16")];
            int32 var_5946 = const()[name = string("op_5946"), val = int32(1)];
            bool attn_output_123_interleave_0 = const()[name = string("attn_output_123_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_123_cast_fp16 = concat(axis = var_5946, interleave = attn_output_123_interleave_0, values = (var_5932_cast_fp16, attn_output_121_cast_fp16))[name = string("attn_output_123_cast_fp16")];
            tensor<int32, [4]> var_5950_perm_0 = const()[name = string("op_5950_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_191x = const()[name = string("concat_191x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_5950_cast_fp16 = transpose(perm = var_5950_perm_0, x = attn_output_123_cast_fp16)[name = string("transpose_558")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_127_cast_fp16 = reshape(shape = concat_191x, x = var_5950_cast_fp16)[name = string("attn_output_127_cast_fp16")];
            tensor<int32, [2]> hidden_states_153_strides_0 = const()[name = string("hidden_states_153_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_153_pad_type_0 = const()[name = string("hidden_states_153_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_153_pad_0 = const()[name = string("hidden_states_153_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_153_dilations_0 = const()[name = string("hidden_states_153_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_153_groups_0 = const()[name = string("hidden_states_153_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_153_cast_fp16 = conv(dilations = hidden_states_153_dilations_0, groups = hidden_states_153_groups_0, pad = hidden_states_153_pad_0, pad_type = hidden_states_153_pad_type_0, strides = hidden_states_153_strides_0, weight = layers_15_self_attn_o_proj_weight_cast_fp16, x = attn_output_127_cast_fp16)[name = string("hidden_states_153_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_155_cast_fp16 = add(x = hidden_states_149_cast_fp16, y = hidden_states_153_cast_fp16)[name = string("hidden_states_155_cast_fp16")];
            fp16 const_160_promoted_to_fp16 = const()[name = string("const_160_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_5983_cast_fp16 = mul(x = hidden_states_155_cast_fp16, y = const_160_promoted_to_fp16)[name = string("op_5983_cast_fp16")];
            int32 var_5981 = const()[name = string("op_5981"), val = int32(1)];
            bool doubled_125_interleave_0 = const()[name = string("doubled_125_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_125_cast_fp16 = concat(axis = var_5981, interleave = doubled_125_interleave_0, values = (hidden_states_155_cast_fp16, var_5983_cast_fp16))[name = string("doubled_125_cast_fp16")];
            tensor<int32, [1]> out_63_axes_0 = const()[name = string("out_63_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_63_gamma_0_to_fp16 = const()[name = string("out_63_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433266560)))];
            fp16 var_5993_to_fp16 = const()[name = string("op_5993_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_63_cast_fp16 = layer_norm(axes = out_63_axes_0, epsilon = var_5993_to_fp16, gamma = out_63_gamma_0_to_fp16, x = doubled_125_cast_fp16)[name = string("out_63_cast_fp16")];
            tensor<int32, [2]> var_6004_split_sizes_0 = const()[name = string("op_6004_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6004_axis_0 = const()[name = string("op_6004_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_6004_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_6004_cast_fp16_1 = split(axis = var_6004_axis_0, split_sizes = var_6004_split_sizes_0, x = out_63_cast_fp16)[name = string("op_6004_cast_fp16")];
            tensor<int32, [2]> input_31_strides_0 = const()[name = string("input_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_31_pad_type_0 = const()[name = string("input_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_31_pad_0 = const()[name = string("input_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_31_dilations_0 = const()[name = string("input_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_31_groups_0 = const()[name = string("input_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_31_cast_fp16 = conv(dilations = input_31_dilations_0, groups = input_31_groups_0, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = input_31_strides_0, weight = layers_15_mlp_gate_proj_weight_cast_fp16, x = var_6004_cast_fp16_0)[name = string("input_31_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_6021_cast_fp16 = silu(x = input_31_cast_fp16)[name = string("op_6021_cast_fp16")];
            tensor<int32, [2]> var_6027_strides_0 = const()[name = string("op_6027_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6027_pad_type_0 = const()[name = string("op_6027_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6027_pad_0 = const()[name = string("op_6027_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6027_dilations_0 = const()[name = string("op_6027_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6027_groups_0 = const()[name = string("op_6027_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_6027_cast_fp16 = conv(dilations = var_6027_dilations_0, groups = var_6027_groups_0, pad = var_6027_pad_0, pad_type = var_6027_pad_type_0, strides = var_6027_strides_0, weight = layers_15_mlp_up_proj_weight_cast_fp16, x = var_6004_cast_fp16_0)[name = string("op_6027_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_159_cast_fp16 = mul(x = var_6021_cast_fp16, y = var_6027_cast_fp16)[name = string("x_159_cast_fp16")];
            tensor<int32, [2]> hidden_states_157_strides_0 = const()[name = string("hidden_states_157_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_157_pad_type_0 = const()[name = string("hidden_states_157_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_157_pad_0 = const()[name = string("hidden_states_157_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_157_dilations_0 = const()[name = string("hidden_states_157_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_157_groups_0 = const()[name = string("hidden_states_157_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_157_cast_fp16 = conv(dilations = hidden_states_157_dilations_0, groups = hidden_states_157_groups_0, pad = hidden_states_157_pad_0, pad_type = hidden_states_157_pad_type_0, strides = hidden_states_157_strides_0, weight = layers_15_mlp_down_proj_weight_cast_fp16, x = x_159_cast_fp16)[name = string("hidden_states_157_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_159_cast_fp16 = add(x = hidden_states_155_cast_fp16, y = hidden_states_157_cast_fp16)[name = string("hidden_states_159_cast_fp16")];
            fp16 const_162_promoted_to_fp16 = const()[name = string("const_162_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_6045_cast_fp16 = mul(x = hidden_states_159_cast_fp16, y = const_162_promoted_to_fp16)[name = string("op_6045_cast_fp16")];
            int32 var_6043 = const()[name = string("op_6043"), val = int32(1)];
            bool doubled_129_interleave_0 = const()[name = string("doubled_129_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_129_cast_fp16 = concat(axis = var_6043, interleave = doubled_129_interleave_0, values = (hidden_states_159_cast_fp16, var_6045_cast_fp16))[name = string("doubled_129_cast_fp16")];
            tensor<int32, [1]> out_65_axes_0 = const()[name = string("out_65_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_65_gamma_0_to_fp16 = const()[name = string("out_65_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433274816)))];
            fp16 var_6055_to_fp16 = const()[name = string("op_6055_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_65_cast_fp16 = layer_norm(axes = out_65_axes_0, epsilon = var_6055_to_fp16, gamma = out_65_gamma_0_to_fp16, x = doubled_129_cast_fp16)[name = string("out_65_cast_fp16")];
            tensor<int32, [2]> var_6066_split_sizes_0 = const()[name = string("op_6066_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6066_axis_0 = const()[name = string("op_6066_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_6066_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_6066_cast_fp16_1 = split(axis = var_6066_axis_0, split_sizes = var_6066_split_sizes_0, x = out_65_cast_fp16)[name = string("op_6066_cast_fp16")];
            tensor<int32, [2]> query_states_97_strides_0 = const()[name = string("query_states_97_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_97_pad_type_0 = const()[name = string("query_states_97_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_97_pad_0 = const()[name = string("query_states_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_97_dilations_0 = const()[name = string("query_states_97_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_97_groups_0 = const()[name = string("query_states_97_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_97_cast_fp16 = conv(dilations = query_states_97_dilations_0, groups = query_states_97_groups_0, pad = query_states_97_pad_0, pad_type = query_states_97_pad_type_0, strides = query_states_97_strides_0, weight = layers_16_self_attn_q_proj_weight_cast_fp16, x = var_6066_cast_fp16_0)[name = string("query_states_97_cast_fp16")];
            tensor<int32, [2]> key_states_161_strides_0 = const()[name = string("key_states_161_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_161_pad_type_0 = const()[name = string("key_states_161_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_161_pad_0 = const()[name = string("key_states_161_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_161_dilations_0 = const()[name = string("key_states_161_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_161_groups_0 = const()[name = string("key_states_161_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_161_cast_fp16 = conv(dilations = key_states_161_dilations_0, groups = key_states_161_groups_0, pad = key_states_161_pad_0, pad_type = key_states_161_pad_type_0, strides = key_states_161_strides_0, weight = layers_16_self_attn_k_proj_weight_cast_fp16, x = var_6066_cast_fp16_0)[name = string("key_states_161_cast_fp16")];
            tensor<int32, [2]> value_states_97_strides_0 = const()[name = string("value_states_97_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_97_pad_type_0 = const()[name = string("value_states_97_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_97_pad_0 = const()[name = string("value_states_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_97_dilations_0 = const()[name = string("value_states_97_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_97_groups_0 = const()[name = string("value_states_97_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_97_cast_fp16 = conv(dilations = value_states_97_dilations_0, groups = value_states_97_groups_0, pad = value_states_97_pad_0, pad_type = value_states_97_pad_type_0, strides = value_states_97_strides_0, weight = layers_16_self_attn_v_proj_weight_cast_fp16, x = var_6066_cast_fp16_0)[name = string("value_states_97_cast_fp16")];
            tensor<int32, [4]> concat_192x = const()[name = string("concat_192x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_161_cast_fp16 = reshape(shape = concat_192x, x = query_states_97_cast_fp16)[name = string("x_161_cast_fp16")];
            tensor<int32, [4]> concat_193x = const()[name = string("concat_193x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_6123_cast_fp16 = reshape(shape = concat_193x, x = key_states_161_cast_fp16)[name = string("op_6123_cast_fp16")];
            tensor<int32, [4]> concat_194x = const()[name = string("concat_194x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_6130_cast_fp16 = reshape(shape = concat_194x, x = value_states_97_cast_fp16)[name = string("op_6130_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_6134_cast_fp16 = mul(x = x_161_cast_fp16, y = var_859_cast_fp16)[name = string("op_6134_cast_fp16")];
            tensor<int32, [2]> var_6135_split_sizes_0 = const()[name = string("op_6135_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6135_axis_0 = const()[name = string("op_6135_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_6135_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_6135_cast_fp16_1 = split(axis = var_6135_axis_0, split_sizes = var_6135_split_sizes_0, x = x_161_cast_fp16)[name = string("op_6135_cast_fp16")];
            fp16 const_164_promoted_to_fp16 = const()[name = string("const_164_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_6137_cast_fp16 = mul(x = var_6135_cast_fp16_1, y = const_164_promoted_to_fp16)[name = string("op_6137_cast_fp16")];
            int32 var_6139 = const()[name = string("op_6139"), val = int32(-2)];
            bool var_6140_interleave_0 = const()[name = string("op_6140_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_6140_cast_fp16 = concat(axis = var_6139, interleave = var_6140_interleave_0, values = (var_6137_cast_fp16, var_6135_cast_fp16_0))[name = string("op_6140_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_6141_cast_fp16 = mul(x = var_6140_cast_fp16, y = var_866_cast_fp16)[name = string("op_6141_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_99_cast_fp16 = add(x = var_6134_cast_fp16, y = var_6141_cast_fp16)[name = string("query_states_99_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_6147_cast_fp16 = mul(x = var_6123_cast_fp16, y = var_859_cast_fp16)[name = string("op_6147_cast_fp16")];
            tensor<int32, [2]> var_6148_split_sizes_0 = const()[name = string("op_6148_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6148_axis_0 = const()[name = string("op_6148_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_6148_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_6148_cast_fp16_1 = split(axis = var_6148_axis_0, split_sizes = var_6148_split_sizes_0, x = var_6123_cast_fp16)[name = string("op_6148_cast_fp16")];
            fp16 const_165_promoted_to_fp16 = const()[name = string("const_165_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_6150_cast_fp16 = mul(x = var_6148_cast_fp16_1, y = const_165_promoted_to_fp16)[name = string("op_6150_cast_fp16")];
            int32 var_6152 = const()[name = string("op_6152"), val = int32(-2)];
            bool var_6153_interleave_0 = const()[name = string("op_6153_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_6153_cast_fp16 = concat(axis = var_6152, interleave = var_6153_interleave_0, values = (var_6150_cast_fp16, var_6148_cast_fp16_0))[name = string("op_6153_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_6154_cast_fp16 = mul(x = var_6153_cast_fp16, y = var_866_cast_fp16)[name = string("op_6154_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_165_cast_fp16 = add(x = var_6147_cast_fp16, y = var_6154_cast_fp16)[name = string("key_states_165_cast_fp16")];
            tensor<int32, [1]> expand_dims_192 = const()[name = string("expand_dims_192"), val = tensor<int32, [1]>([16])];
            tensor<int32, [1]> expand_dims_193 = const()[name = string("expand_dims_193"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_195 = const()[name = string("expand_dims_195"), val = tensor<int32, [1]>([0])];
            int32 concat_197_axis_0 = const()[name = string("concat_197_axis_0"), val = int32(0)];
            bool concat_197_interleave_0 = const()[name = string("concat_197_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_197 = concat(axis = concat_197_axis_0, interleave = concat_197_interleave_0, values = (expand_dims_192, expand_dims_193, position_id, expand_dims_195))[name = string("concat_197")];
            tensor<int32, [1]> expand_dims_196 = const()[name = string("expand_dims_196"), val = tensor<int32, [1]>([17])];
            tensor<int32, [1]> concat_198_values1_0 = const()[name = string("concat_198_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_198_values3_0 = const()[name = string("concat_198_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_198_axis_0 = const()[name = string("concat_198_axis_0"), val = int32(0)];
            bool concat_198_interleave_0 = const()[name = string("concat_198_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_198 = concat(axis = concat_198_axis_0, interleave = concat_198_interleave_0, values = (expand_dims_196, concat_198_values1_0, cache_position_end, concat_198_values3_0))[name = string("concat_198")];
            tensor<int32, [4]> key_states_167_perm_0 = const()[name = string("key_states_167_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_17_stride_0 = const()[name = string("key_cache_internal_tensor_assign_17_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_17_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_17_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_17_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_17_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_17_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_17_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_167_cast_fp16 = transpose(perm = key_states_167_perm_0, x = key_states_165_cast_fp16)[name = string("transpose_557")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_17_cast_fp16 = slice_update(begin = concat_197, begin_mask = key_cache_internal_tensor_assign_17_begin_mask_0, end = concat_198, end_mask = key_cache_internal_tensor_assign_17_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_17_squeeze_mask_0, stride = key_cache_internal_tensor_assign_17_stride_0, update = key_states_167_cast_fp16, x = coreml_update_state_366)[name = string("key_cache_internal_tensor_assign_17_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_17_cast_fp16, input = key_cache)[name = string("coreml_update_state_368_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_368 = read_state(input = key_cache)[name = string("coreml_update_state_368")];
            tensor<int32, [4]> value_states_99_perm_0 = const()[name = string("value_states_99_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_17_stride_0 = const()[name = string("value_cache_internal_tensor_assign_17_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_17_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_17_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_17_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_17_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_17_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_17_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_99_cast_fp16 = transpose(perm = value_states_99_perm_0, x = var_6130_cast_fp16)[name = string("transpose_556")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_17_cast_fp16 = slice_update(begin = concat_197, begin_mask = value_cache_internal_tensor_assign_17_begin_mask_0, end = concat_198, end_mask = value_cache_internal_tensor_assign_17_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_17_squeeze_mask_0, stride = value_cache_internal_tensor_assign_17_stride_0, update = value_states_99_cast_fp16, x = coreml_update_state_367)[name = string("value_cache_internal_tensor_assign_17_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_17_cast_fp16, input = value_cache)[name = string("coreml_update_state_369_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_369 = read_state(input = value_cache)[name = string("coreml_update_state_369")];
            tensor<int32, [4]> var_6224_begin_0 = const()[name = string("op_6224_begin_0"), val = tensor<int32, [4]>([16, 0, 0, 0])];
            tensor<int32, [4]> var_6224_end_0 = const()[name = string("op_6224_end_0"), val = tensor<int32, [4]>([17, 2, 2048, 128])];
            tensor<bool, [4]> var_6224_end_mask_0 = const()[name = string("op_6224_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6224_cast_fp16 = slice_by_index(begin = var_6224_begin_0, end = var_6224_end_0, end_mask = var_6224_end_mask_0, x = coreml_update_state_368)[name = string("op_6224_cast_fp16")];
            tensor<int32, [2]> tile_32 = const()[name = string("tile_32"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6227_axis_0 = const()[name = string("op_6227_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6227_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6227_cast_fp16_1 = split(axis = var_6227_axis_0, split_sizes = tile_32, x = var_6224_cast_fp16)[name = string("op_6227_cast_fp16")];
            tensor<int32, [4]> var_6234_begin_0 = const()[name = string("op_6234_begin_0"), val = tensor<int32, [4]>([16, 0, 0, 0])];
            tensor<int32, [4]> var_6234_end_0 = const()[name = string("op_6234_end_0"), val = tensor<int32, [4]>([17, 2, 2048, 128])];
            tensor<bool, [4]> var_6234_end_mask_0 = const()[name = string("op_6234_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6234_cast_fp16 = slice_by_index(begin = var_6234_begin_0, end = var_6234_end_0, end_mask = var_6234_end_mask_0, x = coreml_update_state_369)[name = string("op_6234_cast_fp16")];
            tensor<int32, [2]> tile_33 = const()[name = string("tile_33"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6237_axis_0 = const()[name = string("op_6237_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6237_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6237_cast_fp16_1 = split(axis = var_6237_axis_0, split_sizes = tile_33, x = var_6234_cast_fp16)[name = string("op_6237_cast_fp16")];
            tensor<int32, [2]> var_6240_split_sizes_0 = const()[name = string("op_6240_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_6240_axis_0 = const()[name = string("op_6240_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_6240_0, tensor<fp16, [1, 8, 128, 128]> var_6240_1 = split(axis = var_6240_axis_0, split_sizes = var_6240_split_sizes_0, x = query_states_99_cast_fp16)[name = string("op_6240")];
            bool attn_weights_257_transpose_x_0 = const()[name = string("attn_weights_257_transpose_x_0"), val = bool(false)];
            bool attn_weights_257_transpose_y_0 = const()[name = string("attn_weights_257_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_257_cast_fp16 = matmul(transpose_x = attn_weights_257_transpose_x_0, transpose_y = attn_weights_257_transpose_y_0, x = var_6227_cast_fp16_0, y = var_6240_0)[name = string("attn_weights_257_cast_fp16")];
            fp16 var_6243_to_fp16 = const()[name = string("op_6243_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_259_cast_fp16 = mul(x = attn_weights_257_cast_fp16, y = var_6243_to_fp16)[name = string("attn_weights_259_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_261_cast_fp16 = add(x = attn_weights_259_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_261_cast_fp16")];
            int32 var_6247 = const()[name = string("op_6247"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_263_cast_fp16 = softmax(axis = var_6247, x = attn_weights_261_cast_fp16)[name = string("attn_weights_263_cast_fp16")];
            bool var_6253_transpose_x_1 = const()[name = string("op_6253_transpose_x_1"), val = bool(true)];
            bool var_6253_transpose_y_1 = const()[name = string("op_6253_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_6253_cast_fp16 = matmul(transpose_x = var_6253_transpose_x_1, transpose_y = var_6253_transpose_y_1, x = attn_weights_263_cast_fp16, y = var_6237_cast_fp16_0)[name = string("op_6253_cast_fp16")];
            bool attn_weights_265_transpose_x_0 = const()[name = string("attn_weights_265_transpose_x_0"), val = bool(false)];
            bool attn_weights_265_transpose_y_0 = const()[name = string("attn_weights_265_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_265_cast_fp16 = matmul(transpose_x = attn_weights_265_transpose_x_0, transpose_y = attn_weights_265_transpose_y_0, x = var_6227_cast_fp16_1, y = var_6240_1)[name = string("attn_weights_265_cast_fp16")];
            fp16 var_6255_to_fp16 = const()[name = string("op_6255_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_267_cast_fp16 = mul(x = attn_weights_265_cast_fp16, y = var_6255_to_fp16)[name = string("attn_weights_267_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_269_cast_fp16 = add(x = attn_weights_267_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_269_cast_fp16")];
            int32 var_6259 = const()[name = string("op_6259"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_271_cast_fp16 = softmax(axis = var_6259, x = attn_weights_269_cast_fp16)[name = string("attn_weights_271_cast_fp16")];
            bool attn_output_129_transpose_x_1 = const()[name = string("attn_output_129_transpose_x_1"), val = bool(true)];
            bool attn_output_129_transpose_y_1 = const()[name = string("attn_output_129_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_129_cast_fp16 = matmul(transpose_x = attn_output_129_transpose_x_1, transpose_y = attn_output_129_transpose_y_1, x = attn_weights_271_cast_fp16, y = var_6237_cast_fp16_1)[name = string("attn_output_129_cast_fp16")];
            int32 var_6267 = const()[name = string("op_6267"), val = int32(1)];
            bool attn_output_131_interleave_0 = const()[name = string("attn_output_131_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_131_cast_fp16 = concat(axis = var_6267, interleave = attn_output_131_interleave_0, values = (var_6253_cast_fp16, attn_output_129_cast_fp16))[name = string("attn_output_131_cast_fp16")];
            tensor<int32, [4]> var_6271_perm_0 = const()[name = string("op_6271_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_203x = const()[name = string("concat_203x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_6271_cast_fp16 = transpose(perm = var_6271_perm_0, x = attn_output_131_cast_fp16)[name = string("transpose_555")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_135_cast_fp16 = reshape(shape = concat_203x, x = var_6271_cast_fp16)[name = string("attn_output_135_cast_fp16")];
            tensor<int32, [2]> hidden_states_163_strides_0 = const()[name = string("hidden_states_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_163_pad_type_0 = const()[name = string("hidden_states_163_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_163_pad_0 = const()[name = string("hidden_states_163_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_163_dilations_0 = const()[name = string("hidden_states_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_163_groups_0 = const()[name = string("hidden_states_163_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_163_cast_fp16 = conv(dilations = hidden_states_163_dilations_0, groups = hidden_states_163_groups_0, pad = hidden_states_163_pad_0, pad_type = hidden_states_163_pad_type_0, strides = hidden_states_163_strides_0, weight = layers_16_self_attn_o_proj_weight_cast_fp16, x = attn_output_135_cast_fp16)[name = string("hidden_states_163_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_165_cast_fp16 = add(x = hidden_states_159_cast_fp16, y = hidden_states_163_cast_fp16)[name = string("hidden_states_165_cast_fp16")];
            fp16 const_170_promoted_to_fp16 = const()[name = string("const_170_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_6304_cast_fp16 = mul(x = hidden_states_165_cast_fp16, y = const_170_promoted_to_fp16)[name = string("op_6304_cast_fp16")];
            int32 var_6302 = const()[name = string("op_6302"), val = int32(1)];
            bool doubled_133_interleave_0 = const()[name = string("doubled_133_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_133_cast_fp16 = concat(axis = var_6302, interleave = doubled_133_interleave_0, values = (hidden_states_165_cast_fp16, var_6304_cast_fp16))[name = string("doubled_133_cast_fp16")];
            tensor<int32, [1]> out_67_axes_0 = const()[name = string("out_67_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_67_gamma_0_to_fp16 = const()[name = string("out_67_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433283072)))];
            fp16 var_6314_to_fp16 = const()[name = string("op_6314_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_67_cast_fp16 = layer_norm(axes = out_67_axes_0, epsilon = var_6314_to_fp16, gamma = out_67_gamma_0_to_fp16, x = doubled_133_cast_fp16)[name = string("out_67_cast_fp16")];
            tensor<int32, [2]> var_6325_split_sizes_0 = const()[name = string("op_6325_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6325_axis_0 = const()[name = string("op_6325_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_6325_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_6325_cast_fp16_1 = split(axis = var_6325_axis_0, split_sizes = var_6325_split_sizes_0, x = out_67_cast_fp16)[name = string("op_6325_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_16_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_16_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433291328)))];
            tensor<int32, [2]> input_33_strides_0 = const()[name = string("input_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_33_pad_type_0 = const()[name = string("input_33_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_33_pad_0 = const()[name = string("input_33_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_33_dilations_0 = const()[name = string("input_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_33_groups_0 = const()[name = string("input_33_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_33_cast_fp16 = conv(dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = layers_16_mlp_gate_proj_weight_to_fp16, x = var_6325_cast_fp16_0)[name = string("input_33_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_6342_cast_fp16 = silu(x = input_33_cast_fp16)[name = string("op_6342_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_16_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_16_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1458457216)))];
            tensor<int32, [2]> var_6348_strides_0 = const()[name = string("op_6348_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6348_pad_type_0 = const()[name = string("op_6348_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6348_pad_0 = const()[name = string("op_6348_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6348_dilations_0 = const()[name = string("op_6348_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6348_groups_0 = const()[name = string("op_6348_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_6348_cast_fp16 = conv(dilations = var_6348_dilations_0, groups = var_6348_groups_0, pad = var_6348_pad_0, pad_type = var_6348_pad_type_0, strides = var_6348_strides_0, weight = layers_16_mlp_up_proj_weight_to_fp16, x = var_6325_cast_fp16_0)[name = string("op_6348_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_169_cast_fp16 = mul(x = var_6342_cast_fp16, y = var_6348_cast_fp16)[name = string("x_169_cast_fp16")];
            tensor<int32, [2]> hidden_states_167_strides_0 = const()[name = string("hidden_states_167_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_167_pad_type_0 = const()[name = string("hidden_states_167_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_167_pad_0 = const()[name = string("hidden_states_167_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_167_dilations_0 = const()[name = string("hidden_states_167_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_167_groups_0 = const()[name = string("hidden_states_167_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_167_cast_fp16 = conv(dilations = hidden_states_167_dilations_0, groups = hidden_states_167_groups_0, pad = hidden_states_167_pad_0, pad_type = hidden_states_167_pad_type_0, strides = hidden_states_167_strides_0, weight = layers_16_mlp_down_proj_weight_cast_fp16, x = x_169_cast_fp16)[name = string("hidden_states_167_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_169_cast_fp16 = add(x = hidden_states_165_cast_fp16, y = hidden_states_167_cast_fp16)[name = string("hidden_states_169_cast_fp16")];
            fp16 const_172_promoted_to_fp16 = const()[name = string("const_172_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_6366_cast_fp16 = mul(x = hidden_states_169_cast_fp16, y = const_172_promoted_to_fp16)[name = string("op_6366_cast_fp16")];
            int32 var_6364 = const()[name = string("op_6364"), val = int32(1)];
            bool doubled_137_interleave_0 = const()[name = string("doubled_137_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_137_cast_fp16 = concat(axis = var_6364, interleave = doubled_137_interleave_0, values = (hidden_states_169_cast_fp16, var_6366_cast_fp16))[name = string("doubled_137_cast_fp16")];
            tensor<int32, [1]> out_69_axes_0 = const()[name = string("out_69_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_69_gamma_0_to_fp16 = const()[name = string("out_69_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483623104)))];
            fp16 var_6376_to_fp16 = const()[name = string("op_6376_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_69_cast_fp16 = layer_norm(axes = out_69_axes_0, epsilon = var_6376_to_fp16, gamma = out_69_gamma_0_to_fp16, x = doubled_137_cast_fp16)[name = string("out_69_cast_fp16")];
            tensor<int32, [2]> var_6387_split_sizes_0 = const()[name = string("op_6387_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6387_axis_0 = const()[name = string("op_6387_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_6387_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_6387_cast_fp16_1 = split(axis = var_6387_axis_0, split_sizes = var_6387_split_sizes_0, x = out_69_cast_fp16)[name = string("op_6387_cast_fp16")];
            tensor<int32, [2]> query_states_103_strides_0 = const()[name = string("query_states_103_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_103_pad_type_0 = const()[name = string("query_states_103_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_103_pad_0 = const()[name = string("query_states_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_103_dilations_0 = const()[name = string("query_states_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_103_groups_0 = const()[name = string("query_states_103_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_103_cast_fp16 = conv(dilations = query_states_103_dilations_0, groups = query_states_103_groups_0, pad = query_states_103_pad_0, pad_type = query_states_103_pad_type_0, strides = query_states_103_strides_0, weight = layers_17_self_attn_q_proj_weight_cast_fp16, x = var_6387_cast_fp16_0)[name = string("query_states_103_cast_fp16")];
            tensor<int32, [2]> key_states_171_strides_0 = const()[name = string("key_states_171_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_171_pad_type_0 = const()[name = string("key_states_171_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_171_pad_0 = const()[name = string("key_states_171_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_171_dilations_0 = const()[name = string("key_states_171_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_171_groups_0 = const()[name = string("key_states_171_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_171_cast_fp16 = conv(dilations = key_states_171_dilations_0, groups = key_states_171_groups_0, pad = key_states_171_pad_0, pad_type = key_states_171_pad_type_0, strides = key_states_171_strides_0, weight = layers_17_self_attn_k_proj_weight_cast_fp16, x = var_6387_cast_fp16_0)[name = string("key_states_171_cast_fp16")];
            tensor<int32, [2]> value_states_103_strides_0 = const()[name = string("value_states_103_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_103_pad_type_0 = const()[name = string("value_states_103_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_103_pad_0 = const()[name = string("value_states_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_103_dilations_0 = const()[name = string("value_states_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_103_groups_0 = const()[name = string("value_states_103_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_103_cast_fp16 = conv(dilations = value_states_103_dilations_0, groups = value_states_103_groups_0, pad = value_states_103_pad_0, pad_type = value_states_103_pad_type_0, strides = value_states_103_strides_0, weight = layers_17_self_attn_v_proj_weight_cast_fp16, x = var_6387_cast_fp16_0)[name = string("value_states_103_cast_fp16")];
            tensor<int32, [4]> concat_204x = const()[name = string("concat_204x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_171_cast_fp16 = reshape(shape = concat_204x, x = query_states_103_cast_fp16)[name = string("x_171_cast_fp16")];
            tensor<int32, [4]> concat_205x = const()[name = string("concat_205x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_6444_cast_fp16 = reshape(shape = concat_205x, x = key_states_171_cast_fp16)[name = string("op_6444_cast_fp16")];
            tensor<int32, [4]> concat_206x = const()[name = string("concat_206x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_6451_cast_fp16 = reshape(shape = concat_206x, x = value_states_103_cast_fp16)[name = string("op_6451_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_6455_cast_fp16 = mul(x = x_171_cast_fp16, y = var_859_cast_fp16)[name = string("op_6455_cast_fp16")];
            tensor<int32, [2]> var_6456_split_sizes_0 = const()[name = string("op_6456_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6456_axis_0 = const()[name = string("op_6456_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_6456_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_6456_cast_fp16_1 = split(axis = var_6456_axis_0, split_sizes = var_6456_split_sizes_0, x = x_171_cast_fp16)[name = string("op_6456_cast_fp16")];
            fp16 const_174_promoted_to_fp16 = const()[name = string("const_174_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_6458_cast_fp16 = mul(x = var_6456_cast_fp16_1, y = const_174_promoted_to_fp16)[name = string("op_6458_cast_fp16")];
            int32 var_6460 = const()[name = string("op_6460"), val = int32(-2)];
            bool var_6461_interleave_0 = const()[name = string("op_6461_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_6461_cast_fp16 = concat(axis = var_6460, interleave = var_6461_interleave_0, values = (var_6458_cast_fp16, var_6456_cast_fp16_0))[name = string("op_6461_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_6462_cast_fp16 = mul(x = var_6461_cast_fp16, y = var_866_cast_fp16)[name = string("op_6462_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_105_cast_fp16 = add(x = var_6455_cast_fp16, y = var_6462_cast_fp16)[name = string("query_states_105_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_6468_cast_fp16 = mul(x = var_6444_cast_fp16, y = var_859_cast_fp16)[name = string("op_6468_cast_fp16")];
            tensor<int32, [2]> var_6469_split_sizes_0 = const()[name = string("op_6469_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6469_axis_0 = const()[name = string("op_6469_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_6469_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_6469_cast_fp16_1 = split(axis = var_6469_axis_0, split_sizes = var_6469_split_sizes_0, x = var_6444_cast_fp16)[name = string("op_6469_cast_fp16")];
            fp16 const_175_promoted_to_fp16 = const()[name = string("const_175_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_6471_cast_fp16 = mul(x = var_6469_cast_fp16_1, y = const_175_promoted_to_fp16)[name = string("op_6471_cast_fp16")];
            int32 var_6473 = const()[name = string("op_6473"), val = int32(-2)];
            bool var_6474_interleave_0 = const()[name = string("op_6474_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_6474_cast_fp16 = concat(axis = var_6473, interleave = var_6474_interleave_0, values = (var_6471_cast_fp16, var_6469_cast_fp16_0))[name = string("op_6474_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_6475_cast_fp16 = mul(x = var_6474_cast_fp16, y = var_866_cast_fp16)[name = string("op_6475_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_175_cast_fp16 = add(x = var_6468_cast_fp16, y = var_6475_cast_fp16)[name = string("key_states_175_cast_fp16")];
            tensor<int32, [1]> expand_dims_204 = const()[name = string("expand_dims_204"), val = tensor<int32, [1]>([17])];
            tensor<int32, [1]> expand_dims_205 = const()[name = string("expand_dims_205"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_207 = const()[name = string("expand_dims_207"), val = tensor<int32, [1]>([0])];
            int32 concat_209_axis_0 = const()[name = string("concat_209_axis_0"), val = int32(0)];
            bool concat_209_interleave_0 = const()[name = string("concat_209_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_209 = concat(axis = concat_209_axis_0, interleave = concat_209_interleave_0, values = (expand_dims_204, expand_dims_205, position_id, expand_dims_207))[name = string("concat_209")];
            tensor<int32, [1]> expand_dims_208 = const()[name = string("expand_dims_208"), val = tensor<int32, [1]>([18])];
            tensor<int32, [1]> concat_210_values1_0 = const()[name = string("concat_210_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_210_values3_0 = const()[name = string("concat_210_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_210_axis_0 = const()[name = string("concat_210_axis_0"), val = int32(0)];
            bool concat_210_interleave_0 = const()[name = string("concat_210_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_210 = concat(axis = concat_210_axis_0, interleave = concat_210_interleave_0, values = (expand_dims_208, concat_210_values1_0, cache_position_end, concat_210_values3_0))[name = string("concat_210")];
            tensor<int32, [4]> key_states_177_perm_0 = const()[name = string("key_states_177_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_18_stride_0 = const()[name = string("key_cache_internal_tensor_assign_18_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_18_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_18_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_18_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_18_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_18_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_18_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_177_cast_fp16 = transpose(perm = key_states_177_perm_0, x = key_states_175_cast_fp16)[name = string("transpose_554")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_18_cast_fp16 = slice_update(begin = concat_209, begin_mask = key_cache_internal_tensor_assign_18_begin_mask_0, end = concat_210, end_mask = key_cache_internal_tensor_assign_18_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_18_squeeze_mask_0, stride = key_cache_internal_tensor_assign_18_stride_0, update = key_states_177_cast_fp16, x = coreml_update_state_368)[name = string("key_cache_internal_tensor_assign_18_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_18_cast_fp16, input = key_cache)[name = string("coreml_update_state_370_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_370 = read_state(input = key_cache)[name = string("coreml_update_state_370")];
            tensor<int32, [4]> value_states_105_perm_0 = const()[name = string("value_states_105_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_18_stride_0 = const()[name = string("value_cache_internal_tensor_assign_18_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_18_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_18_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_18_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_18_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_18_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_18_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_105_cast_fp16 = transpose(perm = value_states_105_perm_0, x = var_6451_cast_fp16)[name = string("transpose_553")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_18_cast_fp16 = slice_update(begin = concat_209, begin_mask = value_cache_internal_tensor_assign_18_begin_mask_0, end = concat_210, end_mask = value_cache_internal_tensor_assign_18_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_18_squeeze_mask_0, stride = value_cache_internal_tensor_assign_18_stride_0, update = value_states_105_cast_fp16, x = coreml_update_state_369)[name = string("value_cache_internal_tensor_assign_18_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_18_cast_fp16, input = value_cache)[name = string("coreml_update_state_371_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_371 = read_state(input = value_cache)[name = string("coreml_update_state_371")];
            tensor<int32, [4]> var_6545_begin_0 = const()[name = string("op_6545_begin_0"), val = tensor<int32, [4]>([17, 0, 0, 0])];
            tensor<int32, [4]> var_6545_end_0 = const()[name = string("op_6545_end_0"), val = tensor<int32, [4]>([18, 2, 2048, 128])];
            tensor<bool, [4]> var_6545_end_mask_0 = const()[name = string("op_6545_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6545_cast_fp16 = slice_by_index(begin = var_6545_begin_0, end = var_6545_end_0, end_mask = var_6545_end_mask_0, x = coreml_update_state_370)[name = string("op_6545_cast_fp16")];
            tensor<int32, [2]> tile_34 = const()[name = string("tile_34"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6548_axis_0 = const()[name = string("op_6548_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6548_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6548_cast_fp16_1 = split(axis = var_6548_axis_0, split_sizes = tile_34, x = var_6545_cast_fp16)[name = string("op_6548_cast_fp16")];
            tensor<int32, [4]> var_6555_begin_0 = const()[name = string("op_6555_begin_0"), val = tensor<int32, [4]>([17, 0, 0, 0])];
            tensor<int32, [4]> var_6555_end_0 = const()[name = string("op_6555_end_0"), val = tensor<int32, [4]>([18, 2, 2048, 128])];
            tensor<bool, [4]> var_6555_end_mask_0 = const()[name = string("op_6555_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6555_cast_fp16 = slice_by_index(begin = var_6555_begin_0, end = var_6555_end_0, end_mask = var_6555_end_mask_0, x = coreml_update_state_371)[name = string("op_6555_cast_fp16")];
            tensor<int32, [2]> tile_35 = const()[name = string("tile_35"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6558_axis_0 = const()[name = string("op_6558_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6558_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6558_cast_fp16_1 = split(axis = var_6558_axis_0, split_sizes = tile_35, x = var_6555_cast_fp16)[name = string("op_6558_cast_fp16")];
            tensor<int32, [2]> var_6561_split_sizes_0 = const()[name = string("op_6561_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_6561_axis_0 = const()[name = string("op_6561_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_6561_0, tensor<fp16, [1, 8, 128, 128]> var_6561_1 = split(axis = var_6561_axis_0, split_sizes = var_6561_split_sizes_0, x = query_states_105_cast_fp16)[name = string("op_6561")];
            bool attn_weights_273_transpose_x_0 = const()[name = string("attn_weights_273_transpose_x_0"), val = bool(false)];
            bool attn_weights_273_transpose_y_0 = const()[name = string("attn_weights_273_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_273_cast_fp16 = matmul(transpose_x = attn_weights_273_transpose_x_0, transpose_y = attn_weights_273_transpose_y_0, x = var_6548_cast_fp16_0, y = var_6561_0)[name = string("attn_weights_273_cast_fp16")];
            fp16 var_6564_to_fp16 = const()[name = string("op_6564_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_275_cast_fp16 = mul(x = attn_weights_273_cast_fp16, y = var_6564_to_fp16)[name = string("attn_weights_275_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_277_cast_fp16 = add(x = attn_weights_275_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_277_cast_fp16")];
            int32 var_6568 = const()[name = string("op_6568"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_279_cast_fp16 = softmax(axis = var_6568, x = attn_weights_277_cast_fp16)[name = string("attn_weights_279_cast_fp16")];
            bool var_6574_transpose_x_1 = const()[name = string("op_6574_transpose_x_1"), val = bool(true)];
            bool var_6574_transpose_y_1 = const()[name = string("op_6574_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_6574_cast_fp16 = matmul(transpose_x = var_6574_transpose_x_1, transpose_y = var_6574_transpose_y_1, x = attn_weights_279_cast_fp16, y = var_6558_cast_fp16_0)[name = string("op_6574_cast_fp16")];
            bool attn_weights_281_transpose_x_0 = const()[name = string("attn_weights_281_transpose_x_0"), val = bool(false)];
            bool attn_weights_281_transpose_y_0 = const()[name = string("attn_weights_281_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_281_cast_fp16 = matmul(transpose_x = attn_weights_281_transpose_x_0, transpose_y = attn_weights_281_transpose_y_0, x = var_6548_cast_fp16_1, y = var_6561_1)[name = string("attn_weights_281_cast_fp16")];
            fp16 var_6576_to_fp16 = const()[name = string("op_6576_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_283_cast_fp16 = mul(x = attn_weights_281_cast_fp16, y = var_6576_to_fp16)[name = string("attn_weights_283_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_285_cast_fp16 = add(x = attn_weights_283_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_285_cast_fp16")];
            int32 var_6580 = const()[name = string("op_6580"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_287_cast_fp16 = softmax(axis = var_6580, x = attn_weights_285_cast_fp16)[name = string("attn_weights_287_cast_fp16")];
            bool attn_output_137_transpose_x_1 = const()[name = string("attn_output_137_transpose_x_1"), val = bool(true)];
            bool attn_output_137_transpose_y_1 = const()[name = string("attn_output_137_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_137_cast_fp16 = matmul(transpose_x = attn_output_137_transpose_x_1, transpose_y = attn_output_137_transpose_y_1, x = attn_weights_287_cast_fp16, y = var_6558_cast_fp16_1)[name = string("attn_output_137_cast_fp16")];
            int32 var_6588 = const()[name = string("op_6588"), val = int32(1)];
            bool attn_output_139_interleave_0 = const()[name = string("attn_output_139_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_139_cast_fp16 = concat(axis = var_6588, interleave = attn_output_139_interleave_0, values = (var_6574_cast_fp16, attn_output_137_cast_fp16))[name = string("attn_output_139_cast_fp16")];
            tensor<int32, [4]> var_6592_perm_0 = const()[name = string("op_6592_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_215x = const()[name = string("concat_215x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_6592_cast_fp16 = transpose(perm = var_6592_perm_0, x = attn_output_139_cast_fp16)[name = string("transpose_552")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_143_cast_fp16 = reshape(shape = concat_215x, x = var_6592_cast_fp16)[name = string("attn_output_143_cast_fp16")];
            tensor<int32, [2]> hidden_states_173_strides_0 = const()[name = string("hidden_states_173_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_173_pad_type_0 = const()[name = string("hidden_states_173_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_173_pad_0 = const()[name = string("hidden_states_173_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_173_dilations_0 = const()[name = string("hidden_states_173_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_173_groups_0 = const()[name = string("hidden_states_173_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_173_cast_fp16 = conv(dilations = hidden_states_173_dilations_0, groups = hidden_states_173_groups_0, pad = hidden_states_173_pad_0, pad_type = hidden_states_173_pad_type_0, strides = hidden_states_173_strides_0, weight = layers_17_self_attn_o_proj_weight_cast_fp16, x = attn_output_143_cast_fp16)[name = string("hidden_states_173_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_175_cast_fp16 = add(x = hidden_states_169_cast_fp16, y = hidden_states_173_cast_fp16)[name = string("hidden_states_175_cast_fp16")];
            fp16 const_180_promoted_to_fp16 = const()[name = string("const_180_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_6625_cast_fp16 = mul(x = hidden_states_175_cast_fp16, y = const_180_promoted_to_fp16)[name = string("op_6625_cast_fp16")];
            int32 var_6623 = const()[name = string("op_6623"), val = int32(1)];
            bool doubled_141_interleave_0 = const()[name = string("doubled_141_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_141_cast_fp16 = concat(axis = var_6623, interleave = doubled_141_interleave_0, values = (hidden_states_175_cast_fp16, var_6625_cast_fp16))[name = string("doubled_141_cast_fp16")];
            tensor<int32, [1]> out_71_axes_0 = const()[name = string("out_71_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_71_gamma_0_to_fp16 = const()[name = string("out_71_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483631360)))];
            fp16 var_6635_to_fp16 = const()[name = string("op_6635_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_71_cast_fp16 = layer_norm(axes = out_71_axes_0, epsilon = var_6635_to_fp16, gamma = out_71_gamma_0_to_fp16, x = doubled_141_cast_fp16)[name = string("out_71_cast_fp16")];
            tensor<int32, [2]> var_6646_split_sizes_0 = const()[name = string("op_6646_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6646_axis_0 = const()[name = string("op_6646_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_6646_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_6646_cast_fp16_1 = split(axis = var_6646_axis_0, split_sizes = var_6646_split_sizes_0, x = out_71_cast_fp16)[name = string("op_6646_cast_fp16")];
            tensor<int32, [2]> input_35_strides_0 = const()[name = string("input_35_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_35_pad_type_0 = const()[name = string("input_35_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_35_pad_0 = const()[name = string("input_35_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_35_dilations_0 = const()[name = string("input_35_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_35_groups_0 = const()[name = string("input_35_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_35_cast_fp16 = conv(dilations = input_35_dilations_0, groups = input_35_groups_0, pad = input_35_pad_0, pad_type = input_35_pad_type_0, strides = input_35_strides_0, weight = layers_17_mlp_gate_proj_weight_cast_fp16, x = var_6646_cast_fp16_0)[name = string("input_35_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_6663_cast_fp16 = silu(x = input_35_cast_fp16)[name = string("op_6663_cast_fp16")];
            tensor<int32, [2]> var_6669_strides_0 = const()[name = string("op_6669_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6669_pad_type_0 = const()[name = string("op_6669_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6669_pad_0 = const()[name = string("op_6669_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6669_dilations_0 = const()[name = string("op_6669_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6669_groups_0 = const()[name = string("op_6669_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_6669_cast_fp16 = conv(dilations = var_6669_dilations_0, groups = var_6669_groups_0, pad = var_6669_pad_0, pad_type = var_6669_pad_type_0, strides = var_6669_strides_0, weight = layers_17_mlp_up_proj_weight_cast_fp16, x = var_6646_cast_fp16_0)[name = string("op_6669_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_179_cast_fp16 = mul(x = var_6663_cast_fp16, y = var_6669_cast_fp16)[name = string("x_179_cast_fp16")];
            tensor<int32, [2]> hidden_states_177_strides_0 = const()[name = string("hidden_states_177_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_177_pad_type_0 = const()[name = string("hidden_states_177_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_177_pad_0 = const()[name = string("hidden_states_177_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_177_dilations_0 = const()[name = string("hidden_states_177_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_177_groups_0 = const()[name = string("hidden_states_177_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_177_cast_fp16 = conv(dilations = hidden_states_177_dilations_0, groups = hidden_states_177_groups_0, pad = hidden_states_177_pad_0, pad_type = hidden_states_177_pad_type_0, strides = hidden_states_177_strides_0, weight = layers_17_mlp_down_proj_weight_cast_fp16, x = x_179_cast_fp16)[name = string("hidden_states_177_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_179_cast_fp16 = add(x = hidden_states_175_cast_fp16, y = hidden_states_177_cast_fp16)[name = string("hidden_states_179_cast_fp16")];
            fp16 const_182_promoted_to_fp16 = const()[name = string("const_182_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_6687_cast_fp16 = mul(x = hidden_states_179_cast_fp16, y = const_182_promoted_to_fp16)[name = string("op_6687_cast_fp16")];
            int32 var_6685 = const()[name = string("op_6685"), val = int32(1)];
            bool doubled_145_interleave_0 = const()[name = string("doubled_145_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_145_cast_fp16 = concat(axis = var_6685, interleave = doubled_145_interleave_0, values = (hidden_states_179_cast_fp16, var_6687_cast_fp16))[name = string("doubled_145_cast_fp16")];
            tensor<int32, [1]> out_73_axes_0 = const()[name = string("out_73_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_73_gamma_0_to_fp16 = const()[name = string("out_73_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483639616)))];
            fp16 var_6697_to_fp16 = const()[name = string("op_6697_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_73_cast_fp16 = layer_norm(axes = out_73_axes_0, epsilon = var_6697_to_fp16, gamma = out_73_gamma_0_to_fp16, x = doubled_145_cast_fp16)[name = string("out_73_cast_fp16")];
            tensor<int32, [2]> var_6708_split_sizes_0 = const()[name = string("op_6708_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6708_axis_0 = const()[name = string("op_6708_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_6708_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_6708_cast_fp16_1 = split(axis = var_6708_axis_0, split_sizes = var_6708_split_sizes_0, x = out_73_cast_fp16)[name = string("op_6708_cast_fp16")];
            tensor<int32, [2]> query_states_109_strides_0 = const()[name = string("query_states_109_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_109_pad_type_0 = const()[name = string("query_states_109_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_109_pad_0 = const()[name = string("query_states_109_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_109_dilations_0 = const()[name = string("query_states_109_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_109_groups_0 = const()[name = string("query_states_109_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_109_cast_fp16 = conv(dilations = query_states_109_dilations_0, groups = query_states_109_groups_0, pad = query_states_109_pad_0, pad_type = query_states_109_pad_type_0, strides = query_states_109_strides_0, weight = layers_18_self_attn_q_proj_weight_cast_fp16, x = var_6708_cast_fp16_0)[name = string("query_states_109_cast_fp16")];
            tensor<int32, [2]> key_states_181_strides_0 = const()[name = string("key_states_181_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_181_pad_type_0 = const()[name = string("key_states_181_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_181_pad_0 = const()[name = string("key_states_181_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_181_dilations_0 = const()[name = string("key_states_181_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_181_groups_0 = const()[name = string("key_states_181_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_181_cast_fp16 = conv(dilations = key_states_181_dilations_0, groups = key_states_181_groups_0, pad = key_states_181_pad_0, pad_type = key_states_181_pad_type_0, strides = key_states_181_strides_0, weight = layers_18_self_attn_k_proj_weight_cast_fp16, x = var_6708_cast_fp16_0)[name = string("key_states_181_cast_fp16")];
            tensor<int32, [2]> value_states_109_strides_0 = const()[name = string("value_states_109_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_109_pad_type_0 = const()[name = string("value_states_109_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_109_pad_0 = const()[name = string("value_states_109_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_109_dilations_0 = const()[name = string("value_states_109_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_109_groups_0 = const()[name = string("value_states_109_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_109_cast_fp16 = conv(dilations = value_states_109_dilations_0, groups = value_states_109_groups_0, pad = value_states_109_pad_0, pad_type = value_states_109_pad_type_0, strides = value_states_109_strides_0, weight = layers_18_self_attn_v_proj_weight_cast_fp16, x = var_6708_cast_fp16_0)[name = string("value_states_109_cast_fp16")];
            tensor<int32, [4]> concat_216x = const()[name = string("concat_216x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_181_cast_fp16 = reshape(shape = concat_216x, x = query_states_109_cast_fp16)[name = string("x_181_cast_fp16")];
            tensor<int32, [4]> concat_217x = const()[name = string("concat_217x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_6765_cast_fp16 = reshape(shape = concat_217x, x = key_states_181_cast_fp16)[name = string("op_6765_cast_fp16")];
            tensor<int32, [4]> concat_218x = const()[name = string("concat_218x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_6772_cast_fp16 = reshape(shape = concat_218x, x = value_states_109_cast_fp16)[name = string("op_6772_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_6776_cast_fp16 = mul(x = x_181_cast_fp16, y = var_859_cast_fp16)[name = string("op_6776_cast_fp16")];
            tensor<int32, [2]> var_6777_split_sizes_0 = const()[name = string("op_6777_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6777_axis_0 = const()[name = string("op_6777_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_6777_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_6777_cast_fp16_1 = split(axis = var_6777_axis_0, split_sizes = var_6777_split_sizes_0, x = x_181_cast_fp16)[name = string("op_6777_cast_fp16")];
            fp16 const_184_promoted_to_fp16 = const()[name = string("const_184_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_6779_cast_fp16 = mul(x = var_6777_cast_fp16_1, y = const_184_promoted_to_fp16)[name = string("op_6779_cast_fp16")];
            int32 var_6781 = const()[name = string("op_6781"), val = int32(-2)];
            bool var_6782_interleave_0 = const()[name = string("op_6782_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_6782_cast_fp16 = concat(axis = var_6781, interleave = var_6782_interleave_0, values = (var_6779_cast_fp16, var_6777_cast_fp16_0))[name = string("op_6782_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_6783_cast_fp16 = mul(x = var_6782_cast_fp16, y = var_866_cast_fp16)[name = string("op_6783_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_111_cast_fp16 = add(x = var_6776_cast_fp16, y = var_6783_cast_fp16)[name = string("query_states_111_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_6789_cast_fp16 = mul(x = var_6765_cast_fp16, y = var_859_cast_fp16)[name = string("op_6789_cast_fp16")];
            tensor<int32, [2]> var_6790_split_sizes_0 = const()[name = string("op_6790_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6790_axis_0 = const()[name = string("op_6790_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_6790_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_6790_cast_fp16_1 = split(axis = var_6790_axis_0, split_sizes = var_6790_split_sizes_0, x = var_6765_cast_fp16)[name = string("op_6790_cast_fp16")];
            fp16 const_185_promoted_to_fp16 = const()[name = string("const_185_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_6792_cast_fp16 = mul(x = var_6790_cast_fp16_1, y = const_185_promoted_to_fp16)[name = string("op_6792_cast_fp16")];
            int32 var_6794 = const()[name = string("op_6794"), val = int32(-2)];
            bool var_6795_interleave_0 = const()[name = string("op_6795_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_6795_cast_fp16 = concat(axis = var_6794, interleave = var_6795_interleave_0, values = (var_6792_cast_fp16, var_6790_cast_fp16_0))[name = string("op_6795_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_6796_cast_fp16 = mul(x = var_6795_cast_fp16, y = var_866_cast_fp16)[name = string("op_6796_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_185_cast_fp16 = add(x = var_6789_cast_fp16, y = var_6796_cast_fp16)[name = string("key_states_185_cast_fp16")];
            tensor<int32, [1]> expand_dims_216 = const()[name = string("expand_dims_216"), val = tensor<int32, [1]>([18])];
            tensor<int32, [1]> expand_dims_217 = const()[name = string("expand_dims_217"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_219 = const()[name = string("expand_dims_219"), val = tensor<int32, [1]>([0])];
            int32 concat_221_axis_0 = const()[name = string("concat_221_axis_0"), val = int32(0)];
            bool concat_221_interleave_0 = const()[name = string("concat_221_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_221 = concat(axis = concat_221_axis_0, interleave = concat_221_interleave_0, values = (expand_dims_216, expand_dims_217, position_id, expand_dims_219))[name = string("concat_221")];
            tensor<int32, [1]> expand_dims_220 = const()[name = string("expand_dims_220"), val = tensor<int32, [1]>([19])];
            tensor<int32, [1]> concat_222_values1_0 = const()[name = string("concat_222_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_222_values3_0 = const()[name = string("concat_222_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_222_axis_0 = const()[name = string("concat_222_axis_0"), val = int32(0)];
            bool concat_222_interleave_0 = const()[name = string("concat_222_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_222 = concat(axis = concat_222_axis_0, interleave = concat_222_interleave_0, values = (expand_dims_220, concat_222_values1_0, cache_position_end, concat_222_values3_0))[name = string("concat_222")];
            tensor<int32, [4]> key_states_187_perm_0 = const()[name = string("key_states_187_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_19_stride_0 = const()[name = string("key_cache_internal_tensor_assign_19_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_19_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_19_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_19_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_19_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_19_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_19_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_187_cast_fp16 = transpose(perm = key_states_187_perm_0, x = key_states_185_cast_fp16)[name = string("transpose_551")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_19_cast_fp16 = slice_update(begin = concat_221, begin_mask = key_cache_internal_tensor_assign_19_begin_mask_0, end = concat_222, end_mask = key_cache_internal_tensor_assign_19_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_19_squeeze_mask_0, stride = key_cache_internal_tensor_assign_19_stride_0, update = key_states_187_cast_fp16, x = coreml_update_state_370)[name = string("key_cache_internal_tensor_assign_19_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_19_cast_fp16, input = key_cache)[name = string("coreml_update_state_372_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_372 = read_state(input = key_cache)[name = string("coreml_update_state_372")];
            tensor<int32, [4]> value_states_111_perm_0 = const()[name = string("value_states_111_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_19_stride_0 = const()[name = string("value_cache_internal_tensor_assign_19_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_19_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_19_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_19_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_19_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_19_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_19_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_111_cast_fp16 = transpose(perm = value_states_111_perm_0, x = var_6772_cast_fp16)[name = string("transpose_550")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_19_cast_fp16 = slice_update(begin = concat_221, begin_mask = value_cache_internal_tensor_assign_19_begin_mask_0, end = concat_222, end_mask = value_cache_internal_tensor_assign_19_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_19_squeeze_mask_0, stride = value_cache_internal_tensor_assign_19_stride_0, update = value_states_111_cast_fp16, x = coreml_update_state_371)[name = string("value_cache_internal_tensor_assign_19_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_19_cast_fp16, input = value_cache)[name = string("coreml_update_state_373_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_373 = read_state(input = value_cache)[name = string("coreml_update_state_373")];
            tensor<int32, [4]> var_6866_begin_0 = const()[name = string("op_6866_begin_0"), val = tensor<int32, [4]>([18, 0, 0, 0])];
            tensor<int32, [4]> var_6866_end_0 = const()[name = string("op_6866_end_0"), val = tensor<int32, [4]>([19, 2, 2048, 128])];
            tensor<bool, [4]> var_6866_end_mask_0 = const()[name = string("op_6866_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6866_cast_fp16 = slice_by_index(begin = var_6866_begin_0, end = var_6866_end_0, end_mask = var_6866_end_mask_0, x = coreml_update_state_372)[name = string("op_6866_cast_fp16")];
            tensor<int32, [2]> tile_36 = const()[name = string("tile_36"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6869_axis_0 = const()[name = string("op_6869_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6869_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6869_cast_fp16_1 = split(axis = var_6869_axis_0, split_sizes = tile_36, x = var_6866_cast_fp16)[name = string("op_6869_cast_fp16")];
            tensor<int32, [4]> var_6876_begin_0 = const()[name = string("op_6876_begin_0"), val = tensor<int32, [4]>([18, 0, 0, 0])];
            tensor<int32, [4]> var_6876_end_0 = const()[name = string("op_6876_end_0"), val = tensor<int32, [4]>([19, 2, 2048, 128])];
            tensor<bool, [4]> var_6876_end_mask_0 = const()[name = string("op_6876_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6876_cast_fp16 = slice_by_index(begin = var_6876_begin_0, end = var_6876_end_0, end_mask = var_6876_end_mask_0, x = coreml_update_state_373)[name = string("op_6876_cast_fp16")];
            tensor<int32, [2]> tile_37 = const()[name = string("tile_37"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6879_axis_0 = const()[name = string("op_6879_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6879_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6879_cast_fp16_1 = split(axis = var_6879_axis_0, split_sizes = tile_37, x = var_6876_cast_fp16)[name = string("op_6879_cast_fp16")];
            tensor<int32, [2]> var_6882_split_sizes_0 = const()[name = string("op_6882_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_6882_axis_0 = const()[name = string("op_6882_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_6882_0, tensor<fp16, [1, 8, 128, 128]> var_6882_1 = split(axis = var_6882_axis_0, split_sizes = var_6882_split_sizes_0, x = query_states_111_cast_fp16)[name = string("op_6882")];
            bool attn_weights_289_transpose_x_0 = const()[name = string("attn_weights_289_transpose_x_0"), val = bool(false)];
            bool attn_weights_289_transpose_y_0 = const()[name = string("attn_weights_289_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_289_cast_fp16 = matmul(transpose_x = attn_weights_289_transpose_x_0, transpose_y = attn_weights_289_transpose_y_0, x = var_6869_cast_fp16_0, y = var_6882_0)[name = string("attn_weights_289_cast_fp16")];
            fp16 var_6885_to_fp16 = const()[name = string("op_6885_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_291_cast_fp16 = mul(x = attn_weights_289_cast_fp16, y = var_6885_to_fp16)[name = string("attn_weights_291_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_293_cast_fp16 = add(x = attn_weights_291_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_293_cast_fp16")];
            int32 var_6889 = const()[name = string("op_6889"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_295_cast_fp16 = softmax(axis = var_6889, x = attn_weights_293_cast_fp16)[name = string("attn_weights_295_cast_fp16")];
            bool var_6895_transpose_x_1 = const()[name = string("op_6895_transpose_x_1"), val = bool(true)];
            bool var_6895_transpose_y_1 = const()[name = string("op_6895_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_6895_cast_fp16 = matmul(transpose_x = var_6895_transpose_x_1, transpose_y = var_6895_transpose_y_1, x = attn_weights_295_cast_fp16, y = var_6879_cast_fp16_0)[name = string("op_6895_cast_fp16")];
            bool attn_weights_297_transpose_x_0 = const()[name = string("attn_weights_297_transpose_x_0"), val = bool(false)];
            bool attn_weights_297_transpose_y_0 = const()[name = string("attn_weights_297_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_297_cast_fp16 = matmul(transpose_x = attn_weights_297_transpose_x_0, transpose_y = attn_weights_297_transpose_y_0, x = var_6869_cast_fp16_1, y = var_6882_1)[name = string("attn_weights_297_cast_fp16")];
            fp16 var_6897_to_fp16 = const()[name = string("op_6897_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_299_cast_fp16 = mul(x = attn_weights_297_cast_fp16, y = var_6897_to_fp16)[name = string("attn_weights_299_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_301_cast_fp16 = add(x = attn_weights_299_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_301_cast_fp16")];
            int32 var_6901 = const()[name = string("op_6901"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_303_cast_fp16 = softmax(axis = var_6901, x = attn_weights_301_cast_fp16)[name = string("attn_weights_303_cast_fp16")];
            bool attn_output_145_transpose_x_1 = const()[name = string("attn_output_145_transpose_x_1"), val = bool(true)];
            bool attn_output_145_transpose_y_1 = const()[name = string("attn_output_145_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_145_cast_fp16 = matmul(transpose_x = attn_output_145_transpose_x_1, transpose_y = attn_output_145_transpose_y_1, x = attn_weights_303_cast_fp16, y = var_6879_cast_fp16_1)[name = string("attn_output_145_cast_fp16")];
            int32 var_6909 = const()[name = string("op_6909"), val = int32(1)];
            bool attn_output_147_interleave_0 = const()[name = string("attn_output_147_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_147_cast_fp16 = concat(axis = var_6909, interleave = attn_output_147_interleave_0, values = (var_6895_cast_fp16, attn_output_145_cast_fp16))[name = string("attn_output_147_cast_fp16")];
            tensor<int32, [4]> var_6913_perm_0 = const()[name = string("op_6913_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_227x = const()[name = string("concat_227x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_6913_cast_fp16 = transpose(perm = var_6913_perm_0, x = attn_output_147_cast_fp16)[name = string("transpose_549")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_151_cast_fp16 = reshape(shape = concat_227x, x = var_6913_cast_fp16)[name = string("attn_output_151_cast_fp16")];
            tensor<int32, [2]> hidden_states_183_strides_0 = const()[name = string("hidden_states_183_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_183_pad_type_0 = const()[name = string("hidden_states_183_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_183_pad_0 = const()[name = string("hidden_states_183_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_183_dilations_0 = const()[name = string("hidden_states_183_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_183_groups_0 = const()[name = string("hidden_states_183_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_183_cast_fp16 = conv(dilations = hidden_states_183_dilations_0, groups = hidden_states_183_groups_0, pad = hidden_states_183_pad_0, pad_type = hidden_states_183_pad_type_0, strides = hidden_states_183_strides_0, weight = layers_18_self_attn_o_proj_weight_cast_fp16, x = attn_output_151_cast_fp16)[name = string("hidden_states_183_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_185_cast_fp16 = add(x = hidden_states_179_cast_fp16, y = hidden_states_183_cast_fp16)[name = string("hidden_states_185_cast_fp16")];
            fp16 const_190_promoted_to_fp16 = const()[name = string("const_190_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_6946_cast_fp16 = mul(x = hidden_states_185_cast_fp16, y = const_190_promoted_to_fp16)[name = string("op_6946_cast_fp16")];
            int32 var_6944 = const()[name = string("op_6944"), val = int32(1)];
            bool doubled_149_interleave_0 = const()[name = string("doubled_149_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_149_cast_fp16 = concat(axis = var_6944, interleave = doubled_149_interleave_0, values = (hidden_states_185_cast_fp16, var_6946_cast_fp16))[name = string("doubled_149_cast_fp16")];
            tensor<int32, [1]> out_75_axes_0 = const()[name = string("out_75_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_75_gamma_0_to_fp16 = const()[name = string("out_75_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483647872)))];
            fp16 var_6956_to_fp16 = const()[name = string("op_6956_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_75_cast_fp16 = layer_norm(axes = out_75_axes_0, epsilon = var_6956_to_fp16, gamma = out_75_gamma_0_to_fp16, x = doubled_149_cast_fp16)[name = string("out_75_cast_fp16")];
            tensor<int32, [2]> var_6967_split_sizes_0 = const()[name = string("op_6967_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6967_axis_0 = const()[name = string("op_6967_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_6967_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_6967_cast_fp16_1 = split(axis = var_6967_axis_0, split_sizes = var_6967_split_sizes_0, x = out_75_cast_fp16)[name = string("op_6967_cast_fp16")];
            tensor<int32, [2]> input_37_strides_0 = const()[name = string("input_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_37_pad_type_0 = const()[name = string("input_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_37_pad_0 = const()[name = string("input_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_37_dilations_0 = const()[name = string("input_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_37_groups_0 = const()[name = string("input_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_37_cast_fp16 = conv(dilations = input_37_dilations_0, groups = input_37_groups_0, pad = input_37_pad_0, pad_type = input_37_pad_type_0, strides = input_37_strides_0, weight = layers_18_mlp_gate_proj_weight_cast_fp16, x = var_6967_cast_fp16_0)[name = string("input_37_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_6984_cast_fp16 = silu(x = input_37_cast_fp16)[name = string("op_6984_cast_fp16")];
            tensor<int32, [2]> var_6990_strides_0 = const()[name = string("op_6990_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6990_pad_type_0 = const()[name = string("op_6990_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6990_pad_0 = const()[name = string("op_6990_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6990_dilations_0 = const()[name = string("op_6990_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6990_groups_0 = const()[name = string("op_6990_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_6990_cast_fp16 = conv(dilations = var_6990_dilations_0, groups = var_6990_groups_0, pad = var_6990_pad_0, pad_type = var_6990_pad_type_0, strides = var_6990_strides_0, weight = layers_18_mlp_up_proj_weight_cast_fp16, x = var_6967_cast_fp16_0)[name = string("op_6990_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_189_cast_fp16 = mul(x = var_6984_cast_fp16, y = var_6990_cast_fp16)[name = string("x_189_cast_fp16")];
            tensor<int32, [2]> hidden_states_187_strides_0 = const()[name = string("hidden_states_187_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_187_pad_type_0 = const()[name = string("hidden_states_187_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_187_pad_0 = const()[name = string("hidden_states_187_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_187_dilations_0 = const()[name = string("hidden_states_187_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_187_groups_0 = const()[name = string("hidden_states_187_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_187_cast_fp16 = conv(dilations = hidden_states_187_dilations_0, groups = hidden_states_187_groups_0, pad = hidden_states_187_pad_0, pad_type = hidden_states_187_pad_type_0, strides = hidden_states_187_strides_0, weight = layers_18_mlp_down_proj_weight_cast_fp16, x = x_189_cast_fp16)[name = string("hidden_states_187_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_189_cast_fp16 = add(x = hidden_states_185_cast_fp16, y = hidden_states_187_cast_fp16)[name = string("hidden_states_189_cast_fp16")];
            fp16 const_192_promoted_to_fp16 = const()[name = string("const_192_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_7008_cast_fp16 = mul(x = hidden_states_189_cast_fp16, y = const_192_promoted_to_fp16)[name = string("op_7008_cast_fp16")];
            int32 var_7006 = const()[name = string("op_7006"), val = int32(1)];
            bool doubled_153_interleave_0 = const()[name = string("doubled_153_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_153_cast_fp16 = concat(axis = var_7006, interleave = doubled_153_interleave_0, values = (hidden_states_189_cast_fp16, var_7008_cast_fp16))[name = string("doubled_153_cast_fp16")];
            tensor<int32, [1]> out_77_axes_0 = const()[name = string("out_77_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_77_gamma_0_to_fp16 = const()[name = string("out_77_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483656128)))];
            fp16 var_7018_to_fp16 = const()[name = string("op_7018_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_77_cast_fp16 = layer_norm(axes = out_77_axes_0, epsilon = var_7018_to_fp16, gamma = out_77_gamma_0_to_fp16, x = doubled_153_cast_fp16)[name = string("out_77_cast_fp16")];
            tensor<int32, [2]> var_7029_split_sizes_0 = const()[name = string("op_7029_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7029_axis_0 = const()[name = string("op_7029_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_7029_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_7029_cast_fp16_1 = split(axis = var_7029_axis_0, split_sizes = var_7029_split_sizes_0, x = out_77_cast_fp16)[name = string("op_7029_cast_fp16")];
            tensor<int32, [2]> query_states_115_strides_0 = const()[name = string("query_states_115_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_115_pad_type_0 = const()[name = string("query_states_115_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_115_pad_0 = const()[name = string("query_states_115_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_115_dilations_0 = const()[name = string("query_states_115_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_115_groups_0 = const()[name = string("query_states_115_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_115_cast_fp16 = conv(dilations = query_states_115_dilations_0, groups = query_states_115_groups_0, pad = query_states_115_pad_0, pad_type = query_states_115_pad_type_0, strides = query_states_115_strides_0, weight = layers_19_self_attn_q_proj_weight_cast_fp16, x = var_7029_cast_fp16_0)[name = string("query_states_115_cast_fp16")];
            tensor<int32, [2]> key_states_191_strides_0 = const()[name = string("key_states_191_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_191_pad_type_0 = const()[name = string("key_states_191_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_191_pad_0 = const()[name = string("key_states_191_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_191_dilations_0 = const()[name = string("key_states_191_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_191_groups_0 = const()[name = string("key_states_191_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_191_cast_fp16 = conv(dilations = key_states_191_dilations_0, groups = key_states_191_groups_0, pad = key_states_191_pad_0, pad_type = key_states_191_pad_type_0, strides = key_states_191_strides_0, weight = layers_19_self_attn_k_proj_weight_cast_fp16, x = var_7029_cast_fp16_0)[name = string("key_states_191_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_19_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_19_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483664384)))];
            tensor<int32, [2]> value_states_115_strides_0 = const()[name = string("value_states_115_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_115_pad_type_0 = const()[name = string("value_states_115_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_115_pad_0 = const()[name = string("value_states_115_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_115_dilations_0 = const()[name = string("value_states_115_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_115_groups_0 = const()[name = string("value_states_115_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_115_cast_fp16 = conv(dilations = value_states_115_dilations_0, groups = value_states_115_groups_0, pad = value_states_115_pad_0, pad_type = value_states_115_pad_type_0, strides = value_states_115_strides_0, weight = layers_19_self_attn_v_proj_weight_to_fp16, x = var_7029_cast_fp16_0)[name = string("value_states_115_cast_fp16")];
            tensor<int32, [4]> concat_228x = const()[name = string("concat_228x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_191_cast_fp16 = reshape(shape = concat_228x, x = query_states_115_cast_fp16)[name = string("x_191_cast_fp16")];
            tensor<int32, [4]> concat_229x = const()[name = string("concat_229x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_7086_cast_fp16 = reshape(shape = concat_229x, x = key_states_191_cast_fp16)[name = string("op_7086_cast_fp16")];
            tensor<int32, [4]> concat_230x = const()[name = string("concat_230x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_7093_cast_fp16 = reshape(shape = concat_230x, x = value_states_115_cast_fp16)[name = string("op_7093_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_7097_cast_fp16 = mul(x = x_191_cast_fp16, y = var_859_cast_fp16)[name = string("op_7097_cast_fp16")];
            tensor<int32, [2]> var_7098_split_sizes_0 = const()[name = string("op_7098_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7098_axis_0 = const()[name = string("op_7098_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_7098_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_7098_cast_fp16_1 = split(axis = var_7098_axis_0, split_sizes = var_7098_split_sizes_0, x = x_191_cast_fp16)[name = string("op_7098_cast_fp16")];
            fp16 const_194_promoted_to_fp16 = const()[name = string("const_194_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_7100_cast_fp16 = mul(x = var_7098_cast_fp16_1, y = const_194_promoted_to_fp16)[name = string("op_7100_cast_fp16")];
            int32 var_7102 = const()[name = string("op_7102"), val = int32(-2)];
            bool var_7103_interleave_0 = const()[name = string("op_7103_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_7103_cast_fp16 = concat(axis = var_7102, interleave = var_7103_interleave_0, values = (var_7100_cast_fp16, var_7098_cast_fp16_0))[name = string("op_7103_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_7104_cast_fp16 = mul(x = var_7103_cast_fp16, y = var_866_cast_fp16)[name = string("op_7104_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_117_cast_fp16 = add(x = var_7097_cast_fp16, y = var_7104_cast_fp16)[name = string("query_states_117_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_7110_cast_fp16 = mul(x = var_7086_cast_fp16, y = var_859_cast_fp16)[name = string("op_7110_cast_fp16")];
            tensor<int32, [2]> var_7111_split_sizes_0 = const()[name = string("op_7111_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7111_axis_0 = const()[name = string("op_7111_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_7111_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_7111_cast_fp16_1 = split(axis = var_7111_axis_0, split_sizes = var_7111_split_sizes_0, x = var_7086_cast_fp16)[name = string("op_7111_cast_fp16")];
            fp16 const_195_promoted_to_fp16 = const()[name = string("const_195_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_7113_cast_fp16 = mul(x = var_7111_cast_fp16_1, y = const_195_promoted_to_fp16)[name = string("op_7113_cast_fp16")];
            int32 var_7115 = const()[name = string("op_7115"), val = int32(-2)];
            bool var_7116_interleave_0 = const()[name = string("op_7116_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_7116_cast_fp16 = concat(axis = var_7115, interleave = var_7116_interleave_0, values = (var_7113_cast_fp16, var_7111_cast_fp16_0))[name = string("op_7116_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_7117_cast_fp16 = mul(x = var_7116_cast_fp16, y = var_866_cast_fp16)[name = string("op_7117_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_195_cast_fp16 = add(x = var_7110_cast_fp16, y = var_7117_cast_fp16)[name = string("key_states_195_cast_fp16")];
            tensor<int32, [1]> expand_dims_228 = const()[name = string("expand_dims_228"), val = tensor<int32, [1]>([19])];
            tensor<int32, [1]> expand_dims_229 = const()[name = string("expand_dims_229"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_231 = const()[name = string("expand_dims_231"), val = tensor<int32, [1]>([0])];
            int32 concat_233_axis_0 = const()[name = string("concat_233_axis_0"), val = int32(0)];
            bool concat_233_interleave_0 = const()[name = string("concat_233_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_233 = concat(axis = concat_233_axis_0, interleave = concat_233_interleave_0, values = (expand_dims_228, expand_dims_229, position_id, expand_dims_231))[name = string("concat_233")];
            tensor<int32, [1]> expand_dims_232 = const()[name = string("expand_dims_232"), val = tensor<int32, [1]>([20])];
            tensor<int32, [1]> concat_234_values1_0 = const()[name = string("concat_234_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_234_values3_0 = const()[name = string("concat_234_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_234_axis_0 = const()[name = string("concat_234_axis_0"), val = int32(0)];
            bool concat_234_interleave_0 = const()[name = string("concat_234_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_234 = concat(axis = concat_234_axis_0, interleave = concat_234_interleave_0, values = (expand_dims_232, concat_234_values1_0, cache_position_end, concat_234_values3_0))[name = string("concat_234")];
            tensor<int32, [4]> key_states_197_perm_0 = const()[name = string("key_states_197_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_20_stride_0 = const()[name = string("key_cache_internal_tensor_assign_20_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_20_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_20_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_20_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_20_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_20_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_20_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_197_cast_fp16 = transpose(perm = key_states_197_perm_0, x = key_states_195_cast_fp16)[name = string("transpose_548")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_20_cast_fp16 = slice_update(begin = concat_233, begin_mask = key_cache_internal_tensor_assign_20_begin_mask_0, end = concat_234, end_mask = key_cache_internal_tensor_assign_20_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_20_squeeze_mask_0, stride = key_cache_internal_tensor_assign_20_stride_0, update = key_states_197_cast_fp16, x = coreml_update_state_372)[name = string("key_cache_internal_tensor_assign_20_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_20_cast_fp16, input = key_cache)[name = string("coreml_update_state_374_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_374 = read_state(input = key_cache)[name = string("coreml_update_state_374")];
            tensor<int32, [4]> value_states_117_perm_0 = const()[name = string("value_states_117_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_20_stride_0 = const()[name = string("value_cache_internal_tensor_assign_20_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_20_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_20_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_20_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_20_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_20_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_20_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_117_cast_fp16 = transpose(perm = value_states_117_perm_0, x = var_7093_cast_fp16)[name = string("transpose_547")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_20_cast_fp16 = slice_update(begin = concat_233, begin_mask = value_cache_internal_tensor_assign_20_begin_mask_0, end = concat_234, end_mask = value_cache_internal_tensor_assign_20_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_20_squeeze_mask_0, stride = value_cache_internal_tensor_assign_20_stride_0, update = value_states_117_cast_fp16, x = coreml_update_state_373)[name = string("value_cache_internal_tensor_assign_20_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_20_cast_fp16, input = value_cache)[name = string("coreml_update_state_375_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_375 = read_state(input = value_cache)[name = string("coreml_update_state_375")];
            tensor<int32, [4]> var_7187_begin_0 = const()[name = string("op_7187_begin_0"), val = tensor<int32, [4]>([19, 0, 0, 0])];
            tensor<int32, [4]> var_7187_end_0 = const()[name = string("op_7187_end_0"), val = tensor<int32, [4]>([20, 2, 2048, 128])];
            tensor<bool, [4]> var_7187_end_mask_0 = const()[name = string("op_7187_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7187_cast_fp16 = slice_by_index(begin = var_7187_begin_0, end = var_7187_end_0, end_mask = var_7187_end_mask_0, x = coreml_update_state_374)[name = string("op_7187_cast_fp16")];
            tensor<int32, [2]> tile_38 = const()[name = string("tile_38"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7190_axis_0 = const()[name = string("op_7190_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7190_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7190_cast_fp16_1 = split(axis = var_7190_axis_0, split_sizes = tile_38, x = var_7187_cast_fp16)[name = string("op_7190_cast_fp16")];
            tensor<int32, [4]> var_7197_begin_0 = const()[name = string("op_7197_begin_0"), val = tensor<int32, [4]>([19, 0, 0, 0])];
            tensor<int32, [4]> var_7197_end_0 = const()[name = string("op_7197_end_0"), val = tensor<int32, [4]>([20, 2, 2048, 128])];
            tensor<bool, [4]> var_7197_end_mask_0 = const()[name = string("op_7197_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7197_cast_fp16 = slice_by_index(begin = var_7197_begin_0, end = var_7197_end_0, end_mask = var_7197_end_mask_0, x = coreml_update_state_375)[name = string("op_7197_cast_fp16")];
            tensor<int32, [2]> tile_39 = const()[name = string("tile_39"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7200_axis_0 = const()[name = string("op_7200_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7200_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7200_cast_fp16_1 = split(axis = var_7200_axis_0, split_sizes = tile_39, x = var_7197_cast_fp16)[name = string("op_7200_cast_fp16")];
            tensor<int32, [2]> var_7203_split_sizes_0 = const()[name = string("op_7203_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_7203_axis_0 = const()[name = string("op_7203_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_7203_0, tensor<fp16, [1, 8, 128, 128]> var_7203_1 = split(axis = var_7203_axis_0, split_sizes = var_7203_split_sizes_0, x = query_states_117_cast_fp16)[name = string("op_7203")];
            bool attn_weights_305_transpose_x_0 = const()[name = string("attn_weights_305_transpose_x_0"), val = bool(false)];
            bool attn_weights_305_transpose_y_0 = const()[name = string("attn_weights_305_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_305_cast_fp16 = matmul(transpose_x = attn_weights_305_transpose_x_0, transpose_y = attn_weights_305_transpose_y_0, x = var_7190_cast_fp16_0, y = var_7203_0)[name = string("attn_weights_305_cast_fp16")];
            fp16 var_7206_to_fp16 = const()[name = string("op_7206_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_307_cast_fp16 = mul(x = attn_weights_305_cast_fp16, y = var_7206_to_fp16)[name = string("attn_weights_307_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_309_cast_fp16 = add(x = attn_weights_307_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_309_cast_fp16")];
            int32 var_7210 = const()[name = string("op_7210"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_311_cast_fp16 = softmax(axis = var_7210, x = attn_weights_309_cast_fp16)[name = string("attn_weights_311_cast_fp16")];
            bool var_7216_transpose_x_1 = const()[name = string("op_7216_transpose_x_1"), val = bool(true)];
            bool var_7216_transpose_y_1 = const()[name = string("op_7216_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_7216_cast_fp16 = matmul(transpose_x = var_7216_transpose_x_1, transpose_y = var_7216_transpose_y_1, x = attn_weights_311_cast_fp16, y = var_7200_cast_fp16_0)[name = string("op_7216_cast_fp16")];
            bool attn_weights_313_transpose_x_0 = const()[name = string("attn_weights_313_transpose_x_0"), val = bool(false)];
            bool attn_weights_313_transpose_y_0 = const()[name = string("attn_weights_313_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_313_cast_fp16 = matmul(transpose_x = attn_weights_313_transpose_x_0, transpose_y = attn_weights_313_transpose_y_0, x = var_7190_cast_fp16_1, y = var_7203_1)[name = string("attn_weights_313_cast_fp16")];
            fp16 var_7218_to_fp16 = const()[name = string("op_7218_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_315_cast_fp16 = mul(x = attn_weights_313_cast_fp16, y = var_7218_to_fp16)[name = string("attn_weights_315_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_317_cast_fp16 = add(x = attn_weights_315_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_317_cast_fp16")];
            int32 var_7222 = const()[name = string("op_7222"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_319_cast_fp16 = softmax(axis = var_7222, x = attn_weights_317_cast_fp16)[name = string("attn_weights_319_cast_fp16")];
            bool attn_output_153_transpose_x_1 = const()[name = string("attn_output_153_transpose_x_1"), val = bool(true)];
            bool attn_output_153_transpose_y_1 = const()[name = string("attn_output_153_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_153_cast_fp16 = matmul(transpose_x = attn_output_153_transpose_x_1, transpose_y = attn_output_153_transpose_y_1, x = attn_weights_319_cast_fp16, y = var_7200_cast_fp16_1)[name = string("attn_output_153_cast_fp16")];
            int32 var_7230 = const()[name = string("op_7230"), val = int32(1)];
            bool attn_output_155_interleave_0 = const()[name = string("attn_output_155_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_155_cast_fp16 = concat(axis = var_7230, interleave = attn_output_155_interleave_0, values = (var_7216_cast_fp16, attn_output_153_cast_fp16))[name = string("attn_output_155_cast_fp16")];
            tensor<int32, [4]> var_7234_perm_0 = const()[name = string("op_7234_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_239x = const()[name = string("concat_239x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_7234_cast_fp16 = transpose(perm = var_7234_perm_0, x = attn_output_155_cast_fp16)[name = string("transpose_546")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_159_cast_fp16 = reshape(shape = concat_239x, x = var_7234_cast_fp16)[name = string("attn_output_159_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_19_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_19_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1484713024)))];
            tensor<int32, [2]> hidden_states_193_strides_0 = const()[name = string("hidden_states_193_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_193_pad_type_0 = const()[name = string("hidden_states_193_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_193_pad_0 = const()[name = string("hidden_states_193_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_193_dilations_0 = const()[name = string("hidden_states_193_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_193_groups_0 = const()[name = string("hidden_states_193_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_193_cast_fp16 = conv(dilations = hidden_states_193_dilations_0, groups = hidden_states_193_groups_0, pad = hidden_states_193_pad_0, pad_type = hidden_states_193_pad_type_0, strides = hidden_states_193_strides_0, weight = layers_19_self_attn_o_proj_weight_to_fp16, x = attn_output_159_cast_fp16)[name = string("hidden_states_193_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_195_cast_fp16 = add(x = hidden_states_189_cast_fp16, y = hidden_states_193_cast_fp16)[name = string("hidden_states_195_cast_fp16")];
            fp16 const_200_promoted_to_fp16 = const()[name = string("const_200_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_7267_cast_fp16 = mul(x = hidden_states_195_cast_fp16, y = const_200_promoted_to_fp16)[name = string("op_7267_cast_fp16")];
            int32 var_7265 = const()[name = string("op_7265"), val = int32(1)];
            bool doubled_157_interleave_0 = const()[name = string("doubled_157_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_157_cast_fp16 = concat(axis = var_7265, interleave = doubled_157_interleave_0, values = (hidden_states_195_cast_fp16, var_7267_cast_fp16))[name = string("doubled_157_cast_fp16")];
            tensor<int32, [1]> out_79_axes_0 = const()[name = string("out_79_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_79_gamma_0_to_fp16 = const()[name = string("out_79_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1493101696)))];
            fp16 var_7277_to_fp16 = const()[name = string("op_7277_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_79_cast_fp16 = layer_norm(axes = out_79_axes_0, epsilon = var_7277_to_fp16, gamma = out_79_gamma_0_to_fp16, x = doubled_157_cast_fp16)[name = string("out_79_cast_fp16")];
            tensor<int32, [2]> var_7288_split_sizes_0 = const()[name = string("op_7288_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7288_axis_0 = const()[name = string("op_7288_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_7288_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_7288_cast_fp16_1 = split(axis = var_7288_axis_0, split_sizes = var_7288_split_sizes_0, x = out_79_cast_fp16)[name = string("op_7288_cast_fp16")];
            tensor<int32, [2]> input_39_strides_0 = const()[name = string("input_39_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_39_pad_type_0 = const()[name = string("input_39_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_39_pad_0 = const()[name = string("input_39_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_39_dilations_0 = const()[name = string("input_39_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_39_groups_0 = const()[name = string("input_39_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_39_cast_fp16 = conv(dilations = input_39_dilations_0, groups = input_39_groups_0, pad = input_39_pad_0, pad_type = input_39_pad_type_0, strides = input_39_strides_0, weight = layers_19_mlp_gate_proj_weight_cast_fp16, x = var_7288_cast_fp16_0)[name = string("input_39_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_7305_cast_fp16 = silu(x = input_39_cast_fp16)[name = string("op_7305_cast_fp16")];
            tensor<int32, [2]> var_7311_strides_0 = const()[name = string("op_7311_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_7311_pad_type_0 = const()[name = string("op_7311_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_7311_pad_0 = const()[name = string("op_7311_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_7311_dilations_0 = const()[name = string("op_7311_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7311_groups_0 = const()[name = string("op_7311_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_7311_cast_fp16 = conv(dilations = var_7311_dilations_0, groups = var_7311_groups_0, pad = var_7311_pad_0, pad_type = var_7311_pad_type_0, strides = var_7311_strides_0, weight = layers_19_mlp_up_proj_weight_cast_fp16, x = var_7288_cast_fp16_0)[name = string("op_7311_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_199_cast_fp16 = mul(x = var_7305_cast_fp16, y = var_7311_cast_fp16)[name = string("x_199_cast_fp16")];
            tensor<int32, [2]> hidden_states_197_strides_0 = const()[name = string("hidden_states_197_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_197_pad_type_0 = const()[name = string("hidden_states_197_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_197_pad_0 = const()[name = string("hidden_states_197_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_197_dilations_0 = const()[name = string("hidden_states_197_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_197_groups_0 = const()[name = string("hidden_states_197_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_197_cast_fp16 = conv(dilations = hidden_states_197_dilations_0, groups = hidden_states_197_groups_0, pad = hidden_states_197_pad_0, pad_type = hidden_states_197_pad_type_0, strides = hidden_states_197_strides_0, weight = layers_19_mlp_down_proj_weight_cast_fp16, x = x_199_cast_fp16)[name = string("hidden_states_197_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_199_cast_fp16 = add(x = hidden_states_195_cast_fp16, y = hidden_states_197_cast_fp16)[name = string("hidden_states_199_cast_fp16")];
            fp16 const_202_promoted_to_fp16 = const()[name = string("const_202_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_7329_cast_fp16 = mul(x = hidden_states_199_cast_fp16, y = const_202_promoted_to_fp16)[name = string("op_7329_cast_fp16")];
            int32 var_7327 = const()[name = string("op_7327"), val = int32(1)];
            bool doubled_161_interleave_0 = const()[name = string("doubled_161_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_161_cast_fp16 = concat(axis = var_7327, interleave = doubled_161_interleave_0, values = (hidden_states_199_cast_fp16, var_7329_cast_fp16))[name = string("doubled_161_cast_fp16")];
            tensor<int32, [1]> out_81_axes_0 = const()[name = string("out_81_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_81_gamma_0_to_fp16 = const()[name = string("out_81_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1493109952)))];
            fp16 var_7339_to_fp16 = const()[name = string("op_7339_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_81_cast_fp16 = layer_norm(axes = out_81_axes_0, epsilon = var_7339_to_fp16, gamma = out_81_gamma_0_to_fp16, x = doubled_161_cast_fp16)[name = string("out_81_cast_fp16")];
            tensor<int32, [2]> var_7350_split_sizes_0 = const()[name = string("op_7350_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7350_axis_0 = const()[name = string("op_7350_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_7350_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_7350_cast_fp16_1 = split(axis = var_7350_axis_0, split_sizes = var_7350_split_sizes_0, x = out_81_cast_fp16)[name = string("op_7350_cast_fp16")];
            tensor<int32, [2]> query_states_121_strides_0 = const()[name = string("query_states_121_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_121_pad_type_0 = const()[name = string("query_states_121_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_121_pad_0 = const()[name = string("query_states_121_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_121_dilations_0 = const()[name = string("query_states_121_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_121_groups_0 = const()[name = string("query_states_121_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_121_cast_fp16 = conv(dilations = query_states_121_dilations_0, groups = query_states_121_groups_0, pad = query_states_121_pad_0, pad_type = query_states_121_pad_type_0, strides = query_states_121_strides_0, weight = layers_20_self_attn_q_proj_weight_cast_fp16, x = var_7350_cast_fp16_0)[name = string("query_states_121_cast_fp16")];
            tensor<int32, [2]> key_states_201_strides_0 = const()[name = string("key_states_201_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_201_pad_type_0 = const()[name = string("key_states_201_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_201_pad_0 = const()[name = string("key_states_201_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_201_dilations_0 = const()[name = string("key_states_201_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_201_groups_0 = const()[name = string("key_states_201_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_201_cast_fp16 = conv(dilations = key_states_201_dilations_0, groups = key_states_201_groups_0, pad = key_states_201_pad_0, pad_type = key_states_201_pad_type_0, strides = key_states_201_strides_0, weight = layers_20_self_attn_k_proj_weight_cast_fp16, x = var_7350_cast_fp16_0)[name = string("key_states_201_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_20_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_20_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1493118208)))];
            tensor<int32, [2]> value_states_121_strides_0 = const()[name = string("value_states_121_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_121_pad_type_0 = const()[name = string("value_states_121_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_121_pad_0 = const()[name = string("value_states_121_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_121_dilations_0 = const()[name = string("value_states_121_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_121_groups_0 = const()[name = string("value_states_121_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_121_cast_fp16 = conv(dilations = value_states_121_dilations_0, groups = value_states_121_groups_0, pad = value_states_121_pad_0, pad_type = value_states_121_pad_type_0, strides = value_states_121_strides_0, weight = layers_20_self_attn_v_proj_weight_to_fp16, x = var_7350_cast_fp16_0)[name = string("value_states_121_cast_fp16")];
            tensor<int32, [4]> concat_240x = const()[name = string("concat_240x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_201_cast_fp16 = reshape(shape = concat_240x, x = query_states_121_cast_fp16)[name = string("x_201_cast_fp16")];
            tensor<int32, [4]> concat_241x = const()[name = string("concat_241x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_7407_cast_fp16 = reshape(shape = concat_241x, x = key_states_201_cast_fp16)[name = string("op_7407_cast_fp16")];
            tensor<int32, [4]> concat_242x = const()[name = string("concat_242x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_7414_cast_fp16 = reshape(shape = concat_242x, x = value_states_121_cast_fp16)[name = string("op_7414_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_7418_cast_fp16 = mul(x = x_201_cast_fp16, y = var_859_cast_fp16)[name = string("op_7418_cast_fp16")];
            tensor<int32, [2]> var_7419_split_sizes_0 = const()[name = string("op_7419_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7419_axis_0 = const()[name = string("op_7419_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_7419_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_7419_cast_fp16_1 = split(axis = var_7419_axis_0, split_sizes = var_7419_split_sizes_0, x = x_201_cast_fp16)[name = string("op_7419_cast_fp16")];
            fp16 const_204_promoted_to_fp16 = const()[name = string("const_204_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_7421_cast_fp16 = mul(x = var_7419_cast_fp16_1, y = const_204_promoted_to_fp16)[name = string("op_7421_cast_fp16")];
            int32 var_7423 = const()[name = string("op_7423"), val = int32(-2)];
            bool var_7424_interleave_0 = const()[name = string("op_7424_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_7424_cast_fp16 = concat(axis = var_7423, interleave = var_7424_interleave_0, values = (var_7421_cast_fp16, var_7419_cast_fp16_0))[name = string("op_7424_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_7425_cast_fp16 = mul(x = var_7424_cast_fp16, y = var_866_cast_fp16)[name = string("op_7425_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_123_cast_fp16 = add(x = var_7418_cast_fp16, y = var_7425_cast_fp16)[name = string("query_states_123_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_7431_cast_fp16 = mul(x = var_7407_cast_fp16, y = var_859_cast_fp16)[name = string("op_7431_cast_fp16")];
            tensor<int32, [2]> var_7432_split_sizes_0 = const()[name = string("op_7432_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7432_axis_0 = const()[name = string("op_7432_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_7432_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_7432_cast_fp16_1 = split(axis = var_7432_axis_0, split_sizes = var_7432_split_sizes_0, x = var_7407_cast_fp16)[name = string("op_7432_cast_fp16")];
            fp16 const_205_promoted_to_fp16 = const()[name = string("const_205_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_7434_cast_fp16 = mul(x = var_7432_cast_fp16_1, y = const_205_promoted_to_fp16)[name = string("op_7434_cast_fp16")];
            int32 var_7436 = const()[name = string("op_7436"), val = int32(-2)];
            bool var_7437_interleave_0 = const()[name = string("op_7437_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_7437_cast_fp16 = concat(axis = var_7436, interleave = var_7437_interleave_0, values = (var_7434_cast_fp16, var_7432_cast_fp16_0))[name = string("op_7437_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_7438_cast_fp16 = mul(x = var_7437_cast_fp16, y = var_866_cast_fp16)[name = string("op_7438_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_205_cast_fp16 = add(x = var_7431_cast_fp16, y = var_7438_cast_fp16)[name = string("key_states_205_cast_fp16")];
            tensor<int32, [1]> expand_dims_240 = const()[name = string("expand_dims_240"), val = tensor<int32, [1]>([20])];
            tensor<int32, [1]> expand_dims_241 = const()[name = string("expand_dims_241"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_243 = const()[name = string("expand_dims_243"), val = tensor<int32, [1]>([0])];
            int32 concat_245_axis_0 = const()[name = string("concat_245_axis_0"), val = int32(0)];
            bool concat_245_interleave_0 = const()[name = string("concat_245_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_245 = concat(axis = concat_245_axis_0, interleave = concat_245_interleave_0, values = (expand_dims_240, expand_dims_241, position_id, expand_dims_243))[name = string("concat_245")];
            tensor<int32, [1]> expand_dims_244 = const()[name = string("expand_dims_244"), val = tensor<int32, [1]>([21])];
            tensor<int32, [1]> concat_246_values1_0 = const()[name = string("concat_246_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_246_values3_0 = const()[name = string("concat_246_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_246_axis_0 = const()[name = string("concat_246_axis_0"), val = int32(0)];
            bool concat_246_interleave_0 = const()[name = string("concat_246_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_246 = concat(axis = concat_246_axis_0, interleave = concat_246_interleave_0, values = (expand_dims_244, concat_246_values1_0, cache_position_end, concat_246_values3_0))[name = string("concat_246")];
            tensor<int32, [4]> key_states_207_perm_0 = const()[name = string("key_states_207_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_21_stride_0 = const()[name = string("key_cache_internal_tensor_assign_21_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_21_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_21_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_21_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_21_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_21_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_21_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_207_cast_fp16 = transpose(perm = key_states_207_perm_0, x = key_states_205_cast_fp16)[name = string("transpose_545")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_21_cast_fp16 = slice_update(begin = concat_245, begin_mask = key_cache_internal_tensor_assign_21_begin_mask_0, end = concat_246, end_mask = key_cache_internal_tensor_assign_21_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_21_squeeze_mask_0, stride = key_cache_internal_tensor_assign_21_stride_0, update = key_states_207_cast_fp16, x = coreml_update_state_374)[name = string("key_cache_internal_tensor_assign_21_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_21_cast_fp16, input = key_cache)[name = string("coreml_update_state_376_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_376 = read_state(input = key_cache)[name = string("coreml_update_state_376")];
            tensor<int32, [4]> value_states_123_perm_0 = const()[name = string("value_states_123_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_21_stride_0 = const()[name = string("value_cache_internal_tensor_assign_21_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_21_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_21_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_21_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_21_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_21_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_21_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_123_cast_fp16 = transpose(perm = value_states_123_perm_0, x = var_7414_cast_fp16)[name = string("transpose_544")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_21_cast_fp16 = slice_update(begin = concat_245, begin_mask = value_cache_internal_tensor_assign_21_begin_mask_0, end = concat_246, end_mask = value_cache_internal_tensor_assign_21_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_21_squeeze_mask_0, stride = value_cache_internal_tensor_assign_21_stride_0, update = value_states_123_cast_fp16, x = coreml_update_state_375)[name = string("value_cache_internal_tensor_assign_21_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_21_cast_fp16, input = value_cache)[name = string("coreml_update_state_377_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_377 = read_state(input = value_cache)[name = string("coreml_update_state_377")];
            tensor<int32, [4]> var_7508_begin_0 = const()[name = string("op_7508_begin_0"), val = tensor<int32, [4]>([20, 0, 0, 0])];
            tensor<int32, [4]> var_7508_end_0 = const()[name = string("op_7508_end_0"), val = tensor<int32, [4]>([21, 2, 2048, 128])];
            tensor<bool, [4]> var_7508_end_mask_0 = const()[name = string("op_7508_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7508_cast_fp16 = slice_by_index(begin = var_7508_begin_0, end = var_7508_end_0, end_mask = var_7508_end_mask_0, x = coreml_update_state_376)[name = string("op_7508_cast_fp16")];
            tensor<int32, [2]> tile_40 = const()[name = string("tile_40"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7511_axis_0 = const()[name = string("op_7511_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7511_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7511_cast_fp16_1 = split(axis = var_7511_axis_0, split_sizes = tile_40, x = var_7508_cast_fp16)[name = string("op_7511_cast_fp16")];
            tensor<int32, [4]> var_7518_begin_0 = const()[name = string("op_7518_begin_0"), val = tensor<int32, [4]>([20, 0, 0, 0])];
            tensor<int32, [4]> var_7518_end_0 = const()[name = string("op_7518_end_0"), val = tensor<int32, [4]>([21, 2, 2048, 128])];
            tensor<bool, [4]> var_7518_end_mask_0 = const()[name = string("op_7518_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7518_cast_fp16 = slice_by_index(begin = var_7518_begin_0, end = var_7518_end_0, end_mask = var_7518_end_mask_0, x = coreml_update_state_377)[name = string("op_7518_cast_fp16")];
            tensor<int32, [2]> tile_41 = const()[name = string("tile_41"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7521_axis_0 = const()[name = string("op_7521_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7521_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7521_cast_fp16_1 = split(axis = var_7521_axis_0, split_sizes = tile_41, x = var_7518_cast_fp16)[name = string("op_7521_cast_fp16")];
            tensor<int32, [2]> var_7524_split_sizes_0 = const()[name = string("op_7524_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_7524_axis_0 = const()[name = string("op_7524_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_7524_0, tensor<fp16, [1, 8, 128, 128]> var_7524_1 = split(axis = var_7524_axis_0, split_sizes = var_7524_split_sizes_0, x = query_states_123_cast_fp16)[name = string("op_7524")];
            bool attn_weights_321_transpose_x_0 = const()[name = string("attn_weights_321_transpose_x_0"), val = bool(false)];
            bool attn_weights_321_transpose_y_0 = const()[name = string("attn_weights_321_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_321_cast_fp16 = matmul(transpose_x = attn_weights_321_transpose_x_0, transpose_y = attn_weights_321_transpose_y_0, x = var_7511_cast_fp16_0, y = var_7524_0)[name = string("attn_weights_321_cast_fp16")];
            fp16 var_7527_to_fp16 = const()[name = string("op_7527_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_323_cast_fp16 = mul(x = attn_weights_321_cast_fp16, y = var_7527_to_fp16)[name = string("attn_weights_323_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_325_cast_fp16 = add(x = attn_weights_323_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_325_cast_fp16")];
            int32 var_7531 = const()[name = string("op_7531"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_327_cast_fp16 = softmax(axis = var_7531, x = attn_weights_325_cast_fp16)[name = string("attn_weights_327_cast_fp16")];
            bool var_7537_transpose_x_1 = const()[name = string("op_7537_transpose_x_1"), val = bool(true)];
            bool var_7537_transpose_y_1 = const()[name = string("op_7537_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_7537_cast_fp16 = matmul(transpose_x = var_7537_transpose_x_1, transpose_y = var_7537_transpose_y_1, x = attn_weights_327_cast_fp16, y = var_7521_cast_fp16_0)[name = string("op_7537_cast_fp16")];
            bool attn_weights_329_transpose_x_0 = const()[name = string("attn_weights_329_transpose_x_0"), val = bool(false)];
            bool attn_weights_329_transpose_y_0 = const()[name = string("attn_weights_329_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_329_cast_fp16 = matmul(transpose_x = attn_weights_329_transpose_x_0, transpose_y = attn_weights_329_transpose_y_0, x = var_7511_cast_fp16_1, y = var_7524_1)[name = string("attn_weights_329_cast_fp16")];
            fp16 var_7539_to_fp16 = const()[name = string("op_7539_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_331_cast_fp16 = mul(x = attn_weights_329_cast_fp16, y = var_7539_to_fp16)[name = string("attn_weights_331_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_333_cast_fp16 = add(x = attn_weights_331_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_333_cast_fp16")];
            int32 var_7543 = const()[name = string("op_7543"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_335_cast_fp16 = softmax(axis = var_7543, x = attn_weights_333_cast_fp16)[name = string("attn_weights_335_cast_fp16")];
            bool attn_output_161_transpose_x_1 = const()[name = string("attn_output_161_transpose_x_1"), val = bool(true)];
            bool attn_output_161_transpose_y_1 = const()[name = string("attn_output_161_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_161_cast_fp16 = matmul(transpose_x = attn_output_161_transpose_x_1, transpose_y = attn_output_161_transpose_y_1, x = attn_weights_335_cast_fp16, y = var_7521_cast_fp16_1)[name = string("attn_output_161_cast_fp16")];
            int32 var_7551 = const()[name = string("op_7551"), val = int32(1)];
            bool attn_output_163_interleave_0 = const()[name = string("attn_output_163_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_163_cast_fp16 = concat(axis = var_7551, interleave = attn_output_163_interleave_0, values = (var_7537_cast_fp16, attn_output_161_cast_fp16))[name = string("attn_output_163_cast_fp16")];
            tensor<int32, [4]> var_7555_perm_0 = const()[name = string("op_7555_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_251x = const()[name = string("concat_251x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_7555_cast_fp16 = transpose(perm = var_7555_perm_0, x = attn_output_163_cast_fp16)[name = string("transpose_543")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_167_cast_fp16 = reshape(shape = concat_251x, x = var_7555_cast_fp16)[name = string("attn_output_167_cast_fp16")];
            tensor<int32, [2]> hidden_states_203_strides_0 = const()[name = string("hidden_states_203_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_203_pad_type_0 = const()[name = string("hidden_states_203_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_203_pad_0 = const()[name = string("hidden_states_203_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_203_dilations_0 = const()[name = string("hidden_states_203_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_203_groups_0 = const()[name = string("hidden_states_203_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_203_cast_fp16 = conv(dilations = hidden_states_203_dilations_0, groups = hidden_states_203_groups_0, pad = hidden_states_203_pad_0, pad_type = hidden_states_203_pad_type_0, strides = hidden_states_203_strides_0, weight = layers_20_self_attn_o_proj_weight_cast_fp16, x = attn_output_167_cast_fp16)[name = string("hidden_states_203_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_205_cast_fp16 = add(x = hidden_states_199_cast_fp16, y = hidden_states_203_cast_fp16)[name = string("hidden_states_205_cast_fp16")];
            fp16 const_210_promoted_to_fp16 = const()[name = string("const_210_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_7588_cast_fp16 = mul(x = hidden_states_205_cast_fp16, y = const_210_promoted_to_fp16)[name = string("op_7588_cast_fp16")];
            int32 var_7586 = const()[name = string("op_7586"), val = int32(1)];
            bool doubled_165_interleave_0 = const()[name = string("doubled_165_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_165_cast_fp16 = concat(axis = var_7586, interleave = doubled_165_interleave_0, values = (hidden_states_205_cast_fp16, var_7588_cast_fp16))[name = string("doubled_165_cast_fp16")];
            tensor<int32, [1]> out_83_axes_0 = const()[name = string("out_83_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_83_gamma_0_to_fp16 = const()[name = string("out_83_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1494166848)))];
            fp16 var_7598_to_fp16 = const()[name = string("op_7598_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_83_cast_fp16 = layer_norm(axes = out_83_axes_0, epsilon = var_7598_to_fp16, gamma = out_83_gamma_0_to_fp16, x = doubled_165_cast_fp16)[name = string("out_83_cast_fp16")];
            tensor<int32, [2]> var_7609_split_sizes_0 = const()[name = string("op_7609_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7609_axis_0 = const()[name = string("op_7609_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_7609_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_7609_cast_fp16_1 = split(axis = var_7609_axis_0, split_sizes = var_7609_split_sizes_0, x = out_83_cast_fp16)[name = string("op_7609_cast_fp16")];
            tensor<int32, [2]> input_41_strides_0 = const()[name = string("input_41_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_41_pad_type_0 = const()[name = string("input_41_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_41_pad_0 = const()[name = string("input_41_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_41_dilations_0 = const()[name = string("input_41_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_41_groups_0 = const()[name = string("input_41_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_41_cast_fp16 = conv(dilations = input_41_dilations_0, groups = input_41_groups_0, pad = input_41_pad_0, pad_type = input_41_pad_type_0, strides = input_41_strides_0, weight = layers_20_mlp_gate_proj_weight_cast_fp16, x = var_7609_cast_fp16_0)[name = string("input_41_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_7626_cast_fp16 = silu(x = input_41_cast_fp16)[name = string("op_7626_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_20_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_20_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1494175104)))];
            tensor<int32, [2]> var_7632_strides_0 = const()[name = string("op_7632_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_7632_pad_type_0 = const()[name = string("op_7632_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_7632_pad_0 = const()[name = string("op_7632_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_7632_dilations_0 = const()[name = string("op_7632_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7632_groups_0 = const()[name = string("op_7632_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_7632_cast_fp16 = conv(dilations = var_7632_dilations_0, groups = var_7632_groups_0, pad = var_7632_pad_0, pad_type = var_7632_pad_type_0, strides = var_7632_strides_0, weight = layers_20_mlp_up_proj_weight_to_fp16, x = var_7609_cast_fp16_0)[name = string("op_7632_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_209_cast_fp16 = mul(x = var_7626_cast_fp16, y = var_7632_cast_fp16)[name = string("x_209_cast_fp16")];
            tensor<int32, [2]> hidden_states_207_strides_0 = const()[name = string("hidden_states_207_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_207_pad_type_0 = const()[name = string("hidden_states_207_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_207_pad_0 = const()[name = string("hidden_states_207_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_207_dilations_0 = const()[name = string("hidden_states_207_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_207_groups_0 = const()[name = string("hidden_states_207_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_207_cast_fp16 = conv(dilations = hidden_states_207_dilations_0, groups = hidden_states_207_groups_0, pad = hidden_states_207_pad_0, pad_type = hidden_states_207_pad_type_0, strides = hidden_states_207_strides_0, weight = layers_20_mlp_down_proj_weight_cast_fp16, x = x_209_cast_fp16)[name = string("hidden_states_207_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_209_cast_fp16 = add(x = hidden_states_205_cast_fp16, y = hidden_states_207_cast_fp16)[name = string("hidden_states_209_cast_fp16")];
            fp16 const_212_promoted_to_fp16 = const()[name = string("const_212_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_7650_cast_fp16 = mul(x = hidden_states_209_cast_fp16, y = const_212_promoted_to_fp16)[name = string("op_7650_cast_fp16")];
            int32 var_7648 = const()[name = string("op_7648"), val = int32(1)];
            bool doubled_169_interleave_0 = const()[name = string("doubled_169_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_169_cast_fp16 = concat(axis = var_7648, interleave = doubled_169_interleave_0, values = (hidden_states_209_cast_fp16, var_7650_cast_fp16))[name = string("doubled_169_cast_fp16")];
            tensor<int32, [1]> out_85_axes_0 = const()[name = string("out_85_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_85_gamma_0_to_fp16 = const()[name = string("out_85_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1519340992)))];
            fp16 var_7660_to_fp16 = const()[name = string("op_7660_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_85_cast_fp16 = layer_norm(axes = out_85_axes_0, epsilon = var_7660_to_fp16, gamma = out_85_gamma_0_to_fp16, x = doubled_169_cast_fp16)[name = string("out_85_cast_fp16")];
            tensor<int32, [2]> var_7671_split_sizes_0 = const()[name = string("op_7671_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7671_axis_0 = const()[name = string("op_7671_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_7671_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_7671_cast_fp16_1 = split(axis = var_7671_axis_0, split_sizes = var_7671_split_sizes_0, x = out_85_cast_fp16)[name = string("op_7671_cast_fp16")];
            tensor<int32, [2]> query_states_127_strides_0 = const()[name = string("query_states_127_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_127_pad_type_0 = const()[name = string("query_states_127_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_127_pad_0 = const()[name = string("query_states_127_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_127_dilations_0 = const()[name = string("query_states_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_127_groups_0 = const()[name = string("query_states_127_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_127_cast_fp16 = conv(dilations = query_states_127_dilations_0, groups = query_states_127_groups_0, pad = query_states_127_pad_0, pad_type = query_states_127_pad_type_0, strides = query_states_127_strides_0, weight = layers_21_self_attn_q_proj_weight_cast_fp16, x = var_7671_cast_fp16_0)[name = string("query_states_127_cast_fp16")];
            tensor<int32, [2]> key_states_211_strides_0 = const()[name = string("key_states_211_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_211_pad_type_0 = const()[name = string("key_states_211_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_211_pad_0 = const()[name = string("key_states_211_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_211_dilations_0 = const()[name = string("key_states_211_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_211_groups_0 = const()[name = string("key_states_211_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_211_cast_fp16 = conv(dilations = key_states_211_dilations_0, groups = key_states_211_groups_0, pad = key_states_211_pad_0, pad_type = key_states_211_pad_type_0, strides = key_states_211_strides_0, weight = layers_21_self_attn_k_proj_weight_cast_fp16, x = var_7671_cast_fp16_0)[name = string("key_states_211_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_21_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_21_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1519349248)))];
            tensor<int32, [2]> value_states_127_strides_0 = const()[name = string("value_states_127_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_127_pad_type_0 = const()[name = string("value_states_127_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_127_pad_0 = const()[name = string("value_states_127_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_127_dilations_0 = const()[name = string("value_states_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_127_groups_0 = const()[name = string("value_states_127_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_127_cast_fp16 = conv(dilations = value_states_127_dilations_0, groups = value_states_127_groups_0, pad = value_states_127_pad_0, pad_type = value_states_127_pad_type_0, strides = value_states_127_strides_0, weight = layers_21_self_attn_v_proj_weight_to_fp16, x = var_7671_cast_fp16_0)[name = string("value_states_127_cast_fp16")];
            tensor<int32, [4]> concat_252x = const()[name = string("concat_252x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_211_cast_fp16 = reshape(shape = concat_252x, x = query_states_127_cast_fp16)[name = string("x_211_cast_fp16")];
            tensor<int32, [4]> concat_253x = const()[name = string("concat_253x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_7728_cast_fp16 = reshape(shape = concat_253x, x = key_states_211_cast_fp16)[name = string("op_7728_cast_fp16")];
            tensor<int32, [4]> concat_254x = const()[name = string("concat_254x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_7735_cast_fp16 = reshape(shape = concat_254x, x = value_states_127_cast_fp16)[name = string("op_7735_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_7739_cast_fp16 = mul(x = x_211_cast_fp16, y = var_859_cast_fp16)[name = string("op_7739_cast_fp16")];
            tensor<int32, [2]> var_7740_split_sizes_0 = const()[name = string("op_7740_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7740_axis_0 = const()[name = string("op_7740_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_7740_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_7740_cast_fp16_1 = split(axis = var_7740_axis_0, split_sizes = var_7740_split_sizes_0, x = x_211_cast_fp16)[name = string("op_7740_cast_fp16")];
            fp16 const_214_promoted_to_fp16 = const()[name = string("const_214_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_7742_cast_fp16 = mul(x = var_7740_cast_fp16_1, y = const_214_promoted_to_fp16)[name = string("op_7742_cast_fp16")];
            int32 var_7744 = const()[name = string("op_7744"), val = int32(-2)];
            bool var_7745_interleave_0 = const()[name = string("op_7745_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_7745_cast_fp16 = concat(axis = var_7744, interleave = var_7745_interleave_0, values = (var_7742_cast_fp16, var_7740_cast_fp16_0))[name = string("op_7745_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_7746_cast_fp16 = mul(x = var_7745_cast_fp16, y = var_866_cast_fp16)[name = string("op_7746_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_129_cast_fp16 = add(x = var_7739_cast_fp16, y = var_7746_cast_fp16)[name = string("query_states_129_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_7752_cast_fp16 = mul(x = var_7728_cast_fp16, y = var_859_cast_fp16)[name = string("op_7752_cast_fp16")];
            tensor<int32, [2]> var_7753_split_sizes_0 = const()[name = string("op_7753_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7753_axis_0 = const()[name = string("op_7753_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_7753_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_7753_cast_fp16_1 = split(axis = var_7753_axis_0, split_sizes = var_7753_split_sizes_0, x = var_7728_cast_fp16)[name = string("op_7753_cast_fp16")];
            fp16 const_215_promoted_to_fp16 = const()[name = string("const_215_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_7755_cast_fp16 = mul(x = var_7753_cast_fp16_1, y = const_215_promoted_to_fp16)[name = string("op_7755_cast_fp16")];
            int32 var_7757 = const()[name = string("op_7757"), val = int32(-2)];
            bool var_7758_interleave_0 = const()[name = string("op_7758_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_7758_cast_fp16 = concat(axis = var_7757, interleave = var_7758_interleave_0, values = (var_7755_cast_fp16, var_7753_cast_fp16_0))[name = string("op_7758_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_7759_cast_fp16 = mul(x = var_7758_cast_fp16, y = var_866_cast_fp16)[name = string("op_7759_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_215_cast_fp16 = add(x = var_7752_cast_fp16, y = var_7759_cast_fp16)[name = string("key_states_215_cast_fp16")];
            tensor<int32, [1]> expand_dims_252 = const()[name = string("expand_dims_252"), val = tensor<int32, [1]>([21])];
            tensor<int32, [1]> expand_dims_253 = const()[name = string("expand_dims_253"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_255 = const()[name = string("expand_dims_255"), val = tensor<int32, [1]>([0])];
            int32 concat_257_axis_0 = const()[name = string("concat_257_axis_0"), val = int32(0)];
            bool concat_257_interleave_0 = const()[name = string("concat_257_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_257 = concat(axis = concat_257_axis_0, interleave = concat_257_interleave_0, values = (expand_dims_252, expand_dims_253, position_id, expand_dims_255))[name = string("concat_257")];
            tensor<int32, [1]> expand_dims_256 = const()[name = string("expand_dims_256"), val = tensor<int32, [1]>([22])];
            tensor<int32, [1]> concat_258_values1_0 = const()[name = string("concat_258_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_258_values3_0 = const()[name = string("concat_258_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_258_axis_0 = const()[name = string("concat_258_axis_0"), val = int32(0)];
            bool concat_258_interleave_0 = const()[name = string("concat_258_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_258 = concat(axis = concat_258_axis_0, interleave = concat_258_interleave_0, values = (expand_dims_256, concat_258_values1_0, cache_position_end, concat_258_values3_0))[name = string("concat_258")];
            tensor<int32, [4]> key_states_217_perm_0 = const()[name = string("key_states_217_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_22_stride_0 = const()[name = string("key_cache_internal_tensor_assign_22_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_22_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_22_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_22_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_22_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_22_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_22_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_217_cast_fp16 = transpose(perm = key_states_217_perm_0, x = key_states_215_cast_fp16)[name = string("transpose_542")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_22_cast_fp16 = slice_update(begin = concat_257, begin_mask = key_cache_internal_tensor_assign_22_begin_mask_0, end = concat_258, end_mask = key_cache_internal_tensor_assign_22_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_22_squeeze_mask_0, stride = key_cache_internal_tensor_assign_22_stride_0, update = key_states_217_cast_fp16, x = coreml_update_state_376)[name = string("key_cache_internal_tensor_assign_22_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_22_cast_fp16, input = key_cache)[name = string("coreml_update_state_378_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_378 = read_state(input = key_cache)[name = string("coreml_update_state_378")];
            tensor<int32, [4]> value_states_129_perm_0 = const()[name = string("value_states_129_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_22_stride_0 = const()[name = string("value_cache_internal_tensor_assign_22_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_22_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_22_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_22_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_22_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_22_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_22_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_129_cast_fp16 = transpose(perm = value_states_129_perm_0, x = var_7735_cast_fp16)[name = string("transpose_541")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_22_cast_fp16 = slice_update(begin = concat_257, begin_mask = value_cache_internal_tensor_assign_22_begin_mask_0, end = concat_258, end_mask = value_cache_internal_tensor_assign_22_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_22_squeeze_mask_0, stride = value_cache_internal_tensor_assign_22_stride_0, update = value_states_129_cast_fp16, x = coreml_update_state_377)[name = string("value_cache_internal_tensor_assign_22_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_22_cast_fp16, input = value_cache)[name = string("coreml_update_state_379_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_379 = read_state(input = value_cache)[name = string("coreml_update_state_379")];
            tensor<int32, [4]> var_7829_begin_0 = const()[name = string("op_7829_begin_0"), val = tensor<int32, [4]>([21, 0, 0, 0])];
            tensor<int32, [4]> var_7829_end_0 = const()[name = string("op_7829_end_0"), val = tensor<int32, [4]>([22, 2, 2048, 128])];
            tensor<bool, [4]> var_7829_end_mask_0 = const()[name = string("op_7829_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7829_cast_fp16 = slice_by_index(begin = var_7829_begin_0, end = var_7829_end_0, end_mask = var_7829_end_mask_0, x = coreml_update_state_378)[name = string("op_7829_cast_fp16")];
            tensor<int32, [2]> tile_42 = const()[name = string("tile_42"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7832_axis_0 = const()[name = string("op_7832_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7832_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7832_cast_fp16_1 = split(axis = var_7832_axis_0, split_sizes = tile_42, x = var_7829_cast_fp16)[name = string("op_7832_cast_fp16")];
            tensor<int32, [4]> var_7839_begin_0 = const()[name = string("op_7839_begin_0"), val = tensor<int32, [4]>([21, 0, 0, 0])];
            tensor<int32, [4]> var_7839_end_0 = const()[name = string("op_7839_end_0"), val = tensor<int32, [4]>([22, 2, 2048, 128])];
            tensor<bool, [4]> var_7839_end_mask_0 = const()[name = string("op_7839_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7839_cast_fp16 = slice_by_index(begin = var_7839_begin_0, end = var_7839_end_0, end_mask = var_7839_end_mask_0, x = coreml_update_state_379)[name = string("op_7839_cast_fp16")];
            tensor<int32, [2]> tile_43 = const()[name = string("tile_43"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7842_axis_0 = const()[name = string("op_7842_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7842_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7842_cast_fp16_1 = split(axis = var_7842_axis_0, split_sizes = tile_43, x = var_7839_cast_fp16)[name = string("op_7842_cast_fp16")];
            tensor<int32, [2]> var_7845_split_sizes_0 = const()[name = string("op_7845_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_7845_axis_0 = const()[name = string("op_7845_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_7845_0, tensor<fp16, [1, 8, 128, 128]> var_7845_1 = split(axis = var_7845_axis_0, split_sizes = var_7845_split_sizes_0, x = query_states_129_cast_fp16)[name = string("op_7845")];
            bool attn_weights_337_transpose_x_0 = const()[name = string("attn_weights_337_transpose_x_0"), val = bool(false)];
            bool attn_weights_337_transpose_y_0 = const()[name = string("attn_weights_337_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_337_cast_fp16 = matmul(transpose_x = attn_weights_337_transpose_x_0, transpose_y = attn_weights_337_transpose_y_0, x = var_7832_cast_fp16_0, y = var_7845_0)[name = string("attn_weights_337_cast_fp16")];
            fp16 var_7848_to_fp16 = const()[name = string("op_7848_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_339_cast_fp16 = mul(x = attn_weights_337_cast_fp16, y = var_7848_to_fp16)[name = string("attn_weights_339_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_341_cast_fp16 = add(x = attn_weights_339_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_341_cast_fp16")];
            int32 var_7852 = const()[name = string("op_7852"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_343_cast_fp16 = softmax(axis = var_7852, x = attn_weights_341_cast_fp16)[name = string("attn_weights_343_cast_fp16")];
            bool var_7858_transpose_x_1 = const()[name = string("op_7858_transpose_x_1"), val = bool(true)];
            bool var_7858_transpose_y_1 = const()[name = string("op_7858_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_7858_cast_fp16 = matmul(transpose_x = var_7858_transpose_x_1, transpose_y = var_7858_transpose_y_1, x = attn_weights_343_cast_fp16, y = var_7842_cast_fp16_0)[name = string("op_7858_cast_fp16")];
            bool attn_weights_345_transpose_x_0 = const()[name = string("attn_weights_345_transpose_x_0"), val = bool(false)];
            bool attn_weights_345_transpose_y_0 = const()[name = string("attn_weights_345_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_345_cast_fp16 = matmul(transpose_x = attn_weights_345_transpose_x_0, transpose_y = attn_weights_345_transpose_y_0, x = var_7832_cast_fp16_1, y = var_7845_1)[name = string("attn_weights_345_cast_fp16")];
            fp16 var_7860_to_fp16 = const()[name = string("op_7860_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_347_cast_fp16 = mul(x = attn_weights_345_cast_fp16, y = var_7860_to_fp16)[name = string("attn_weights_347_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_349_cast_fp16 = add(x = attn_weights_347_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_349_cast_fp16")];
            int32 var_7864 = const()[name = string("op_7864"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_351_cast_fp16 = softmax(axis = var_7864, x = attn_weights_349_cast_fp16)[name = string("attn_weights_351_cast_fp16")];
            bool attn_output_169_transpose_x_1 = const()[name = string("attn_output_169_transpose_x_1"), val = bool(true)];
            bool attn_output_169_transpose_y_1 = const()[name = string("attn_output_169_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_169_cast_fp16 = matmul(transpose_x = attn_output_169_transpose_x_1, transpose_y = attn_output_169_transpose_y_1, x = attn_weights_351_cast_fp16, y = var_7842_cast_fp16_1)[name = string("attn_output_169_cast_fp16")];
            int32 var_7872 = const()[name = string("op_7872"), val = int32(1)];
            bool attn_output_171_interleave_0 = const()[name = string("attn_output_171_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_171_cast_fp16 = concat(axis = var_7872, interleave = attn_output_171_interleave_0, values = (var_7858_cast_fp16, attn_output_169_cast_fp16))[name = string("attn_output_171_cast_fp16")];
            tensor<int32, [4]> var_7876_perm_0 = const()[name = string("op_7876_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_263x = const()[name = string("concat_263x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_7876_cast_fp16 = transpose(perm = var_7876_perm_0, x = attn_output_171_cast_fp16)[name = string("transpose_540")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_175_cast_fp16 = reshape(shape = concat_263x, x = var_7876_cast_fp16)[name = string("attn_output_175_cast_fp16")];
            tensor<int32, [2]> hidden_states_213_strides_0 = const()[name = string("hidden_states_213_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_213_pad_type_0 = const()[name = string("hidden_states_213_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_213_pad_0 = const()[name = string("hidden_states_213_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_213_dilations_0 = const()[name = string("hidden_states_213_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_213_groups_0 = const()[name = string("hidden_states_213_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_213_cast_fp16 = conv(dilations = hidden_states_213_dilations_0, groups = hidden_states_213_groups_0, pad = hidden_states_213_pad_0, pad_type = hidden_states_213_pad_type_0, strides = hidden_states_213_strides_0, weight = layers_21_self_attn_o_proj_weight_cast_fp16, x = attn_output_175_cast_fp16)[name = string("hidden_states_213_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_215_cast_fp16 = add(x = hidden_states_209_cast_fp16, y = hidden_states_213_cast_fp16)[name = string("hidden_states_215_cast_fp16")];
            fp16 const_220_promoted_to_fp16 = const()[name = string("const_220_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_7909_cast_fp16 = mul(x = hidden_states_215_cast_fp16, y = const_220_promoted_to_fp16)[name = string("op_7909_cast_fp16")];
            int32 var_7907 = const()[name = string("op_7907"), val = int32(1)];
            bool doubled_173_interleave_0 = const()[name = string("doubled_173_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_173_cast_fp16 = concat(axis = var_7907, interleave = doubled_173_interleave_0, values = (hidden_states_215_cast_fp16, var_7909_cast_fp16))[name = string("doubled_173_cast_fp16")];
            tensor<int32, [1]> out_87_axes_0 = const()[name = string("out_87_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_87_gamma_0_to_fp16 = const()[name = string("out_87_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1520397888)))];
            fp16 var_7919_to_fp16 = const()[name = string("op_7919_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_87_cast_fp16 = layer_norm(axes = out_87_axes_0, epsilon = var_7919_to_fp16, gamma = out_87_gamma_0_to_fp16, x = doubled_173_cast_fp16)[name = string("out_87_cast_fp16")];
            tensor<int32, [2]> var_7930_split_sizes_0 = const()[name = string("op_7930_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7930_axis_0 = const()[name = string("op_7930_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_7930_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_7930_cast_fp16_1 = split(axis = var_7930_axis_0, split_sizes = var_7930_split_sizes_0, x = out_87_cast_fp16)[name = string("op_7930_cast_fp16")];
            tensor<int32, [2]> input_43_strides_0 = const()[name = string("input_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_43_pad_type_0 = const()[name = string("input_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_43_pad_0 = const()[name = string("input_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_43_dilations_0 = const()[name = string("input_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_43_groups_0 = const()[name = string("input_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_43_cast_fp16 = conv(dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = layers_21_mlp_gate_proj_weight_cast_fp16, x = var_7930_cast_fp16_0)[name = string("input_43_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_7947_cast_fp16 = silu(x = input_43_cast_fp16)[name = string("op_7947_cast_fp16")];
            tensor<int32, [2]> var_7953_strides_0 = const()[name = string("op_7953_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_7953_pad_type_0 = const()[name = string("op_7953_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_7953_pad_0 = const()[name = string("op_7953_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_7953_dilations_0 = const()[name = string("op_7953_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7953_groups_0 = const()[name = string("op_7953_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_7953_cast_fp16 = conv(dilations = var_7953_dilations_0, groups = var_7953_groups_0, pad = var_7953_pad_0, pad_type = var_7953_pad_type_0, strides = var_7953_strides_0, weight = layers_21_mlp_up_proj_weight_cast_fp16, x = var_7930_cast_fp16_0)[name = string("op_7953_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_219_cast_fp16 = mul(x = var_7947_cast_fp16, y = var_7953_cast_fp16)[name = string("x_219_cast_fp16")];
            tensor<int32, [2]> hidden_states_217_strides_0 = const()[name = string("hidden_states_217_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_217_pad_type_0 = const()[name = string("hidden_states_217_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_217_pad_0 = const()[name = string("hidden_states_217_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_217_dilations_0 = const()[name = string("hidden_states_217_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_217_groups_0 = const()[name = string("hidden_states_217_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_217_cast_fp16 = conv(dilations = hidden_states_217_dilations_0, groups = hidden_states_217_groups_0, pad = hidden_states_217_pad_0, pad_type = hidden_states_217_pad_type_0, strides = hidden_states_217_strides_0, weight = layers_21_mlp_down_proj_weight_cast_fp16, x = x_219_cast_fp16)[name = string("hidden_states_217_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_219_cast_fp16 = add(x = hidden_states_215_cast_fp16, y = hidden_states_217_cast_fp16)[name = string("hidden_states_219_cast_fp16")];
            fp16 const_222_promoted_to_fp16 = const()[name = string("const_222_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_7971_cast_fp16 = mul(x = hidden_states_219_cast_fp16, y = const_222_promoted_to_fp16)[name = string("op_7971_cast_fp16")];
            int32 var_7969 = const()[name = string("op_7969"), val = int32(1)];
            bool doubled_177_interleave_0 = const()[name = string("doubled_177_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_177_cast_fp16 = concat(axis = var_7969, interleave = doubled_177_interleave_0, values = (hidden_states_219_cast_fp16, var_7971_cast_fp16))[name = string("doubled_177_cast_fp16")];
            tensor<int32, [1]> out_89_axes_0 = const()[name = string("out_89_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_89_gamma_0_to_fp16 = const()[name = string("out_89_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1520406144)))];
            fp16 var_7981_to_fp16 = const()[name = string("op_7981_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_89_cast_fp16 = layer_norm(axes = out_89_axes_0, epsilon = var_7981_to_fp16, gamma = out_89_gamma_0_to_fp16, x = doubled_177_cast_fp16)[name = string("out_89_cast_fp16")];
            tensor<int32, [2]> var_7992_split_sizes_0 = const()[name = string("op_7992_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7992_axis_0 = const()[name = string("op_7992_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_7992_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_7992_cast_fp16_1 = split(axis = var_7992_axis_0, split_sizes = var_7992_split_sizes_0, x = out_89_cast_fp16)[name = string("op_7992_cast_fp16")];
            tensor<int32, [2]> query_states_133_strides_0 = const()[name = string("query_states_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_133_pad_type_0 = const()[name = string("query_states_133_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_133_pad_0 = const()[name = string("query_states_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_133_dilations_0 = const()[name = string("query_states_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_133_groups_0 = const()[name = string("query_states_133_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_133_cast_fp16 = conv(dilations = query_states_133_dilations_0, groups = query_states_133_groups_0, pad = query_states_133_pad_0, pad_type = query_states_133_pad_type_0, strides = query_states_133_strides_0, weight = layers_22_self_attn_q_proj_weight_cast_fp16, x = var_7992_cast_fp16_0)[name = string("query_states_133_cast_fp16")];
            tensor<int32, [2]> key_states_221_strides_0 = const()[name = string("key_states_221_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_221_pad_type_0 = const()[name = string("key_states_221_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_221_pad_0 = const()[name = string("key_states_221_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_221_dilations_0 = const()[name = string("key_states_221_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_221_groups_0 = const()[name = string("key_states_221_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_221_cast_fp16 = conv(dilations = key_states_221_dilations_0, groups = key_states_221_groups_0, pad = key_states_221_pad_0, pad_type = key_states_221_pad_type_0, strides = key_states_221_strides_0, weight = layers_22_self_attn_k_proj_weight_cast_fp16, x = var_7992_cast_fp16_0)[name = string("key_states_221_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_22_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_22_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1520414400)))];
            tensor<int32, [2]> value_states_133_strides_0 = const()[name = string("value_states_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_133_pad_type_0 = const()[name = string("value_states_133_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_133_pad_0 = const()[name = string("value_states_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_133_dilations_0 = const()[name = string("value_states_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_133_groups_0 = const()[name = string("value_states_133_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_133_cast_fp16 = conv(dilations = value_states_133_dilations_0, groups = value_states_133_groups_0, pad = value_states_133_pad_0, pad_type = value_states_133_pad_type_0, strides = value_states_133_strides_0, weight = layers_22_self_attn_v_proj_weight_to_fp16, x = var_7992_cast_fp16_0)[name = string("value_states_133_cast_fp16")];
            tensor<int32, [4]> concat_264x = const()[name = string("concat_264x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_221_cast_fp16 = reshape(shape = concat_264x, x = query_states_133_cast_fp16)[name = string("x_221_cast_fp16")];
            tensor<int32, [4]> concat_265x = const()[name = string("concat_265x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_8049_cast_fp16 = reshape(shape = concat_265x, x = key_states_221_cast_fp16)[name = string("op_8049_cast_fp16")];
            tensor<int32, [4]> concat_266x = const()[name = string("concat_266x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_8056_cast_fp16 = reshape(shape = concat_266x, x = value_states_133_cast_fp16)[name = string("op_8056_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_8060_cast_fp16 = mul(x = x_221_cast_fp16, y = var_859_cast_fp16)[name = string("op_8060_cast_fp16")];
            tensor<int32, [2]> var_8061_split_sizes_0 = const()[name = string("op_8061_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8061_axis_0 = const()[name = string("op_8061_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_8061_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_8061_cast_fp16_1 = split(axis = var_8061_axis_0, split_sizes = var_8061_split_sizes_0, x = x_221_cast_fp16)[name = string("op_8061_cast_fp16")];
            fp16 const_224_promoted_to_fp16 = const()[name = string("const_224_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_8063_cast_fp16 = mul(x = var_8061_cast_fp16_1, y = const_224_promoted_to_fp16)[name = string("op_8063_cast_fp16")];
            int32 var_8065 = const()[name = string("op_8065"), val = int32(-2)];
            bool var_8066_interleave_0 = const()[name = string("op_8066_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_8066_cast_fp16 = concat(axis = var_8065, interleave = var_8066_interleave_0, values = (var_8063_cast_fp16, var_8061_cast_fp16_0))[name = string("op_8066_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_8067_cast_fp16 = mul(x = var_8066_cast_fp16, y = var_866_cast_fp16)[name = string("op_8067_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_135_cast_fp16 = add(x = var_8060_cast_fp16, y = var_8067_cast_fp16)[name = string("query_states_135_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_8073_cast_fp16 = mul(x = var_8049_cast_fp16, y = var_859_cast_fp16)[name = string("op_8073_cast_fp16")];
            tensor<int32, [2]> var_8074_split_sizes_0 = const()[name = string("op_8074_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8074_axis_0 = const()[name = string("op_8074_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_8074_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_8074_cast_fp16_1 = split(axis = var_8074_axis_0, split_sizes = var_8074_split_sizes_0, x = var_8049_cast_fp16)[name = string("op_8074_cast_fp16")];
            fp16 const_225_promoted_to_fp16 = const()[name = string("const_225_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_8076_cast_fp16 = mul(x = var_8074_cast_fp16_1, y = const_225_promoted_to_fp16)[name = string("op_8076_cast_fp16")];
            int32 var_8078 = const()[name = string("op_8078"), val = int32(-2)];
            bool var_8079_interleave_0 = const()[name = string("op_8079_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_8079_cast_fp16 = concat(axis = var_8078, interleave = var_8079_interleave_0, values = (var_8076_cast_fp16, var_8074_cast_fp16_0))[name = string("op_8079_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_8080_cast_fp16 = mul(x = var_8079_cast_fp16, y = var_866_cast_fp16)[name = string("op_8080_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_225_cast_fp16 = add(x = var_8073_cast_fp16, y = var_8080_cast_fp16)[name = string("key_states_225_cast_fp16")];
            tensor<int32, [1]> expand_dims_264 = const()[name = string("expand_dims_264"), val = tensor<int32, [1]>([22])];
            tensor<int32, [1]> expand_dims_265 = const()[name = string("expand_dims_265"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_267 = const()[name = string("expand_dims_267"), val = tensor<int32, [1]>([0])];
            int32 concat_269_axis_0 = const()[name = string("concat_269_axis_0"), val = int32(0)];
            bool concat_269_interleave_0 = const()[name = string("concat_269_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_269 = concat(axis = concat_269_axis_0, interleave = concat_269_interleave_0, values = (expand_dims_264, expand_dims_265, position_id, expand_dims_267))[name = string("concat_269")];
            tensor<int32, [1]> expand_dims_268 = const()[name = string("expand_dims_268"), val = tensor<int32, [1]>([23])];
            tensor<int32, [1]> concat_270_values1_0 = const()[name = string("concat_270_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_270_values3_0 = const()[name = string("concat_270_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_270_axis_0 = const()[name = string("concat_270_axis_0"), val = int32(0)];
            bool concat_270_interleave_0 = const()[name = string("concat_270_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_270 = concat(axis = concat_270_axis_0, interleave = concat_270_interleave_0, values = (expand_dims_268, concat_270_values1_0, cache_position_end, concat_270_values3_0))[name = string("concat_270")];
            tensor<int32, [4]> key_states_227_perm_0 = const()[name = string("key_states_227_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_23_stride_0 = const()[name = string("key_cache_internal_tensor_assign_23_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_23_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_23_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_23_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_23_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_23_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_23_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_227_cast_fp16 = transpose(perm = key_states_227_perm_0, x = key_states_225_cast_fp16)[name = string("transpose_539")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_23_cast_fp16 = slice_update(begin = concat_269, begin_mask = key_cache_internal_tensor_assign_23_begin_mask_0, end = concat_270, end_mask = key_cache_internal_tensor_assign_23_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_23_squeeze_mask_0, stride = key_cache_internal_tensor_assign_23_stride_0, update = key_states_227_cast_fp16, x = coreml_update_state_378)[name = string("key_cache_internal_tensor_assign_23_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_23_cast_fp16, input = key_cache)[name = string("coreml_update_state_380_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_380 = read_state(input = key_cache)[name = string("coreml_update_state_380")];
            tensor<int32, [4]> value_states_135_perm_0 = const()[name = string("value_states_135_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_23_stride_0 = const()[name = string("value_cache_internal_tensor_assign_23_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_23_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_23_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_23_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_23_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_23_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_23_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_135_cast_fp16 = transpose(perm = value_states_135_perm_0, x = var_8056_cast_fp16)[name = string("transpose_538")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_23_cast_fp16 = slice_update(begin = concat_269, begin_mask = value_cache_internal_tensor_assign_23_begin_mask_0, end = concat_270, end_mask = value_cache_internal_tensor_assign_23_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_23_squeeze_mask_0, stride = value_cache_internal_tensor_assign_23_stride_0, update = value_states_135_cast_fp16, x = coreml_update_state_379)[name = string("value_cache_internal_tensor_assign_23_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_23_cast_fp16, input = value_cache)[name = string("coreml_update_state_381_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_381 = read_state(input = value_cache)[name = string("coreml_update_state_381")];
            tensor<int32, [4]> var_8150_begin_0 = const()[name = string("op_8150_begin_0"), val = tensor<int32, [4]>([22, 0, 0, 0])];
            tensor<int32, [4]> var_8150_end_0 = const()[name = string("op_8150_end_0"), val = tensor<int32, [4]>([23, 2, 2048, 128])];
            tensor<bool, [4]> var_8150_end_mask_0 = const()[name = string("op_8150_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8150_cast_fp16 = slice_by_index(begin = var_8150_begin_0, end = var_8150_end_0, end_mask = var_8150_end_mask_0, x = coreml_update_state_380)[name = string("op_8150_cast_fp16")];
            tensor<int32, [2]> tile_44 = const()[name = string("tile_44"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8153_axis_0 = const()[name = string("op_8153_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8153_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8153_cast_fp16_1 = split(axis = var_8153_axis_0, split_sizes = tile_44, x = var_8150_cast_fp16)[name = string("op_8153_cast_fp16")];
            tensor<int32, [4]> var_8160_begin_0 = const()[name = string("op_8160_begin_0"), val = tensor<int32, [4]>([22, 0, 0, 0])];
            tensor<int32, [4]> var_8160_end_0 = const()[name = string("op_8160_end_0"), val = tensor<int32, [4]>([23, 2, 2048, 128])];
            tensor<bool, [4]> var_8160_end_mask_0 = const()[name = string("op_8160_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8160_cast_fp16 = slice_by_index(begin = var_8160_begin_0, end = var_8160_end_0, end_mask = var_8160_end_mask_0, x = coreml_update_state_381)[name = string("op_8160_cast_fp16")];
            tensor<int32, [2]> tile_45 = const()[name = string("tile_45"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8163_axis_0 = const()[name = string("op_8163_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8163_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8163_cast_fp16_1 = split(axis = var_8163_axis_0, split_sizes = tile_45, x = var_8160_cast_fp16)[name = string("op_8163_cast_fp16")];
            tensor<int32, [2]> var_8166_split_sizes_0 = const()[name = string("op_8166_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_8166_axis_0 = const()[name = string("op_8166_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_8166_0, tensor<fp16, [1, 8, 128, 128]> var_8166_1 = split(axis = var_8166_axis_0, split_sizes = var_8166_split_sizes_0, x = query_states_135_cast_fp16)[name = string("op_8166")];
            bool attn_weights_353_transpose_x_0 = const()[name = string("attn_weights_353_transpose_x_0"), val = bool(false)];
            bool attn_weights_353_transpose_y_0 = const()[name = string("attn_weights_353_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_353_cast_fp16 = matmul(transpose_x = attn_weights_353_transpose_x_0, transpose_y = attn_weights_353_transpose_y_0, x = var_8153_cast_fp16_0, y = var_8166_0)[name = string("attn_weights_353_cast_fp16")];
            fp16 var_8169_to_fp16 = const()[name = string("op_8169_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_355_cast_fp16 = mul(x = attn_weights_353_cast_fp16, y = var_8169_to_fp16)[name = string("attn_weights_355_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_357_cast_fp16 = add(x = attn_weights_355_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_357_cast_fp16")];
            int32 var_8173 = const()[name = string("op_8173"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_359_cast_fp16 = softmax(axis = var_8173, x = attn_weights_357_cast_fp16)[name = string("attn_weights_359_cast_fp16")];
            bool var_8179_transpose_x_1 = const()[name = string("op_8179_transpose_x_1"), val = bool(true)];
            bool var_8179_transpose_y_1 = const()[name = string("op_8179_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_8179_cast_fp16 = matmul(transpose_x = var_8179_transpose_x_1, transpose_y = var_8179_transpose_y_1, x = attn_weights_359_cast_fp16, y = var_8163_cast_fp16_0)[name = string("op_8179_cast_fp16")];
            bool attn_weights_361_transpose_x_0 = const()[name = string("attn_weights_361_transpose_x_0"), val = bool(false)];
            bool attn_weights_361_transpose_y_0 = const()[name = string("attn_weights_361_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_361_cast_fp16 = matmul(transpose_x = attn_weights_361_transpose_x_0, transpose_y = attn_weights_361_transpose_y_0, x = var_8153_cast_fp16_1, y = var_8166_1)[name = string("attn_weights_361_cast_fp16")];
            fp16 var_8181_to_fp16 = const()[name = string("op_8181_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_363_cast_fp16 = mul(x = attn_weights_361_cast_fp16, y = var_8181_to_fp16)[name = string("attn_weights_363_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_365_cast_fp16 = add(x = attn_weights_363_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_365_cast_fp16")];
            int32 var_8185 = const()[name = string("op_8185"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_367_cast_fp16 = softmax(axis = var_8185, x = attn_weights_365_cast_fp16)[name = string("attn_weights_367_cast_fp16")];
            bool attn_output_177_transpose_x_1 = const()[name = string("attn_output_177_transpose_x_1"), val = bool(true)];
            bool attn_output_177_transpose_y_1 = const()[name = string("attn_output_177_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_177_cast_fp16 = matmul(transpose_x = attn_output_177_transpose_x_1, transpose_y = attn_output_177_transpose_y_1, x = attn_weights_367_cast_fp16, y = var_8163_cast_fp16_1)[name = string("attn_output_177_cast_fp16")];
            int32 var_8193 = const()[name = string("op_8193"), val = int32(1)];
            bool attn_output_179_interleave_0 = const()[name = string("attn_output_179_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_179_cast_fp16 = concat(axis = var_8193, interleave = attn_output_179_interleave_0, values = (var_8179_cast_fp16, attn_output_177_cast_fp16))[name = string("attn_output_179_cast_fp16")];
            tensor<int32, [4]> var_8197_perm_0 = const()[name = string("op_8197_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_275x = const()[name = string("concat_275x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_8197_cast_fp16 = transpose(perm = var_8197_perm_0, x = attn_output_179_cast_fp16)[name = string("transpose_537")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_183_cast_fp16 = reshape(shape = concat_275x, x = var_8197_cast_fp16)[name = string("attn_output_183_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_22_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_22_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1521463040)))];
            tensor<int32, [2]> hidden_states_223_strides_0 = const()[name = string("hidden_states_223_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_223_pad_type_0 = const()[name = string("hidden_states_223_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_223_pad_0 = const()[name = string("hidden_states_223_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_223_dilations_0 = const()[name = string("hidden_states_223_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_223_groups_0 = const()[name = string("hidden_states_223_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_223_cast_fp16 = conv(dilations = hidden_states_223_dilations_0, groups = hidden_states_223_groups_0, pad = hidden_states_223_pad_0, pad_type = hidden_states_223_pad_type_0, strides = hidden_states_223_strides_0, weight = layers_22_self_attn_o_proj_weight_to_fp16, x = attn_output_183_cast_fp16)[name = string("hidden_states_223_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_225_cast_fp16 = add(x = hidden_states_219_cast_fp16, y = hidden_states_223_cast_fp16)[name = string("hidden_states_225_cast_fp16")];
            fp16 const_230_promoted_to_fp16 = const()[name = string("const_230_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_8230_cast_fp16 = mul(x = hidden_states_225_cast_fp16, y = const_230_promoted_to_fp16)[name = string("op_8230_cast_fp16")];
            int32 var_8228 = const()[name = string("op_8228"), val = int32(1)];
            bool doubled_181_interleave_0 = const()[name = string("doubled_181_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_181_cast_fp16 = concat(axis = var_8228, interleave = doubled_181_interleave_0, values = (hidden_states_225_cast_fp16, var_8230_cast_fp16))[name = string("doubled_181_cast_fp16")];
            tensor<int32, [1]> out_91_axes_0 = const()[name = string("out_91_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_91_gamma_0_to_fp16 = const()[name = string("out_91_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1529851712)))];
            fp16 var_8240_to_fp16 = const()[name = string("op_8240_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_91_cast_fp16 = layer_norm(axes = out_91_axes_0, epsilon = var_8240_to_fp16, gamma = out_91_gamma_0_to_fp16, x = doubled_181_cast_fp16)[name = string("out_91_cast_fp16")];
            tensor<int32, [2]> var_8251_split_sizes_0 = const()[name = string("op_8251_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8251_axis_0 = const()[name = string("op_8251_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_8251_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_8251_cast_fp16_1 = split(axis = var_8251_axis_0, split_sizes = var_8251_split_sizes_0, x = out_91_cast_fp16)[name = string("op_8251_cast_fp16")];
            tensor<int32, [2]> input_45_strides_0 = const()[name = string("input_45_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_45_pad_type_0 = const()[name = string("input_45_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_45_pad_0 = const()[name = string("input_45_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_45_dilations_0 = const()[name = string("input_45_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_45_groups_0 = const()[name = string("input_45_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_45_cast_fp16 = conv(dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = layers_22_mlp_gate_proj_weight_cast_fp16, x = var_8251_cast_fp16_0)[name = string("input_45_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_8268_cast_fp16 = silu(x = input_45_cast_fp16)[name = string("op_8268_cast_fp16")];
            tensor<int32, [2]> var_8274_strides_0 = const()[name = string("op_8274_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_8274_pad_type_0 = const()[name = string("op_8274_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_8274_pad_0 = const()[name = string("op_8274_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_8274_dilations_0 = const()[name = string("op_8274_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8274_groups_0 = const()[name = string("op_8274_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_8274_cast_fp16 = conv(dilations = var_8274_dilations_0, groups = var_8274_groups_0, pad = var_8274_pad_0, pad_type = var_8274_pad_type_0, strides = var_8274_strides_0, weight = layers_22_mlp_up_proj_weight_cast_fp16, x = var_8251_cast_fp16_0)[name = string("op_8274_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_229_cast_fp16 = mul(x = var_8268_cast_fp16, y = var_8274_cast_fp16)[name = string("x_229_cast_fp16")];
            tensor<int32, [2]> hidden_states_227_strides_0 = const()[name = string("hidden_states_227_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_227_pad_type_0 = const()[name = string("hidden_states_227_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_227_pad_0 = const()[name = string("hidden_states_227_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_227_dilations_0 = const()[name = string("hidden_states_227_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_227_groups_0 = const()[name = string("hidden_states_227_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_227_cast_fp16 = conv(dilations = hidden_states_227_dilations_0, groups = hidden_states_227_groups_0, pad = hidden_states_227_pad_0, pad_type = hidden_states_227_pad_type_0, strides = hidden_states_227_strides_0, weight = layers_22_mlp_down_proj_weight_cast_fp16, x = x_229_cast_fp16)[name = string("hidden_states_227_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_229_cast_fp16 = add(x = hidden_states_225_cast_fp16, y = hidden_states_227_cast_fp16)[name = string("hidden_states_229_cast_fp16")];
            fp16 const_232_promoted_to_fp16 = const()[name = string("const_232_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_8292_cast_fp16 = mul(x = hidden_states_229_cast_fp16, y = const_232_promoted_to_fp16)[name = string("op_8292_cast_fp16")];
            int32 var_8290 = const()[name = string("op_8290"), val = int32(1)];
            bool doubled_185_interleave_0 = const()[name = string("doubled_185_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_185_cast_fp16 = concat(axis = var_8290, interleave = doubled_185_interleave_0, values = (hidden_states_229_cast_fp16, var_8292_cast_fp16))[name = string("doubled_185_cast_fp16")];
            tensor<int32, [1]> out_93_axes_0 = const()[name = string("out_93_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_93_gamma_0_to_fp16 = const()[name = string("out_93_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1529859968)))];
            fp16 var_8302_to_fp16 = const()[name = string("op_8302_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_93_cast_fp16 = layer_norm(axes = out_93_axes_0, epsilon = var_8302_to_fp16, gamma = out_93_gamma_0_to_fp16, x = doubled_185_cast_fp16)[name = string("out_93_cast_fp16")];
            tensor<int32, [2]> var_8313_split_sizes_0 = const()[name = string("op_8313_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8313_axis_0 = const()[name = string("op_8313_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_8313_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_8313_cast_fp16_1 = split(axis = var_8313_axis_0, split_sizes = var_8313_split_sizes_0, x = out_93_cast_fp16)[name = string("op_8313_cast_fp16")];
            tensor<int32, [2]> query_states_139_strides_0 = const()[name = string("query_states_139_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_139_pad_type_0 = const()[name = string("query_states_139_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_139_pad_0 = const()[name = string("query_states_139_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_139_dilations_0 = const()[name = string("query_states_139_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_139_groups_0 = const()[name = string("query_states_139_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_139_cast_fp16 = conv(dilations = query_states_139_dilations_0, groups = query_states_139_groups_0, pad = query_states_139_pad_0, pad_type = query_states_139_pad_type_0, strides = query_states_139_strides_0, weight = layers_23_self_attn_q_proj_weight_cast_fp16, x = var_8313_cast_fp16_0)[name = string("query_states_139_cast_fp16")];
            tensor<int32, [2]> key_states_231_strides_0 = const()[name = string("key_states_231_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_231_pad_type_0 = const()[name = string("key_states_231_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_231_pad_0 = const()[name = string("key_states_231_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_231_dilations_0 = const()[name = string("key_states_231_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_231_groups_0 = const()[name = string("key_states_231_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_231_cast_fp16 = conv(dilations = key_states_231_dilations_0, groups = key_states_231_groups_0, pad = key_states_231_pad_0, pad_type = key_states_231_pad_type_0, strides = key_states_231_strides_0, weight = layers_23_self_attn_k_proj_weight_cast_fp16, x = var_8313_cast_fp16_0)[name = string("key_states_231_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_23_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_23_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1529868224)))];
            tensor<int32, [2]> value_states_139_strides_0 = const()[name = string("value_states_139_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_139_pad_type_0 = const()[name = string("value_states_139_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_139_pad_0 = const()[name = string("value_states_139_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_139_dilations_0 = const()[name = string("value_states_139_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_139_groups_0 = const()[name = string("value_states_139_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_139_cast_fp16 = conv(dilations = value_states_139_dilations_0, groups = value_states_139_groups_0, pad = value_states_139_pad_0, pad_type = value_states_139_pad_type_0, strides = value_states_139_strides_0, weight = layers_23_self_attn_v_proj_weight_to_fp16, x = var_8313_cast_fp16_0)[name = string("value_states_139_cast_fp16")];
            tensor<int32, [4]> concat_276x = const()[name = string("concat_276x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_231_cast_fp16 = reshape(shape = concat_276x, x = query_states_139_cast_fp16)[name = string("x_231_cast_fp16")];
            tensor<int32, [4]> concat_277x = const()[name = string("concat_277x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_8370_cast_fp16 = reshape(shape = concat_277x, x = key_states_231_cast_fp16)[name = string("op_8370_cast_fp16")];
            tensor<int32, [4]> concat_278x = const()[name = string("concat_278x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_8377_cast_fp16 = reshape(shape = concat_278x, x = value_states_139_cast_fp16)[name = string("op_8377_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_8381_cast_fp16 = mul(x = x_231_cast_fp16, y = var_859_cast_fp16)[name = string("op_8381_cast_fp16")];
            tensor<int32, [2]> var_8382_split_sizes_0 = const()[name = string("op_8382_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8382_axis_0 = const()[name = string("op_8382_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_8382_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_8382_cast_fp16_1 = split(axis = var_8382_axis_0, split_sizes = var_8382_split_sizes_0, x = x_231_cast_fp16)[name = string("op_8382_cast_fp16")];
            fp16 const_234_promoted_to_fp16 = const()[name = string("const_234_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_8384_cast_fp16 = mul(x = var_8382_cast_fp16_1, y = const_234_promoted_to_fp16)[name = string("op_8384_cast_fp16")];
            int32 var_8386 = const()[name = string("op_8386"), val = int32(-2)];
            bool var_8387_interleave_0 = const()[name = string("op_8387_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_8387_cast_fp16 = concat(axis = var_8386, interleave = var_8387_interleave_0, values = (var_8384_cast_fp16, var_8382_cast_fp16_0))[name = string("op_8387_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_8388_cast_fp16 = mul(x = var_8387_cast_fp16, y = var_866_cast_fp16)[name = string("op_8388_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_141_cast_fp16 = add(x = var_8381_cast_fp16, y = var_8388_cast_fp16)[name = string("query_states_141_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_8394_cast_fp16 = mul(x = var_8370_cast_fp16, y = var_859_cast_fp16)[name = string("op_8394_cast_fp16")];
            tensor<int32, [2]> var_8395_split_sizes_0 = const()[name = string("op_8395_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8395_axis_0 = const()[name = string("op_8395_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_8395_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_8395_cast_fp16_1 = split(axis = var_8395_axis_0, split_sizes = var_8395_split_sizes_0, x = var_8370_cast_fp16)[name = string("op_8395_cast_fp16")];
            fp16 const_235_promoted_to_fp16 = const()[name = string("const_235_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_8397_cast_fp16 = mul(x = var_8395_cast_fp16_1, y = const_235_promoted_to_fp16)[name = string("op_8397_cast_fp16")];
            int32 var_8399 = const()[name = string("op_8399"), val = int32(-2)];
            bool var_8400_interleave_0 = const()[name = string("op_8400_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_8400_cast_fp16 = concat(axis = var_8399, interleave = var_8400_interleave_0, values = (var_8397_cast_fp16, var_8395_cast_fp16_0))[name = string("op_8400_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_8401_cast_fp16 = mul(x = var_8400_cast_fp16, y = var_866_cast_fp16)[name = string("op_8401_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_235_cast_fp16 = add(x = var_8394_cast_fp16, y = var_8401_cast_fp16)[name = string("key_states_235_cast_fp16")];
            tensor<int32, [1]> expand_dims_276 = const()[name = string("expand_dims_276"), val = tensor<int32, [1]>([23])];
            tensor<int32, [1]> expand_dims_277 = const()[name = string("expand_dims_277"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_279 = const()[name = string("expand_dims_279"), val = tensor<int32, [1]>([0])];
            int32 concat_281_axis_0 = const()[name = string("concat_281_axis_0"), val = int32(0)];
            bool concat_281_interleave_0 = const()[name = string("concat_281_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_281 = concat(axis = concat_281_axis_0, interleave = concat_281_interleave_0, values = (expand_dims_276, expand_dims_277, position_id, expand_dims_279))[name = string("concat_281")];
            tensor<int32, [1]> expand_dims_280 = const()[name = string("expand_dims_280"), val = tensor<int32, [1]>([24])];
            tensor<int32, [1]> concat_282_values1_0 = const()[name = string("concat_282_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_282_values3_0 = const()[name = string("concat_282_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_282_axis_0 = const()[name = string("concat_282_axis_0"), val = int32(0)];
            bool concat_282_interleave_0 = const()[name = string("concat_282_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_282 = concat(axis = concat_282_axis_0, interleave = concat_282_interleave_0, values = (expand_dims_280, concat_282_values1_0, cache_position_end, concat_282_values3_0))[name = string("concat_282")];
            tensor<int32, [4]> key_states_237_perm_0 = const()[name = string("key_states_237_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_24_stride_0 = const()[name = string("key_cache_internal_tensor_assign_24_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_24_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_24_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_24_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_24_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_24_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_24_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_237_cast_fp16 = transpose(perm = key_states_237_perm_0, x = key_states_235_cast_fp16)[name = string("transpose_536")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_24_cast_fp16 = slice_update(begin = concat_281, begin_mask = key_cache_internal_tensor_assign_24_begin_mask_0, end = concat_282, end_mask = key_cache_internal_tensor_assign_24_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_24_squeeze_mask_0, stride = key_cache_internal_tensor_assign_24_stride_0, update = key_states_237_cast_fp16, x = coreml_update_state_380)[name = string("key_cache_internal_tensor_assign_24_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_24_cast_fp16, input = key_cache)[name = string("coreml_update_state_382_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_382 = read_state(input = key_cache)[name = string("coreml_update_state_382")];
            tensor<int32, [4]> value_states_141_perm_0 = const()[name = string("value_states_141_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_24_stride_0 = const()[name = string("value_cache_internal_tensor_assign_24_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_24_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_24_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_24_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_24_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_24_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_24_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_141_cast_fp16 = transpose(perm = value_states_141_perm_0, x = var_8377_cast_fp16)[name = string("transpose_535")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_24_cast_fp16 = slice_update(begin = concat_281, begin_mask = value_cache_internal_tensor_assign_24_begin_mask_0, end = concat_282, end_mask = value_cache_internal_tensor_assign_24_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_24_squeeze_mask_0, stride = value_cache_internal_tensor_assign_24_stride_0, update = value_states_141_cast_fp16, x = coreml_update_state_381)[name = string("value_cache_internal_tensor_assign_24_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_24_cast_fp16, input = value_cache)[name = string("coreml_update_state_383_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_383 = read_state(input = value_cache)[name = string("coreml_update_state_383")];
            tensor<int32, [4]> var_8471_begin_0 = const()[name = string("op_8471_begin_0"), val = tensor<int32, [4]>([23, 0, 0, 0])];
            tensor<int32, [4]> var_8471_end_0 = const()[name = string("op_8471_end_0"), val = tensor<int32, [4]>([24, 2, 2048, 128])];
            tensor<bool, [4]> var_8471_end_mask_0 = const()[name = string("op_8471_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8471_cast_fp16 = slice_by_index(begin = var_8471_begin_0, end = var_8471_end_0, end_mask = var_8471_end_mask_0, x = coreml_update_state_382)[name = string("op_8471_cast_fp16")];
            tensor<int32, [2]> tile_46 = const()[name = string("tile_46"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8474_axis_0 = const()[name = string("op_8474_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8474_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8474_cast_fp16_1 = split(axis = var_8474_axis_0, split_sizes = tile_46, x = var_8471_cast_fp16)[name = string("op_8474_cast_fp16")];
            tensor<int32, [4]> var_8481_begin_0 = const()[name = string("op_8481_begin_0"), val = tensor<int32, [4]>([23, 0, 0, 0])];
            tensor<int32, [4]> var_8481_end_0 = const()[name = string("op_8481_end_0"), val = tensor<int32, [4]>([24, 2, 2048, 128])];
            tensor<bool, [4]> var_8481_end_mask_0 = const()[name = string("op_8481_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8481_cast_fp16 = slice_by_index(begin = var_8481_begin_0, end = var_8481_end_0, end_mask = var_8481_end_mask_0, x = coreml_update_state_383)[name = string("op_8481_cast_fp16")];
            tensor<int32, [2]> tile_47 = const()[name = string("tile_47"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8484_axis_0 = const()[name = string("op_8484_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8484_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8484_cast_fp16_1 = split(axis = var_8484_axis_0, split_sizes = tile_47, x = var_8481_cast_fp16)[name = string("op_8484_cast_fp16")];
            tensor<int32, [2]> var_8487_split_sizes_0 = const()[name = string("op_8487_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_8487_axis_0 = const()[name = string("op_8487_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_8487_0, tensor<fp16, [1, 8, 128, 128]> var_8487_1 = split(axis = var_8487_axis_0, split_sizes = var_8487_split_sizes_0, x = query_states_141_cast_fp16)[name = string("op_8487")];
            bool attn_weights_369_transpose_x_0 = const()[name = string("attn_weights_369_transpose_x_0"), val = bool(false)];
            bool attn_weights_369_transpose_y_0 = const()[name = string("attn_weights_369_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_369_cast_fp16 = matmul(transpose_x = attn_weights_369_transpose_x_0, transpose_y = attn_weights_369_transpose_y_0, x = var_8474_cast_fp16_0, y = var_8487_0)[name = string("attn_weights_369_cast_fp16")];
            fp16 var_8490_to_fp16 = const()[name = string("op_8490_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_371_cast_fp16 = mul(x = attn_weights_369_cast_fp16, y = var_8490_to_fp16)[name = string("attn_weights_371_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_373_cast_fp16 = add(x = attn_weights_371_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_373_cast_fp16")];
            int32 var_8494 = const()[name = string("op_8494"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_375_cast_fp16 = softmax(axis = var_8494, x = attn_weights_373_cast_fp16)[name = string("attn_weights_375_cast_fp16")];
            bool var_8500_transpose_x_1 = const()[name = string("op_8500_transpose_x_1"), val = bool(true)];
            bool var_8500_transpose_y_1 = const()[name = string("op_8500_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_8500_cast_fp16 = matmul(transpose_x = var_8500_transpose_x_1, transpose_y = var_8500_transpose_y_1, x = attn_weights_375_cast_fp16, y = var_8484_cast_fp16_0)[name = string("op_8500_cast_fp16")];
            bool attn_weights_377_transpose_x_0 = const()[name = string("attn_weights_377_transpose_x_0"), val = bool(false)];
            bool attn_weights_377_transpose_y_0 = const()[name = string("attn_weights_377_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_377_cast_fp16 = matmul(transpose_x = attn_weights_377_transpose_x_0, transpose_y = attn_weights_377_transpose_y_0, x = var_8474_cast_fp16_1, y = var_8487_1)[name = string("attn_weights_377_cast_fp16")];
            fp16 var_8502_to_fp16 = const()[name = string("op_8502_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_379_cast_fp16 = mul(x = attn_weights_377_cast_fp16, y = var_8502_to_fp16)[name = string("attn_weights_379_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_381_cast_fp16 = add(x = attn_weights_379_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_381_cast_fp16")];
            int32 var_8506 = const()[name = string("op_8506"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_383_cast_fp16 = softmax(axis = var_8506, x = attn_weights_381_cast_fp16)[name = string("attn_weights_383_cast_fp16")];
            bool attn_output_185_transpose_x_1 = const()[name = string("attn_output_185_transpose_x_1"), val = bool(true)];
            bool attn_output_185_transpose_y_1 = const()[name = string("attn_output_185_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_185_cast_fp16 = matmul(transpose_x = attn_output_185_transpose_x_1, transpose_y = attn_output_185_transpose_y_1, x = attn_weights_383_cast_fp16, y = var_8484_cast_fp16_1)[name = string("attn_output_185_cast_fp16")];
            int32 var_8514 = const()[name = string("op_8514"), val = int32(1)];
            bool attn_output_187_interleave_0 = const()[name = string("attn_output_187_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_187_cast_fp16 = concat(axis = var_8514, interleave = attn_output_187_interleave_0, values = (var_8500_cast_fp16, attn_output_185_cast_fp16))[name = string("attn_output_187_cast_fp16")];
            tensor<int32, [4]> var_8518_perm_0 = const()[name = string("op_8518_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_287x = const()[name = string("concat_287x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_8518_cast_fp16 = transpose(perm = var_8518_perm_0, x = attn_output_187_cast_fp16)[name = string("transpose_534")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_191_cast_fp16 = reshape(shape = concat_287x, x = var_8518_cast_fp16)[name = string("attn_output_191_cast_fp16")];
            tensor<int32, [2]> hidden_states_233_strides_0 = const()[name = string("hidden_states_233_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_233_pad_type_0 = const()[name = string("hidden_states_233_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_233_pad_0 = const()[name = string("hidden_states_233_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_233_dilations_0 = const()[name = string("hidden_states_233_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_233_groups_0 = const()[name = string("hidden_states_233_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_233_cast_fp16 = conv(dilations = hidden_states_233_dilations_0, groups = hidden_states_233_groups_0, pad = hidden_states_233_pad_0, pad_type = hidden_states_233_pad_type_0, strides = hidden_states_233_strides_0, weight = layers_23_self_attn_o_proj_weight_cast_fp16, x = attn_output_191_cast_fp16)[name = string("hidden_states_233_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_235_cast_fp16 = add(x = hidden_states_229_cast_fp16, y = hidden_states_233_cast_fp16)[name = string("hidden_states_235_cast_fp16")];
            fp16 const_240_promoted_to_fp16 = const()[name = string("const_240_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_8551_cast_fp16 = mul(x = hidden_states_235_cast_fp16, y = const_240_promoted_to_fp16)[name = string("op_8551_cast_fp16")];
            int32 var_8549 = const()[name = string("op_8549"), val = int32(1)];
            bool doubled_189_interleave_0 = const()[name = string("doubled_189_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_189_cast_fp16 = concat(axis = var_8549, interleave = doubled_189_interleave_0, values = (hidden_states_235_cast_fp16, var_8551_cast_fp16))[name = string("doubled_189_cast_fp16")];
            tensor<int32, [1]> out_95_axes_0 = const()[name = string("out_95_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_95_gamma_0_to_fp16 = const()[name = string("out_95_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1530916864)))];
            fp16 var_8561_to_fp16 = const()[name = string("op_8561_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_95_cast_fp16 = layer_norm(axes = out_95_axes_0, epsilon = var_8561_to_fp16, gamma = out_95_gamma_0_to_fp16, x = doubled_189_cast_fp16)[name = string("out_95_cast_fp16")];
            tensor<int32, [2]> var_8572_split_sizes_0 = const()[name = string("op_8572_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8572_axis_0 = const()[name = string("op_8572_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_8572_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_8572_cast_fp16_1 = split(axis = var_8572_axis_0, split_sizes = var_8572_split_sizes_0, x = out_95_cast_fp16)[name = string("op_8572_cast_fp16")];
            tensor<int32, [2]> input_47_strides_0 = const()[name = string("input_47_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_47_pad_type_0 = const()[name = string("input_47_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_47_dilations_0 = const()[name = string("input_47_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_47_groups_0 = const()[name = string("input_47_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_47_cast_fp16 = conv(dilations = input_47_dilations_0, groups = input_47_groups_0, pad = input_47_pad_0, pad_type = input_47_pad_type_0, strides = input_47_strides_0, weight = layers_23_mlp_gate_proj_weight_cast_fp16, x = var_8572_cast_fp16_0)[name = string("input_47_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_8589_cast_fp16 = silu(x = input_47_cast_fp16)[name = string("op_8589_cast_fp16")];
            tensor<int32, [2]> var_8595_strides_0 = const()[name = string("op_8595_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_8595_pad_type_0 = const()[name = string("op_8595_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_8595_pad_0 = const()[name = string("op_8595_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_8595_dilations_0 = const()[name = string("op_8595_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8595_groups_0 = const()[name = string("op_8595_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_8595_cast_fp16 = conv(dilations = var_8595_dilations_0, groups = var_8595_groups_0, pad = var_8595_pad_0, pad_type = var_8595_pad_type_0, strides = var_8595_strides_0, weight = layers_23_mlp_up_proj_weight_cast_fp16, x = var_8572_cast_fp16_0)[name = string("op_8595_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_239_cast_fp16 = mul(x = var_8589_cast_fp16, y = var_8595_cast_fp16)[name = string("x_239_cast_fp16")];
            tensor<int32, [2]> hidden_states_237_strides_0 = const()[name = string("hidden_states_237_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_237_pad_type_0 = const()[name = string("hidden_states_237_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_237_pad_0 = const()[name = string("hidden_states_237_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_237_dilations_0 = const()[name = string("hidden_states_237_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_237_groups_0 = const()[name = string("hidden_states_237_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_237_cast_fp16 = conv(dilations = hidden_states_237_dilations_0, groups = hidden_states_237_groups_0, pad = hidden_states_237_pad_0, pad_type = hidden_states_237_pad_type_0, strides = hidden_states_237_strides_0, weight = layers_23_mlp_down_proj_weight_cast_fp16, x = x_239_cast_fp16)[name = string("hidden_states_237_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_239_cast_fp16 = add(x = hidden_states_235_cast_fp16, y = hidden_states_237_cast_fp16)[name = string("hidden_states_239_cast_fp16")];
            fp16 const_242_promoted_to_fp16 = const()[name = string("const_242_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_8613_cast_fp16 = mul(x = hidden_states_239_cast_fp16, y = const_242_promoted_to_fp16)[name = string("op_8613_cast_fp16")];
            int32 var_8611 = const()[name = string("op_8611"), val = int32(1)];
            bool doubled_193_interleave_0 = const()[name = string("doubled_193_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_193_cast_fp16 = concat(axis = var_8611, interleave = doubled_193_interleave_0, values = (hidden_states_239_cast_fp16, var_8613_cast_fp16))[name = string("doubled_193_cast_fp16")];
            tensor<int32, [1]> out_97_axes_0 = const()[name = string("out_97_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_97_gamma_0_to_fp16 = const()[name = string("out_97_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1530925120)))];
            fp16 var_8623_to_fp16 = const()[name = string("op_8623_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_97_cast_fp16 = layer_norm(axes = out_97_axes_0, epsilon = var_8623_to_fp16, gamma = out_97_gamma_0_to_fp16, x = doubled_193_cast_fp16)[name = string("out_97_cast_fp16")];
            tensor<int32, [2]> var_8634_split_sizes_0 = const()[name = string("op_8634_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8634_axis_0 = const()[name = string("op_8634_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_8634_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_8634_cast_fp16_1 = split(axis = var_8634_axis_0, split_sizes = var_8634_split_sizes_0, x = out_97_cast_fp16)[name = string("op_8634_cast_fp16")];
            tensor<int32, [2]> query_states_145_strides_0 = const()[name = string("query_states_145_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_145_pad_type_0 = const()[name = string("query_states_145_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_145_pad_0 = const()[name = string("query_states_145_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_145_dilations_0 = const()[name = string("query_states_145_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_145_groups_0 = const()[name = string("query_states_145_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_145_cast_fp16 = conv(dilations = query_states_145_dilations_0, groups = query_states_145_groups_0, pad = query_states_145_pad_0, pad_type = query_states_145_pad_type_0, strides = query_states_145_strides_0, weight = layers_24_self_attn_q_proj_weight_cast_fp16, x = var_8634_cast_fp16_0)[name = string("query_states_145_cast_fp16")];
            tensor<int32, [2]> key_states_241_strides_0 = const()[name = string("key_states_241_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_241_pad_type_0 = const()[name = string("key_states_241_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_241_pad_0 = const()[name = string("key_states_241_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_241_dilations_0 = const()[name = string("key_states_241_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_241_groups_0 = const()[name = string("key_states_241_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_241_cast_fp16 = conv(dilations = key_states_241_dilations_0, groups = key_states_241_groups_0, pad = key_states_241_pad_0, pad_type = key_states_241_pad_type_0, strides = key_states_241_strides_0, weight = layers_24_self_attn_k_proj_weight_cast_fp16, x = var_8634_cast_fp16_0)[name = string("key_states_241_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_24_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_24_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1530933376)))];
            tensor<int32, [2]> value_states_145_strides_0 = const()[name = string("value_states_145_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_145_pad_type_0 = const()[name = string("value_states_145_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_145_pad_0 = const()[name = string("value_states_145_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_145_dilations_0 = const()[name = string("value_states_145_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_145_groups_0 = const()[name = string("value_states_145_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_145_cast_fp16 = conv(dilations = value_states_145_dilations_0, groups = value_states_145_groups_0, pad = value_states_145_pad_0, pad_type = value_states_145_pad_type_0, strides = value_states_145_strides_0, weight = layers_24_self_attn_v_proj_weight_to_fp16, x = var_8634_cast_fp16_0)[name = string("value_states_145_cast_fp16")];
            tensor<int32, [4]> concat_288x = const()[name = string("concat_288x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_241_cast_fp16 = reshape(shape = concat_288x, x = query_states_145_cast_fp16)[name = string("x_241_cast_fp16")];
            tensor<int32, [4]> concat_289x = const()[name = string("concat_289x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_8691_cast_fp16 = reshape(shape = concat_289x, x = key_states_241_cast_fp16)[name = string("op_8691_cast_fp16")];
            tensor<int32, [4]> concat_290x = const()[name = string("concat_290x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_8698_cast_fp16 = reshape(shape = concat_290x, x = value_states_145_cast_fp16)[name = string("op_8698_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_8702_cast_fp16 = mul(x = x_241_cast_fp16, y = var_859_cast_fp16)[name = string("op_8702_cast_fp16")];
            tensor<int32, [2]> var_8703_split_sizes_0 = const()[name = string("op_8703_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8703_axis_0 = const()[name = string("op_8703_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_8703_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_8703_cast_fp16_1 = split(axis = var_8703_axis_0, split_sizes = var_8703_split_sizes_0, x = x_241_cast_fp16)[name = string("op_8703_cast_fp16")];
            fp16 const_244_promoted_to_fp16 = const()[name = string("const_244_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_8705_cast_fp16 = mul(x = var_8703_cast_fp16_1, y = const_244_promoted_to_fp16)[name = string("op_8705_cast_fp16")];
            int32 var_8707 = const()[name = string("op_8707"), val = int32(-2)];
            bool var_8708_interleave_0 = const()[name = string("op_8708_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_8708_cast_fp16 = concat(axis = var_8707, interleave = var_8708_interleave_0, values = (var_8705_cast_fp16, var_8703_cast_fp16_0))[name = string("op_8708_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_8709_cast_fp16 = mul(x = var_8708_cast_fp16, y = var_866_cast_fp16)[name = string("op_8709_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_147_cast_fp16 = add(x = var_8702_cast_fp16, y = var_8709_cast_fp16)[name = string("query_states_147_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_8715_cast_fp16 = mul(x = var_8691_cast_fp16, y = var_859_cast_fp16)[name = string("op_8715_cast_fp16")];
            tensor<int32, [2]> var_8716_split_sizes_0 = const()[name = string("op_8716_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8716_axis_0 = const()[name = string("op_8716_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_8716_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_8716_cast_fp16_1 = split(axis = var_8716_axis_0, split_sizes = var_8716_split_sizes_0, x = var_8691_cast_fp16)[name = string("op_8716_cast_fp16")];
            fp16 const_245_promoted_to_fp16 = const()[name = string("const_245_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_8718_cast_fp16 = mul(x = var_8716_cast_fp16_1, y = const_245_promoted_to_fp16)[name = string("op_8718_cast_fp16")];
            int32 var_8720 = const()[name = string("op_8720"), val = int32(-2)];
            bool var_8721_interleave_0 = const()[name = string("op_8721_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_8721_cast_fp16 = concat(axis = var_8720, interleave = var_8721_interleave_0, values = (var_8718_cast_fp16, var_8716_cast_fp16_0))[name = string("op_8721_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_8722_cast_fp16 = mul(x = var_8721_cast_fp16, y = var_866_cast_fp16)[name = string("op_8722_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_245_cast_fp16 = add(x = var_8715_cast_fp16, y = var_8722_cast_fp16)[name = string("key_states_245_cast_fp16")];
            tensor<int32, [1]> expand_dims_288 = const()[name = string("expand_dims_288"), val = tensor<int32, [1]>([24])];
            tensor<int32, [1]> expand_dims_289 = const()[name = string("expand_dims_289"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_291 = const()[name = string("expand_dims_291"), val = tensor<int32, [1]>([0])];
            int32 concat_293_axis_0 = const()[name = string("concat_293_axis_0"), val = int32(0)];
            bool concat_293_interleave_0 = const()[name = string("concat_293_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_293 = concat(axis = concat_293_axis_0, interleave = concat_293_interleave_0, values = (expand_dims_288, expand_dims_289, position_id, expand_dims_291))[name = string("concat_293")];
            tensor<int32, [1]> expand_dims_292 = const()[name = string("expand_dims_292"), val = tensor<int32, [1]>([25])];
            tensor<int32, [1]> concat_294_values1_0 = const()[name = string("concat_294_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_294_values3_0 = const()[name = string("concat_294_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_294_axis_0 = const()[name = string("concat_294_axis_0"), val = int32(0)];
            bool concat_294_interleave_0 = const()[name = string("concat_294_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_294 = concat(axis = concat_294_axis_0, interleave = concat_294_interleave_0, values = (expand_dims_292, concat_294_values1_0, cache_position_end, concat_294_values3_0))[name = string("concat_294")];
            tensor<int32, [4]> key_states_247_perm_0 = const()[name = string("key_states_247_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_25_stride_0 = const()[name = string("key_cache_internal_tensor_assign_25_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_25_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_25_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_25_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_25_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_25_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_25_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_247_cast_fp16 = transpose(perm = key_states_247_perm_0, x = key_states_245_cast_fp16)[name = string("transpose_533")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_25_cast_fp16 = slice_update(begin = concat_293, begin_mask = key_cache_internal_tensor_assign_25_begin_mask_0, end = concat_294, end_mask = key_cache_internal_tensor_assign_25_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_25_squeeze_mask_0, stride = key_cache_internal_tensor_assign_25_stride_0, update = key_states_247_cast_fp16, x = coreml_update_state_382)[name = string("key_cache_internal_tensor_assign_25_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_25_cast_fp16, input = key_cache)[name = string("coreml_update_state_384_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_384 = read_state(input = key_cache)[name = string("coreml_update_state_384")];
            tensor<int32, [4]> value_states_147_perm_0 = const()[name = string("value_states_147_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_25_stride_0 = const()[name = string("value_cache_internal_tensor_assign_25_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_25_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_25_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_25_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_25_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_25_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_25_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_147_cast_fp16 = transpose(perm = value_states_147_perm_0, x = var_8698_cast_fp16)[name = string("transpose_532")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_25_cast_fp16 = slice_update(begin = concat_293, begin_mask = value_cache_internal_tensor_assign_25_begin_mask_0, end = concat_294, end_mask = value_cache_internal_tensor_assign_25_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_25_squeeze_mask_0, stride = value_cache_internal_tensor_assign_25_stride_0, update = value_states_147_cast_fp16, x = coreml_update_state_383)[name = string("value_cache_internal_tensor_assign_25_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_25_cast_fp16, input = value_cache)[name = string("coreml_update_state_385_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_385 = read_state(input = value_cache)[name = string("coreml_update_state_385")];
            tensor<int32, [4]> var_8792_begin_0 = const()[name = string("op_8792_begin_0"), val = tensor<int32, [4]>([24, 0, 0, 0])];
            tensor<int32, [4]> var_8792_end_0 = const()[name = string("op_8792_end_0"), val = tensor<int32, [4]>([25, 2, 2048, 128])];
            tensor<bool, [4]> var_8792_end_mask_0 = const()[name = string("op_8792_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8792_cast_fp16 = slice_by_index(begin = var_8792_begin_0, end = var_8792_end_0, end_mask = var_8792_end_mask_0, x = coreml_update_state_384)[name = string("op_8792_cast_fp16")];
            tensor<int32, [2]> tile_48 = const()[name = string("tile_48"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8795_axis_0 = const()[name = string("op_8795_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8795_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8795_cast_fp16_1 = split(axis = var_8795_axis_0, split_sizes = tile_48, x = var_8792_cast_fp16)[name = string("op_8795_cast_fp16")];
            tensor<int32, [4]> var_8802_begin_0 = const()[name = string("op_8802_begin_0"), val = tensor<int32, [4]>([24, 0, 0, 0])];
            tensor<int32, [4]> var_8802_end_0 = const()[name = string("op_8802_end_0"), val = tensor<int32, [4]>([25, 2, 2048, 128])];
            tensor<bool, [4]> var_8802_end_mask_0 = const()[name = string("op_8802_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8802_cast_fp16 = slice_by_index(begin = var_8802_begin_0, end = var_8802_end_0, end_mask = var_8802_end_mask_0, x = coreml_update_state_385)[name = string("op_8802_cast_fp16")];
            tensor<int32, [2]> tile_49 = const()[name = string("tile_49"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8805_axis_0 = const()[name = string("op_8805_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8805_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8805_cast_fp16_1 = split(axis = var_8805_axis_0, split_sizes = tile_49, x = var_8802_cast_fp16)[name = string("op_8805_cast_fp16")];
            tensor<int32, [2]> var_8808_split_sizes_0 = const()[name = string("op_8808_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_8808_axis_0 = const()[name = string("op_8808_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_8808_0, tensor<fp16, [1, 8, 128, 128]> var_8808_1 = split(axis = var_8808_axis_0, split_sizes = var_8808_split_sizes_0, x = query_states_147_cast_fp16)[name = string("op_8808")];
            bool attn_weights_385_transpose_x_0 = const()[name = string("attn_weights_385_transpose_x_0"), val = bool(false)];
            bool attn_weights_385_transpose_y_0 = const()[name = string("attn_weights_385_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_385_cast_fp16 = matmul(transpose_x = attn_weights_385_transpose_x_0, transpose_y = attn_weights_385_transpose_y_0, x = var_8795_cast_fp16_0, y = var_8808_0)[name = string("attn_weights_385_cast_fp16")];
            fp16 var_8811_to_fp16 = const()[name = string("op_8811_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_387_cast_fp16 = mul(x = attn_weights_385_cast_fp16, y = var_8811_to_fp16)[name = string("attn_weights_387_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_389_cast_fp16 = add(x = attn_weights_387_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_389_cast_fp16")];
            int32 var_8815 = const()[name = string("op_8815"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_391_cast_fp16 = softmax(axis = var_8815, x = attn_weights_389_cast_fp16)[name = string("attn_weights_391_cast_fp16")];
            bool var_8821_transpose_x_1 = const()[name = string("op_8821_transpose_x_1"), val = bool(true)];
            bool var_8821_transpose_y_1 = const()[name = string("op_8821_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_8821_cast_fp16 = matmul(transpose_x = var_8821_transpose_x_1, transpose_y = var_8821_transpose_y_1, x = attn_weights_391_cast_fp16, y = var_8805_cast_fp16_0)[name = string("op_8821_cast_fp16")];
            bool attn_weights_393_transpose_x_0 = const()[name = string("attn_weights_393_transpose_x_0"), val = bool(false)];
            bool attn_weights_393_transpose_y_0 = const()[name = string("attn_weights_393_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_393_cast_fp16 = matmul(transpose_x = attn_weights_393_transpose_x_0, transpose_y = attn_weights_393_transpose_y_0, x = var_8795_cast_fp16_1, y = var_8808_1)[name = string("attn_weights_393_cast_fp16")];
            fp16 var_8823_to_fp16 = const()[name = string("op_8823_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_395_cast_fp16 = mul(x = attn_weights_393_cast_fp16, y = var_8823_to_fp16)[name = string("attn_weights_395_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_397_cast_fp16 = add(x = attn_weights_395_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_397_cast_fp16")];
            int32 var_8827 = const()[name = string("op_8827"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_399_cast_fp16 = softmax(axis = var_8827, x = attn_weights_397_cast_fp16)[name = string("attn_weights_399_cast_fp16")];
            bool attn_output_193_transpose_x_1 = const()[name = string("attn_output_193_transpose_x_1"), val = bool(true)];
            bool attn_output_193_transpose_y_1 = const()[name = string("attn_output_193_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_193_cast_fp16 = matmul(transpose_x = attn_output_193_transpose_x_1, transpose_y = attn_output_193_transpose_y_1, x = attn_weights_399_cast_fp16, y = var_8805_cast_fp16_1)[name = string("attn_output_193_cast_fp16")];
            int32 var_8835 = const()[name = string("op_8835"), val = int32(1)];
            bool attn_output_195_interleave_0 = const()[name = string("attn_output_195_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_195_cast_fp16 = concat(axis = var_8835, interleave = attn_output_195_interleave_0, values = (var_8821_cast_fp16, attn_output_193_cast_fp16))[name = string("attn_output_195_cast_fp16")];
            tensor<int32, [4]> var_8839_perm_0 = const()[name = string("op_8839_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_299x = const()[name = string("concat_299x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_8839_cast_fp16 = transpose(perm = var_8839_perm_0, x = attn_output_195_cast_fp16)[name = string("transpose_531")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_199_cast_fp16 = reshape(shape = concat_299x, x = var_8839_cast_fp16)[name = string("attn_output_199_cast_fp16")];
            tensor<int32, [2]> hidden_states_243_strides_0 = const()[name = string("hidden_states_243_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_243_pad_type_0 = const()[name = string("hidden_states_243_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_243_pad_0 = const()[name = string("hidden_states_243_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_243_dilations_0 = const()[name = string("hidden_states_243_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_243_groups_0 = const()[name = string("hidden_states_243_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_243_cast_fp16 = conv(dilations = hidden_states_243_dilations_0, groups = hidden_states_243_groups_0, pad = hidden_states_243_pad_0, pad_type = hidden_states_243_pad_type_0, strides = hidden_states_243_strides_0, weight = layers_24_self_attn_o_proj_weight_cast_fp16, x = attn_output_199_cast_fp16)[name = string("hidden_states_243_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_245_cast_fp16 = add(x = hidden_states_239_cast_fp16, y = hidden_states_243_cast_fp16)[name = string("hidden_states_245_cast_fp16")];
            fp16 const_250_promoted_to_fp16 = const()[name = string("const_250_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_8872_cast_fp16 = mul(x = hidden_states_245_cast_fp16, y = const_250_promoted_to_fp16)[name = string("op_8872_cast_fp16")];
            int32 var_8870 = const()[name = string("op_8870"), val = int32(1)];
            bool doubled_197_interleave_0 = const()[name = string("doubled_197_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_197_cast_fp16 = concat(axis = var_8870, interleave = doubled_197_interleave_0, values = (hidden_states_245_cast_fp16, var_8872_cast_fp16))[name = string("doubled_197_cast_fp16")];
            tensor<int32, [1]> out_99_axes_0 = const()[name = string("out_99_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_99_gamma_0_to_fp16 = const()[name = string("out_99_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1531982016)))];
            fp16 var_8882_to_fp16 = const()[name = string("op_8882_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_99_cast_fp16 = layer_norm(axes = out_99_axes_0, epsilon = var_8882_to_fp16, gamma = out_99_gamma_0_to_fp16, x = doubled_197_cast_fp16)[name = string("out_99_cast_fp16")];
            tensor<int32, [2]> var_8893_split_sizes_0 = const()[name = string("op_8893_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8893_axis_0 = const()[name = string("op_8893_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_8893_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_8893_cast_fp16_1 = split(axis = var_8893_axis_0, split_sizes = var_8893_split_sizes_0, x = out_99_cast_fp16)[name = string("op_8893_cast_fp16")];
            tensor<int32, [2]> input_49_strides_0 = const()[name = string("input_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_49_pad_type_0 = const()[name = string("input_49_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_49_pad_0 = const()[name = string("input_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_49_dilations_0 = const()[name = string("input_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_49_groups_0 = const()[name = string("input_49_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_49_cast_fp16 = conv(dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = layers_24_mlp_gate_proj_weight_cast_fp16, x = var_8893_cast_fp16_0)[name = string("input_49_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_8910_cast_fp16 = silu(x = input_49_cast_fp16)[name = string("op_8910_cast_fp16")];
            tensor<int32, [2]> var_8916_strides_0 = const()[name = string("op_8916_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_8916_pad_type_0 = const()[name = string("op_8916_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_8916_pad_0 = const()[name = string("op_8916_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_8916_dilations_0 = const()[name = string("op_8916_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8916_groups_0 = const()[name = string("op_8916_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_8916_cast_fp16 = conv(dilations = var_8916_dilations_0, groups = var_8916_groups_0, pad = var_8916_pad_0, pad_type = var_8916_pad_type_0, strides = var_8916_strides_0, weight = layers_24_mlp_up_proj_weight_cast_fp16, x = var_8893_cast_fp16_0)[name = string("op_8916_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_249_cast_fp16 = mul(x = var_8910_cast_fp16, y = var_8916_cast_fp16)[name = string("x_249_cast_fp16")];
            tensor<int32, [2]> hidden_states_247_strides_0 = const()[name = string("hidden_states_247_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_247_pad_type_0 = const()[name = string("hidden_states_247_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_247_pad_0 = const()[name = string("hidden_states_247_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_247_dilations_0 = const()[name = string("hidden_states_247_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_247_groups_0 = const()[name = string("hidden_states_247_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_247_cast_fp16 = conv(dilations = hidden_states_247_dilations_0, groups = hidden_states_247_groups_0, pad = hidden_states_247_pad_0, pad_type = hidden_states_247_pad_type_0, strides = hidden_states_247_strides_0, weight = layers_24_mlp_down_proj_weight_cast_fp16, x = x_249_cast_fp16)[name = string("hidden_states_247_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_249_cast_fp16 = add(x = hidden_states_245_cast_fp16, y = hidden_states_247_cast_fp16)[name = string("hidden_states_249_cast_fp16")];
            fp16 const_252_promoted_to_fp16 = const()[name = string("const_252_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_8934_cast_fp16 = mul(x = hidden_states_249_cast_fp16, y = const_252_promoted_to_fp16)[name = string("op_8934_cast_fp16")];
            int32 var_8932 = const()[name = string("op_8932"), val = int32(1)];
            bool doubled_201_interleave_0 = const()[name = string("doubled_201_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_201_cast_fp16 = concat(axis = var_8932, interleave = doubled_201_interleave_0, values = (hidden_states_249_cast_fp16, var_8934_cast_fp16))[name = string("doubled_201_cast_fp16")];
            tensor<int32, [1]> out_101_axes_0 = const()[name = string("out_101_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_101_gamma_0_to_fp16 = const()[name = string("out_101_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1531990272)))];
            fp16 var_8944_to_fp16 = const()[name = string("op_8944_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_101_cast_fp16 = layer_norm(axes = out_101_axes_0, epsilon = var_8944_to_fp16, gamma = out_101_gamma_0_to_fp16, x = doubled_201_cast_fp16)[name = string("out_101_cast_fp16")];
            tensor<int32, [2]> var_8955_split_sizes_0 = const()[name = string("op_8955_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8955_axis_0 = const()[name = string("op_8955_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_8955_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_8955_cast_fp16_1 = split(axis = var_8955_axis_0, split_sizes = var_8955_split_sizes_0, x = out_101_cast_fp16)[name = string("op_8955_cast_fp16")];
            tensor<int32, [2]> query_states_151_strides_0 = const()[name = string("query_states_151_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_151_pad_type_0 = const()[name = string("query_states_151_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_151_pad_0 = const()[name = string("query_states_151_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_151_dilations_0 = const()[name = string("query_states_151_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_151_groups_0 = const()[name = string("query_states_151_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_151_cast_fp16 = conv(dilations = query_states_151_dilations_0, groups = query_states_151_groups_0, pad = query_states_151_pad_0, pad_type = query_states_151_pad_type_0, strides = query_states_151_strides_0, weight = layers_25_self_attn_q_proj_weight_cast_fp16, x = var_8955_cast_fp16_0)[name = string("query_states_151_cast_fp16")];
            tensor<int32, [2]> key_states_251_strides_0 = const()[name = string("key_states_251_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_251_pad_type_0 = const()[name = string("key_states_251_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_251_pad_0 = const()[name = string("key_states_251_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_251_dilations_0 = const()[name = string("key_states_251_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_251_groups_0 = const()[name = string("key_states_251_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_251_cast_fp16 = conv(dilations = key_states_251_dilations_0, groups = key_states_251_groups_0, pad = key_states_251_pad_0, pad_type = key_states_251_pad_type_0, strides = key_states_251_strides_0, weight = layers_25_self_attn_k_proj_weight_cast_fp16, x = var_8955_cast_fp16_0)[name = string("key_states_251_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_25_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_25_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1531998528)))];
            tensor<int32, [2]> value_states_151_strides_0 = const()[name = string("value_states_151_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_151_pad_type_0 = const()[name = string("value_states_151_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_151_pad_0 = const()[name = string("value_states_151_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_151_dilations_0 = const()[name = string("value_states_151_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_151_groups_0 = const()[name = string("value_states_151_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_151_cast_fp16 = conv(dilations = value_states_151_dilations_0, groups = value_states_151_groups_0, pad = value_states_151_pad_0, pad_type = value_states_151_pad_type_0, strides = value_states_151_strides_0, weight = layers_25_self_attn_v_proj_weight_to_fp16, x = var_8955_cast_fp16_0)[name = string("value_states_151_cast_fp16")];
            tensor<int32, [4]> concat_300x = const()[name = string("concat_300x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_251_cast_fp16 = reshape(shape = concat_300x, x = query_states_151_cast_fp16)[name = string("x_251_cast_fp16")];
            tensor<int32, [4]> concat_301x = const()[name = string("concat_301x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_9012_cast_fp16 = reshape(shape = concat_301x, x = key_states_251_cast_fp16)[name = string("op_9012_cast_fp16")];
            tensor<int32, [4]> concat_302x = const()[name = string("concat_302x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_9019_cast_fp16 = reshape(shape = concat_302x, x = value_states_151_cast_fp16)[name = string("op_9019_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_9023_cast_fp16 = mul(x = x_251_cast_fp16, y = var_859_cast_fp16)[name = string("op_9023_cast_fp16")];
            tensor<int32, [2]> var_9024_split_sizes_0 = const()[name = string("op_9024_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9024_axis_0 = const()[name = string("op_9024_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_9024_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_9024_cast_fp16_1 = split(axis = var_9024_axis_0, split_sizes = var_9024_split_sizes_0, x = x_251_cast_fp16)[name = string("op_9024_cast_fp16")];
            fp16 const_254_promoted_to_fp16 = const()[name = string("const_254_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_9026_cast_fp16 = mul(x = var_9024_cast_fp16_1, y = const_254_promoted_to_fp16)[name = string("op_9026_cast_fp16")];
            int32 var_9028 = const()[name = string("op_9028"), val = int32(-2)];
            bool var_9029_interleave_0 = const()[name = string("op_9029_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_9029_cast_fp16 = concat(axis = var_9028, interleave = var_9029_interleave_0, values = (var_9026_cast_fp16, var_9024_cast_fp16_0))[name = string("op_9029_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_9030_cast_fp16 = mul(x = var_9029_cast_fp16, y = var_866_cast_fp16)[name = string("op_9030_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_153_cast_fp16 = add(x = var_9023_cast_fp16, y = var_9030_cast_fp16)[name = string("query_states_153_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_9036_cast_fp16 = mul(x = var_9012_cast_fp16, y = var_859_cast_fp16)[name = string("op_9036_cast_fp16")];
            tensor<int32, [2]> var_9037_split_sizes_0 = const()[name = string("op_9037_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9037_axis_0 = const()[name = string("op_9037_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_9037_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_9037_cast_fp16_1 = split(axis = var_9037_axis_0, split_sizes = var_9037_split_sizes_0, x = var_9012_cast_fp16)[name = string("op_9037_cast_fp16")];
            fp16 const_255_promoted_to_fp16 = const()[name = string("const_255_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_9039_cast_fp16 = mul(x = var_9037_cast_fp16_1, y = const_255_promoted_to_fp16)[name = string("op_9039_cast_fp16")];
            int32 var_9041 = const()[name = string("op_9041"), val = int32(-2)];
            bool var_9042_interleave_0 = const()[name = string("op_9042_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_9042_cast_fp16 = concat(axis = var_9041, interleave = var_9042_interleave_0, values = (var_9039_cast_fp16, var_9037_cast_fp16_0))[name = string("op_9042_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_9043_cast_fp16 = mul(x = var_9042_cast_fp16, y = var_866_cast_fp16)[name = string("op_9043_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_255_cast_fp16 = add(x = var_9036_cast_fp16, y = var_9043_cast_fp16)[name = string("key_states_255_cast_fp16")];
            tensor<int32, [1]> expand_dims_300 = const()[name = string("expand_dims_300"), val = tensor<int32, [1]>([25])];
            tensor<int32, [1]> expand_dims_301 = const()[name = string("expand_dims_301"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_303 = const()[name = string("expand_dims_303"), val = tensor<int32, [1]>([0])];
            int32 concat_305_axis_0 = const()[name = string("concat_305_axis_0"), val = int32(0)];
            bool concat_305_interleave_0 = const()[name = string("concat_305_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_305 = concat(axis = concat_305_axis_0, interleave = concat_305_interleave_0, values = (expand_dims_300, expand_dims_301, position_id, expand_dims_303))[name = string("concat_305")];
            tensor<int32, [1]> expand_dims_304 = const()[name = string("expand_dims_304"), val = tensor<int32, [1]>([26])];
            tensor<int32, [1]> concat_306_values1_0 = const()[name = string("concat_306_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_306_values3_0 = const()[name = string("concat_306_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_306_axis_0 = const()[name = string("concat_306_axis_0"), val = int32(0)];
            bool concat_306_interleave_0 = const()[name = string("concat_306_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_306 = concat(axis = concat_306_axis_0, interleave = concat_306_interleave_0, values = (expand_dims_304, concat_306_values1_0, cache_position_end, concat_306_values3_0))[name = string("concat_306")];
            tensor<int32, [4]> key_states_257_perm_0 = const()[name = string("key_states_257_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_26_stride_0 = const()[name = string("key_cache_internal_tensor_assign_26_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_26_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_26_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_26_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_26_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_26_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_26_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_257_cast_fp16 = transpose(perm = key_states_257_perm_0, x = key_states_255_cast_fp16)[name = string("transpose_530")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_26_cast_fp16 = slice_update(begin = concat_305, begin_mask = key_cache_internal_tensor_assign_26_begin_mask_0, end = concat_306, end_mask = key_cache_internal_tensor_assign_26_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_26_squeeze_mask_0, stride = key_cache_internal_tensor_assign_26_stride_0, update = key_states_257_cast_fp16, x = coreml_update_state_384)[name = string("key_cache_internal_tensor_assign_26_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_26_cast_fp16, input = key_cache)[name = string("coreml_update_state_386_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_386 = read_state(input = key_cache)[name = string("coreml_update_state_386")];
            tensor<int32, [4]> value_states_153_perm_0 = const()[name = string("value_states_153_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_26_stride_0 = const()[name = string("value_cache_internal_tensor_assign_26_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_26_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_26_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_26_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_26_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_26_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_26_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_153_cast_fp16 = transpose(perm = value_states_153_perm_0, x = var_9019_cast_fp16)[name = string("transpose_529")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_26_cast_fp16 = slice_update(begin = concat_305, begin_mask = value_cache_internal_tensor_assign_26_begin_mask_0, end = concat_306, end_mask = value_cache_internal_tensor_assign_26_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_26_squeeze_mask_0, stride = value_cache_internal_tensor_assign_26_stride_0, update = value_states_153_cast_fp16, x = coreml_update_state_385)[name = string("value_cache_internal_tensor_assign_26_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_26_cast_fp16, input = value_cache)[name = string("coreml_update_state_387_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_387 = read_state(input = value_cache)[name = string("coreml_update_state_387")];
            tensor<int32, [4]> var_9113_begin_0 = const()[name = string("op_9113_begin_0"), val = tensor<int32, [4]>([25, 0, 0, 0])];
            tensor<int32, [4]> var_9113_end_0 = const()[name = string("op_9113_end_0"), val = tensor<int32, [4]>([26, 2, 2048, 128])];
            tensor<bool, [4]> var_9113_end_mask_0 = const()[name = string("op_9113_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9113_cast_fp16 = slice_by_index(begin = var_9113_begin_0, end = var_9113_end_0, end_mask = var_9113_end_mask_0, x = coreml_update_state_386)[name = string("op_9113_cast_fp16")];
            tensor<int32, [2]> tile_50 = const()[name = string("tile_50"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9116_axis_0 = const()[name = string("op_9116_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9116_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9116_cast_fp16_1 = split(axis = var_9116_axis_0, split_sizes = tile_50, x = var_9113_cast_fp16)[name = string("op_9116_cast_fp16")];
            tensor<int32, [4]> var_9123_begin_0 = const()[name = string("op_9123_begin_0"), val = tensor<int32, [4]>([25, 0, 0, 0])];
            tensor<int32, [4]> var_9123_end_0 = const()[name = string("op_9123_end_0"), val = tensor<int32, [4]>([26, 2, 2048, 128])];
            tensor<bool, [4]> var_9123_end_mask_0 = const()[name = string("op_9123_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9123_cast_fp16 = slice_by_index(begin = var_9123_begin_0, end = var_9123_end_0, end_mask = var_9123_end_mask_0, x = coreml_update_state_387)[name = string("op_9123_cast_fp16")];
            tensor<int32, [2]> tile_51 = const()[name = string("tile_51"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9126_axis_0 = const()[name = string("op_9126_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9126_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9126_cast_fp16_1 = split(axis = var_9126_axis_0, split_sizes = tile_51, x = var_9123_cast_fp16)[name = string("op_9126_cast_fp16")];
            tensor<int32, [2]> var_9129_split_sizes_0 = const()[name = string("op_9129_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_9129_axis_0 = const()[name = string("op_9129_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_9129_0, tensor<fp16, [1, 8, 128, 128]> var_9129_1 = split(axis = var_9129_axis_0, split_sizes = var_9129_split_sizes_0, x = query_states_153_cast_fp16)[name = string("op_9129")];
            bool attn_weights_401_transpose_x_0 = const()[name = string("attn_weights_401_transpose_x_0"), val = bool(false)];
            bool attn_weights_401_transpose_y_0 = const()[name = string("attn_weights_401_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_401_cast_fp16 = matmul(transpose_x = attn_weights_401_transpose_x_0, transpose_y = attn_weights_401_transpose_y_0, x = var_9116_cast_fp16_0, y = var_9129_0)[name = string("attn_weights_401_cast_fp16")];
            fp16 var_9132_to_fp16 = const()[name = string("op_9132_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_403_cast_fp16 = mul(x = attn_weights_401_cast_fp16, y = var_9132_to_fp16)[name = string("attn_weights_403_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_405_cast_fp16 = add(x = attn_weights_403_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_405_cast_fp16")];
            int32 var_9136 = const()[name = string("op_9136"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_407_cast_fp16 = softmax(axis = var_9136, x = attn_weights_405_cast_fp16)[name = string("attn_weights_407_cast_fp16")];
            bool var_9142_transpose_x_1 = const()[name = string("op_9142_transpose_x_1"), val = bool(true)];
            bool var_9142_transpose_y_1 = const()[name = string("op_9142_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_9142_cast_fp16 = matmul(transpose_x = var_9142_transpose_x_1, transpose_y = var_9142_transpose_y_1, x = attn_weights_407_cast_fp16, y = var_9126_cast_fp16_0)[name = string("op_9142_cast_fp16")];
            bool attn_weights_409_transpose_x_0 = const()[name = string("attn_weights_409_transpose_x_0"), val = bool(false)];
            bool attn_weights_409_transpose_y_0 = const()[name = string("attn_weights_409_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_409_cast_fp16 = matmul(transpose_x = attn_weights_409_transpose_x_0, transpose_y = attn_weights_409_transpose_y_0, x = var_9116_cast_fp16_1, y = var_9129_1)[name = string("attn_weights_409_cast_fp16")];
            fp16 var_9144_to_fp16 = const()[name = string("op_9144_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_411_cast_fp16 = mul(x = attn_weights_409_cast_fp16, y = var_9144_to_fp16)[name = string("attn_weights_411_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_413_cast_fp16 = add(x = attn_weights_411_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_413_cast_fp16")];
            int32 var_9148 = const()[name = string("op_9148"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_415_cast_fp16 = softmax(axis = var_9148, x = attn_weights_413_cast_fp16)[name = string("attn_weights_415_cast_fp16")];
            bool attn_output_201_transpose_x_1 = const()[name = string("attn_output_201_transpose_x_1"), val = bool(true)];
            bool attn_output_201_transpose_y_1 = const()[name = string("attn_output_201_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_201_cast_fp16 = matmul(transpose_x = attn_output_201_transpose_x_1, transpose_y = attn_output_201_transpose_y_1, x = attn_weights_415_cast_fp16, y = var_9126_cast_fp16_1)[name = string("attn_output_201_cast_fp16")];
            int32 var_9156 = const()[name = string("op_9156"), val = int32(1)];
            bool attn_output_203_interleave_0 = const()[name = string("attn_output_203_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_203_cast_fp16 = concat(axis = var_9156, interleave = attn_output_203_interleave_0, values = (var_9142_cast_fp16, attn_output_201_cast_fp16))[name = string("attn_output_203_cast_fp16")];
            tensor<int32, [4]> var_9160_perm_0 = const()[name = string("op_9160_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_311x = const()[name = string("concat_311x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_9160_cast_fp16 = transpose(perm = var_9160_perm_0, x = attn_output_203_cast_fp16)[name = string("transpose_528")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_207_cast_fp16 = reshape(shape = concat_311x, x = var_9160_cast_fp16)[name = string("attn_output_207_cast_fp16")];
            tensor<int32, [2]> hidden_states_253_strides_0 = const()[name = string("hidden_states_253_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_253_pad_type_0 = const()[name = string("hidden_states_253_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_253_pad_0 = const()[name = string("hidden_states_253_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_253_dilations_0 = const()[name = string("hidden_states_253_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_253_groups_0 = const()[name = string("hidden_states_253_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_253_cast_fp16 = conv(dilations = hidden_states_253_dilations_0, groups = hidden_states_253_groups_0, pad = hidden_states_253_pad_0, pad_type = hidden_states_253_pad_type_0, strides = hidden_states_253_strides_0, weight = layers_25_self_attn_o_proj_weight_cast_fp16, x = attn_output_207_cast_fp16)[name = string("hidden_states_253_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_255_cast_fp16 = add(x = hidden_states_249_cast_fp16, y = hidden_states_253_cast_fp16)[name = string("hidden_states_255_cast_fp16")];
            fp16 const_260_promoted_to_fp16 = const()[name = string("const_260_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_9193_cast_fp16 = mul(x = hidden_states_255_cast_fp16, y = const_260_promoted_to_fp16)[name = string("op_9193_cast_fp16")];
            int32 var_9191 = const()[name = string("op_9191"), val = int32(1)];
            bool doubled_205_interleave_0 = const()[name = string("doubled_205_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_205_cast_fp16 = concat(axis = var_9191, interleave = doubled_205_interleave_0, values = (hidden_states_255_cast_fp16, var_9193_cast_fp16))[name = string("doubled_205_cast_fp16")];
            tensor<int32, [1]> out_103_axes_0 = const()[name = string("out_103_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_103_gamma_0_to_fp16 = const()[name = string("out_103_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1533047168)))];
            fp16 var_9203_to_fp16 = const()[name = string("op_9203_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_103_cast_fp16 = layer_norm(axes = out_103_axes_0, epsilon = var_9203_to_fp16, gamma = out_103_gamma_0_to_fp16, x = doubled_205_cast_fp16)[name = string("out_103_cast_fp16")];
            tensor<int32, [2]> var_9214_split_sizes_0 = const()[name = string("op_9214_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9214_axis_0 = const()[name = string("op_9214_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_9214_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_9214_cast_fp16_1 = split(axis = var_9214_axis_0, split_sizes = var_9214_split_sizes_0, x = out_103_cast_fp16)[name = string("op_9214_cast_fp16")];
            tensor<int32, [2]> input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_51_cast_fp16 = conv(dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = layers_25_mlp_gate_proj_weight_cast_fp16, x = var_9214_cast_fp16_0)[name = string("input_51_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_9231_cast_fp16 = silu(x = input_51_cast_fp16)[name = string("op_9231_cast_fp16")];
            tensor<int32, [2]> var_9237_strides_0 = const()[name = string("op_9237_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_9237_pad_type_0 = const()[name = string("op_9237_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_9237_pad_0 = const()[name = string("op_9237_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_9237_dilations_0 = const()[name = string("op_9237_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9237_groups_0 = const()[name = string("op_9237_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_9237_cast_fp16 = conv(dilations = var_9237_dilations_0, groups = var_9237_groups_0, pad = var_9237_pad_0, pad_type = var_9237_pad_type_0, strides = var_9237_strides_0, weight = layers_25_mlp_up_proj_weight_cast_fp16, x = var_9214_cast_fp16_0)[name = string("op_9237_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_259_cast_fp16 = mul(x = var_9231_cast_fp16, y = var_9237_cast_fp16)[name = string("x_259_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_25_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_25_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1533055424)))];
            tensor<int32, [2]> hidden_states_257_strides_0 = const()[name = string("hidden_states_257_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_257_pad_type_0 = const()[name = string("hidden_states_257_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_257_pad_0 = const()[name = string("hidden_states_257_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_257_dilations_0 = const()[name = string("hidden_states_257_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_257_groups_0 = const()[name = string("hidden_states_257_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_257_cast_fp16 = conv(dilations = hidden_states_257_dilations_0, groups = hidden_states_257_groups_0, pad = hidden_states_257_pad_0, pad_type = hidden_states_257_pad_type_0, strides = hidden_states_257_strides_0, weight = layers_25_mlp_down_proj_weight_to_fp16, x = x_259_cast_fp16)[name = string("hidden_states_257_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_259_cast_fp16 = add(x = hidden_states_255_cast_fp16, y = hidden_states_257_cast_fp16)[name = string("hidden_states_259_cast_fp16")];
            fp16 const_262_promoted_to_fp16 = const()[name = string("const_262_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_9255_cast_fp16 = mul(x = hidden_states_259_cast_fp16, y = const_262_promoted_to_fp16)[name = string("op_9255_cast_fp16")];
            int32 var_9253 = const()[name = string("op_9253"), val = int32(1)];
            bool doubled_209_interleave_0 = const()[name = string("doubled_209_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_209_cast_fp16 = concat(axis = var_9253, interleave = doubled_209_interleave_0, values = (hidden_states_259_cast_fp16, var_9255_cast_fp16))[name = string("doubled_209_cast_fp16")];
            tensor<int32, [1]> out_105_axes_0 = const()[name = string("out_105_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_105_gamma_0_to_fp16 = const()[name = string("out_105_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1558221312)))];
            fp16 var_9265_to_fp16 = const()[name = string("op_9265_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_105_cast_fp16 = layer_norm(axes = out_105_axes_0, epsilon = var_9265_to_fp16, gamma = out_105_gamma_0_to_fp16, x = doubled_209_cast_fp16)[name = string("out_105_cast_fp16")];
            tensor<int32, [2]> var_9276_split_sizes_0 = const()[name = string("op_9276_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9276_axis_0 = const()[name = string("op_9276_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_9276_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_9276_cast_fp16_1 = split(axis = var_9276_axis_0, split_sizes = var_9276_split_sizes_0, x = out_105_cast_fp16)[name = string("op_9276_cast_fp16")];
            tensor<int32, [2]> query_states_157_strides_0 = const()[name = string("query_states_157_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_157_pad_type_0 = const()[name = string("query_states_157_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_157_pad_0 = const()[name = string("query_states_157_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_157_dilations_0 = const()[name = string("query_states_157_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_157_groups_0 = const()[name = string("query_states_157_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_157_cast_fp16 = conv(dilations = query_states_157_dilations_0, groups = query_states_157_groups_0, pad = query_states_157_pad_0, pad_type = query_states_157_pad_type_0, strides = query_states_157_strides_0, weight = layers_26_self_attn_q_proj_weight_cast_fp16, x = var_9276_cast_fp16_0)[name = string("query_states_157_cast_fp16")];
            tensor<int32, [2]> key_states_261_strides_0 = const()[name = string("key_states_261_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_261_pad_type_0 = const()[name = string("key_states_261_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_261_pad_0 = const()[name = string("key_states_261_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_261_dilations_0 = const()[name = string("key_states_261_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_261_groups_0 = const()[name = string("key_states_261_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_261_cast_fp16 = conv(dilations = key_states_261_dilations_0, groups = key_states_261_groups_0, pad = key_states_261_pad_0, pad_type = key_states_261_pad_type_0, strides = key_states_261_strides_0, weight = layers_26_self_attn_k_proj_weight_cast_fp16, x = var_9276_cast_fp16_0)[name = string("key_states_261_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_26_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_26_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1558229568)))];
            tensor<int32, [2]> value_states_157_strides_0 = const()[name = string("value_states_157_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_157_pad_type_0 = const()[name = string("value_states_157_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_157_pad_0 = const()[name = string("value_states_157_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_157_dilations_0 = const()[name = string("value_states_157_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_157_groups_0 = const()[name = string("value_states_157_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_157_cast_fp16 = conv(dilations = value_states_157_dilations_0, groups = value_states_157_groups_0, pad = value_states_157_pad_0, pad_type = value_states_157_pad_type_0, strides = value_states_157_strides_0, weight = layers_26_self_attn_v_proj_weight_to_fp16, x = var_9276_cast_fp16_0)[name = string("value_states_157_cast_fp16")];
            tensor<int32, [4]> concat_312x = const()[name = string("concat_312x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_261_cast_fp16 = reshape(shape = concat_312x, x = query_states_157_cast_fp16)[name = string("x_261_cast_fp16")];
            tensor<int32, [4]> concat_313x = const()[name = string("concat_313x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_9333_cast_fp16 = reshape(shape = concat_313x, x = key_states_261_cast_fp16)[name = string("op_9333_cast_fp16")];
            tensor<int32, [4]> concat_314x = const()[name = string("concat_314x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_9340_cast_fp16 = reshape(shape = concat_314x, x = value_states_157_cast_fp16)[name = string("op_9340_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_9344_cast_fp16 = mul(x = x_261_cast_fp16, y = var_859_cast_fp16)[name = string("op_9344_cast_fp16")];
            tensor<int32, [2]> var_9345_split_sizes_0 = const()[name = string("op_9345_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9345_axis_0 = const()[name = string("op_9345_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_9345_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_9345_cast_fp16_1 = split(axis = var_9345_axis_0, split_sizes = var_9345_split_sizes_0, x = x_261_cast_fp16)[name = string("op_9345_cast_fp16")];
            fp16 const_264_promoted_to_fp16 = const()[name = string("const_264_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_9347_cast_fp16 = mul(x = var_9345_cast_fp16_1, y = const_264_promoted_to_fp16)[name = string("op_9347_cast_fp16")];
            int32 var_9349 = const()[name = string("op_9349"), val = int32(-2)];
            bool var_9350_interleave_0 = const()[name = string("op_9350_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_9350_cast_fp16 = concat(axis = var_9349, interleave = var_9350_interleave_0, values = (var_9347_cast_fp16, var_9345_cast_fp16_0))[name = string("op_9350_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_9351_cast_fp16 = mul(x = var_9350_cast_fp16, y = var_866_cast_fp16)[name = string("op_9351_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_159_cast_fp16 = add(x = var_9344_cast_fp16, y = var_9351_cast_fp16)[name = string("query_states_159_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_9357_cast_fp16 = mul(x = var_9333_cast_fp16, y = var_859_cast_fp16)[name = string("op_9357_cast_fp16")];
            tensor<int32, [2]> var_9358_split_sizes_0 = const()[name = string("op_9358_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9358_axis_0 = const()[name = string("op_9358_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_9358_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_9358_cast_fp16_1 = split(axis = var_9358_axis_0, split_sizes = var_9358_split_sizes_0, x = var_9333_cast_fp16)[name = string("op_9358_cast_fp16")];
            fp16 const_265_promoted_to_fp16 = const()[name = string("const_265_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_9360_cast_fp16 = mul(x = var_9358_cast_fp16_1, y = const_265_promoted_to_fp16)[name = string("op_9360_cast_fp16")];
            int32 var_9362 = const()[name = string("op_9362"), val = int32(-2)];
            bool var_9363_interleave_0 = const()[name = string("op_9363_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_9363_cast_fp16 = concat(axis = var_9362, interleave = var_9363_interleave_0, values = (var_9360_cast_fp16, var_9358_cast_fp16_0))[name = string("op_9363_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_9364_cast_fp16 = mul(x = var_9363_cast_fp16, y = var_866_cast_fp16)[name = string("op_9364_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_265_cast_fp16 = add(x = var_9357_cast_fp16, y = var_9364_cast_fp16)[name = string("key_states_265_cast_fp16")];
            tensor<int32, [1]> expand_dims_312 = const()[name = string("expand_dims_312"), val = tensor<int32, [1]>([26])];
            tensor<int32, [1]> expand_dims_313 = const()[name = string("expand_dims_313"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_315 = const()[name = string("expand_dims_315"), val = tensor<int32, [1]>([0])];
            int32 concat_317_axis_0 = const()[name = string("concat_317_axis_0"), val = int32(0)];
            bool concat_317_interleave_0 = const()[name = string("concat_317_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_317 = concat(axis = concat_317_axis_0, interleave = concat_317_interleave_0, values = (expand_dims_312, expand_dims_313, position_id, expand_dims_315))[name = string("concat_317")];
            tensor<int32, [1]> expand_dims_316 = const()[name = string("expand_dims_316"), val = tensor<int32, [1]>([27])];
            tensor<int32, [1]> concat_318_values1_0 = const()[name = string("concat_318_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_318_values3_0 = const()[name = string("concat_318_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_318_axis_0 = const()[name = string("concat_318_axis_0"), val = int32(0)];
            bool concat_318_interleave_0 = const()[name = string("concat_318_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_318 = concat(axis = concat_318_axis_0, interleave = concat_318_interleave_0, values = (expand_dims_316, concat_318_values1_0, cache_position_end, concat_318_values3_0))[name = string("concat_318")];
            tensor<int32, [4]> key_states_267_perm_0 = const()[name = string("key_states_267_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_27_stride_0 = const()[name = string("key_cache_internal_tensor_assign_27_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_27_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_27_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_27_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_27_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_27_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_27_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_267_cast_fp16 = transpose(perm = key_states_267_perm_0, x = key_states_265_cast_fp16)[name = string("transpose_527")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_27_cast_fp16 = slice_update(begin = concat_317, begin_mask = key_cache_internal_tensor_assign_27_begin_mask_0, end = concat_318, end_mask = key_cache_internal_tensor_assign_27_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_27_squeeze_mask_0, stride = key_cache_internal_tensor_assign_27_stride_0, update = key_states_267_cast_fp16, x = coreml_update_state_386)[name = string("key_cache_internal_tensor_assign_27_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_27_cast_fp16, input = key_cache)[name = string("coreml_update_state_388_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_388 = read_state(input = key_cache)[name = string("coreml_update_state_388")];
            tensor<int32, [4]> value_states_159_perm_0 = const()[name = string("value_states_159_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_27_stride_0 = const()[name = string("value_cache_internal_tensor_assign_27_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_27_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_27_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_27_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_27_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_27_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_27_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_159_cast_fp16 = transpose(perm = value_states_159_perm_0, x = var_9340_cast_fp16)[name = string("transpose_526")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_27_cast_fp16 = slice_update(begin = concat_317, begin_mask = value_cache_internal_tensor_assign_27_begin_mask_0, end = concat_318, end_mask = value_cache_internal_tensor_assign_27_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_27_squeeze_mask_0, stride = value_cache_internal_tensor_assign_27_stride_0, update = value_states_159_cast_fp16, x = coreml_update_state_387)[name = string("value_cache_internal_tensor_assign_27_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_27_cast_fp16, input = value_cache)[name = string("coreml_update_state_389_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_389 = read_state(input = value_cache)[name = string("coreml_update_state_389")];
            tensor<int32, [4]> var_9434_begin_0 = const()[name = string("op_9434_begin_0"), val = tensor<int32, [4]>([26, 0, 0, 0])];
            tensor<int32, [4]> var_9434_end_0 = const()[name = string("op_9434_end_0"), val = tensor<int32, [4]>([27, 2, 2048, 128])];
            tensor<bool, [4]> var_9434_end_mask_0 = const()[name = string("op_9434_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9434_cast_fp16 = slice_by_index(begin = var_9434_begin_0, end = var_9434_end_0, end_mask = var_9434_end_mask_0, x = coreml_update_state_388)[name = string("op_9434_cast_fp16")];
            tensor<int32, [2]> tile_52 = const()[name = string("tile_52"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9437_axis_0 = const()[name = string("op_9437_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9437_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9437_cast_fp16_1 = split(axis = var_9437_axis_0, split_sizes = tile_52, x = var_9434_cast_fp16)[name = string("op_9437_cast_fp16")];
            tensor<int32, [4]> var_9444_begin_0 = const()[name = string("op_9444_begin_0"), val = tensor<int32, [4]>([26, 0, 0, 0])];
            tensor<int32, [4]> var_9444_end_0 = const()[name = string("op_9444_end_0"), val = tensor<int32, [4]>([27, 2, 2048, 128])];
            tensor<bool, [4]> var_9444_end_mask_0 = const()[name = string("op_9444_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9444_cast_fp16 = slice_by_index(begin = var_9444_begin_0, end = var_9444_end_0, end_mask = var_9444_end_mask_0, x = coreml_update_state_389)[name = string("op_9444_cast_fp16")];
            tensor<int32, [2]> tile_53 = const()[name = string("tile_53"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9447_axis_0 = const()[name = string("op_9447_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9447_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9447_cast_fp16_1 = split(axis = var_9447_axis_0, split_sizes = tile_53, x = var_9444_cast_fp16)[name = string("op_9447_cast_fp16")];
            tensor<int32, [2]> var_9450_split_sizes_0 = const()[name = string("op_9450_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_9450_axis_0 = const()[name = string("op_9450_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_9450_0, tensor<fp16, [1, 8, 128, 128]> var_9450_1 = split(axis = var_9450_axis_0, split_sizes = var_9450_split_sizes_0, x = query_states_159_cast_fp16)[name = string("op_9450")];
            bool attn_weights_417_transpose_x_0 = const()[name = string("attn_weights_417_transpose_x_0"), val = bool(false)];
            bool attn_weights_417_transpose_y_0 = const()[name = string("attn_weights_417_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_417_cast_fp16 = matmul(transpose_x = attn_weights_417_transpose_x_0, transpose_y = attn_weights_417_transpose_y_0, x = var_9437_cast_fp16_0, y = var_9450_0)[name = string("attn_weights_417_cast_fp16")];
            fp16 var_9453_to_fp16 = const()[name = string("op_9453_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_419_cast_fp16 = mul(x = attn_weights_417_cast_fp16, y = var_9453_to_fp16)[name = string("attn_weights_419_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_421_cast_fp16 = add(x = attn_weights_419_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_421_cast_fp16")];
            int32 var_9457 = const()[name = string("op_9457"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_423_cast_fp16 = softmax(axis = var_9457, x = attn_weights_421_cast_fp16)[name = string("attn_weights_423_cast_fp16")];
            bool var_9463_transpose_x_1 = const()[name = string("op_9463_transpose_x_1"), val = bool(true)];
            bool var_9463_transpose_y_1 = const()[name = string("op_9463_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_9463_cast_fp16 = matmul(transpose_x = var_9463_transpose_x_1, transpose_y = var_9463_transpose_y_1, x = attn_weights_423_cast_fp16, y = var_9447_cast_fp16_0)[name = string("op_9463_cast_fp16")];
            bool attn_weights_425_transpose_x_0 = const()[name = string("attn_weights_425_transpose_x_0"), val = bool(false)];
            bool attn_weights_425_transpose_y_0 = const()[name = string("attn_weights_425_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_425_cast_fp16 = matmul(transpose_x = attn_weights_425_transpose_x_0, transpose_y = attn_weights_425_transpose_y_0, x = var_9437_cast_fp16_1, y = var_9450_1)[name = string("attn_weights_425_cast_fp16")];
            fp16 var_9465_to_fp16 = const()[name = string("op_9465_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_427_cast_fp16 = mul(x = attn_weights_425_cast_fp16, y = var_9465_to_fp16)[name = string("attn_weights_427_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_429_cast_fp16 = add(x = attn_weights_427_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_429_cast_fp16")];
            int32 var_9469 = const()[name = string("op_9469"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_431_cast_fp16 = softmax(axis = var_9469, x = attn_weights_429_cast_fp16)[name = string("attn_weights_431_cast_fp16")];
            bool attn_output_209_transpose_x_1 = const()[name = string("attn_output_209_transpose_x_1"), val = bool(true)];
            bool attn_output_209_transpose_y_1 = const()[name = string("attn_output_209_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_209_cast_fp16 = matmul(transpose_x = attn_output_209_transpose_x_1, transpose_y = attn_output_209_transpose_y_1, x = attn_weights_431_cast_fp16, y = var_9447_cast_fp16_1)[name = string("attn_output_209_cast_fp16")];
            int32 var_9477 = const()[name = string("op_9477"), val = int32(1)];
            bool attn_output_211_interleave_0 = const()[name = string("attn_output_211_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_211_cast_fp16 = concat(axis = var_9477, interleave = attn_output_211_interleave_0, values = (var_9463_cast_fp16, attn_output_209_cast_fp16))[name = string("attn_output_211_cast_fp16")];
            tensor<int32, [4]> var_9481_perm_0 = const()[name = string("op_9481_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_323x = const()[name = string("concat_323x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_9481_cast_fp16 = transpose(perm = var_9481_perm_0, x = attn_output_211_cast_fp16)[name = string("transpose_525")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_215_cast_fp16 = reshape(shape = concat_323x, x = var_9481_cast_fp16)[name = string("attn_output_215_cast_fp16")];
            tensor<int32, [2]> hidden_states_263_strides_0 = const()[name = string("hidden_states_263_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_263_pad_type_0 = const()[name = string("hidden_states_263_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_263_pad_0 = const()[name = string("hidden_states_263_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_263_dilations_0 = const()[name = string("hidden_states_263_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_263_groups_0 = const()[name = string("hidden_states_263_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_263_cast_fp16 = conv(dilations = hidden_states_263_dilations_0, groups = hidden_states_263_groups_0, pad = hidden_states_263_pad_0, pad_type = hidden_states_263_pad_type_0, strides = hidden_states_263_strides_0, weight = layers_26_self_attn_o_proj_weight_cast_fp16, x = attn_output_215_cast_fp16)[name = string("hidden_states_263_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_265_cast_fp16 = add(x = hidden_states_259_cast_fp16, y = hidden_states_263_cast_fp16)[name = string("hidden_states_265_cast_fp16")];
            fp16 const_270_promoted_to_fp16 = const()[name = string("const_270_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_9514_cast_fp16 = mul(x = hidden_states_265_cast_fp16, y = const_270_promoted_to_fp16)[name = string("op_9514_cast_fp16")];
            int32 var_9512 = const()[name = string("op_9512"), val = int32(1)];
            bool doubled_213_interleave_0 = const()[name = string("doubled_213_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_213_cast_fp16 = concat(axis = var_9512, interleave = doubled_213_interleave_0, values = (hidden_states_265_cast_fp16, var_9514_cast_fp16))[name = string("doubled_213_cast_fp16")];
            tensor<int32, [1]> out_107_axes_0 = const()[name = string("out_107_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_107_gamma_0_to_fp16 = const()[name = string("out_107_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1559278208)))];
            fp16 var_9524_to_fp16 = const()[name = string("op_9524_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_107_cast_fp16 = layer_norm(axes = out_107_axes_0, epsilon = var_9524_to_fp16, gamma = out_107_gamma_0_to_fp16, x = doubled_213_cast_fp16)[name = string("out_107_cast_fp16")];
            tensor<int32, [2]> var_9535_split_sizes_0 = const()[name = string("op_9535_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9535_axis_0 = const()[name = string("op_9535_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_9535_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_9535_cast_fp16_1 = split(axis = var_9535_axis_0, split_sizes = var_9535_split_sizes_0, x = out_107_cast_fp16)[name = string("op_9535_cast_fp16")];
            tensor<int32, [2]> input_53_strides_0 = const()[name = string("input_53_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_53_pad_type_0 = const()[name = string("input_53_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_53_dilations_0 = const()[name = string("input_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_53_groups_0 = const()[name = string("input_53_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_53_cast_fp16 = conv(dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = layers_26_mlp_gate_proj_weight_cast_fp16, x = var_9535_cast_fp16_0)[name = string("input_53_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_9552_cast_fp16 = silu(x = input_53_cast_fp16)[name = string("op_9552_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_26_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_26_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1559286464)))];
            tensor<int32, [2]> var_9558_strides_0 = const()[name = string("op_9558_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_9558_pad_type_0 = const()[name = string("op_9558_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_9558_pad_0 = const()[name = string("op_9558_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_9558_dilations_0 = const()[name = string("op_9558_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9558_groups_0 = const()[name = string("op_9558_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_9558_cast_fp16 = conv(dilations = var_9558_dilations_0, groups = var_9558_groups_0, pad = var_9558_pad_0, pad_type = var_9558_pad_type_0, strides = var_9558_strides_0, weight = layers_26_mlp_up_proj_weight_to_fp16, x = var_9535_cast_fp16_0)[name = string("op_9558_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_269_cast_fp16 = mul(x = var_9552_cast_fp16, y = var_9558_cast_fp16)[name = string("x_269_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_26_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_26_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1584452352)))];
            tensor<int32, [2]> hidden_states_267_strides_0 = const()[name = string("hidden_states_267_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_267_pad_type_0 = const()[name = string("hidden_states_267_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_267_pad_0 = const()[name = string("hidden_states_267_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_267_dilations_0 = const()[name = string("hidden_states_267_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_267_groups_0 = const()[name = string("hidden_states_267_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_267_cast_fp16 = conv(dilations = hidden_states_267_dilations_0, groups = hidden_states_267_groups_0, pad = hidden_states_267_pad_0, pad_type = hidden_states_267_pad_type_0, strides = hidden_states_267_strides_0, weight = layers_26_mlp_down_proj_weight_to_fp16, x = x_269_cast_fp16)[name = string("hidden_states_267_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_269_cast_fp16 = add(x = hidden_states_265_cast_fp16, y = hidden_states_267_cast_fp16)[name = string("hidden_states_269_cast_fp16")];
            fp16 const_272_promoted_to_fp16 = const()[name = string("const_272_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_9576_cast_fp16 = mul(x = hidden_states_269_cast_fp16, y = const_272_promoted_to_fp16)[name = string("op_9576_cast_fp16")];
            int32 var_9574 = const()[name = string("op_9574"), val = int32(1)];
            bool doubled_217_interleave_0 = const()[name = string("doubled_217_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_217_cast_fp16 = concat(axis = var_9574, interleave = doubled_217_interleave_0, values = (hidden_states_269_cast_fp16, var_9576_cast_fp16))[name = string("doubled_217_cast_fp16")];
            tensor<int32, [1]> out_109_axes_0 = const()[name = string("out_109_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_109_gamma_0_to_fp16 = const()[name = string("out_109_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1609618240)))];
            fp16 var_9586_to_fp16 = const()[name = string("op_9586_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_109_cast_fp16 = layer_norm(axes = out_109_axes_0, epsilon = var_9586_to_fp16, gamma = out_109_gamma_0_to_fp16, x = doubled_217_cast_fp16)[name = string("out_109_cast_fp16")];
            tensor<int32, [2]> var_9597_split_sizes_0 = const()[name = string("op_9597_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9597_axis_0 = const()[name = string("op_9597_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_9597_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_9597_cast_fp16_1 = split(axis = var_9597_axis_0, split_sizes = var_9597_split_sizes_0, x = out_109_cast_fp16)[name = string("op_9597_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_27_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1609626496)))];
            tensor<int32, [2]> query_states_163_strides_0 = const()[name = string("query_states_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_163_pad_type_0 = const()[name = string("query_states_163_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_163_pad_0 = const()[name = string("query_states_163_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_163_dilations_0 = const()[name = string("query_states_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_163_groups_0 = const()[name = string("query_states_163_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> query_states_163_cast_fp16 = conv(dilations = query_states_163_dilations_0, groups = query_states_163_groups_0, pad = query_states_163_pad_0, pad_type = query_states_163_pad_type_0, strides = query_states_163_strides_0, weight = layers_27_self_attn_q_proj_weight_to_fp16, x = var_9597_cast_fp16_0)[name = string("query_states_163_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_27_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1618015168)))];
            tensor<int32, [2]> key_states_271_strides_0 = const()[name = string("key_states_271_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_271_pad_type_0 = const()[name = string("key_states_271_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_271_pad_0 = const()[name = string("key_states_271_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_271_dilations_0 = const()[name = string("key_states_271_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_271_groups_0 = const()[name = string("key_states_271_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> key_states_271_cast_fp16 = conv(dilations = key_states_271_dilations_0, groups = key_states_271_groups_0, pad = key_states_271_pad_0, pad_type = key_states_271_pad_type_0, strides = key_states_271_strides_0, weight = layers_27_self_attn_k_proj_weight_to_fp16, x = var_9597_cast_fp16_0)[name = string("key_states_271_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_27_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1619063808)))];
            tensor<int32, [2]> value_states_163_strides_0 = const()[name = string("value_states_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_163_pad_type_0 = const()[name = string("value_states_163_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_163_pad_0 = const()[name = string("value_states_163_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_163_dilations_0 = const()[name = string("value_states_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_163_groups_0 = const()[name = string("value_states_163_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 128]> value_states_163_cast_fp16 = conv(dilations = value_states_163_dilations_0, groups = value_states_163_groups_0, pad = value_states_163_pad_0, pad_type = value_states_163_pad_type_0, strides = value_states_163_strides_0, weight = layers_27_self_attn_v_proj_weight_to_fp16, x = var_9597_cast_fp16_0)[name = string("value_states_163_cast_fp16")];
            tensor<int32, [4]> concat_324x = const()[name = string("concat_324x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 128]> x_271_cast_fp16 = reshape(shape = concat_324x, x = query_states_163_cast_fp16)[name = string("x_271_cast_fp16")];
            tensor<int32, [4]> concat_325x = const()[name = string("concat_325x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_9654_cast_fp16 = reshape(shape = concat_325x, x = key_states_271_cast_fp16)[name = string("op_9654_cast_fp16")];
            tensor<int32, [4]> concat_326x = const()[name = string("concat_326x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 128]> var_9661_cast_fp16 = reshape(shape = concat_326x, x = value_states_163_cast_fp16)[name = string("op_9661_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_9665_cast_fp16 = mul(x = x_271_cast_fp16, y = var_859_cast_fp16)[name = string("op_9665_cast_fp16")];
            tensor<int32, [2]> var_9666_split_sizes_0 = const()[name = string("op_9666_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9666_axis_0 = const()[name = string("op_9666_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 128]> var_9666_cast_fp16_0, tensor<fp16, [1, 16, 64, 128]> var_9666_cast_fp16_1 = split(axis = var_9666_axis_0, split_sizes = var_9666_split_sizes_0, x = x_271_cast_fp16)[name = string("op_9666_cast_fp16")];
            fp16 const_274_promoted_to_fp16 = const()[name = string("const_274_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 128]> var_9668_cast_fp16 = mul(x = var_9666_cast_fp16_1, y = const_274_promoted_to_fp16)[name = string("op_9668_cast_fp16")];
            int32 var_9670 = const()[name = string("op_9670"), val = int32(-2)];
            bool var_9671_interleave_0 = const()[name = string("op_9671_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> var_9671_cast_fp16 = concat(axis = var_9670, interleave = var_9671_interleave_0, values = (var_9668_cast_fp16, var_9666_cast_fp16_0))[name = string("op_9671_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> var_9672_cast_fp16 = mul(x = var_9671_cast_fp16, y = var_866_cast_fp16)[name = string("op_9672_cast_fp16")];
            tensor<fp16, [1, 16, 128, 128]> query_states_165_cast_fp16 = add(x = var_9665_cast_fp16, y = var_9672_cast_fp16)[name = string("query_states_165_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_9678_cast_fp16 = mul(x = var_9654_cast_fp16, y = var_859_cast_fp16)[name = string("op_9678_cast_fp16")];
            tensor<int32, [2]> var_9679_split_sizes_0 = const()[name = string("op_9679_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9679_axis_0 = const()[name = string("op_9679_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 128]> var_9679_cast_fp16_0, tensor<fp16, [1, 2, 64, 128]> var_9679_cast_fp16_1 = split(axis = var_9679_axis_0, split_sizes = var_9679_split_sizes_0, x = var_9654_cast_fp16)[name = string("op_9679_cast_fp16")];
            fp16 const_275_promoted_to_fp16 = const()[name = string("const_275_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 128]> var_9681_cast_fp16 = mul(x = var_9679_cast_fp16_1, y = const_275_promoted_to_fp16)[name = string("op_9681_cast_fp16")];
            int32 var_9683 = const()[name = string("op_9683"), val = int32(-2)];
            bool var_9684_interleave_0 = const()[name = string("op_9684_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 128]> var_9684_cast_fp16 = concat(axis = var_9683, interleave = var_9684_interleave_0, values = (var_9681_cast_fp16, var_9679_cast_fp16_0))[name = string("op_9684_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> var_9685_cast_fp16 = mul(x = var_9684_cast_fp16, y = var_866_cast_fp16)[name = string("op_9685_cast_fp16")];
            tensor<fp16, [1, 2, 128, 128]> key_states_275_cast_fp16 = add(x = var_9678_cast_fp16, y = var_9685_cast_fp16)[name = string("key_states_275_cast_fp16")];
            tensor<int32, [1]> expand_dims_324 = const()[name = string("expand_dims_324"), val = tensor<int32, [1]>([27])];
            tensor<int32, [1]> expand_dims_325 = const()[name = string("expand_dims_325"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_327 = const()[name = string("expand_dims_327"), val = tensor<int32, [1]>([0])];
            int32 concat_329_axis_0 = const()[name = string("concat_329_axis_0"), val = int32(0)];
            bool concat_329_interleave_0 = const()[name = string("concat_329_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_329 = concat(axis = concat_329_axis_0, interleave = concat_329_interleave_0, values = (expand_dims_324, expand_dims_325, position_id, expand_dims_327))[name = string("concat_329")];
            tensor<int32, [1]> expand_dims_328 = const()[name = string("expand_dims_328"), val = tensor<int32, [1]>([28])];
            tensor<int32, [1]> concat_330_values1_0 = const()[name = string("concat_330_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_330_values3_0 = const()[name = string("concat_330_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_330_axis_0 = const()[name = string("concat_330_axis_0"), val = int32(0)];
            bool concat_330_interleave_0 = const()[name = string("concat_330_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_330 = concat(axis = concat_330_axis_0, interleave = concat_330_interleave_0, values = (expand_dims_328, concat_330_values1_0, cache_position_end, concat_330_values3_0))[name = string("concat_330")];
            tensor<int32, [4]> key_states_277_perm_0 = const()[name = string("key_states_277_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_28_stride_0 = const()[name = string("key_cache_internal_tensor_assign_28_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_28_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_28_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_28_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_28_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_28_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_28_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> key_states_277_cast_fp16 = transpose(perm = key_states_277_perm_0, x = key_states_275_cast_fp16)[name = string("transpose_524")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_28_cast_fp16 = slice_update(begin = concat_329, begin_mask = key_cache_internal_tensor_assign_28_begin_mask_0, end = concat_330, end_mask = key_cache_internal_tensor_assign_28_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_28_squeeze_mask_0, stride = key_cache_internal_tensor_assign_28_stride_0, update = key_states_277_cast_fp16, x = coreml_update_state_388)[name = string("key_cache_internal_tensor_assign_28_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_28_cast_fp16, input = key_cache)[name = string("coreml_update_state_390_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_390 = read_state(input = key_cache)[name = string("coreml_update_state_390")];
            tensor<int32, [4]> value_states_165_perm_0 = const()[name = string("value_states_165_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_28_stride_0 = const()[name = string("value_cache_internal_tensor_assign_28_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_28_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_28_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_28_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_28_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_28_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_28_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 128, 128]> value_states_165_cast_fp16 = transpose(perm = value_states_165_perm_0, x = var_9661_cast_fp16)[name = string("transpose_523")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_28_cast_fp16 = slice_update(begin = concat_329, begin_mask = value_cache_internal_tensor_assign_28_begin_mask_0, end = concat_330, end_mask = value_cache_internal_tensor_assign_28_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_28_squeeze_mask_0, stride = value_cache_internal_tensor_assign_28_stride_0, update = value_states_165_cast_fp16, x = coreml_update_state_389)[name = string("value_cache_internal_tensor_assign_28_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_28_cast_fp16, input = value_cache)[name = string("coreml_update_state_391_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_391 = read_state(input = value_cache)[name = string("coreml_update_state_391")];
            tensor<int32, [4]> var_9755_begin_0 = const()[name = string("op_9755_begin_0"), val = tensor<int32, [4]>([27, 0, 0, 0])];
            tensor<int32, [4]> var_9755_end_0 = const()[name = string("op_9755_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_9755_end_mask_0 = const()[name = string("op_9755_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9755_cast_fp16 = slice_by_index(begin = var_9755_begin_0, end = var_9755_end_0, end_mask = var_9755_end_mask_0, x = coreml_update_state_390)[name = string("op_9755_cast_fp16")];
            tensor<int32, [2]> tile_54 = const()[name = string("tile_54"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9758_axis_0 = const()[name = string("op_9758_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9758_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9758_cast_fp16_1 = split(axis = var_9758_axis_0, split_sizes = tile_54, x = var_9755_cast_fp16)[name = string("op_9758_cast_fp16")];
            tensor<int32, [4]> var_9765_begin_0 = const()[name = string("op_9765_begin_0"), val = tensor<int32, [4]>([27, 0, 0, 0])];
            tensor<int32, [4]> var_9765_end_0 = const()[name = string("op_9765_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_9765_end_mask_0 = const()[name = string("op_9765_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9765_cast_fp16 = slice_by_index(begin = var_9765_begin_0, end = var_9765_end_0, end_mask = var_9765_end_mask_0, x = coreml_update_state_391)[name = string("op_9765_cast_fp16")];
            tensor<int32, [2]> tile_55 = const()[name = string("tile_55"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9768_axis_0 = const()[name = string("op_9768_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9768_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9768_cast_fp16_1 = split(axis = var_9768_axis_0, split_sizes = tile_55, x = var_9765_cast_fp16)[name = string("op_9768_cast_fp16")];
            tensor<int32, [2]> var_9771_split_sizes_0 = const()[name = string("op_9771_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_9771_axis_0 = const()[name = string("op_9771_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 128]> var_9771_0, tensor<fp16, [1, 8, 128, 128]> var_9771_1 = split(axis = var_9771_axis_0, split_sizes = var_9771_split_sizes_0, x = query_states_165_cast_fp16)[name = string("op_9771")];
            bool attn_weights_433_transpose_x_0 = const()[name = string("attn_weights_433_transpose_x_0"), val = bool(false)];
            bool attn_weights_433_transpose_y_0 = const()[name = string("attn_weights_433_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_433_cast_fp16 = matmul(transpose_x = attn_weights_433_transpose_x_0, transpose_y = attn_weights_433_transpose_y_0, x = var_9758_cast_fp16_0, y = var_9771_0)[name = string("attn_weights_433_cast_fp16")];
            fp16 var_9774_to_fp16 = const()[name = string("op_9774_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_435_cast_fp16 = mul(x = attn_weights_433_cast_fp16, y = var_9774_to_fp16)[name = string("attn_weights_435_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_437_cast_fp16 = add(x = attn_weights_435_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_437_cast_fp16")];
            int32 var_9778 = const()[name = string("op_9778"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_439_cast_fp16 = softmax(axis = var_9778, x = attn_weights_437_cast_fp16)[name = string("attn_weights_439_cast_fp16")];
            bool var_9784_transpose_x_1 = const()[name = string("op_9784_transpose_x_1"), val = bool(true)];
            bool var_9784_transpose_y_1 = const()[name = string("op_9784_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> var_9784_cast_fp16 = matmul(transpose_x = var_9784_transpose_x_1, transpose_y = var_9784_transpose_y_1, x = attn_weights_439_cast_fp16, y = var_9768_cast_fp16_0)[name = string("op_9784_cast_fp16")];
            bool attn_weights_441_transpose_x_0 = const()[name = string("attn_weights_441_transpose_x_0"), val = bool(false)];
            bool attn_weights_441_transpose_y_0 = const()[name = string("attn_weights_441_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_441_cast_fp16 = matmul(transpose_x = attn_weights_441_transpose_x_0, transpose_y = attn_weights_441_transpose_y_0, x = var_9758_cast_fp16_1, y = var_9771_1)[name = string("attn_weights_441_cast_fp16")];
            fp16 var_9786_to_fp16 = const()[name = string("op_9786_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_443_cast_fp16 = mul(x = attn_weights_441_cast_fp16, y = var_9786_to_fp16)[name = string("attn_weights_443_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_445_cast_fp16 = add(x = attn_weights_443_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_445_cast_fp16")];
            int32 var_9790 = const()[name = string("op_9790"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 128]> attn_weights_cast_fp16 = softmax(axis = var_9790, x = attn_weights_445_cast_fp16)[name = string("attn_weights_cast_fp16")];
            bool attn_output_217_transpose_x_1 = const()[name = string("attn_output_217_transpose_x_1"), val = bool(true)];
            bool attn_output_217_transpose_y_1 = const()[name = string("attn_output_217_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 128, 128]> attn_output_217_cast_fp16 = matmul(transpose_x = attn_output_217_transpose_x_1, transpose_y = attn_output_217_transpose_y_1, x = attn_weights_cast_fp16, y = var_9768_cast_fp16_1)[name = string("attn_output_217_cast_fp16")];
            int32 var_9798 = const()[name = string("op_9798"), val = int32(1)];
            bool attn_output_219_interleave_0 = const()[name = string("attn_output_219_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 128]> attn_output_219_cast_fp16 = concat(axis = var_9798, interleave = attn_output_219_interleave_0, values = (var_9784_cast_fp16, attn_output_217_cast_fp16))[name = string("attn_output_219_cast_fp16")];
            tensor<int32, [4]> var_9802_perm_0 = const()[name = string("op_9802_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_335x = const()[name = string("concat_335x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 128]> var_9802_cast_fp16 = transpose(perm = var_9802_perm_0, x = attn_output_219_cast_fp16)[name = string("transpose_522")];
            tensor<fp16, [1, 2048, 1, 128]> attn_output_cast_fp16 = reshape(shape = concat_335x, x = var_9802_cast_fp16)[name = string("attn_output_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_27_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1620112448)))];
            tensor<int32, [2]> hidden_states_273_strides_0 = const()[name = string("hidden_states_273_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_273_pad_type_0 = const()[name = string("hidden_states_273_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_273_pad_0 = const()[name = string("hidden_states_273_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_273_dilations_0 = const()[name = string("hidden_states_273_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_273_groups_0 = const()[name = string("hidden_states_273_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_273_cast_fp16 = conv(dilations = hidden_states_273_dilations_0, groups = hidden_states_273_groups_0, pad = hidden_states_273_pad_0, pad_type = hidden_states_273_pad_type_0, strides = hidden_states_273_strides_0, weight = layers_27_self_attn_o_proj_weight_to_fp16, x = attn_output_cast_fp16)[name = string("hidden_states_273_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_275_cast_fp16 = add(x = hidden_states_269_cast_fp16, y = hidden_states_273_cast_fp16)[name = string("hidden_states_275_cast_fp16")];
            fp16 const_280_promoted_to_fp16 = const()[name = string("const_280_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_9835_cast_fp16 = mul(x = hidden_states_275_cast_fp16, y = const_280_promoted_to_fp16)[name = string("op_9835_cast_fp16")];
            int32 var_9833 = const()[name = string("op_9833"), val = int32(1)];
            bool doubled_221_interleave_0 = const()[name = string("doubled_221_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_221_cast_fp16 = concat(axis = var_9833, interleave = doubled_221_interleave_0, values = (hidden_states_275_cast_fp16, var_9835_cast_fp16))[name = string("doubled_221_cast_fp16")];
            tensor<int32, [1]> out_111_axes_0 = const()[name = string("out_111_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_111_gamma_0_to_fp16 = const()[name = string("out_111_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1628501120)))];
            fp16 var_9845_to_fp16 = const()[name = string("op_9845_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_111_cast_fp16 = layer_norm(axes = out_111_axes_0, epsilon = var_9845_to_fp16, gamma = out_111_gamma_0_to_fp16, x = doubled_221_cast_fp16)[name = string("out_111_cast_fp16")];
            tensor<int32, [2]> var_9856_split_sizes_0 = const()[name = string("op_9856_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9856_axis_0 = const()[name = string("op_9856_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> var_9856_cast_fp16_0, tensor<fp16, [1, 2048, 1, 128]> var_9856_cast_fp16_1 = split(axis = var_9856_axis_0, split_sizes = var_9856_split_sizes_0, x = out_111_cast_fp16)[name = string("op_9856_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_27_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_27_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1628509376)))];
            tensor<int32, [2]> input_strides_0 = const()[name = string("input_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_pad_type_0 = const()[name = string("input_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_pad_0 = const()[name = string("input_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_dilations_0 = const()[name = string("input_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_groups_0 = const()[name = string("input_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> input_cast_fp16 = conv(dilations = input_dilations_0, groups = input_groups_0, pad = input_pad_0, pad_type = input_pad_type_0, strides = input_strides_0, weight = layers_27_mlp_gate_proj_weight_to_fp16, x = var_9856_cast_fp16_0)[name = string("input_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> var_9873_cast_fp16 = silu(x = input_cast_fp16)[name = string("op_9873_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_27_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_27_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1653675264)))];
            tensor<int32, [2]> var_9879_strides_0 = const()[name = string("op_9879_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_9879_pad_type_0 = const()[name = string("op_9879_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_9879_pad_0 = const()[name = string("op_9879_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_9879_dilations_0 = const()[name = string("op_9879_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9879_groups_0 = const()[name = string("op_9879_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 128]> var_9879_cast_fp16 = conv(dilations = var_9879_dilations_0, groups = var_9879_groups_0, pad = var_9879_pad_0, pad_type = var_9879_pad_type_0, strides = var_9879_strides_0, weight = layers_27_mlp_up_proj_weight_to_fp16, x = var_9856_cast_fp16_0)[name = string("op_9879_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 128]> x_cast_fp16 = mul(x = var_9873_cast_fp16, y = var_9879_cast_fp16)[name = string("x_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_27_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_27_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1678841152)))];
            tensor<int32, [2]> hidden_states_277_strides_0 = const()[name = string("hidden_states_277_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_277_pad_type_0 = const()[name = string("hidden_states_277_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_277_pad_0 = const()[name = string("hidden_states_277_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_277_dilations_0 = const()[name = string("hidden_states_277_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_277_groups_0 = const()[name = string("hidden_states_277_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_277_cast_fp16 = conv(dilations = hidden_states_277_dilations_0, groups = hidden_states_277_groups_0, pad = hidden_states_277_pad_0, pad_type = hidden_states_277_pad_type_0, strides = hidden_states_277_strides_0, weight = layers_27_mlp_down_proj_weight_to_fp16, x = x_cast_fp16)[name = string("hidden_states_277_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states_cast_fp16 = add(x = hidden_states_275_cast_fp16, y = hidden_states_277_cast_fp16)[name = string("hidden_states_cast_fp16")];
            fp16 const_282_promoted_to_fp16 = const()[name = string("const_282_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 128]> var_9897_cast_fp16 = mul(x = hidden_states_cast_fp16, y = const_282_promoted_to_fp16)[name = string("op_9897_cast_fp16")];
            int32 var_9895 = const()[name = string("op_9895"), val = int32(1)];
            bool doubled_225_interleave_0 = const()[name = string("doubled_225_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 128]> doubled_225_cast_fp16 = concat(axis = var_9895, interleave = doubled_225_interleave_0, values = (hidden_states_cast_fp16, var_9897_cast_fp16))[name = string("doubled_225_cast_fp16")];
            tensor<int32, [1]> out_axes_0 = const()[name = string("out_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_gamma_0_to_fp16 = const()[name = string("out_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1704007040)))];
            fp16 var_9907_to_fp16 = const()[name = string("op_9907_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 128]> out_cast_fp16 = layer_norm(axes = out_axes_0, epsilon = var_9907_to_fp16, gamma = out_gamma_0_to_fp16, x = doubled_225_cast_fp16)[name = string("out_cast_fp16")];
            tensor<int32, [2]> var_9918_split_sizes_0 = const()[name = string("op_9918_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9918_axis_0 = const()[name = string("op_9918_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 128]> hidden_states, tensor<fp16, [1, 2048, 1, 128]> var_9918_cast_fp16_1 = split(axis = var_9918_axis_0, split_sizes = var_9918_split_sizes_0, x = out_cast_fp16)[name = string("op_9918_cast_fp16")];
        } -> (hidden_states);
    func length_16<ios18>(tensor<fp32, [1, 2048, 1, 16]> inputs_embeds, state<tensor<fp16, [28, 2, 2048, 128]>> key_cache, tensor<int32, [1]> position_id, tensor<int32, [16]> position_index_seed, state<tensor<fp16, [28, 2, 2048, 128]>> value_cache) {
            tensor<fp16, [256, 2048, 1, 1]> layers_1_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524992))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524416))))[name = string("layers_1_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_1_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(525312))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13120640))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13108288))))[name = string("layers_1_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_2_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13126848))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13651776))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13651200))))[name = string("layers_2_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_2_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13652096))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26247424))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26235072))))[name = string("layers_2_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_3_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26253632))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26778560))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26777984))))[name = string("layers_3_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_3_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26778880))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30977408))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30973248))))[name = string("layers_3_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_3_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30979520))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43566656))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43562496))))[name = string("layers_3_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_4_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43568768))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44093696))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44093120))))[name = string("layers_4_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_4_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44094016))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48292544))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48288384))))[name = string("layers_4_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_4_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48294656))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60889984))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60877632))))[name = string("layers_4_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_4_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60896192))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73491520))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73479168))))[name = string("layers_4_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_4_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73497728))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86084864))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86080704))))[name = string("layers_4_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_5_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86086976))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86611904))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86611328))))[name = string("layers_5_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_5_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86612224))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90810752))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90806592))))[name = string("layers_5_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_5_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90812864))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103408192))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103395840))))[name = string("layers_5_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_5_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103414400))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116001536))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115997376))))[name = string("layers_5_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_6_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116003648))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116528576))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116528000))))[name = string("layers_6_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_6_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116528896))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120727424))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120723264))))[name = string("layers_6_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_6_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120729536))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133324864))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133312512))))[name = string("layers_6_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_6_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133331072))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145926400))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145914048))))[name = string("layers_6_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_6_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145932608))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158519744))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158515584))))[name = string("layers_6_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_7_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158521856))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159046784))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159046208))))[name = string("layers_7_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_7_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159047104))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163245632))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163241472))))[name = string("layers_7_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_7_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163247744))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175843072))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175830720))))[name = string("layers_7_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_8_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175849280))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176374208))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176373632))))[name = string("layers_8_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_8_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176374528))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180573056))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180568896))))[name = string("layers_8_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_8_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180575168))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193170496))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193158144))))[name = string("layers_8_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_8_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193176704))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205772032))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205759680))))[name = string("layers_8_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_8_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205778240))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218365376))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218361216))))[name = string("layers_8_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_9_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218367488))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218892416))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218891840))))[name = string("layers_9_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_9_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218892736))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223091264))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223087104))))[name = string("layers_9_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_9_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223093376))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235688704))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235676352))))[name = string("layers_9_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_9_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235694912))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248290240))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248277888))))[name = string("layers_9_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_9_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248296448))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260883584))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260879424))))[name = string("layers_9_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_10_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260885696))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261410624))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261410048))))[name = string("layers_10_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_10_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261410944))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265609472))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265605312))))[name = string("layers_10_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_10_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265611584))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278206912))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278194560))))[name = string("layers_10_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_10_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278213120))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290808448))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290796096))))[name = string("layers_10_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_10_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290814656))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303401792))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303397632))))[name = string("layers_10_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_11_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303403904))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307602432))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307598272))))[name = string("layers_11_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_11_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307604544))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308129472))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308128896))))[name = string("layers_11_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_11_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308129792))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308654720))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308654144))))[name = string("layers_11_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_11_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308655040))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312853568))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312849408))))[name = string("layers_11_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_11_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312855680))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325451008))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325438656))))[name = string("layers_11_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_11_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325457216))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338052544))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338040192))))[name = string("layers_11_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_11_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338058752))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350645888))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350641728))))[name = string("layers_11_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_12_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350648000))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354846528))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354842368))))[name = string("layers_12_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_12_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354848640))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355373568))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355372992))))[name = string("layers_12_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_12_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355373888))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355898816))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355898240))))[name = string("layers_12_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_12_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355899136))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360097664))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360093504))))[name = string("layers_12_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_12_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360099776))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372695104))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372682752))))[name = string("layers_12_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_12_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372701312))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385296640))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385284288))))[name = string("layers_12_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_12_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385302848))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397889984))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397885824))))[name = string("layers_12_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_13_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397892096))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402090624))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402086464))))[name = string("layers_13_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_13_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402092736))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402617664))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402617088))))[name = string("layers_13_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_13_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402617984))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403142912))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403142336))))[name = string("layers_13_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_13_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403143232))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407341760))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407337600))))[name = string("layers_13_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_13_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407343872))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419939200))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419926848))))[name = string("layers_13_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_13_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419945408))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432532544))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432528384))))[name = string("layers_13_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_14_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432534656))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436733184))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436729024))))[name = string("layers_14_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_14_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436735296))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437260224))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437259648))))[name = string("layers_14_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_14_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437260544))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441459072))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441454912))))[name = string("layers_14_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_14_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441461184))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454056512))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454044160))))[name = string("layers_14_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_14_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454062720))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(466658048))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(466645696))))[name = string("layers_14_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_14_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(466664256))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(479251392))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(479247232))))[name = string("layers_14_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_15_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(479253504))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483452032))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483447872))))[name = string("layers_15_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_15_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483454144))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483979072))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483978496))))[name = string("layers_15_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_15_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483979392))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484504320))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484503744))))[name = string("layers_15_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_15_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484504640))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488703168))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488699008))))[name = string("layers_15_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_15_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488705280))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(501300608))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(501288256))))[name = string("layers_15_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_15_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(501306816))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513902144))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513889792))))[name = string("layers_15_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_15_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513908352))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526495488))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526491328))))[name = string("layers_15_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_16_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526497600))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530696128))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530691968))))[name = string("layers_16_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_16_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530698240))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531223168))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531222592))))[name = string("layers_16_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_16_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531223488))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531748416))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531747840))))[name = string("layers_16_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_16_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531748736))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535947264))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535943104))))[name = string("layers_16_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_16_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535949376))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(548536512))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(548532352))))[name = string("layers_16_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_17_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(548538624))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552737152))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552732992))))[name = string("layers_17_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_17_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552739264))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553264192))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553263616))))[name = string("layers_17_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_17_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553264512))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553789440))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553788864))))[name = string("layers_17_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_17_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553789760))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557988288))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557984128))))[name = string("layers_17_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_17_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557990400))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570585728))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570573376))))[name = string("layers_17_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_17_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570591936))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583187264))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583174912))))[name = string("layers_17_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_17_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583193472))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(595780608))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(595776448))))[name = string("layers_17_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_18_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(595782720))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(599981248))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(599977088))))[name = string("layers_18_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_18_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(599983360))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(600508288))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(600507712))))[name = string("layers_18_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_18_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(600508608))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(601033536))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(601032960))))[name = string("layers_18_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_18_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(601033856))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(605232384))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(605228224))))[name = string("layers_18_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_18_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(605234496))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(617829824))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(617817472))))[name = string("layers_18_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_18_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(617836032))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(630431360))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(630419008))))[name = string("layers_18_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_18_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(630437568))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(643024704))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(643020544))))[name = string("layers_18_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_19_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(643026816))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647225344))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647221184))))[name = string("layers_19_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_19_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647227456))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647752384))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647751808))))[name = string("layers_19_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_19_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647752704))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(660348032))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(660335680))))[name = string("layers_19_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_19_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(660354240))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(672949568))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(672937216))))[name = string("layers_19_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_19_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(672955776))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(685542912))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(685538752))))[name = string("layers_19_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_20_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(685545024))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(689743552))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(689739392))))[name = string("layers_20_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_20_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(689745664))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(690270592))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(690270016))))[name = string("layers_20_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_20_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(690270912))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(694469440))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(694465280))))[name = string("layers_20_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_20_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(694471552))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(707066880))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(707054528))))[name = string("layers_20_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_20_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(707073088))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(719660224))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(719656064))))[name = string("layers_20_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_21_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(719662336))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(723860864))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(723856704))))[name = string("layers_21_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_21_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(723862976))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724387904))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724387328))))[name = string("layers_21_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_21_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724388224))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(728586752))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(728582592))))[name = string("layers_21_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_21_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(728588864))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(741184192))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(741171840))))[name = string("layers_21_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_21_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(741190400))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(753785728))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(753773376))))[name = string("layers_21_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_21_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(753791936))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(766379072))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(766374912))))[name = string("layers_21_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_22_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(766381184))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(770579712))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(770575552))))[name = string("layers_22_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_22_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(770581824))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(771106752))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(771106176))))[name = string("layers_22_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_22_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(771107072))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783702400))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783690048))))[name = string("layers_22_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_22_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783708608))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(796303936))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(796291584))))[name = string("layers_22_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_22_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(796310144))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808897280))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808893120))))[name = string("layers_22_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_23_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808899392))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813097920))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813093760))))[name = string("layers_23_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_23_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813100032))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813624960))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813624384))))[name = string("layers_23_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_23_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813625280))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(817823808))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(817819648))))[name = string("layers_23_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_23_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(817825920))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(830421248))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(830408896))))[name = string("layers_23_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_23_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(830427456))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(843022784))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(843010432))))[name = string("layers_23_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_23_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(843028992))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855616128))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855611968))))[name = string("layers_23_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_24_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855618240))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(859816768))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(859812608))))[name = string("layers_24_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_24_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(859818880))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(860343808))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(860343232))))[name = string("layers_24_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_24_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(860344128))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(864542656))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(864538496))))[name = string("layers_24_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_24_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(864544768))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(877140096))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(877127744))))[name = string("layers_24_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_24_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(877146304))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889741632))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889729280))))[name = string("layers_24_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_24_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889747840))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(902334976))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(902330816))))[name = string("layers_24_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_25_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(902337088))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(906535616))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(906531456))))[name = string("layers_25_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_25_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(906537728))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(907062656))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(907062080))))[name = string("layers_25_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_25_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(907062976))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(911261504))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(911257344))))[name = string("layers_25_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_25_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(911263616))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(923858944))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(923846592))))[name = string("layers_25_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_25_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(923865152))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936460480))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936448128))))[name = string("layers_25_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_26_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936466688))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(940665216))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(940661056))))[name = string("layers_26_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_26_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(940667328))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(941192256))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(941191680))))[name = string("layers_26_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_26_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(941192576))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(945391104))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(945386944))))[name = string("layers_26_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_26_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(945393216))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(957988544))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(957976192))))[name = string("layers_26_mlp_gate_proj_weight_cast_fp16")];
            int32 gather_0_cast_uint16_to_int32 = const()[name = string("gather_0_cast_uint16_to_int32"), val = int32(16)];
            tensor<int32, [1]> cache_position_end = add(x = position_id, y = gather_0_cast_uint16_to_int32)[name = string("cache_position_end")];
            fp16 fill_like_0_value_0_to_fp16 = const()[name = string("fill_like_0_value_0_to_fp16"), val = fp16(0x1p+0)];
            tensor<fp16, [16]> fill_like_0_cast_fp16 = fill_like(ref_tensor = position_index_seed, value = fill_like_0_value_0_to_fp16)[name = string("fill_like_0_cast_fp16")];
            int32 var_831 = const()[name = string("op_831"), val = int32(0)];
            bool var_833_exclusive_0 = const()[name = string("op_833_exclusive_0"), val = bool(false)];
            bool var_833_reverse_0 = const()[name = string("op_833_reverse_0"), val = bool(false)];
            tensor<fp16, [16]> var_833_cast_fp16 = cumsum(axis = var_831, exclusive = var_833_exclusive_0, reverse = var_833_reverse_0, x = fill_like_0_cast_fp16)[name = string("op_833_cast_fp16")];
            fp16 var_835_promoted_to_fp16 = const()[name = string("op_835_promoted_to_fp16"), val = fp16(0x1p+0)];
            tensor<fp16, [16]> position_offsets_cast_fp16 = sub(x = var_833_cast_fp16, y = var_835_promoted_to_fp16)[name = string("position_offsets_cast_fp16")];
            tensor<int32, [1]> var_838_axes_0 = const()[name = string("op_838_axes_0"), val = tensor<int32, [1]>([0])];
            tensor<fp16, [1, 16]> var_838_cast_fp16 = expand_dims(axes = var_838_axes_0, x = position_offsets_cast_fp16)[name = string("op_838_cast_fp16")];
            string position_id_promoted_to_fp16_dtype_0 = const()[name = string("position_id_promoted_to_fp16_dtype_0"), val = string("fp16")];
            tensor<fp16, [1]> position_id_to_fp16 = cast(dtype = position_id_promoted_to_fp16_dtype_0, x = position_id)[name = string("cast_15")];
            tensor<fp16, [1, 16]> position_ids_1_cast_fp16 = add(x = var_838_cast_fp16, y = position_id_to_fp16)[name = string("position_ids_1_cast_fp16")];
            string position_ids_dtype_0 = const()[name = string("position_ids_dtype_0"), val = string("int32")];
            int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)];
            tensor<int32, [1, 16]> position_ids_1_cast_fp16_to_int32 = cast(dtype = position_ids_dtype_0, x = position_ids_1_cast_fp16)[name = string("cast_14")];
            tensor<bool, [1, 16]> greater_equal_0 = greater_equal(x = position_ids_1_cast_fp16_to_int32, y = greater_equal_0_y_0)[name = string("greater_equal_0")];
            int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(32768)];
            tensor<int32, [1, 16]> add_0 = add(x = position_ids_1_cast_fp16_to_int32, y = slice_by_index_0)[name = string("add_0")];
            tensor<int32, [1, 16]> select_0 = select(a = position_ids_1_cast_fp16_to_int32, b = add_0, cond = greater_equal_0)[name = string("select_0")];
            tensor<fp16, [32768, 128]> rope_emb_cos_cached_to_fp16 = const()[name = string("rope_emb_cos_cached_to_fp16"), val = tensor<fp16, [32768, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(957994752)))];
            int32 cos_1_batch_dims_0 = const()[name = string("cos_1_batch_dims_0"), val = int32(0)];
            bool cos_1_validate_indices_0 = const()[name = string("cos_1_validate_indices_0"), val = bool(false)];
            int32 greater_equal_6_y_0 = const()[name = string("greater_equal_6_y_0"), val = int32(0)];
            tensor<bool, [1, 16]> greater_equal_6 = greater_equal(x = select_0, y = greater_equal_6_y_0)[name = string("greater_equal_6")];
            int32 slice_by_index_6 = const()[name = string("slice_by_index_6"), val = int32(32768)];
            tensor<int32, [1, 16]> add_6 = add(x = select_0, y = slice_by_index_6)[name = string("add_6")];
            tensor<int32, [1, 16]> select_6 = select(a = select_0, b = add_6, cond = greater_equal_6)[name = string("select_6")];
            int32 cos_1_cast_fp16_axis_3 = const()[name = string("cos_1_cast_fp16_axis_3"), val = int32(0)];
            tensor<fp16, [1, 16, 128]> cos_1_cast_fp16 = gather(axis = cos_1_cast_fp16_axis_3, batch_dims = cos_1_batch_dims_0, indices = select_6, validate_indices = cos_1_validate_indices_0, x = rope_emb_cos_cached_to_fp16)[name = string("cos_1_cast_fp16")];
            tensor<fp16, [32768, 128]> rope_emb_sin_cached_to_fp16 = const()[name = string("rope_emb_sin_cached_to_fp16"), val = tensor<fp16, [32768, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(966383424)))];
            int32 sin_1_batch_dims_0 = const()[name = string("sin_1_batch_dims_0"), val = int32(0)];
            bool sin_1_validate_indices_0 = const()[name = string("sin_1_validate_indices_0"), val = bool(false)];
            int32 sin_1_cast_fp16_axis_3 = const()[name = string("sin_1_cast_fp16_axis_3"), val = int32(0)];
            tensor<fp16, [1, 16, 128]> sin_1_cast_fp16 = gather(axis = sin_1_cast_fp16_axis_3, batch_dims = sin_1_batch_dims_0, indices = select_6, validate_indices = sin_1_validate_indices_0, x = rope_emb_sin_cached_to_fp16)[name = string("sin_1_cast_fp16")];
            tensor<int32, [3]> var_857_perm_0 = const()[name = string("op_857_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<int32, [1]> var_859_axes_0 = const()[name = string("op_859_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 128, 16]> var_857_cast_fp16 = transpose(perm = var_857_perm_0, x = cos_1_cast_fp16)[name = string("transpose_347")];
            tensor<fp16, [1, 1, 128, 16]> var_859_cast_fp16 = expand_dims(axes = var_859_axes_0, x = var_857_cast_fp16)[name = string("op_859_cast_fp16")];
            tensor<int32, [3]> var_864_perm_0 = const()[name = string("op_864_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<int32, [1]> var_866_axes_0 = const()[name = string("op_866_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 128, 16]> var_864_cast_fp16 = transpose(perm = var_864_perm_0, x = sin_1_cast_fp16)[name = string("transpose_346")];
            tensor<fp16, [1, 1, 128, 16]> var_866_cast_fp16 = expand_dims(axes = var_866_axes_0, x = var_864_cast_fp16)[name = string("op_866_cast_fp16")];
            tensor<int32, [1]> var_885_axes_0 = const()[name = string("op_885_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1, 16, 1]> var_885 = expand_dims(axes = var_885_axes_0, x = position_ids_1_cast_fp16_to_int32)[name = string("op_885")];
            tensor<int32, [1, 1, 2048]> var_878 = const()[name = string("op_878"), val = tensor<int32, [1, 1, 2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(974772096)))];
            tensor<bool, [1, 16, 2048]> var_886 = greater(x = var_878, y = var_885)[name = string("op_886")];
            string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")];
            tensor<int32, [1]> var_893_axes_0 = const()[name = string("op_893_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 16, 2048]> var_886_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = var_886)[name = string("cast_13")];
            tensor<fp16, [1, 1, 16, 2048]> var_893_cast_fp16 = expand_dims(axes = var_893_axes_0, x = var_886_to_fp16)[name = string("op_893_cast_fp16")];
            tensor<int32, [4]> mask_perm_0 = const()[name = string("mask_perm_0"), val = tensor<int32, [4]>([0, 1, -1, -2])];
            fp16 var_897_promoted_to_fp16 = const()[name = string("op_897_promoted_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1, 2048, 16]> mask_cast_fp16 = transpose(perm = mask_perm_0, x = var_893_cast_fp16)[name = string("transpose_345")];
            tensor<bool, [1, 1, 2048, 16]> var_898_cast_fp16 = equal(x = mask_cast_fp16, y = var_897_promoted_to_fp16)[name = string("op_898_cast_fp16")];
            fp16 var_899_to_fp16 = const()[name = string("op_899_to_fp16"), val = fp16(-inf)];
            tensor<fp16, [1, 1, 2048, 16]> attn_mask_1_cast_fp16 = select(a = mask_cast_fp16, b = var_899_to_fp16, cond = var_898_cast_fp16)[name = string("attn_mask_1_cast_fp16")];
            string inputs_embeds_to_fp16_dtype_0 = const()[name = string("inputs_embeds_to_fp16_dtype_0"), val = string("fp16")];
            fp16 const_2_promoted_to_fp16 = const()[name = string("const_2_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> inputs_embeds_to_fp16 = cast(dtype = inputs_embeds_to_fp16_dtype_0, x = inputs_embeds)[name = string("cast_12")];
            tensor<fp16, [1, 2048, 1, 16]> var_909_cast_fp16 = mul(x = inputs_embeds_to_fp16, y = const_2_promoted_to_fp16)[name = string("op_909_cast_fp16")];
            int32 var_907 = const()[name = string("op_907"), val = int32(1)];
            bool doubled_1_interleave_0 = const()[name = string("doubled_1_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_1_cast_fp16 = concat(axis = var_907, interleave = doubled_1_interleave_0, values = (inputs_embeds_to_fp16, var_909_cast_fp16))[name = string("doubled_1_cast_fp16")];
            tensor<int32, [1]> out_1_axes_0 = const()[name = string("out_1_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_1_gamma_0_to_fp16 = const()[name = string("out_1_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(974780352)))];
            fp16 var_919_to_fp16 = const()[name = string("op_919_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_1_cast_fp16 = layer_norm(axes = out_1_axes_0, epsilon = var_919_to_fp16, gamma = out_1_gamma_0_to_fp16, x = doubled_1_cast_fp16)[name = string("out_1_cast_fp16")];
            tensor<int32, [2]> var_930_split_sizes_0 = const()[name = string("op_930_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_930_axis_0 = const()[name = string("op_930_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_930_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_930_cast_fp16_1 = split(axis = var_930_axis_0, split_sizes = var_930_split_sizes_0, x = out_1_cast_fp16)[name = string("op_930_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_0_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(974788608)))];
            tensor<int32, [2]> query_states_1_strides_0 = const()[name = string("query_states_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_1_pad_type_0 = const()[name = string("query_states_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_1_pad_0 = const()[name = string("query_states_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_1_dilations_0 = const()[name = string("query_states_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_1_groups_0 = const()[name = string("query_states_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_1_cast_fp16 = conv(dilations = query_states_1_dilations_0, groups = query_states_1_groups_0, pad = query_states_1_pad_0, pad_type = query_states_1_pad_type_0, strides = query_states_1_strides_0, weight = layers_0_self_attn_q_proj_weight_to_fp16, x = var_930_cast_fp16_0)[name = string("query_states_1_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_0_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(983177280)))];
            tensor<int32, [2]> key_states_1_strides_0 = const()[name = string("key_states_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_1_pad_type_0 = const()[name = string("key_states_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_1_pad_0 = const()[name = string("key_states_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_1_dilations_0 = const()[name = string("key_states_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_1_groups_0 = const()[name = string("key_states_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_1_cast_fp16 = conv(dilations = key_states_1_dilations_0, groups = key_states_1_groups_0, pad = key_states_1_pad_0, pad_type = key_states_1_pad_type_0, strides = key_states_1_strides_0, weight = layers_0_self_attn_k_proj_weight_to_fp16, x = var_930_cast_fp16_0)[name = string("key_states_1_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_0_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(984225920)))];
            tensor<int32, [2]> value_states_1_strides_0 = const()[name = string("value_states_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_1_pad_type_0 = const()[name = string("value_states_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_1_pad_0 = const()[name = string("value_states_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_1_dilations_0 = const()[name = string("value_states_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_1_groups_0 = const()[name = string("value_states_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_1_cast_fp16 = conv(dilations = value_states_1_dilations_0, groups = value_states_1_groups_0, pad = value_states_1_pad_0, pad_type = value_states_1_pad_type_0, strides = value_states_1_strides_0, weight = layers_0_self_attn_v_proj_weight_to_fp16, x = var_930_cast_fp16_0)[name = string("value_states_1_cast_fp16")];
            tensor<int32, [4]> concat_0x = const()[name = string("concat_0x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_1_cast_fp16 = reshape(shape = concat_0x, x = query_states_1_cast_fp16)[name = string("x_1_cast_fp16")];
            tensor<int32, [4]> concat_1x = const()[name = string("concat_1x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_987_cast_fp16 = reshape(shape = concat_1x, x = key_states_1_cast_fp16)[name = string("op_987_cast_fp16")];
            tensor<int32, [4]> concat_2x = const()[name = string("concat_2x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_994_cast_fp16 = reshape(shape = concat_2x, x = value_states_1_cast_fp16)[name = string("op_994_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_998_cast_fp16 = mul(x = x_1_cast_fp16, y = var_859_cast_fp16)[name = string("op_998_cast_fp16")];
            tensor<int32, [2]> var_999_split_sizes_0 = const()[name = string("op_999_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_999_axis_0 = const()[name = string("op_999_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_999_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_999_cast_fp16_1 = split(axis = var_999_axis_0, split_sizes = var_999_split_sizes_0, x = x_1_cast_fp16)[name = string("op_999_cast_fp16")];
            fp16 const_4_promoted_to_fp16 = const()[name = string("const_4_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_1001_cast_fp16 = mul(x = var_999_cast_fp16_1, y = const_4_promoted_to_fp16)[name = string("op_1001_cast_fp16")];
            int32 var_1003 = const()[name = string("op_1003"), val = int32(-2)];
            bool var_1004_interleave_0 = const()[name = string("op_1004_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_1004_cast_fp16 = concat(axis = var_1003, interleave = var_1004_interleave_0, values = (var_1001_cast_fp16, var_999_cast_fp16_0))[name = string("op_1004_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_1005_cast_fp16 = mul(x = var_1004_cast_fp16, y = var_866_cast_fp16)[name = string("op_1005_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_3_cast_fp16 = add(x = var_998_cast_fp16, y = var_1005_cast_fp16)[name = string("query_states_3_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_1011_cast_fp16 = mul(x = var_987_cast_fp16, y = var_859_cast_fp16)[name = string("op_1011_cast_fp16")];
            tensor<int32, [2]> var_1012_split_sizes_0 = const()[name = string("op_1012_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1012_axis_0 = const()[name = string("op_1012_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_1012_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_1012_cast_fp16_1 = split(axis = var_1012_axis_0, split_sizes = var_1012_split_sizes_0, x = var_987_cast_fp16)[name = string("op_1012_cast_fp16")];
            fp16 const_5_promoted_to_fp16 = const()[name = string("const_5_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_1014_cast_fp16 = mul(x = var_1012_cast_fp16_1, y = const_5_promoted_to_fp16)[name = string("op_1014_cast_fp16")];
            int32 var_1016 = const()[name = string("op_1016"), val = int32(-2)];
            bool var_1017_interleave_0 = const()[name = string("op_1017_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_1017_cast_fp16 = concat(axis = var_1016, interleave = var_1017_interleave_0, values = (var_1014_cast_fp16, var_1012_cast_fp16_0))[name = string("op_1017_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_1018_cast_fp16 = mul(x = var_1017_cast_fp16, y = var_866_cast_fp16)[name = string("op_1018_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_5_cast_fp16 = add(x = var_1011_cast_fp16, y = var_1018_cast_fp16)[name = string("key_states_5_cast_fp16")];
            tensor<fp16, [28, 2, 2048, 128]> read_state_0 = read_state(input = key_cache)[name = string("read_state_0")];
            tensor<int32, [1]> expand_dims_0 = const()[name = string("expand_dims_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_1 = const()[name = string("expand_dims_1"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_3 = const()[name = string("expand_dims_3"), val = tensor<int32, [1]>([0])];
            int32 concat_5_axis_0 = const()[name = string("concat_5_axis_0"), val = int32(0)];
            bool concat_5_interleave_0 = const()[name = string("concat_5_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_5 = concat(axis = concat_5_axis_0, interleave = concat_5_interleave_0, values = (expand_dims_0, expand_dims_1, position_id, expand_dims_3))[name = string("concat_5")];
            tensor<int32, [1]> expand_dims_4 = const()[name = string("expand_dims_4"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> concat_6_values1_0 = const()[name = string("concat_6_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_6_values3_0 = const()[name = string("concat_6_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_6_axis_0 = const()[name = string("concat_6_axis_0"), val = int32(0)];
            bool concat_6_interleave_0 = const()[name = string("concat_6_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_6 = concat(axis = concat_6_axis_0, interleave = concat_6_interleave_0, values = (expand_dims_4, concat_6_values1_0, cache_position_end, concat_6_values3_0))[name = string("concat_6")];
            tensor<int32, [4]> key_states_7_perm_0 = const()[name = string("key_states_7_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_1_stride_0 = const()[name = string("key_cache_internal_tensor_assign_1_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_1_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_1_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_1_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_1_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_1_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_1_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_7_cast_fp16 = transpose(perm = key_states_7_perm_0, x = key_states_5_cast_fp16)[name = string("transpose_344")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_1_cast_fp16 = slice_update(begin = concat_5, begin_mask = key_cache_internal_tensor_assign_1_begin_mask_0, end = concat_6, end_mask = key_cache_internal_tensor_assign_1_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_1_squeeze_mask_0, stride = key_cache_internal_tensor_assign_1_stride_0, update = key_states_7_cast_fp16, x = read_state_0)[name = string("key_cache_internal_tensor_assign_1_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_1_cast_fp16, input = key_cache)[name = string("coreml_update_state_168_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_168 = read_state(input = key_cache)[name = string("coreml_update_state_168")];
            tensor<fp16, [28, 2, 2048, 128]> read_state_1 = read_state(input = value_cache)[name = string("read_state_1")];
            tensor<int32, [4]> value_states_3_perm_0 = const()[name = string("value_states_3_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_1_stride_0 = const()[name = string("value_cache_internal_tensor_assign_1_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_1_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_1_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_1_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_1_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_1_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_1_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_3_cast_fp16 = transpose(perm = value_states_3_perm_0, x = var_994_cast_fp16)[name = string("transpose_343")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_1_cast_fp16 = slice_update(begin = concat_5, begin_mask = value_cache_internal_tensor_assign_1_begin_mask_0, end = concat_6, end_mask = value_cache_internal_tensor_assign_1_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_1_squeeze_mask_0, stride = value_cache_internal_tensor_assign_1_stride_0, update = value_states_3_cast_fp16, x = read_state_1)[name = string("value_cache_internal_tensor_assign_1_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_1_cast_fp16, input = value_cache)[name = string("coreml_update_state_169_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_169 = read_state(input = value_cache)[name = string("coreml_update_state_169")];
            tensor<int32, [4]> var_1088_begin_0 = const()[name = string("op_1088_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> var_1088_end_0 = const()[name = string("op_1088_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_1088_end_mask_0 = const()[name = string("op_1088_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1088_cast_fp16 = slice_by_index(begin = var_1088_begin_0, end = var_1088_end_0, end_mask = var_1088_end_mask_0, x = coreml_update_state_168)[name = string("op_1088_cast_fp16")];
            tensor<int32, [2]> tile_0 = const()[name = string("tile_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1091_axis_0 = const()[name = string("op_1091_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1091_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1091_cast_fp16_1 = split(axis = var_1091_axis_0, split_sizes = tile_0, x = var_1088_cast_fp16)[name = string("op_1091_cast_fp16")];
            tensor<int32, [4]> var_1098_begin_0 = const()[name = string("op_1098_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> var_1098_end_0 = const()[name = string("op_1098_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_1098_end_mask_0 = const()[name = string("op_1098_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1098_cast_fp16 = slice_by_index(begin = var_1098_begin_0, end = var_1098_end_0, end_mask = var_1098_end_mask_0, x = coreml_update_state_169)[name = string("op_1098_cast_fp16")];
            tensor<int32, [2]> tile_1 = const()[name = string("tile_1"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1101_axis_0 = const()[name = string("op_1101_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1101_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1101_cast_fp16_1 = split(axis = var_1101_axis_0, split_sizes = tile_1, x = var_1098_cast_fp16)[name = string("op_1101_cast_fp16")];
            tensor<int32, [2]> var_1104_split_sizes_0 = const()[name = string("op_1104_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_1104_axis_0 = const()[name = string("op_1104_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_1104_0, tensor<fp16, [1, 8, 128, 16]> var_1104_1 = split(axis = var_1104_axis_0, split_sizes = var_1104_split_sizes_0, x = query_states_3_cast_fp16)[name = string("op_1104")];
            bool attn_weights_1_transpose_x_0 = const()[name = string("attn_weights_1_transpose_x_0"), val = bool(false)];
            bool attn_weights_1_transpose_y_0 = const()[name = string("attn_weights_1_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_1_cast_fp16 = matmul(transpose_x = attn_weights_1_transpose_x_0, transpose_y = attn_weights_1_transpose_y_0, x = var_1091_cast_fp16_0, y = var_1104_0)[name = string("attn_weights_1_cast_fp16")];
            fp16 var_1107_to_fp16 = const()[name = string("op_1107_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_3_cast_fp16 = mul(x = attn_weights_1_cast_fp16, y = var_1107_to_fp16)[name = string("attn_weights_3_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_5_cast_fp16 = add(x = attn_weights_3_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_5_cast_fp16")];
            int32 var_1111 = const()[name = string("op_1111"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_7_cast_fp16 = softmax(axis = var_1111, x = attn_weights_5_cast_fp16)[name = string("attn_weights_7_cast_fp16")];
            bool var_1117_transpose_x_1 = const()[name = string("op_1117_transpose_x_1"), val = bool(true)];
            bool var_1117_transpose_y_1 = const()[name = string("op_1117_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_1117_cast_fp16 = matmul(transpose_x = var_1117_transpose_x_1, transpose_y = var_1117_transpose_y_1, x = attn_weights_7_cast_fp16, y = var_1101_cast_fp16_0)[name = string("op_1117_cast_fp16")];
            bool attn_weights_9_transpose_x_0 = const()[name = string("attn_weights_9_transpose_x_0"), val = bool(false)];
            bool attn_weights_9_transpose_y_0 = const()[name = string("attn_weights_9_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_9_cast_fp16 = matmul(transpose_x = attn_weights_9_transpose_x_0, transpose_y = attn_weights_9_transpose_y_0, x = var_1091_cast_fp16_1, y = var_1104_1)[name = string("attn_weights_9_cast_fp16")];
            fp16 var_1119_to_fp16 = const()[name = string("op_1119_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_11_cast_fp16 = mul(x = attn_weights_9_cast_fp16, y = var_1119_to_fp16)[name = string("attn_weights_11_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_13_cast_fp16 = add(x = attn_weights_11_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_13_cast_fp16")];
            int32 var_1123 = const()[name = string("op_1123"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_15_cast_fp16 = softmax(axis = var_1123, x = attn_weights_13_cast_fp16)[name = string("attn_weights_15_cast_fp16")];
            bool attn_output_1_transpose_x_1 = const()[name = string("attn_output_1_transpose_x_1"), val = bool(true)];
            bool attn_output_1_transpose_y_1 = const()[name = string("attn_output_1_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_1_cast_fp16 = matmul(transpose_x = attn_output_1_transpose_x_1, transpose_y = attn_output_1_transpose_y_1, x = attn_weights_15_cast_fp16, y = var_1101_cast_fp16_1)[name = string("attn_output_1_cast_fp16")];
            int32 var_1131 = const()[name = string("op_1131"), val = int32(1)];
            bool attn_output_3_interleave_0 = const()[name = string("attn_output_3_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_3_cast_fp16 = concat(axis = var_1131, interleave = attn_output_3_interleave_0, values = (var_1117_cast_fp16, attn_output_1_cast_fp16))[name = string("attn_output_3_cast_fp16")];
            tensor<int32, [4]> var_1135_perm_0 = const()[name = string("op_1135_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_11x = const()[name = string("concat_11x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_1135_cast_fp16 = transpose(perm = var_1135_perm_0, x = attn_output_3_cast_fp16)[name = string("transpose_342")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_7_cast_fp16 = reshape(shape = concat_11x, x = var_1135_cast_fp16)[name = string("attn_output_7_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_0_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(985274560)))];
            tensor<int32, [2]> hidden_states_3_strides_0 = const()[name = string("hidden_states_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_3_pad_type_0 = const()[name = string("hidden_states_3_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_3_pad_0 = const()[name = string("hidden_states_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_3_dilations_0 = const()[name = string("hidden_states_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_3_groups_0 = const()[name = string("hidden_states_3_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_3_cast_fp16 = conv(dilations = hidden_states_3_dilations_0, groups = hidden_states_3_groups_0, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = hidden_states_3_strides_0, weight = layers_0_self_attn_o_proj_weight_to_fp16, x = attn_output_7_cast_fp16)[name = string("hidden_states_3_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_5_cast_fp16 = add(x = inputs_embeds_to_fp16, y = hidden_states_3_cast_fp16)[name = string("hidden_states_5_cast_fp16")];
            fp16 const_10_promoted_to_fp16 = const()[name = string("const_10_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_1168_cast_fp16 = mul(x = hidden_states_5_cast_fp16, y = const_10_promoted_to_fp16)[name = string("op_1168_cast_fp16")];
            int32 var_1166 = const()[name = string("op_1166"), val = int32(1)];
            bool doubled_5_interleave_0 = const()[name = string("doubled_5_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_5_cast_fp16 = concat(axis = var_1166, interleave = doubled_5_interleave_0, values = (hidden_states_5_cast_fp16, var_1168_cast_fp16))[name = string("doubled_5_cast_fp16")];
            tensor<int32, [1]> out_3_axes_0 = const()[name = string("out_3_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_3_gamma_0_to_fp16 = const()[name = string("out_3_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(993663232)))];
            fp16 var_1178_to_fp16 = const()[name = string("op_1178_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_3_cast_fp16 = layer_norm(axes = out_3_axes_0, epsilon = var_1178_to_fp16, gamma = out_3_gamma_0_to_fp16, x = doubled_5_cast_fp16)[name = string("out_3_cast_fp16")];
            tensor<int32, [2]> var_1189_split_sizes_0 = const()[name = string("op_1189_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1189_axis_0 = const()[name = string("op_1189_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_1189_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_1189_cast_fp16_1 = split(axis = var_1189_axis_0, split_sizes = var_1189_split_sizes_0, x = out_3_cast_fp16)[name = string("op_1189_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_0_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_0_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(993671488)))];
            tensor<int32, [2]> input_1_strides_0 = const()[name = string("input_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_1_pad_type_0 = const()[name = string("input_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_1_pad_0 = const()[name = string("input_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_1_dilations_0 = const()[name = string("input_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_1_groups_0 = const()[name = string("input_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_1_cast_fp16 = conv(dilations = input_1_dilations_0, groups = input_1_groups_0, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = input_1_strides_0, weight = layers_0_mlp_gate_proj_weight_to_fp16, x = var_1189_cast_fp16_0)[name = string("input_1_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_1206_cast_fp16 = silu(x = input_1_cast_fp16)[name = string("op_1206_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_0_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_0_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1018837376)))];
            tensor<int32, [2]> var_1212_strides_0 = const()[name = string("op_1212_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_1212_pad_type_0 = const()[name = string("op_1212_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_1212_pad_0 = const()[name = string("op_1212_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_1212_dilations_0 = const()[name = string("op_1212_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1212_groups_0 = const()[name = string("op_1212_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_1212_cast_fp16 = conv(dilations = var_1212_dilations_0, groups = var_1212_groups_0, pad = var_1212_pad_0, pad_type = var_1212_pad_type_0, strides = var_1212_strides_0, weight = layers_0_mlp_up_proj_weight_to_fp16, x = var_1189_cast_fp16_0)[name = string("op_1212_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_9_cast_fp16 = mul(x = var_1206_cast_fp16, y = var_1212_cast_fp16)[name = string("x_9_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_0_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_0_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1044003264)))];
            tensor<int32, [2]> hidden_states_7_strides_0 = const()[name = string("hidden_states_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_7_pad_type_0 = const()[name = string("hidden_states_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_7_pad_0 = const()[name = string("hidden_states_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_7_dilations_0 = const()[name = string("hidden_states_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_7_groups_0 = const()[name = string("hidden_states_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_7_cast_fp16 = conv(dilations = hidden_states_7_dilations_0, groups = hidden_states_7_groups_0, pad = hidden_states_7_pad_0, pad_type = hidden_states_7_pad_type_0, strides = hidden_states_7_strides_0, weight = layers_0_mlp_down_proj_weight_to_fp16, x = x_9_cast_fp16)[name = string("hidden_states_7_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_9_cast_fp16 = add(x = hidden_states_5_cast_fp16, y = hidden_states_7_cast_fp16)[name = string("hidden_states_9_cast_fp16")];
            fp16 const_12_promoted_to_fp16 = const()[name = string("const_12_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_1230_cast_fp16 = mul(x = hidden_states_9_cast_fp16, y = const_12_promoted_to_fp16)[name = string("op_1230_cast_fp16")];
            int32 var_1228 = const()[name = string("op_1228"), val = int32(1)];
            bool doubled_9_interleave_0 = const()[name = string("doubled_9_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_9_cast_fp16 = concat(axis = var_1228, interleave = doubled_9_interleave_0, values = (hidden_states_9_cast_fp16, var_1230_cast_fp16))[name = string("doubled_9_cast_fp16")];
            tensor<int32, [1]> out_5_axes_0 = const()[name = string("out_5_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_5_gamma_0_to_fp16 = const()[name = string("out_5_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1069169152)))];
            fp16 var_1240_to_fp16 = const()[name = string("op_1240_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_5_cast_fp16 = layer_norm(axes = out_5_axes_0, epsilon = var_1240_to_fp16, gamma = out_5_gamma_0_to_fp16, x = doubled_9_cast_fp16)[name = string("out_5_cast_fp16")];
            tensor<int32, [2]> var_1251_split_sizes_0 = const()[name = string("op_1251_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1251_axis_0 = const()[name = string("op_1251_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_1251_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_1251_cast_fp16_1 = split(axis = var_1251_axis_0, split_sizes = var_1251_split_sizes_0, x = out_5_cast_fp16)[name = string("op_1251_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_1_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_1_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1069177408)))];
            tensor<int32, [2]> query_states_7_strides_0 = const()[name = string("query_states_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_7_pad_type_0 = const()[name = string("query_states_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_7_pad_0 = const()[name = string("query_states_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_7_dilations_0 = const()[name = string("query_states_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_7_groups_0 = const()[name = string("query_states_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_7_cast_fp16 = conv(dilations = query_states_7_dilations_0, groups = query_states_7_groups_0, pad = query_states_7_pad_0, pad_type = query_states_7_pad_type_0, strides = query_states_7_strides_0, weight = layers_1_self_attn_q_proj_weight_to_fp16, x = var_1251_cast_fp16_0)[name = string("query_states_7_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_1_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_1_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1077566080)))];
            tensor<int32, [2]> key_states_11_strides_0 = const()[name = string("key_states_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_11_pad_type_0 = const()[name = string("key_states_11_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_11_pad_0 = const()[name = string("key_states_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_11_dilations_0 = const()[name = string("key_states_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_11_groups_0 = const()[name = string("key_states_11_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_11_cast_fp16 = conv(dilations = key_states_11_dilations_0, groups = key_states_11_groups_0, pad = key_states_11_pad_0, pad_type = key_states_11_pad_type_0, strides = key_states_11_strides_0, weight = layers_1_self_attn_k_proj_weight_to_fp16, x = var_1251_cast_fp16_0)[name = string("key_states_11_cast_fp16")];
            tensor<int32, [2]> value_states_7_strides_0 = const()[name = string("value_states_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_7_pad_type_0 = const()[name = string("value_states_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_7_pad_0 = const()[name = string("value_states_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_7_dilations_0 = const()[name = string("value_states_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_7_groups_0 = const()[name = string("value_states_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_7_cast_fp16 = conv(dilations = value_states_7_dilations_0, groups = value_states_7_groups_0, pad = value_states_7_pad_0, pad_type = value_states_7_pad_type_0, strides = value_states_7_strides_0, weight = layers_1_self_attn_v_proj_weight_cast_fp16, x = var_1251_cast_fp16_0)[name = string("value_states_7_cast_fp16")];
            tensor<int32, [4]> concat_12x = const()[name = string("concat_12x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_11_cast_fp16 = reshape(shape = concat_12x, x = query_states_7_cast_fp16)[name = string("x_11_cast_fp16")];
            tensor<int32, [4]> concat_13x = const()[name = string("concat_13x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_1308_cast_fp16 = reshape(shape = concat_13x, x = key_states_11_cast_fp16)[name = string("op_1308_cast_fp16")];
            tensor<int32, [4]> concat_14x = const()[name = string("concat_14x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_1315_cast_fp16 = reshape(shape = concat_14x, x = value_states_7_cast_fp16)[name = string("op_1315_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_1319_cast_fp16 = mul(x = x_11_cast_fp16, y = var_859_cast_fp16)[name = string("op_1319_cast_fp16")];
            tensor<int32, [2]> var_1320_split_sizes_0 = const()[name = string("op_1320_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1320_axis_0 = const()[name = string("op_1320_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_1320_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_1320_cast_fp16_1 = split(axis = var_1320_axis_0, split_sizes = var_1320_split_sizes_0, x = x_11_cast_fp16)[name = string("op_1320_cast_fp16")];
            fp16 const_14_promoted_to_fp16 = const()[name = string("const_14_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_1322_cast_fp16 = mul(x = var_1320_cast_fp16_1, y = const_14_promoted_to_fp16)[name = string("op_1322_cast_fp16")];
            int32 var_1324 = const()[name = string("op_1324"), val = int32(-2)];
            bool var_1325_interleave_0 = const()[name = string("op_1325_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_1325_cast_fp16 = concat(axis = var_1324, interleave = var_1325_interleave_0, values = (var_1322_cast_fp16, var_1320_cast_fp16_0))[name = string("op_1325_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_1326_cast_fp16 = mul(x = var_1325_cast_fp16, y = var_866_cast_fp16)[name = string("op_1326_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_9_cast_fp16 = add(x = var_1319_cast_fp16, y = var_1326_cast_fp16)[name = string("query_states_9_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_1332_cast_fp16 = mul(x = var_1308_cast_fp16, y = var_859_cast_fp16)[name = string("op_1332_cast_fp16")];
            tensor<int32, [2]> var_1333_split_sizes_0 = const()[name = string("op_1333_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1333_axis_0 = const()[name = string("op_1333_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_1333_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_1333_cast_fp16_1 = split(axis = var_1333_axis_0, split_sizes = var_1333_split_sizes_0, x = var_1308_cast_fp16)[name = string("op_1333_cast_fp16")];
            fp16 const_15_promoted_to_fp16 = const()[name = string("const_15_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_1335_cast_fp16 = mul(x = var_1333_cast_fp16_1, y = const_15_promoted_to_fp16)[name = string("op_1335_cast_fp16")];
            int32 var_1337 = const()[name = string("op_1337"), val = int32(-2)];
            bool var_1338_interleave_0 = const()[name = string("op_1338_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_1338_cast_fp16 = concat(axis = var_1337, interleave = var_1338_interleave_0, values = (var_1335_cast_fp16, var_1333_cast_fp16_0))[name = string("op_1338_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_1339_cast_fp16 = mul(x = var_1338_cast_fp16, y = var_866_cast_fp16)[name = string("op_1339_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_15_cast_fp16 = add(x = var_1332_cast_fp16, y = var_1339_cast_fp16)[name = string("key_states_15_cast_fp16")];
            tensor<int32, [1]> expand_dims_12 = const()[name = string("expand_dims_12"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> expand_dims_13 = const()[name = string("expand_dims_13"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_15 = const()[name = string("expand_dims_15"), val = tensor<int32, [1]>([0])];
            int32 concat_17_axis_0 = const()[name = string("concat_17_axis_0"), val = int32(0)];
            bool concat_17_interleave_0 = const()[name = string("concat_17_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_17 = concat(axis = concat_17_axis_0, interleave = concat_17_interleave_0, values = (expand_dims_12, expand_dims_13, position_id, expand_dims_15))[name = string("concat_17")];
            tensor<int32, [1]> expand_dims_16 = const()[name = string("expand_dims_16"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> concat_18_values1_0 = const()[name = string("concat_18_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_18_values3_0 = const()[name = string("concat_18_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_18_axis_0 = const()[name = string("concat_18_axis_0"), val = int32(0)];
            bool concat_18_interleave_0 = const()[name = string("concat_18_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_18 = concat(axis = concat_18_axis_0, interleave = concat_18_interleave_0, values = (expand_dims_16, concat_18_values1_0, cache_position_end, concat_18_values3_0))[name = string("concat_18")];
            tensor<int32, [4]> key_states_17_perm_0 = const()[name = string("key_states_17_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_2_stride_0 = const()[name = string("key_cache_internal_tensor_assign_2_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_2_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_2_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_2_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_2_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_2_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_2_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_17_cast_fp16 = transpose(perm = key_states_17_perm_0, x = key_states_15_cast_fp16)[name = string("transpose_341")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_2_cast_fp16 = slice_update(begin = concat_17, begin_mask = key_cache_internal_tensor_assign_2_begin_mask_0, end = concat_18, end_mask = key_cache_internal_tensor_assign_2_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_2_squeeze_mask_0, stride = key_cache_internal_tensor_assign_2_stride_0, update = key_states_17_cast_fp16, x = coreml_update_state_168)[name = string("key_cache_internal_tensor_assign_2_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_2_cast_fp16, input = key_cache)[name = string("coreml_update_state_170_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_170 = read_state(input = key_cache)[name = string("coreml_update_state_170")];
            tensor<int32, [4]> value_states_9_perm_0 = const()[name = string("value_states_9_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_2_stride_0 = const()[name = string("value_cache_internal_tensor_assign_2_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_2_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_2_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_2_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_2_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_2_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_2_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_9_cast_fp16 = transpose(perm = value_states_9_perm_0, x = var_1315_cast_fp16)[name = string("transpose_340")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_2_cast_fp16 = slice_update(begin = concat_17, begin_mask = value_cache_internal_tensor_assign_2_begin_mask_0, end = concat_18, end_mask = value_cache_internal_tensor_assign_2_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_2_squeeze_mask_0, stride = value_cache_internal_tensor_assign_2_stride_0, update = value_states_9_cast_fp16, x = coreml_update_state_169)[name = string("value_cache_internal_tensor_assign_2_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_2_cast_fp16, input = value_cache)[name = string("coreml_update_state_171_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_171 = read_state(input = value_cache)[name = string("coreml_update_state_171")];
            tensor<int32, [4]> var_1409_begin_0 = const()[name = string("op_1409_begin_0"), val = tensor<int32, [4]>([1, 0, 0, 0])];
            tensor<int32, [4]> var_1409_end_0 = const()[name = string("op_1409_end_0"), val = tensor<int32, [4]>([2, 2, 2048, 128])];
            tensor<bool, [4]> var_1409_end_mask_0 = const()[name = string("op_1409_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1409_cast_fp16 = slice_by_index(begin = var_1409_begin_0, end = var_1409_end_0, end_mask = var_1409_end_mask_0, x = coreml_update_state_170)[name = string("op_1409_cast_fp16")];
            tensor<int32, [2]> tile_2 = const()[name = string("tile_2"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1412_axis_0 = const()[name = string("op_1412_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1412_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1412_cast_fp16_1 = split(axis = var_1412_axis_0, split_sizes = tile_2, x = var_1409_cast_fp16)[name = string("op_1412_cast_fp16")];
            tensor<int32, [4]> var_1419_begin_0 = const()[name = string("op_1419_begin_0"), val = tensor<int32, [4]>([1, 0, 0, 0])];
            tensor<int32, [4]> var_1419_end_0 = const()[name = string("op_1419_end_0"), val = tensor<int32, [4]>([2, 2, 2048, 128])];
            tensor<bool, [4]> var_1419_end_mask_0 = const()[name = string("op_1419_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1419_cast_fp16 = slice_by_index(begin = var_1419_begin_0, end = var_1419_end_0, end_mask = var_1419_end_mask_0, x = coreml_update_state_171)[name = string("op_1419_cast_fp16")];
            tensor<int32, [2]> tile_3 = const()[name = string("tile_3"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1422_axis_0 = const()[name = string("op_1422_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1422_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1422_cast_fp16_1 = split(axis = var_1422_axis_0, split_sizes = tile_3, x = var_1419_cast_fp16)[name = string("op_1422_cast_fp16")];
            tensor<int32, [2]> var_1425_split_sizes_0 = const()[name = string("op_1425_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_1425_axis_0 = const()[name = string("op_1425_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_1425_0, tensor<fp16, [1, 8, 128, 16]> var_1425_1 = split(axis = var_1425_axis_0, split_sizes = var_1425_split_sizes_0, x = query_states_9_cast_fp16)[name = string("op_1425")];
            bool attn_weights_17_transpose_x_0 = const()[name = string("attn_weights_17_transpose_x_0"), val = bool(false)];
            bool attn_weights_17_transpose_y_0 = const()[name = string("attn_weights_17_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_17_cast_fp16 = matmul(transpose_x = attn_weights_17_transpose_x_0, transpose_y = attn_weights_17_transpose_y_0, x = var_1412_cast_fp16_0, y = var_1425_0)[name = string("attn_weights_17_cast_fp16")];
            fp16 var_1428_to_fp16 = const()[name = string("op_1428_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_19_cast_fp16 = mul(x = attn_weights_17_cast_fp16, y = var_1428_to_fp16)[name = string("attn_weights_19_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_21_cast_fp16 = add(x = attn_weights_19_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_21_cast_fp16")];
            int32 var_1432 = const()[name = string("op_1432"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_23_cast_fp16 = softmax(axis = var_1432, x = attn_weights_21_cast_fp16)[name = string("attn_weights_23_cast_fp16")];
            bool var_1438_transpose_x_1 = const()[name = string("op_1438_transpose_x_1"), val = bool(true)];
            bool var_1438_transpose_y_1 = const()[name = string("op_1438_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_1438_cast_fp16 = matmul(transpose_x = var_1438_transpose_x_1, transpose_y = var_1438_transpose_y_1, x = attn_weights_23_cast_fp16, y = var_1422_cast_fp16_0)[name = string("op_1438_cast_fp16")];
            bool attn_weights_25_transpose_x_0 = const()[name = string("attn_weights_25_transpose_x_0"), val = bool(false)];
            bool attn_weights_25_transpose_y_0 = const()[name = string("attn_weights_25_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_25_cast_fp16 = matmul(transpose_x = attn_weights_25_transpose_x_0, transpose_y = attn_weights_25_transpose_y_0, x = var_1412_cast_fp16_1, y = var_1425_1)[name = string("attn_weights_25_cast_fp16")];
            fp16 var_1440_to_fp16 = const()[name = string("op_1440_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_27_cast_fp16 = mul(x = attn_weights_25_cast_fp16, y = var_1440_to_fp16)[name = string("attn_weights_27_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_29_cast_fp16 = add(x = attn_weights_27_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_29_cast_fp16")];
            int32 var_1444 = const()[name = string("op_1444"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_31_cast_fp16 = softmax(axis = var_1444, x = attn_weights_29_cast_fp16)[name = string("attn_weights_31_cast_fp16")];
            bool attn_output_9_transpose_x_1 = const()[name = string("attn_output_9_transpose_x_1"), val = bool(true)];
            bool attn_output_9_transpose_y_1 = const()[name = string("attn_output_9_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_9_cast_fp16 = matmul(transpose_x = attn_output_9_transpose_x_1, transpose_y = attn_output_9_transpose_y_1, x = attn_weights_31_cast_fp16, y = var_1422_cast_fp16_1)[name = string("attn_output_9_cast_fp16")];
            int32 var_1452 = const()[name = string("op_1452"), val = int32(1)];
            bool attn_output_11_interleave_0 = const()[name = string("attn_output_11_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_11_cast_fp16 = concat(axis = var_1452, interleave = attn_output_11_interleave_0, values = (var_1438_cast_fp16, attn_output_9_cast_fp16))[name = string("attn_output_11_cast_fp16")];
            tensor<int32, [4]> var_1456_perm_0 = const()[name = string("op_1456_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_23x = const()[name = string("concat_23x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_1456_cast_fp16 = transpose(perm = var_1456_perm_0, x = attn_output_11_cast_fp16)[name = string("transpose_339")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_15_cast_fp16 = reshape(shape = concat_23x, x = var_1456_cast_fp16)[name = string("attn_output_15_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_1_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_1_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1078614720)))];
            tensor<int32, [2]> hidden_states_13_strides_0 = const()[name = string("hidden_states_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_13_pad_type_0 = const()[name = string("hidden_states_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_13_pad_0 = const()[name = string("hidden_states_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_13_dilations_0 = const()[name = string("hidden_states_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_13_groups_0 = const()[name = string("hidden_states_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_13_cast_fp16 = conv(dilations = hidden_states_13_dilations_0, groups = hidden_states_13_groups_0, pad = hidden_states_13_pad_0, pad_type = hidden_states_13_pad_type_0, strides = hidden_states_13_strides_0, weight = layers_1_self_attn_o_proj_weight_to_fp16, x = attn_output_15_cast_fp16)[name = string("hidden_states_13_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_15_cast_fp16 = add(x = hidden_states_9_cast_fp16, y = hidden_states_13_cast_fp16)[name = string("hidden_states_15_cast_fp16")];
            fp16 const_20_promoted_to_fp16 = const()[name = string("const_20_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_1489_cast_fp16 = mul(x = hidden_states_15_cast_fp16, y = const_20_promoted_to_fp16)[name = string("op_1489_cast_fp16")];
            int32 var_1487 = const()[name = string("op_1487"), val = int32(1)];
            bool doubled_13_interleave_0 = const()[name = string("doubled_13_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_13_cast_fp16 = concat(axis = var_1487, interleave = doubled_13_interleave_0, values = (hidden_states_15_cast_fp16, var_1489_cast_fp16))[name = string("doubled_13_cast_fp16")];
            tensor<int32, [1]> out_7_axes_0 = const()[name = string("out_7_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_7_gamma_0_to_fp16 = const()[name = string("out_7_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1087003392)))];
            fp16 var_1499_to_fp16 = const()[name = string("op_1499_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_7_cast_fp16 = layer_norm(axes = out_7_axes_0, epsilon = var_1499_to_fp16, gamma = out_7_gamma_0_to_fp16, x = doubled_13_cast_fp16)[name = string("out_7_cast_fp16")];
            tensor<int32, [2]> var_1510_split_sizes_0 = const()[name = string("op_1510_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1510_axis_0 = const()[name = string("op_1510_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_1510_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_1510_cast_fp16_1 = split(axis = var_1510_axis_0, split_sizes = var_1510_split_sizes_0, x = out_7_cast_fp16)[name = string("op_1510_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_1_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_1_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1087011648)))];
            tensor<int32, [2]> input_3_strides_0 = const()[name = string("input_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_3_dilations_0 = const()[name = string("input_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_3_groups_0 = const()[name = string("input_3_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_3_cast_fp16 = conv(dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = layers_1_mlp_gate_proj_weight_to_fp16, x = var_1510_cast_fp16_0)[name = string("input_3_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_1527_cast_fp16 = silu(x = input_3_cast_fp16)[name = string("op_1527_cast_fp16")];
            tensor<int32, [2]> var_1533_strides_0 = const()[name = string("op_1533_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_1533_pad_type_0 = const()[name = string("op_1533_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_1533_pad_0 = const()[name = string("op_1533_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_1533_dilations_0 = const()[name = string("op_1533_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1533_groups_0 = const()[name = string("op_1533_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_1533_cast_fp16 = conv(dilations = var_1533_dilations_0, groups = var_1533_groups_0, pad = var_1533_pad_0, pad_type = var_1533_pad_type_0, strides = var_1533_strides_0, weight = layers_1_mlp_up_proj_weight_cast_fp16, x = var_1510_cast_fp16_0)[name = string("op_1533_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_19_cast_fp16 = mul(x = var_1527_cast_fp16, y = var_1533_cast_fp16)[name = string("x_19_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_1_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_1_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1112177536)))];
            tensor<int32, [2]> hidden_states_17_strides_0 = const()[name = string("hidden_states_17_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_17_pad_type_0 = const()[name = string("hidden_states_17_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_17_pad_0 = const()[name = string("hidden_states_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_17_dilations_0 = const()[name = string("hidden_states_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_17_groups_0 = const()[name = string("hidden_states_17_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_17_cast_fp16 = conv(dilations = hidden_states_17_dilations_0, groups = hidden_states_17_groups_0, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = hidden_states_17_strides_0, weight = layers_1_mlp_down_proj_weight_to_fp16, x = x_19_cast_fp16)[name = string("hidden_states_17_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_19_cast_fp16 = add(x = hidden_states_15_cast_fp16, y = hidden_states_17_cast_fp16)[name = string("hidden_states_19_cast_fp16")];
            fp16 const_22_promoted_to_fp16 = const()[name = string("const_22_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_1551_cast_fp16 = mul(x = hidden_states_19_cast_fp16, y = const_22_promoted_to_fp16)[name = string("op_1551_cast_fp16")];
            int32 var_1549 = const()[name = string("op_1549"), val = int32(1)];
            bool doubled_17_interleave_0 = const()[name = string("doubled_17_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_17_cast_fp16 = concat(axis = var_1549, interleave = doubled_17_interleave_0, values = (hidden_states_19_cast_fp16, var_1551_cast_fp16))[name = string("doubled_17_cast_fp16")];
            tensor<int32, [1]> out_9_axes_0 = const()[name = string("out_9_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_9_gamma_0_to_fp16 = const()[name = string("out_9_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1137343424)))];
            fp16 var_1561_to_fp16 = const()[name = string("op_1561_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_9_cast_fp16 = layer_norm(axes = out_9_axes_0, epsilon = var_1561_to_fp16, gamma = out_9_gamma_0_to_fp16, x = doubled_17_cast_fp16)[name = string("out_9_cast_fp16")];
            tensor<int32, [2]> var_1572_split_sizes_0 = const()[name = string("op_1572_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1572_axis_0 = const()[name = string("op_1572_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_1572_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_1572_cast_fp16_1 = split(axis = var_1572_axis_0, split_sizes = var_1572_split_sizes_0, x = out_9_cast_fp16)[name = string("op_1572_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_2_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_2_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1137351680)))];
            tensor<int32, [2]> query_states_13_strides_0 = const()[name = string("query_states_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_13_pad_type_0 = const()[name = string("query_states_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_13_pad_0 = const()[name = string("query_states_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_13_dilations_0 = const()[name = string("query_states_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_13_groups_0 = const()[name = string("query_states_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_13_cast_fp16 = conv(dilations = query_states_13_dilations_0, groups = query_states_13_groups_0, pad = query_states_13_pad_0, pad_type = query_states_13_pad_type_0, strides = query_states_13_strides_0, weight = layers_2_self_attn_q_proj_weight_to_fp16, x = var_1572_cast_fp16_0)[name = string("query_states_13_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_2_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_2_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1145740352)))];
            tensor<int32, [2]> key_states_21_strides_0 = const()[name = string("key_states_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_21_pad_type_0 = const()[name = string("key_states_21_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_21_pad_0 = const()[name = string("key_states_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_21_dilations_0 = const()[name = string("key_states_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_21_groups_0 = const()[name = string("key_states_21_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_21_cast_fp16 = conv(dilations = key_states_21_dilations_0, groups = key_states_21_groups_0, pad = key_states_21_pad_0, pad_type = key_states_21_pad_type_0, strides = key_states_21_strides_0, weight = layers_2_self_attn_k_proj_weight_to_fp16, x = var_1572_cast_fp16_0)[name = string("key_states_21_cast_fp16")];
            tensor<int32, [2]> value_states_13_strides_0 = const()[name = string("value_states_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_13_pad_type_0 = const()[name = string("value_states_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_13_pad_0 = const()[name = string("value_states_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_13_dilations_0 = const()[name = string("value_states_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_13_groups_0 = const()[name = string("value_states_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_13_cast_fp16 = conv(dilations = value_states_13_dilations_0, groups = value_states_13_groups_0, pad = value_states_13_pad_0, pad_type = value_states_13_pad_type_0, strides = value_states_13_strides_0, weight = layers_2_self_attn_v_proj_weight_cast_fp16, x = var_1572_cast_fp16_0)[name = string("value_states_13_cast_fp16")];
            tensor<int32, [4]> concat_24x = const()[name = string("concat_24x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_21_cast_fp16 = reshape(shape = concat_24x, x = query_states_13_cast_fp16)[name = string("x_21_cast_fp16")];
            tensor<int32, [4]> concat_25x = const()[name = string("concat_25x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_1629_cast_fp16 = reshape(shape = concat_25x, x = key_states_21_cast_fp16)[name = string("op_1629_cast_fp16")];
            tensor<int32, [4]> concat_26x = const()[name = string("concat_26x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_1636_cast_fp16 = reshape(shape = concat_26x, x = value_states_13_cast_fp16)[name = string("op_1636_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_1640_cast_fp16 = mul(x = x_21_cast_fp16, y = var_859_cast_fp16)[name = string("op_1640_cast_fp16")];
            tensor<int32, [2]> var_1641_split_sizes_0 = const()[name = string("op_1641_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1641_axis_0 = const()[name = string("op_1641_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_1641_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_1641_cast_fp16_1 = split(axis = var_1641_axis_0, split_sizes = var_1641_split_sizes_0, x = x_21_cast_fp16)[name = string("op_1641_cast_fp16")];
            fp16 const_24_promoted_to_fp16 = const()[name = string("const_24_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_1643_cast_fp16 = mul(x = var_1641_cast_fp16_1, y = const_24_promoted_to_fp16)[name = string("op_1643_cast_fp16")];
            int32 var_1645 = const()[name = string("op_1645"), val = int32(-2)];
            bool var_1646_interleave_0 = const()[name = string("op_1646_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_1646_cast_fp16 = concat(axis = var_1645, interleave = var_1646_interleave_0, values = (var_1643_cast_fp16, var_1641_cast_fp16_0))[name = string("op_1646_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_1647_cast_fp16 = mul(x = var_1646_cast_fp16, y = var_866_cast_fp16)[name = string("op_1647_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_15_cast_fp16 = add(x = var_1640_cast_fp16, y = var_1647_cast_fp16)[name = string("query_states_15_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_1653_cast_fp16 = mul(x = var_1629_cast_fp16, y = var_859_cast_fp16)[name = string("op_1653_cast_fp16")];
            tensor<int32, [2]> var_1654_split_sizes_0 = const()[name = string("op_1654_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1654_axis_0 = const()[name = string("op_1654_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_1654_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_1654_cast_fp16_1 = split(axis = var_1654_axis_0, split_sizes = var_1654_split_sizes_0, x = var_1629_cast_fp16)[name = string("op_1654_cast_fp16")];
            fp16 const_25_promoted_to_fp16 = const()[name = string("const_25_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_1656_cast_fp16 = mul(x = var_1654_cast_fp16_1, y = const_25_promoted_to_fp16)[name = string("op_1656_cast_fp16")];
            int32 var_1658 = const()[name = string("op_1658"), val = int32(-2)];
            bool var_1659_interleave_0 = const()[name = string("op_1659_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_1659_cast_fp16 = concat(axis = var_1658, interleave = var_1659_interleave_0, values = (var_1656_cast_fp16, var_1654_cast_fp16_0))[name = string("op_1659_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_1660_cast_fp16 = mul(x = var_1659_cast_fp16, y = var_866_cast_fp16)[name = string("op_1660_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_25_cast_fp16 = add(x = var_1653_cast_fp16, y = var_1660_cast_fp16)[name = string("key_states_25_cast_fp16")];
            tensor<int32, [1]> expand_dims_24 = const()[name = string("expand_dims_24"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> expand_dims_25 = const()[name = string("expand_dims_25"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_27 = const()[name = string("expand_dims_27"), val = tensor<int32, [1]>([0])];
            int32 concat_29_axis_0 = const()[name = string("concat_29_axis_0"), val = int32(0)];
            bool concat_29_interleave_0 = const()[name = string("concat_29_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_29 = concat(axis = concat_29_axis_0, interleave = concat_29_interleave_0, values = (expand_dims_24, expand_dims_25, position_id, expand_dims_27))[name = string("concat_29")];
            tensor<int32, [1]> expand_dims_28 = const()[name = string("expand_dims_28"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> concat_30_values1_0 = const()[name = string("concat_30_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_30_values3_0 = const()[name = string("concat_30_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_30_axis_0 = const()[name = string("concat_30_axis_0"), val = int32(0)];
            bool concat_30_interleave_0 = const()[name = string("concat_30_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_30 = concat(axis = concat_30_axis_0, interleave = concat_30_interleave_0, values = (expand_dims_28, concat_30_values1_0, cache_position_end, concat_30_values3_0))[name = string("concat_30")];
            tensor<int32, [4]> key_states_27_perm_0 = const()[name = string("key_states_27_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_3_stride_0 = const()[name = string("key_cache_internal_tensor_assign_3_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_3_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_3_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_3_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_3_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_3_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_3_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_27_cast_fp16 = transpose(perm = key_states_27_perm_0, x = key_states_25_cast_fp16)[name = string("transpose_338")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_3_cast_fp16 = slice_update(begin = concat_29, begin_mask = key_cache_internal_tensor_assign_3_begin_mask_0, end = concat_30, end_mask = key_cache_internal_tensor_assign_3_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_3_squeeze_mask_0, stride = key_cache_internal_tensor_assign_3_stride_0, update = key_states_27_cast_fp16, x = coreml_update_state_170)[name = string("key_cache_internal_tensor_assign_3_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_3_cast_fp16, input = key_cache)[name = string("coreml_update_state_172_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_172 = read_state(input = key_cache)[name = string("coreml_update_state_172")];
            tensor<int32, [4]> value_states_15_perm_0 = const()[name = string("value_states_15_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_3_stride_0 = const()[name = string("value_cache_internal_tensor_assign_3_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_3_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_3_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_3_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_3_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_3_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_3_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_15_cast_fp16 = transpose(perm = value_states_15_perm_0, x = var_1636_cast_fp16)[name = string("transpose_337")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_3_cast_fp16 = slice_update(begin = concat_29, begin_mask = value_cache_internal_tensor_assign_3_begin_mask_0, end = concat_30, end_mask = value_cache_internal_tensor_assign_3_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_3_squeeze_mask_0, stride = value_cache_internal_tensor_assign_3_stride_0, update = value_states_15_cast_fp16, x = coreml_update_state_171)[name = string("value_cache_internal_tensor_assign_3_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_3_cast_fp16, input = value_cache)[name = string("coreml_update_state_173_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_173 = read_state(input = value_cache)[name = string("coreml_update_state_173")];
            tensor<int32, [4]> var_1730_begin_0 = const()[name = string("op_1730_begin_0"), val = tensor<int32, [4]>([2, 0, 0, 0])];
            tensor<int32, [4]> var_1730_end_0 = const()[name = string("op_1730_end_0"), val = tensor<int32, [4]>([3, 2, 2048, 128])];
            tensor<bool, [4]> var_1730_end_mask_0 = const()[name = string("op_1730_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1730_cast_fp16 = slice_by_index(begin = var_1730_begin_0, end = var_1730_end_0, end_mask = var_1730_end_mask_0, x = coreml_update_state_172)[name = string("op_1730_cast_fp16")];
            tensor<int32, [2]> tile_4 = const()[name = string("tile_4"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1733_axis_0 = const()[name = string("op_1733_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1733_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1733_cast_fp16_1 = split(axis = var_1733_axis_0, split_sizes = tile_4, x = var_1730_cast_fp16)[name = string("op_1733_cast_fp16")];
            tensor<int32, [4]> var_1740_begin_0 = const()[name = string("op_1740_begin_0"), val = tensor<int32, [4]>([2, 0, 0, 0])];
            tensor<int32, [4]> var_1740_end_0 = const()[name = string("op_1740_end_0"), val = tensor<int32, [4]>([3, 2, 2048, 128])];
            tensor<bool, [4]> var_1740_end_mask_0 = const()[name = string("op_1740_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1740_cast_fp16 = slice_by_index(begin = var_1740_begin_0, end = var_1740_end_0, end_mask = var_1740_end_mask_0, x = coreml_update_state_173)[name = string("op_1740_cast_fp16")];
            tensor<int32, [2]> tile_5 = const()[name = string("tile_5"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1743_axis_0 = const()[name = string("op_1743_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1743_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1743_cast_fp16_1 = split(axis = var_1743_axis_0, split_sizes = tile_5, x = var_1740_cast_fp16)[name = string("op_1743_cast_fp16")];
            tensor<int32, [2]> var_1746_split_sizes_0 = const()[name = string("op_1746_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_1746_axis_0 = const()[name = string("op_1746_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_1746_0, tensor<fp16, [1, 8, 128, 16]> var_1746_1 = split(axis = var_1746_axis_0, split_sizes = var_1746_split_sizes_0, x = query_states_15_cast_fp16)[name = string("op_1746")];
            bool attn_weights_33_transpose_x_0 = const()[name = string("attn_weights_33_transpose_x_0"), val = bool(false)];
            bool attn_weights_33_transpose_y_0 = const()[name = string("attn_weights_33_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_33_cast_fp16 = matmul(transpose_x = attn_weights_33_transpose_x_0, transpose_y = attn_weights_33_transpose_y_0, x = var_1733_cast_fp16_0, y = var_1746_0)[name = string("attn_weights_33_cast_fp16")];
            fp16 var_1749_to_fp16 = const()[name = string("op_1749_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_35_cast_fp16 = mul(x = attn_weights_33_cast_fp16, y = var_1749_to_fp16)[name = string("attn_weights_35_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_37_cast_fp16 = add(x = attn_weights_35_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_37_cast_fp16")];
            int32 var_1753 = const()[name = string("op_1753"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_39_cast_fp16 = softmax(axis = var_1753, x = attn_weights_37_cast_fp16)[name = string("attn_weights_39_cast_fp16")];
            bool var_1759_transpose_x_1 = const()[name = string("op_1759_transpose_x_1"), val = bool(true)];
            bool var_1759_transpose_y_1 = const()[name = string("op_1759_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_1759_cast_fp16 = matmul(transpose_x = var_1759_transpose_x_1, transpose_y = var_1759_transpose_y_1, x = attn_weights_39_cast_fp16, y = var_1743_cast_fp16_0)[name = string("op_1759_cast_fp16")];
            bool attn_weights_41_transpose_x_0 = const()[name = string("attn_weights_41_transpose_x_0"), val = bool(false)];
            bool attn_weights_41_transpose_y_0 = const()[name = string("attn_weights_41_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_41_cast_fp16 = matmul(transpose_x = attn_weights_41_transpose_x_0, transpose_y = attn_weights_41_transpose_y_0, x = var_1733_cast_fp16_1, y = var_1746_1)[name = string("attn_weights_41_cast_fp16")];
            fp16 var_1761_to_fp16 = const()[name = string("op_1761_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_43_cast_fp16 = mul(x = attn_weights_41_cast_fp16, y = var_1761_to_fp16)[name = string("attn_weights_43_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_45_cast_fp16 = add(x = attn_weights_43_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_45_cast_fp16")];
            int32 var_1765 = const()[name = string("op_1765"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_47_cast_fp16 = softmax(axis = var_1765, x = attn_weights_45_cast_fp16)[name = string("attn_weights_47_cast_fp16")];
            bool attn_output_17_transpose_x_1 = const()[name = string("attn_output_17_transpose_x_1"), val = bool(true)];
            bool attn_output_17_transpose_y_1 = const()[name = string("attn_output_17_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_17_cast_fp16 = matmul(transpose_x = attn_output_17_transpose_x_1, transpose_y = attn_output_17_transpose_y_1, x = attn_weights_47_cast_fp16, y = var_1743_cast_fp16_1)[name = string("attn_output_17_cast_fp16")];
            int32 var_1773 = const()[name = string("op_1773"), val = int32(1)];
            bool attn_output_19_interleave_0 = const()[name = string("attn_output_19_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_19_cast_fp16 = concat(axis = var_1773, interleave = attn_output_19_interleave_0, values = (var_1759_cast_fp16, attn_output_17_cast_fp16))[name = string("attn_output_19_cast_fp16")];
            tensor<int32, [4]> var_1777_perm_0 = const()[name = string("op_1777_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_35x = const()[name = string("concat_35x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_1777_cast_fp16 = transpose(perm = var_1777_perm_0, x = attn_output_19_cast_fp16)[name = string("transpose_336")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_23_cast_fp16 = reshape(shape = concat_35x, x = var_1777_cast_fp16)[name = string("attn_output_23_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_2_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_2_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1146788992)))];
            tensor<int32, [2]> hidden_states_23_strides_0 = const()[name = string("hidden_states_23_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_23_pad_type_0 = const()[name = string("hidden_states_23_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_23_pad_0 = const()[name = string("hidden_states_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_23_dilations_0 = const()[name = string("hidden_states_23_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_23_groups_0 = const()[name = string("hidden_states_23_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_23_cast_fp16 = conv(dilations = hidden_states_23_dilations_0, groups = hidden_states_23_groups_0, pad = hidden_states_23_pad_0, pad_type = hidden_states_23_pad_type_0, strides = hidden_states_23_strides_0, weight = layers_2_self_attn_o_proj_weight_to_fp16, x = attn_output_23_cast_fp16)[name = string("hidden_states_23_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_25_cast_fp16 = add(x = hidden_states_19_cast_fp16, y = hidden_states_23_cast_fp16)[name = string("hidden_states_25_cast_fp16")];
            fp16 const_30_promoted_to_fp16 = const()[name = string("const_30_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_1810_cast_fp16 = mul(x = hidden_states_25_cast_fp16, y = const_30_promoted_to_fp16)[name = string("op_1810_cast_fp16")];
            int32 var_1808 = const()[name = string("op_1808"), val = int32(1)];
            bool doubled_21_interleave_0 = const()[name = string("doubled_21_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_21_cast_fp16 = concat(axis = var_1808, interleave = doubled_21_interleave_0, values = (hidden_states_25_cast_fp16, var_1810_cast_fp16))[name = string("doubled_21_cast_fp16")];
            tensor<int32, [1]> out_11_axes_0 = const()[name = string("out_11_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_11_gamma_0_to_fp16 = const()[name = string("out_11_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1155177664)))];
            fp16 var_1820_to_fp16 = const()[name = string("op_1820_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_11_cast_fp16 = layer_norm(axes = out_11_axes_0, epsilon = var_1820_to_fp16, gamma = out_11_gamma_0_to_fp16, x = doubled_21_cast_fp16)[name = string("out_11_cast_fp16")];
            tensor<int32, [2]> var_1831_split_sizes_0 = const()[name = string("op_1831_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1831_axis_0 = const()[name = string("op_1831_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_1831_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_1831_cast_fp16_1 = split(axis = var_1831_axis_0, split_sizes = var_1831_split_sizes_0, x = out_11_cast_fp16)[name = string("op_1831_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_2_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_2_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1155185920)))];
            tensor<int32, [2]> input_5_strides_0 = const()[name = string("input_5_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_5_pad_type_0 = const()[name = string("input_5_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_5_dilations_0 = const()[name = string("input_5_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_5_groups_0 = const()[name = string("input_5_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_5_cast_fp16 = conv(dilations = input_5_dilations_0, groups = input_5_groups_0, pad = input_5_pad_0, pad_type = input_5_pad_type_0, strides = input_5_strides_0, weight = layers_2_mlp_gate_proj_weight_to_fp16, x = var_1831_cast_fp16_0)[name = string("input_5_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_1848_cast_fp16 = silu(x = input_5_cast_fp16)[name = string("op_1848_cast_fp16")];
            tensor<int32, [2]> var_1854_strides_0 = const()[name = string("op_1854_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_1854_pad_type_0 = const()[name = string("op_1854_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_1854_pad_0 = const()[name = string("op_1854_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_1854_dilations_0 = const()[name = string("op_1854_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1854_groups_0 = const()[name = string("op_1854_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_1854_cast_fp16 = conv(dilations = var_1854_dilations_0, groups = var_1854_groups_0, pad = var_1854_pad_0, pad_type = var_1854_pad_type_0, strides = var_1854_strides_0, weight = layers_2_mlp_up_proj_weight_cast_fp16, x = var_1831_cast_fp16_0)[name = string("op_1854_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_29_cast_fp16 = mul(x = var_1848_cast_fp16, y = var_1854_cast_fp16)[name = string("x_29_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_2_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_2_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1180351808)))];
            tensor<int32, [2]> hidden_states_27_strides_0 = const()[name = string("hidden_states_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_27_pad_type_0 = const()[name = string("hidden_states_27_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_27_pad_0 = const()[name = string("hidden_states_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_27_dilations_0 = const()[name = string("hidden_states_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_27_groups_0 = const()[name = string("hidden_states_27_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_27_cast_fp16 = conv(dilations = hidden_states_27_dilations_0, groups = hidden_states_27_groups_0, pad = hidden_states_27_pad_0, pad_type = hidden_states_27_pad_type_0, strides = hidden_states_27_strides_0, weight = layers_2_mlp_down_proj_weight_to_fp16, x = x_29_cast_fp16)[name = string("hidden_states_27_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_29_cast_fp16 = add(x = hidden_states_25_cast_fp16, y = hidden_states_27_cast_fp16)[name = string("hidden_states_29_cast_fp16")];
            fp16 const_32_promoted_to_fp16 = const()[name = string("const_32_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_1872_cast_fp16 = mul(x = hidden_states_29_cast_fp16, y = const_32_promoted_to_fp16)[name = string("op_1872_cast_fp16")];
            int32 var_1870 = const()[name = string("op_1870"), val = int32(1)];
            bool doubled_25_interleave_0 = const()[name = string("doubled_25_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_25_cast_fp16 = concat(axis = var_1870, interleave = doubled_25_interleave_0, values = (hidden_states_29_cast_fp16, var_1872_cast_fp16))[name = string("doubled_25_cast_fp16")];
            tensor<int32, [1]> out_13_axes_0 = const()[name = string("out_13_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_13_gamma_0_to_fp16 = const()[name = string("out_13_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1205517696)))];
            fp16 var_1882_to_fp16 = const()[name = string("op_1882_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_13_cast_fp16 = layer_norm(axes = out_13_axes_0, epsilon = var_1882_to_fp16, gamma = out_13_gamma_0_to_fp16, x = doubled_25_cast_fp16)[name = string("out_13_cast_fp16")];
            tensor<int32, [2]> var_1893_split_sizes_0 = const()[name = string("op_1893_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1893_axis_0 = const()[name = string("op_1893_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_1893_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_1893_cast_fp16_1 = split(axis = var_1893_axis_0, split_sizes = var_1893_split_sizes_0, x = out_13_cast_fp16)[name = string("op_1893_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_3_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_3_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1205525952)))];
            tensor<int32, [2]> query_states_19_strides_0 = const()[name = string("query_states_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_19_pad_type_0 = const()[name = string("query_states_19_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_19_pad_0 = const()[name = string("query_states_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_19_dilations_0 = const()[name = string("query_states_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_19_groups_0 = const()[name = string("query_states_19_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_19_cast_fp16 = conv(dilations = query_states_19_dilations_0, groups = query_states_19_groups_0, pad = query_states_19_pad_0, pad_type = query_states_19_pad_type_0, strides = query_states_19_strides_0, weight = layers_3_self_attn_q_proj_weight_to_fp16, x = var_1893_cast_fp16_0)[name = string("query_states_19_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_3_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_3_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1213914624)))];
            tensor<int32, [2]> key_states_31_strides_0 = const()[name = string("key_states_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_31_pad_type_0 = const()[name = string("key_states_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_31_pad_0 = const()[name = string("key_states_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_31_dilations_0 = const()[name = string("key_states_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_31_groups_0 = const()[name = string("key_states_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_31_cast_fp16 = conv(dilations = key_states_31_dilations_0, groups = key_states_31_groups_0, pad = key_states_31_pad_0, pad_type = key_states_31_pad_type_0, strides = key_states_31_strides_0, weight = layers_3_self_attn_k_proj_weight_to_fp16, x = var_1893_cast_fp16_0)[name = string("key_states_31_cast_fp16")];
            tensor<int32, [2]> value_states_19_strides_0 = const()[name = string("value_states_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_19_pad_type_0 = const()[name = string("value_states_19_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_19_pad_0 = const()[name = string("value_states_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_19_dilations_0 = const()[name = string("value_states_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_19_groups_0 = const()[name = string("value_states_19_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_19_cast_fp16 = conv(dilations = value_states_19_dilations_0, groups = value_states_19_groups_0, pad = value_states_19_pad_0, pad_type = value_states_19_pad_type_0, strides = value_states_19_strides_0, weight = layers_3_self_attn_v_proj_weight_cast_fp16, x = var_1893_cast_fp16_0)[name = string("value_states_19_cast_fp16")];
            tensor<int32, [4]> concat_36x = const()[name = string("concat_36x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_31_cast_fp16 = reshape(shape = concat_36x, x = query_states_19_cast_fp16)[name = string("x_31_cast_fp16")];
            tensor<int32, [4]> concat_37x = const()[name = string("concat_37x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_1950_cast_fp16 = reshape(shape = concat_37x, x = key_states_31_cast_fp16)[name = string("op_1950_cast_fp16")];
            tensor<int32, [4]> concat_38x = const()[name = string("concat_38x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_1957_cast_fp16 = reshape(shape = concat_38x, x = value_states_19_cast_fp16)[name = string("op_1957_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_1961_cast_fp16 = mul(x = x_31_cast_fp16, y = var_859_cast_fp16)[name = string("op_1961_cast_fp16")];
            tensor<int32, [2]> var_1962_split_sizes_0 = const()[name = string("op_1962_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1962_axis_0 = const()[name = string("op_1962_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_1962_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_1962_cast_fp16_1 = split(axis = var_1962_axis_0, split_sizes = var_1962_split_sizes_0, x = x_31_cast_fp16)[name = string("op_1962_cast_fp16")];
            fp16 const_34_promoted_to_fp16 = const()[name = string("const_34_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_1964_cast_fp16 = mul(x = var_1962_cast_fp16_1, y = const_34_promoted_to_fp16)[name = string("op_1964_cast_fp16")];
            int32 var_1966 = const()[name = string("op_1966"), val = int32(-2)];
            bool var_1967_interleave_0 = const()[name = string("op_1967_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_1967_cast_fp16 = concat(axis = var_1966, interleave = var_1967_interleave_0, values = (var_1964_cast_fp16, var_1962_cast_fp16_0))[name = string("op_1967_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_1968_cast_fp16 = mul(x = var_1967_cast_fp16, y = var_866_cast_fp16)[name = string("op_1968_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_21_cast_fp16 = add(x = var_1961_cast_fp16, y = var_1968_cast_fp16)[name = string("query_states_21_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_1974_cast_fp16 = mul(x = var_1950_cast_fp16, y = var_859_cast_fp16)[name = string("op_1974_cast_fp16")];
            tensor<int32, [2]> var_1975_split_sizes_0 = const()[name = string("op_1975_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1975_axis_0 = const()[name = string("op_1975_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_1975_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_1975_cast_fp16_1 = split(axis = var_1975_axis_0, split_sizes = var_1975_split_sizes_0, x = var_1950_cast_fp16)[name = string("op_1975_cast_fp16")];
            fp16 const_35_promoted_to_fp16 = const()[name = string("const_35_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_1977_cast_fp16 = mul(x = var_1975_cast_fp16_1, y = const_35_promoted_to_fp16)[name = string("op_1977_cast_fp16")];
            int32 var_1979 = const()[name = string("op_1979"), val = int32(-2)];
            bool var_1980_interleave_0 = const()[name = string("op_1980_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_1980_cast_fp16 = concat(axis = var_1979, interleave = var_1980_interleave_0, values = (var_1977_cast_fp16, var_1975_cast_fp16_0))[name = string("op_1980_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_1981_cast_fp16 = mul(x = var_1980_cast_fp16, y = var_866_cast_fp16)[name = string("op_1981_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_35_cast_fp16 = add(x = var_1974_cast_fp16, y = var_1981_cast_fp16)[name = string("key_states_35_cast_fp16")];
            tensor<int32, [1]> expand_dims_36 = const()[name = string("expand_dims_36"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> expand_dims_37 = const()[name = string("expand_dims_37"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_39 = const()[name = string("expand_dims_39"), val = tensor<int32, [1]>([0])];
            int32 concat_41_axis_0 = const()[name = string("concat_41_axis_0"), val = int32(0)];
            bool concat_41_interleave_0 = const()[name = string("concat_41_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_41 = concat(axis = concat_41_axis_0, interleave = concat_41_interleave_0, values = (expand_dims_36, expand_dims_37, position_id, expand_dims_39))[name = string("concat_41")];
            tensor<int32, [1]> expand_dims_40 = const()[name = string("expand_dims_40"), val = tensor<int32, [1]>([4])];
            tensor<int32, [1]> concat_42_values1_0 = const()[name = string("concat_42_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_42_values3_0 = const()[name = string("concat_42_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_42_axis_0 = const()[name = string("concat_42_axis_0"), val = int32(0)];
            bool concat_42_interleave_0 = const()[name = string("concat_42_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_42 = concat(axis = concat_42_axis_0, interleave = concat_42_interleave_0, values = (expand_dims_40, concat_42_values1_0, cache_position_end, concat_42_values3_0))[name = string("concat_42")];
            tensor<int32, [4]> key_states_37_perm_0 = const()[name = string("key_states_37_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_4_stride_0 = const()[name = string("key_cache_internal_tensor_assign_4_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_4_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_4_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_4_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_4_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_4_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_4_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_37_cast_fp16 = transpose(perm = key_states_37_perm_0, x = key_states_35_cast_fp16)[name = string("transpose_335")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_4_cast_fp16 = slice_update(begin = concat_41, begin_mask = key_cache_internal_tensor_assign_4_begin_mask_0, end = concat_42, end_mask = key_cache_internal_tensor_assign_4_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_4_squeeze_mask_0, stride = key_cache_internal_tensor_assign_4_stride_0, update = key_states_37_cast_fp16, x = coreml_update_state_172)[name = string("key_cache_internal_tensor_assign_4_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_4_cast_fp16, input = key_cache)[name = string("coreml_update_state_174_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_174 = read_state(input = key_cache)[name = string("coreml_update_state_174")];
            tensor<int32, [4]> value_states_21_perm_0 = const()[name = string("value_states_21_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_4_stride_0 = const()[name = string("value_cache_internal_tensor_assign_4_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_4_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_4_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_4_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_4_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_4_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_4_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_21_cast_fp16 = transpose(perm = value_states_21_perm_0, x = var_1957_cast_fp16)[name = string("transpose_334")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_4_cast_fp16 = slice_update(begin = concat_41, begin_mask = value_cache_internal_tensor_assign_4_begin_mask_0, end = concat_42, end_mask = value_cache_internal_tensor_assign_4_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_4_squeeze_mask_0, stride = value_cache_internal_tensor_assign_4_stride_0, update = value_states_21_cast_fp16, x = coreml_update_state_173)[name = string("value_cache_internal_tensor_assign_4_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_4_cast_fp16, input = value_cache)[name = string("coreml_update_state_175_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_175 = read_state(input = value_cache)[name = string("coreml_update_state_175")];
            tensor<int32, [4]> var_2051_begin_0 = const()[name = string("op_2051_begin_0"), val = tensor<int32, [4]>([3, 0, 0, 0])];
            tensor<int32, [4]> var_2051_end_0 = const()[name = string("op_2051_end_0"), val = tensor<int32, [4]>([4, 2, 2048, 128])];
            tensor<bool, [4]> var_2051_end_mask_0 = const()[name = string("op_2051_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2051_cast_fp16 = slice_by_index(begin = var_2051_begin_0, end = var_2051_end_0, end_mask = var_2051_end_mask_0, x = coreml_update_state_174)[name = string("op_2051_cast_fp16")];
            tensor<int32, [2]> tile_6 = const()[name = string("tile_6"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2054_axis_0 = const()[name = string("op_2054_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2054_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2054_cast_fp16_1 = split(axis = var_2054_axis_0, split_sizes = tile_6, x = var_2051_cast_fp16)[name = string("op_2054_cast_fp16")];
            tensor<int32, [4]> var_2061_begin_0 = const()[name = string("op_2061_begin_0"), val = tensor<int32, [4]>([3, 0, 0, 0])];
            tensor<int32, [4]> var_2061_end_0 = const()[name = string("op_2061_end_0"), val = tensor<int32, [4]>([4, 2, 2048, 128])];
            tensor<bool, [4]> var_2061_end_mask_0 = const()[name = string("op_2061_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2061_cast_fp16 = slice_by_index(begin = var_2061_begin_0, end = var_2061_end_0, end_mask = var_2061_end_mask_0, x = coreml_update_state_175)[name = string("op_2061_cast_fp16")];
            tensor<int32, [2]> tile_7 = const()[name = string("tile_7"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2064_axis_0 = const()[name = string("op_2064_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2064_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2064_cast_fp16_1 = split(axis = var_2064_axis_0, split_sizes = tile_7, x = var_2061_cast_fp16)[name = string("op_2064_cast_fp16")];
            tensor<int32, [2]> var_2067_split_sizes_0 = const()[name = string("op_2067_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_2067_axis_0 = const()[name = string("op_2067_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_2067_0, tensor<fp16, [1, 8, 128, 16]> var_2067_1 = split(axis = var_2067_axis_0, split_sizes = var_2067_split_sizes_0, x = query_states_21_cast_fp16)[name = string("op_2067")];
            bool attn_weights_49_transpose_x_0 = const()[name = string("attn_weights_49_transpose_x_0"), val = bool(false)];
            bool attn_weights_49_transpose_y_0 = const()[name = string("attn_weights_49_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_49_cast_fp16 = matmul(transpose_x = attn_weights_49_transpose_x_0, transpose_y = attn_weights_49_transpose_y_0, x = var_2054_cast_fp16_0, y = var_2067_0)[name = string("attn_weights_49_cast_fp16")];
            fp16 var_2070_to_fp16 = const()[name = string("op_2070_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_51_cast_fp16 = mul(x = attn_weights_49_cast_fp16, y = var_2070_to_fp16)[name = string("attn_weights_51_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_53_cast_fp16 = add(x = attn_weights_51_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_53_cast_fp16")];
            int32 var_2074 = const()[name = string("op_2074"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_55_cast_fp16 = softmax(axis = var_2074, x = attn_weights_53_cast_fp16)[name = string("attn_weights_55_cast_fp16")];
            bool var_2080_transpose_x_1 = const()[name = string("op_2080_transpose_x_1"), val = bool(true)];
            bool var_2080_transpose_y_1 = const()[name = string("op_2080_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_2080_cast_fp16 = matmul(transpose_x = var_2080_transpose_x_1, transpose_y = var_2080_transpose_y_1, x = attn_weights_55_cast_fp16, y = var_2064_cast_fp16_0)[name = string("op_2080_cast_fp16")];
            bool attn_weights_57_transpose_x_0 = const()[name = string("attn_weights_57_transpose_x_0"), val = bool(false)];
            bool attn_weights_57_transpose_y_0 = const()[name = string("attn_weights_57_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_57_cast_fp16 = matmul(transpose_x = attn_weights_57_transpose_x_0, transpose_y = attn_weights_57_transpose_y_0, x = var_2054_cast_fp16_1, y = var_2067_1)[name = string("attn_weights_57_cast_fp16")];
            fp16 var_2082_to_fp16 = const()[name = string("op_2082_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_59_cast_fp16 = mul(x = attn_weights_57_cast_fp16, y = var_2082_to_fp16)[name = string("attn_weights_59_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_61_cast_fp16 = add(x = attn_weights_59_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_61_cast_fp16")];
            int32 var_2086 = const()[name = string("op_2086"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_63_cast_fp16 = softmax(axis = var_2086, x = attn_weights_61_cast_fp16)[name = string("attn_weights_63_cast_fp16")];
            bool attn_output_25_transpose_x_1 = const()[name = string("attn_output_25_transpose_x_1"), val = bool(true)];
            bool attn_output_25_transpose_y_1 = const()[name = string("attn_output_25_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_25_cast_fp16 = matmul(transpose_x = attn_output_25_transpose_x_1, transpose_y = attn_output_25_transpose_y_1, x = attn_weights_63_cast_fp16, y = var_2064_cast_fp16_1)[name = string("attn_output_25_cast_fp16")];
            int32 var_2094 = const()[name = string("op_2094"), val = int32(1)];
            bool attn_output_27_interleave_0 = const()[name = string("attn_output_27_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_27_cast_fp16 = concat(axis = var_2094, interleave = attn_output_27_interleave_0, values = (var_2080_cast_fp16, attn_output_25_cast_fp16))[name = string("attn_output_27_cast_fp16")];
            tensor<int32, [4]> var_2098_perm_0 = const()[name = string("op_2098_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_47x = const()[name = string("concat_47x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_2098_cast_fp16 = transpose(perm = var_2098_perm_0, x = attn_output_27_cast_fp16)[name = string("transpose_333")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_31_cast_fp16 = reshape(shape = concat_47x, x = var_2098_cast_fp16)[name = string("attn_output_31_cast_fp16")];
            tensor<int32, [2]> hidden_states_33_strides_0 = const()[name = string("hidden_states_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_33_pad_type_0 = const()[name = string("hidden_states_33_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_33_pad_0 = const()[name = string("hidden_states_33_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_33_dilations_0 = const()[name = string("hidden_states_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_33_groups_0 = const()[name = string("hidden_states_33_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_33_cast_fp16 = conv(dilations = hidden_states_33_dilations_0, groups = hidden_states_33_groups_0, pad = hidden_states_33_pad_0, pad_type = hidden_states_33_pad_type_0, strides = hidden_states_33_strides_0, weight = layers_3_self_attn_o_proj_weight_cast_fp16, x = attn_output_31_cast_fp16)[name = string("hidden_states_33_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_35_cast_fp16 = add(x = hidden_states_29_cast_fp16, y = hidden_states_33_cast_fp16)[name = string("hidden_states_35_cast_fp16")];
            fp16 const_40_promoted_to_fp16 = const()[name = string("const_40_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_2131_cast_fp16 = mul(x = hidden_states_35_cast_fp16, y = const_40_promoted_to_fp16)[name = string("op_2131_cast_fp16")];
            int32 var_2129 = const()[name = string("op_2129"), val = int32(1)];
            bool doubled_29_interleave_0 = const()[name = string("doubled_29_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_29_cast_fp16 = concat(axis = var_2129, interleave = doubled_29_interleave_0, values = (hidden_states_35_cast_fp16, var_2131_cast_fp16))[name = string("doubled_29_cast_fp16")];
            tensor<int32, [1]> out_15_axes_0 = const()[name = string("out_15_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_15_gamma_0_to_fp16 = const()[name = string("out_15_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1214963264)))];
            fp16 var_2141_to_fp16 = const()[name = string("op_2141_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_15_cast_fp16 = layer_norm(axes = out_15_axes_0, epsilon = var_2141_to_fp16, gamma = out_15_gamma_0_to_fp16, x = doubled_29_cast_fp16)[name = string("out_15_cast_fp16")];
            tensor<int32, [2]> var_2152_split_sizes_0 = const()[name = string("op_2152_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2152_axis_0 = const()[name = string("op_2152_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_2152_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_2152_cast_fp16_1 = split(axis = var_2152_axis_0, split_sizes = var_2152_split_sizes_0, x = out_15_cast_fp16)[name = string("op_2152_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_3_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_3_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1214971520)))];
            tensor<int32, [2]> input_7_strides_0 = const()[name = string("input_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_7_pad_type_0 = const()[name = string("input_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_7_pad_0 = const()[name = string("input_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_7_dilations_0 = const()[name = string("input_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_7_groups_0 = const()[name = string("input_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_7_cast_fp16 = conv(dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = layers_3_mlp_gate_proj_weight_to_fp16, x = var_2152_cast_fp16_0)[name = string("input_7_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_2169_cast_fp16 = silu(x = input_7_cast_fp16)[name = string("op_2169_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_3_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_3_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1240137408)))];
            tensor<int32, [2]> var_2175_strides_0 = const()[name = string("op_2175_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_2175_pad_type_0 = const()[name = string("op_2175_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_2175_pad_0 = const()[name = string("op_2175_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_2175_dilations_0 = const()[name = string("op_2175_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2175_groups_0 = const()[name = string("op_2175_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_2175_cast_fp16 = conv(dilations = var_2175_dilations_0, groups = var_2175_groups_0, pad = var_2175_pad_0, pad_type = var_2175_pad_type_0, strides = var_2175_strides_0, weight = layers_3_mlp_up_proj_weight_to_fp16, x = var_2152_cast_fp16_0)[name = string("op_2175_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_39_cast_fp16 = mul(x = var_2169_cast_fp16, y = var_2175_cast_fp16)[name = string("x_39_cast_fp16")];
            tensor<int32, [2]> hidden_states_37_strides_0 = const()[name = string("hidden_states_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_37_pad_type_0 = const()[name = string("hidden_states_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_37_pad_0 = const()[name = string("hidden_states_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_37_dilations_0 = const()[name = string("hidden_states_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_37_groups_0 = const()[name = string("hidden_states_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_37_cast_fp16 = conv(dilations = hidden_states_37_dilations_0, groups = hidden_states_37_groups_0, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = hidden_states_37_strides_0, weight = layers_3_mlp_down_proj_weight_cast_fp16, x = x_39_cast_fp16)[name = string("hidden_states_37_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_39_cast_fp16 = add(x = hidden_states_35_cast_fp16, y = hidden_states_37_cast_fp16)[name = string("hidden_states_39_cast_fp16")];
            fp16 const_42_promoted_to_fp16 = const()[name = string("const_42_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_2193_cast_fp16 = mul(x = hidden_states_39_cast_fp16, y = const_42_promoted_to_fp16)[name = string("op_2193_cast_fp16")];
            int32 var_2191 = const()[name = string("op_2191"), val = int32(1)];
            bool doubled_33_interleave_0 = const()[name = string("doubled_33_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_33_cast_fp16 = concat(axis = var_2191, interleave = doubled_33_interleave_0, values = (hidden_states_39_cast_fp16, var_2193_cast_fp16))[name = string("doubled_33_cast_fp16")];
            tensor<int32, [1]> out_17_axes_0 = const()[name = string("out_17_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_17_gamma_0_to_fp16 = const()[name = string("out_17_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1265303296)))];
            fp16 var_2203_to_fp16 = const()[name = string("op_2203_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_17_cast_fp16 = layer_norm(axes = out_17_axes_0, epsilon = var_2203_to_fp16, gamma = out_17_gamma_0_to_fp16, x = doubled_33_cast_fp16)[name = string("out_17_cast_fp16")];
            tensor<int32, [2]> var_2214_split_sizes_0 = const()[name = string("op_2214_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2214_axis_0 = const()[name = string("op_2214_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_2214_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_2214_cast_fp16_1 = split(axis = var_2214_axis_0, split_sizes = var_2214_split_sizes_0, x = out_17_cast_fp16)[name = string("op_2214_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_4_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_4_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1265311552)))];
            tensor<int32, [2]> query_states_25_strides_0 = const()[name = string("query_states_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_25_pad_type_0 = const()[name = string("query_states_25_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_25_pad_0 = const()[name = string("query_states_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_25_dilations_0 = const()[name = string("query_states_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_25_groups_0 = const()[name = string("query_states_25_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_25_cast_fp16 = conv(dilations = query_states_25_dilations_0, groups = query_states_25_groups_0, pad = query_states_25_pad_0, pad_type = query_states_25_pad_type_0, strides = query_states_25_strides_0, weight = layers_4_self_attn_q_proj_weight_to_fp16, x = var_2214_cast_fp16_0)[name = string("query_states_25_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_4_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_4_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1273700224)))];
            tensor<int32, [2]> key_states_41_strides_0 = const()[name = string("key_states_41_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_41_pad_type_0 = const()[name = string("key_states_41_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_41_pad_0 = const()[name = string("key_states_41_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_41_dilations_0 = const()[name = string("key_states_41_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_41_groups_0 = const()[name = string("key_states_41_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_41_cast_fp16 = conv(dilations = key_states_41_dilations_0, groups = key_states_41_groups_0, pad = key_states_41_pad_0, pad_type = key_states_41_pad_type_0, strides = key_states_41_strides_0, weight = layers_4_self_attn_k_proj_weight_to_fp16, x = var_2214_cast_fp16_0)[name = string("key_states_41_cast_fp16")];
            tensor<int32, [2]> value_states_25_strides_0 = const()[name = string("value_states_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_25_pad_type_0 = const()[name = string("value_states_25_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_25_pad_0 = const()[name = string("value_states_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_25_dilations_0 = const()[name = string("value_states_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_25_groups_0 = const()[name = string("value_states_25_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_25_cast_fp16 = conv(dilations = value_states_25_dilations_0, groups = value_states_25_groups_0, pad = value_states_25_pad_0, pad_type = value_states_25_pad_type_0, strides = value_states_25_strides_0, weight = layers_4_self_attn_v_proj_weight_cast_fp16, x = var_2214_cast_fp16_0)[name = string("value_states_25_cast_fp16")];
            tensor<int32, [4]> concat_48x = const()[name = string("concat_48x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_41_cast_fp16 = reshape(shape = concat_48x, x = query_states_25_cast_fp16)[name = string("x_41_cast_fp16")];
            tensor<int32, [4]> concat_49x = const()[name = string("concat_49x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_2271_cast_fp16 = reshape(shape = concat_49x, x = key_states_41_cast_fp16)[name = string("op_2271_cast_fp16")];
            tensor<int32, [4]> concat_50x = const()[name = string("concat_50x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_2278_cast_fp16 = reshape(shape = concat_50x, x = value_states_25_cast_fp16)[name = string("op_2278_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_2282_cast_fp16 = mul(x = x_41_cast_fp16, y = var_859_cast_fp16)[name = string("op_2282_cast_fp16")];
            tensor<int32, [2]> var_2283_split_sizes_0 = const()[name = string("op_2283_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2283_axis_0 = const()[name = string("op_2283_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_2283_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_2283_cast_fp16_1 = split(axis = var_2283_axis_0, split_sizes = var_2283_split_sizes_0, x = x_41_cast_fp16)[name = string("op_2283_cast_fp16")];
            fp16 const_44_promoted_to_fp16 = const()[name = string("const_44_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_2285_cast_fp16 = mul(x = var_2283_cast_fp16_1, y = const_44_promoted_to_fp16)[name = string("op_2285_cast_fp16")];
            int32 var_2287 = const()[name = string("op_2287"), val = int32(-2)];
            bool var_2288_interleave_0 = const()[name = string("op_2288_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_2288_cast_fp16 = concat(axis = var_2287, interleave = var_2288_interleave_0, values = (var_2285_cast_fp16, var_2283_cast_fp16_0))[name = string("op_2288_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_2289_cast_fp16 = mul(x = var_2288_cast_fp16, y = var_866_cast_fp16)[name = string("op_2289_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_27_cast_fp16 = add(x = var_2282_cast_fp16, y = var_2289_cast_fp16)[name = string("query_states_27_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_2295_cast_fp16 = mul(x = var_2271_cast_fp16, y = var_859_cast_fp16)[name = string("op_2295_cast_fp16")];
            tensor<int32, [2]> var_2296_split_sizes_0 = const()[name = string("op_2296_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2296_axis_0 = const()[name = string("op_2296_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_2296_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_2296_cast_fp16_1 = split(axis = var_2296_axis_0, split_sizes = var_2296_split_sizes_0, x = var_2271_cast_fp16)[name = string("op_2296_cast_fp16")];
            fp16 const_45_promoted_to_fp16 = const()[name = string("const_45_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_2298_cast_fp16 = mul(x = var_2296_cast_fp16_1, y = const_45_promoted_to_fp16)[name = string("op_2298_cast_fp16")];
            int32 var_2300 = const()[name = string("op_2300"), val = int32(-2)];
            bool var_2301_interleave_0 = const()[name = string("op_2301_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_2301_cast_fp16 = concat(axis = var_2300, interleave = var_2301_interleave_0, values = (var_2298_cast_fp16, var_2296_cast_fp16_0))[name = string("op_2301_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_2302_cast_fp16 = mul(x = var_2301_cast_fp16, y = var_866_cast_fp16)[name = string("op_2302_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_45_cast_fp16 = add(x = var_2295_cast_fp16, y = var_2302_cast_fp16)[name = string("key_states_45_cast_fp16")];
            tensor<int32, [1]> expand_dims_48 = const()[name = string("expand_dims_48"), val = tensor<int32, [1]>([4])];
            tensor<int32, [1]> expand_dims_49 = const()[name = string("expand_dims_49"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_51 = const()[name = string("expand_dims_51"), val = tensor<int32, [1]>([0])];
            int32 concat_53_axis_0 = const()[name = string("concat_53_axis_0"), val = int32(0)];
            bool concat_53_interleave_0 = const()[name = string("concat_53_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_53 = concat(axis = concat_53_axis_0, interleave = concat_53_interleave_0, values = (expand_dims_48, expand_dims_49, position_id, expand_dims_51))[name = string("concat_53")];
            tensor<int32, [1]> expand_dims_52 = const()[name = string("expand_dims_52"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> concat_54_values1_0 = const()[name = string("concat_54_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_54_values3_0 = const()[name = string("concat_54_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_54_axis_0 = const()[name = string("concat_54_axis_0"), val = int32(0)];
            bool concat_54_interleave_0 = const()[name = string("concat_54_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_54 = concat(axis = concat_54_axis_0, interleave = concat_54_interleave_0, values = (expand_dims_52, concat_54_values1_0, cache_position_end, concat_54_values3_0))[name = string("concat_54")];
            tensor<int32, [4]> key_states_47_perm_0 = const()[name = string("key_states_47_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_5_stride_0 = const()[name = string("key_cache_internal_tensor_assign_5_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_5_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_5_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_5_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_5_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_5_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_5_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_47_cast_fp16 = transpose(perm = key_states_47_perm_0, x = key_states_45_cast_fp16)[name = string("transpose_332")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_5_cast_fp16 = slice_update(begin = concat_53, begin_mask = key_cache_internal_tensor_assign_5_begin_mask_0, end = concat_54, end_mask = key_cache_internal_tensor_assign_5_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_5_squeeze_mask_0, stride = key_cache_internal_tensor_assign_5_stride_0, update = key_states_47_cast_fp16, x = coreml_update_state_174)[name = string("key_cache_internal_tensor_assign_5_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_5_cast_fp16, input = key_cache)[name = string("coreml_update_state_176_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_176 = read_state(input = key_cache)[name = string("coreml_update_state_176")];
            tensor<int32, [4]> value_states_27_perm_0 = const()[name = string("value_states_27_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_5_stride_0 = const()[name = string("value_cache_internal_tensor_assign_5_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_5_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_5_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_5_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_5_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_5_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_5_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_27_cast_fp16 = transpose(perm = value_states_27_perm_0, x = var_2278_cast_fp16)[name = string("transpose_331")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_5_cast_fp16 = slice_update(begin = concat_53, begin_mask = value_cache_internal_tensor_assign_5_begin_mask_0, end = concat_54, end_mask = value_cache_internal_tensor_assign_5_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_5_squeeze_mask_0, stride = value_cache_internal_tensor_assign_5_stride_0, update = value_states_27_cast_fp16, x = coreml_update_state_175)[name = string("value_cache_internal_tensor_assign_5_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_5_cast_fp16, input = value_cache)[name = string("coreml_update_state_177_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_177 = read_state(input = value_cache)[name = string("coreml_update_state_177")];
            tensor<int32, [4]> var_2372_begin_0 = const()[name = string("op_2372_begin_0"), val = tensor<int32, [4]>([4, 0, 0, 0])];
            tensor<int32, [4]> var_2372_end_0 = const()[name = string("op_2372_end_0"), val = tensor<int32, [4]>([5, 2, 2048, 128])];
            tensor<bool, [4]> var_2372_end_mask_0 = const()[name = string("op_2372_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2372_cast_fp16 = slice_by_index(begin = var_2372_begin_0, end = var_2372_end_0, end_mask = var_2372_end_mask_0, x = coreml_update_state_176)[name = string("op_2372_cast_fp16")];
            tensor<int32, [2]> tile_8 = const()[name = string("tile_8"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2375_axis_0 = const()[name = string("op_2375_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2375_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2375_cast_fp16_1 = split(axis = var_2375_axis_0, split_sizes = tile_8, x = var_2372_cast_fp16)[name = string("op_2375_cast_fp16")];
            tensor<int32, [4]> var_2382_begin_0 = const()[name = string("op_2382_begin_0"), val = tensor<int32, [4]>([4, 0, 0, 0])];
            tensor<int32, [4]> var_2382_end_0 = const()[name = string("op_2382_end_0"), val = tensor<int32, [4]>([5, 2, 2048, 128])];
            tensor<bool, [4]> var_2382_end_mask_0 = const()[name = string("op_2382_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2382_cast_fp16 = slice_by_index(begin = var_2382_begin_0, end = var_2382_end_0, end_mask = var_2382_end_mask_0, x = coreml_update_state_177)[name = string("op_2382_cast_fp16")];
            tensor<int32, [2]> tile_9 = const()[name = string("tile_9"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2385_axis_0 = const()[name = string("op_2385_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2385_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2385_cast_fp16_1 = split(axis = var_2385_axis_0, split_sizes = tile_9, x = var_2382_cast_fp16)[name = string("op_2385_cast_fp16")];
            tensor<int32, [2]> var_2388_split_sizes_0 = const()[name = string("op_2388_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_2388_axis_0 = const()[name = string("op_2388_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_2388_0, tensor<fp16, [1, 8, 128, 16]> var_2388_1 = split(axis = var_2388_axis_0, split_sizes = var_2388_split_sizes_0, x = query_states_27_cast_fp16)[name = string("op_2388")];
            bool attn_weights_65_transpose_x_0 = const()[name = string("attn_weights_65_transpose_x_0"), val = bool(false)];
            bool attn_weights_65_transpose_y_0 = const()[name = string("attn_weights_65_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_65_cast_fp16 = matmul(transpose_x = attn_weights_65_transpose_x_0, transpose_y = attn_weights_65_transpose_y_0, x = var_2375_cast_fp16_0, y = var_2388_0)[name = string("attn_weights_65_cast_fp16")];
            fp16 var_2391_to_fp16 = const()[name = string("op_2391_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_67_cast_fp16 = mul(x = attn_weights_65_cast_fp16, y = var_2391_to_fp16)[name = string("attn_weights_67_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_69_cast_fp16 = add(x = attn_weights_67_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_69_cast_fp16")];
            int32 var_2395 = const()[name = string("op_2395"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_71_cast_fp16 = softmax(axis = var_2395, x = attn_weights_69_cast_fp16)[name = string("attn_weights_71_cast_fp16")];
            bool var_2401_transpose_x_1 = const()[name = string("op_2401_transpose_x_1"), val = bool(true)];
            bool var_2401_transpose_y_1 = const()[name = string("op_2401_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_2401_cast_fp16 = matmul(transpose_x = var_2401_transpose_x_1, transpose_y = var_2401_transpose_y_1, x = attn_weights_71_cast_fp16, y = var_2385_cast_fp16_0)[name = string("op_2401_cast_fp16")];
            bool attn_weights_73_transpose_x_0 = const()[name = string("attn_weights_73_transpose_x_0"), val = bool(false)];
            bool attn_weights_73_transpose_y_0 = const()[name = string("attn_weights_73_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_73_cast_fp16 = matmul(transpose_x = attn_weights_73_transpose_x_0, transpose_y = attn_weights_73_transpose_y_0, x = var_2375_cast_fp16_1, y = var_2388_1)[name = string("attn_weights_73_cast_fp16")];
            fp16 var_2403_to_fp16 = const()[name = string("op_2403_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_75_cast_fp16 = mul(x = attn_weights_73_cast_fp16, y = var_2403_to_fp16)[name = string("attn_weights_75_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_77_cast_fp16 = add(x = attn_weights_75_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_77_cast_fp16")];
            int32 var_2407 = const()[name = string("op_2407"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_79_cast_fp16 = softmax(axis = var_2407, x = attn_weights_77_cast_fp16)[name = string("attn_weights_79_cast_fp16")];
            bool attn_output_33_transpose_x_1 = const()[name = string("attn_output_33_transpose_x_1"), val = bool(true)];
            bool attn_output_33_transpose_y_1 = const()[name = string("attn_output_33_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_33_cast_fp16 = matmul(transpose_x = attn_output_33_transpose_x_1, transpose_y = attn_output_33_transpose_y_1, x = attn_weights_79_cast_fp16, y = var_2385_cast_fp16_1)[name = string("attn_output_33_cast_fp16")];
            int32 var_2415 = const()[name = string("op_2415"), val = int32(1)];
            bool attn_output_35_interleave_0 = const()[name = string("attn_output_35_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_35_cast_fp16 = concat(axis = var_2415, interleave = attn_output_35_interleave_0, values = (var_2401_cast_fp16, attn_output_33_cast_fp16))[name = string("attn_output_35_cast_fp16")];
            tensor<int32, [4]> var_2419_perm_0 = const()[name = string("op_2419_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_59x = const()[name = string("concat_59x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_2419_cast_fp16 = transpose(perm = var_2419_perm_0, x = attn_output_35_cast_fp16)[name = string("transpose_330")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_39_cast_fp16 = reshape(shape = concat_59x, x = var_2419_cast_fp16)[name = string("attn_output_39_cast_fp16")];
            tensor<int32, [2]> hidden_states_43_strides_0 = const()[name = string("hidden_states_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_43_pad_type_0 = const()[name = string("hidden_states_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_43_pad_0 = const()[name = string("hidden_states_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_43_dilations_0 = const()[name = string("hidden_states_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_43_groups_0 = const()[name = string("hidden_states_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_43_cast_fp16 = conv(dilations = hidden_states_43_dilations_0, groups = hidden_states_43_groups_0, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = hidden_states_43_strides_0, weight = layers_4_self_attn_o_proj_weight_cast_fp16, x = attn_output_39_cast_fp16)[name = string("hidden_states_43_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_45_cast_fp16 = add(x = hidden_states_39_cast_fp16, y = hidden_states_43_cast_fp16)[name = string("hidden_states_45_cast_fp16")];
            fp16 const_50_promoted_to_fp16 = const()[name = string("const_50_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_2452_cast_fp16 = mul(x = hidden_states_45_cast_fp16, y = const_50_promoted_to_fp16)[name = string("op_2452_cast_fp16")];
            int32 var_2450 = const()[name = string("op_2450"), val = int32(1)];
            bool doubled_37_interleave_0 = const()[name = string("doubled_37_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_37_cast_fp16 = concat(axis = var_2450, interleave = doubled_37_interleave_0, values = (hidden_states_45_cast_fp16, var_2452_cast_fp16))[name = string("doubled_37_cast_fp16")];
            tensor<int32, [1]> out_19_axes_0 = const()[name = string("out_19_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_19_gamma_0_to_fp16 = const()[name = string("out_19_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1274748864)))];
            fp16 var_2462_to_fp16 = const()[name = string("op_2462_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_19_cast_fp16 = layer_norm(axes = out_19_axes_0, epsilon = var_2462_to_fp16, gamma = out_19_gamma_0_to_fp16, x = doubled_37_cast_fp16)[name = string("out_19_cast_fp16")];
            tensor<int32, [2]> var_2473_split_sizes_0 = const()[name = string("op_2473_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2473_axis_0 = const()[name = string("op_2473_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_2473_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_2473_cast_fp16_1 = split(axis = var_2473_axis_0, split_sizes = var_2473_split_sizes_0, x = out_19_cast_fp16)[name = string("op_2473_cast_fp16")];
            tensor<int32, [2]> input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_9_cast_fp16 = conv(dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = layers_4_mlp_gate_proj_weight_cast_fp16, x = var_2473_cast_fp16_0)[name = string("input_9_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_2490_cast_fp16 = silu(x = input_9_cast_fp16)[name = string("op_2490_cast_fp16")];
            tensor<int32, [2]> var_2496_strides_0 = const()[name = string("op_2496_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_2496_pad_type_0 = const()[name = string("op_2496_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_2496_pad_0 = const()[name = string("op_2496_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_2496_dilations_0 = const()[name = string("op_2496_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2496_groups_0 = const()[name = string("op_2496_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_2496_cast_fp16 = conv(dilations = var_2496_dilations_0, groups = var_2496_groups_0, pad = var_2496_pad_0, pad_type = var_2496_pad_type_0, strides = var_2496_strides_0, weight = layers_4_mlp_up_proj_weight_cast_fp16, x = var_2473_cast_fp16_0)[name = string("op_2496_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_49_cast_fp16 = mul(x = var_2490_cast_fp16, y = var_2496_cast_fp16)[name = string("x_49_cast_fp16")];
            tensor<int32, [2]> hidden_states_47_strides_0 = const()[name = string("hidden_states_47_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_47_pad_type_0 = const()[name = string("hidden_states_47_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_47_pad_0 = const()[name = string("hidden_states_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_47_dilations_0 = const()[name = string("hidden_states_47_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_47_groups_0 = const()[name = string("hidden_states_47_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_47_cast_fp16 = conv(dilations = hidden_states_47_dilations_0, groups = hidden_states_47_groups_0, pad = hidden_states_47_pad_0, pad_type = hidden_states_47_pad_type_0, strides = hidden_states_47_strides_0, weight = layers_4_mlp_down_proj_weight_cast_fp16, x = x_49_cast_fp16)[name = string("hidden_states_47_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_49_cast_fp16 = add(x = hidden_states_45_cast_fp16, y = hidden_states_47_cast_fp16)[name = string("hidden_states_49_cast_fp16")];
            fp16 const_52_promoted_to_fp16 = const()[name = string("const_52_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_2514_cast_fp16 = mul(x = hidden_states_49_cast_fp16, y = const_52_promoted_to_fp16)[name = string("op_2514_cast_fp16")];
            int32 var_2512 = const()[name = string("op_2512"), val = int32(1)];
            bool doubled_41_interleave_0 = const()[name = string("doubled_41_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_41_cast_fp16 = concat(axis = var_2512, interleave = doubled_41_interleave_0, values = (hidden_states_49_cast_fp16, var_2514_cast_fp16))[name = string("doubled_41_cast_fp16")];
            tensor<int32, [1]> out_21_axes_0 = const()[name = string("out_21_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_21_gamma_0_to_fp16 = const()[name = string("out_21_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1274757120)))];
            fp16 var_2524_to_fp16 = const()[name = string("op_2524_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_21_cast_fp16 = layer_norm(axes = out_21_axes_0, epsilon = var_2524_to_fp16, gamma = out_21_gamma_0_to_fp16, x = doubled_41_cast_fp16)[name = string("out_21_cast_fp16")];
            tensor<int32, [2]> var_2535_split_sizes_0 = const()[name = string("op_2535_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2535_axis_0 = const()[name = string("op_2535_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_2535_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_2535_cast_fp16_1 = split(axis = var_2535_axis_0, split_sizes = var_2535_split_sizes_0, x = out_21_cast_fp16)[name = string("op_2535_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_5_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_5_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1274765376)))];
            tensor<int32, [2]> query_states_31_strides_0 = const()[name = string("query_states_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_31_pad_type_0 = const()[name = string("query_states_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_31_pad_0 = const()[name = string("query_states_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_31_dilations_0 = const()[name = string("query_states_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_31_groups_0 = const()[name = string("query_states_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_31_cast_fp16 = conv(dilations = query_states_31_dilations_0, groups = query_states_31_groups_0, pad = query_states_31_pad_0, pad_type = query_states_31_pad_type_0, strides = query_states_31_strides_0, weight = layers_5_self_attn_q_proj_weight_to_fp16, x = var_2535_cast_fp16_0)[name = string("query_states_31_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_5_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_5_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1283154048)))];
            tensor<int32, [2]> key_states_51_strides_0 = const()[name = string("key_states_51_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_51_pad_type_0 = const()[name = string("key_states_51_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_51_pad_0 = const()[name = string("key_states_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_51_dilations_0 = const()[name = string("key_states_51_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_51_groups_0 = const()[name = string("key_states_51_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_51_cast_fp16 = conv(dilations = key_states_51_dilations_0, groups = key_states_51_groups_0, pad = key_states_51_pad_0, pad_type = key_states_51_pad_type_0, strides = key_states_51_strides_0, weight = layers_5_self_attn_k_proj_weight_to_fp16, x = var_2535_cast_fp16_0)[name = string("key_states_51_cast_fp16")];
            tensor<int32, [2]> value_states_31_strides_0 = const()[name = string("value_states_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_31_pad_type_0 = const()[name = string("value_states_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_31_pad_0 = const()[name = string("value_states_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_31_dilations_0 = const()[name = string("value_states_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_31_groups_0 = const()[name = string("value_states_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_31_cast_fp16 = conv(dilations = value_states_31_dilations_0, groups = value_states_31_groups_0, pad = value_states_31_pad_0, pad_type = value_states_31_pad_type_0, strides = value_states_31_strides_0, weight = layers_5_self_attn_v_proj_weight_cast_fp16, x = var_2535_cast_fp16_0)[name = string("value_states_31_cast_fp16")];
            tensor<int32, [4]> concat_60x = const()[name = string("concat_60x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_51_cast_fp16 = reshape(shape = concat_60x, x = query_states_31_cast_fp16)[name = string("x_51_cast_fp16")];
            tensor<int32, [4]> concat_61x = const()[name = string("concat_61x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_2592_cast_fp16 = reshape(shape = concat_61x, x = key_states_51_cast_fp16)[name = string("op_2592_cast_fp16")];
            tensor<int32, [4]> concat_62x = const()[name = string("concat_62x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_2599_cast_fp16 = reshape(shape = concat_62x, x = value_states_31_cast_fp16)[name = string("op_2599_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_2603_cast_fp16 = mul(x = x_51_cast_fp16, y = var_859_cast_fp16)[name = string("op_2603_cast_fp16")];
            tensor<int32, [2]> var_2604_split_sizes_0 = const()[name = string("op_2604_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2604_axis_0 = const()[name = string("op_2604_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_2604_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_2604_cast_fp16_1 = split(axis = var_2604_axis_0, split_sizes = var_2604_split_sizes_0, x = x_51_cast_fp16)[name = string("op_2604_cast_fp16")];
            fp16 const_54_promoted_to_fp16 = const()[name = string("const_54_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_2606_cast_fp16 = mul(x = var_2604_cast_fp16_1, y = const_54_promoted_to_fp16)[name = string("op_2606_cast_fp16")];
            int32 var_2608 = const()[name = string("op_2608"), val = int32(-2)];
            bool var_2609_interleave_0 = const()[name = string("op_2609_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_2609_cast_fp16 = concat(axis = var_2608, interleave = var_2609_interleave_0, values = (var_2606_cast_fp16, var_2604_cast_fp16_0))[name = string("op_2609_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_2610_cast_fp16 = mul(x = var_2609_cast_fp16, y = var_866_cast_fp16)[name = string("op_2610_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_33_cast_fp16 = add(x = var_2603_cast_fp16, y = var_2610_cast_fp16)[name = string("query_states_33_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_2616_cast_fp16 = mul(x = var_2592_cast_fp16, y = var_859_cast_fp16)[name = string("op_2616_cast_fp16")];
            tensor<int32, [2]> var_2617_split_sizes_0 = const()[name = string("op_2617_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2617_axis_0 = const()[name = string("op_2617_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_2617_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_2617_cast_fp16_1 = split(axis = var_2617_axis_0, split_sizes = var_2617_split_sizes_0, x = var_2592_cast_fp16)[name = string("op_2617_cast_fp16")];
            fp16 const_55_promoted_to_fp16 = const()[name = string("const_55_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_2619_cast_fp16 = mul(x = var_2617_cast_fp16_1, y = const_55_promoted_to_fp16)[name = string("op_2619_cast_fp16")];
            int32 var_2621 = const()[name = string("op_2621"), val = int32(-2)];
            bool var_2622_interleave_0 = const()[name = string("op_2622_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_2622_cast_fp16 = concat(axis = var_2621, interleave = var_2622_interleave_0, values = (var_2619_cast_fp16, var_2617_cast_fp16_0))[name = string("op_2622_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_2623_cast_fp16 = mul(x = var_2622_cast_fp16, y = var_866_cast_fp16)[name = string("op_2623_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_55_cast_fp16 = add(x = var_2616_cast_fp16, y = var_2623_cast_fp16)[name = string("key_states_55_cast_fp16")];
            tensor<int32, [1]> expand_dims_60 = const()[name = string("expand_dims_60"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> expand_dims_61 = const()[name = string("expand_dims_61"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_63 = const()[name = string("expand_dims_63"), val = tensor<int32, [1]>([0])];
            int32 concat_65_axis_0 = const()[name = string("concat_65_axis_0"), val = int32(0)];
            bool concat_65_interleave_0 = const()[name = string("concat_65_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_65 = concat(axis = concat_65_axis_0, interleave = concat_65_interleave_0, values = (expand_dims_60, expand_dims_61, position_id, expand_dims_63))[name = string("concat_65")];
            tensor<int32, [1]> expand_dims_64 = const()[name = string("expand_dims_64"), val = tensor<int32, [1]>([6])];
            tensor<int32, [1]> concat_66_values1_0 = const()[name = string("concat_66_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_66_values3_0 = const()[name = string("concat_66_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_66_axis_0 = const()[name = string("concat_66_axis_0"), val = int32(0)];
            bool concat_66_interleave_0 = const()[name = string("concat_66_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_66 = concat(axis = concat_66_axis_0, interleave = concat_66_interleave_0, values = (expand_dims_64, concat_66_values1_0, cache_position_end, concat_66_values3_0))[name = string("concat_66")];
            tensor<int32, [4]> key_states_57_perm_0 = const()[name = string("key_states_57_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_6_stride_0 = const()[name = string("key_cache_internal_tensor_assign_6_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_6_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_6_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_6_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_6_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_6_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_6_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_57_cast_fp16 = transpose(perm = key_states_57_perm_0, x = key_states_55_cast_fp16)[name = string("transpose_329")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_6_cast_fp16 = slice_update(begin = concat_65, begin_mask = key_cache_internal_tensor_assign_6_begin_mask_0, end = concat_66, end_mask = key_cache_internal_tensor_assign_6_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_6_squeeze_mask_0, stride = key_cache_internal_tensor_assign_6_stride_0, update = key_states_57_cast_fp16, x = coreml_update_state_176)[name = string("key_cache_internal_tensor_assign_6_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_6_cast_fp16, input = key_cache)[name = string("coreml_update_state_178_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_178 = read_state(input = key_cache)[name = string("coreml_update_state_178")];
            tensor<int32, [4]> value_states_33_perm_0 = const()[name = string("value_states_33_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_6_stride_0 = const()[name = string("value_cache_internal_tensor_assign_6_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_6_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_6_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_6_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_6_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_6_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_6_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_33_cast_fp16 = transpose(perm = value_states_33_perm_0, x = var_2599_cast_fp16)[name = string("transpose_328")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_6_cast_fp16 = slice_update(begin = concat_65, begin_mask = value_cache_internal_tensor_assign_6_begin_mask_0, end = concat_66, end_mask = value_cache_internal_tensor_assign_6_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_6_squeeze_mask_0, stride = value_cache_internal_tensor_assign_6_stride_0, update = value_states_33_cast_fp16, x = coreml_update_state_177)[name = string("value_cache_internal_tensor_assign_6_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_6_cast_fp16, input = value_cache)[name = string("coreml_update_state_179_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_179 = read_state(input = value_cache)[name = string("coreml_update_state_179")];
            tensor<int32, [4]> var_2693_begin_0 = const()[name = string("op_2693_begin_0"), val = tensor<int32, [4]>([5, 0, 0, 0])];
            tensor<int32, [4]> var_2693_end_0 = const()[name = string("op_2693_end_0"), val = tensor<int32, [4]>([6, 2, 2048, 128])];
            tensor<bool, [4]> var_2693_end_mask_0 = const()[name = string("op_2693_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2693_cast_fp16 = slice_by_index(begin = var_2693_begin_0, end = var_2693_end_0, end_mask = var_2693_end_mask_0, x = coreml_update_state_178)[name = string("op_2693_cast_fp16")];
            tensor<int32, [2]> tile_10 = const()[name = string("tile_10"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2696_axis_0 = const()[name = string("op_2696_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2696_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2696_cast_fp16_1 = split(axis = var_2696_axis_0, split_sizes = tile_10, x = var_2693_cast_fp16)[name = string("op_2696_cast_fp16")];
            tensor<int32, [4]> var_2703_begin_0 = const()[name = string("op_2703_begin_0"), val = tensor<int32, [4]>([5, 0, 0, 0])];
            tensor<int32, [4]> var_2703_end_0 = const()[name = string("op_2703_end_0"), val = tensor<int32, [4]>([6, 2, 2048, 128])];
            tensor<bool, [4]> var_2703_end_mask_0 = const()[name = string("op_2703_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2703_cast_fp16 = slice_by_index(begin = var_2703_begin_0, end = var_2703_end_0, end_mask = var_2703_end_mask_0, x = coreml_update_state_179)[name = string("op_2703_cast_fp16")];
            tensor<int32, [2]> tile_11 = const()[name = string("tile_11"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2706_axis_0 = const()[name = string("op_2706_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2706_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2706_cast_fp16_1 = split(axis = var_2706_axis_0, split_sizes = tile_11, x = var_2703_cast_fp16)[name = string("op_2706_cast_fp16")];
            tensor<int32, [2]> var_2709_split_sizes_0 = const()[name = string("op_2709_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_2709_axis_0 = const()[name = string("op_2709_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_2709_0, tensor<fp16, [1, 8, 128, 16]> var_2709_1 = split(axis = var_2709_axis_0, split_sizes = var_2709_split_sizes_0, x = query_states_33_cast_fp16)[name = string("op_2709")];
            bool attn_weights_81_transpose_x_0 = const()[name = string("attn_weights_81_transpose_x_0"), val = bool(false)];
            bool attn_weights_81_transpose_y_0 = const()[name = string("attn_weights_81_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_81_cast_fp16 = matmul(transpose_x = attn_weights_81_transpose_x_0, transpose_y = attn_weights_81_transpose_y_0, x = var_2696_cast_fp16_0, y = var_2709_0)[name = string("attn_weights_81_cast_fp16")];
            fp16 var_2712_to_fp16 = const()[name = string("op_2712_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_83_cast_fp16 = mul(x = attn_weights_81_cast_fp16, y = var_2712_to_fp16)[name = string("attn_weights_83_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_85_cast_fp16 = add(x = attn_weights_83_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_85_cast_fp16")];
            int32 var_2716 = const()[name = string("op_2716"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_87_cast_fp16 = softmax(axis = var_2716, x = attn_weights_85_cast_fp16)[name = string("attn_weights_87_cast_fp16")];
            bool var_2722_transpose_x_1 = const()[name = string("op_2722_transpose_x_1"), val = bool(true)];
            bool var_2722_transpose_y_1 = const()[name = string("op_2722_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_2722_cast_fp16 = matmul(transpose_x = var_2722_transpose_x_1, transpose_y = var_2722_transpose_y_1, x = attn_weights_87_cast_fp16, y = var_2706_cast_fp16_0)[name = string("op_2722_cast_fp16")];
            bool attn_weights_89_transpose_x_0 = const()[name = string("attn_weights_89_transpose_x_0"), val = bool(false)];
            bool attn_weights_89_transpose_y_0 = const()[name = string("attn_weights_89_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_89_cast_fp16 = matmul(transpose_x = attn_weights_89_transpose_x_0, transpose_y = attn_weights_89_transpose_y_0, x = var_2696_cast_fp16_1, y = var_2709_1)[name = string("attn_weights_89_cast_fp16")];
            fp16 var_2724_to_fp16 = const()[name = string("op_2724_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_91_cast_fp16 = mul(x = attn_weights_89_cast_fp16, y = var_2724_to_fp16)[name = string("attn_weights_91_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_93_cast_fp16 = add(x = attn_weights_91_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_93_cast_fp16")];
            int32 var_2728 = const()[name = string("op_2728"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_95_cast_fp16 = softmax(axis = var_2728, x = attn_weights_93_cast_fp16)[name = string("attn_weights_95_cast_fp16")];
            bool attn_output_41_transpose_x_1 = const()[name = string("attn_output_41_transpose_x_1"), val = bool(true)];
            bool attn_output_41_transpose_y_1 = const()[name = string("attn_output_41_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_41_cast_fp16 = matmul(transpose_x = attn_output_41_transpose_x_1, transpose_y = attn_output_41_transpose_y_1, x = attn_weights_95_cast_fp16, y = var_2706_cast_fp16_1)[name = string("attn_output_41_cast_fp16")];
            int32 var_2736 = const()[name = string("op_2736"), val = int32(1)];
            bool attn_output_43_interleave_0 = const()[name = string("attn_output_43_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_43_cast_fp16 = concat(axis = var_2736, interleave = attn_output_43_interleave_0, values = (var_2722_cast_fp16, attn_output_41_cast_fp16))[name = string("attn_output_43_cast_fp16")];
            tensor<int32, [4]> var_2740_perm_0 = const()[name = string("op_2740_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_71x = const()[name = string("concat_71x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_2740_cast_fp16 = transpose(perm = var_2740_perm_0, x = attn_output_43_cast_fp16)[name = string("transpose_327")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_47_cast_fp16 = reshape(shape = concat_71x, x = var_2740_cast_fp16)[name = string("attn_output_47_cast_fp16")];
            tensor<int32, [2]> hidden_states_53_strides_0 = const()[name = string("hidden_states_53_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_53_pad_type_0 = const()[name = string("hidden_states_53_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_53_pad_0 = const()[name = string("hidden_states_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_53_dilations_0 = const()[name = string("hidden_states_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_53_groups_0 = const()[name = string("hidden_states_53_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_53_cast_fp16 = conv(dilations = hidden_states_53_dilations_0, groups = hidden_states_53_groups_0, pad = hidden_states_53_pad_0, pad_type = hidden_states_53_pad_type_0, strides = hidden_states_53_strides_0, weight = layers_5_self_attn_o_proj_weight_cast_fp16, x = attn_output_47_cast_fp16)[name = string("hidden_states_53_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_55_cast_fp16 = add(x = hidden_states_49_cast_fp16, y = hidden_states_53_cast_fp16)[name = string("hidden_states_55_cast_fp16")];
            fp16 const_60_promoted_to_fp16 = const()[name = string("const_60_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_2773_cast_fp16 = mul(x = hidden_states_55_cast_fp16, y = const_60_promoted_to_fp16)[name = string("op_2773_cast_fp16")];
            int32 var_2771 = const()[name = string("op_2771"), val = int32(1)];
            bool doubled_45_interleave_0 = const()[name = string("doubled_45_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_45_cast_fp16 = concat(axis = var_2771, interleave = doubled_45_interleave_0, values = (hidden_states_55_cast_fp16, var_2773_cast_fp16))[name = string("doubled_45_cast_fp16")];
            tensor<int32, [1]> out_23_axes_0 = const()[name = string("out_23_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_23_gamma_0_to_fp16 = const()[name = string("out_23_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1284202688)))];
            fp16 var_2783_to_fp16 = const()[name = string("op_2783_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_23_cast_fp16 = layer_norm(axes = out_23_axes_0, epsilon = var_2783_to_fp16, gamma = out_23_gamma_0_to_fp16, x = doubled_45_cast_fp16)[name = string("out_23_cast_fp16")];
            tensor<int32, [2]> var_2794_split_sizes_0 = const()[name = string("op_2794_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2794_axis_0 = const()[name = string("op_2794_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_2794_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_2794_cast_fp16_1 = split(axis = var_2794_axis_0, split_sizes = var_2794_split_sizes_0, x = out_23_cast_fp16)[name = string("op_2794_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_5_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_5_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1284210944)))];
            tensor<int32, [2]> input_11_strides_0 = const()[name = string("input_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_11_pad_type_0 = const()[name = string("input_11_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_11_dilations_0 = const()[name = string("input_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_11_groups_0 = const()[name = string("input_11_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_11_cast_fp16 = conv(dilations = input_11_dilations_0, groups = input_11_groups_0, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = input_11_strides_0, weight = layers_5_mlp_gate_proj_weight_to_fp16, x = var_2794_cast_fp16_0)[name = string("input_11_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_2811_cast_fp16 = silu(x = input_11_cast_fp16)[name = string("op_2811_cast_fp16")];
            tensor<int32, [2]> var_2817_strides_0 = const()[name = string("op_2817_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_2817_pad_type_0 = const()[name = string("op_2817_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_2817_pad_0 = const()[name = string("op_2817_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_2817_dilations_0 = const()[name = string("op_2817_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2817_groups_0 = const()[name = string("op_2817_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_2817_cast_fp16 = conv(dilations = var_2817_dilations_0, groups = var_2817_groups_0, pad = var_2817_pad_0, pad_type = var_2817_pad_type_0, strides = var_2817_strides_0, weight = layers_5_mlp_up_proj_weight_cast_fp16, x = var_2794_cast_fp16_0)[name = string("op_2817_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_59_cast_fp16 = mul(x = var_2811_cast_fp16, y = var_2817_cast_fp16)[name = string("x_59_cast_fp16")];
            tensor<int32, [2]> hidden_states_57_strides_0 = const()[name = string("hidden_states_57_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_57_pad_type_0 = const()[name = string("hidden_states_57_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_57_pad_0 = const()[name = string("hidden_states_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_57_dilations_0 = const()[name = string("hidden_states_57_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_57_groups_0 = const()[name = string("hidden_states_57_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_57_cast_fp16 = conv(dilations = hidden_states_57_dilations_0, groups = hidden_states_57_groups_0, pad = hidden_states_57_pad_0, pad_type = hidden_states_57_pad_type_0, strides = hidden_states_57_strides_0, weight = layers_5_mlp_down_proj_weight_cast_fp16, x = x_59_cast_fp16)[name = string("hidden_states_57_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_59_cast_fp16 = add(x = hidden_states_55_cast_fp16, y = hidden_states_57_cast_fp16)[name = string("hidden_states_59_cast_fp16")];
            fp16 const_62_promoted_to_fp16 = const()[name = string("const_62_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_2835_cast_fp16 = mul(x = hidden_states_59_cast_fp16, y = const_62_promoted_to_fp16)[name = string("op_2835_cast_fp16")];
            int32 var_2833 = const()[name = string("op_2833"), val = int32(1)];
            bool doubled_49_interleave_0 = const()[name = string("doubled_49_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_49_cast_fp16 = concat(axis = var_2833, interleave = doubled_49_interleave_0, values = (hidden_states_59_cast_fp16, var_2835_cast_fp16))[name = string("doubled_49_cast_fp16")];
            tensor<int32, [1]> out_25_axes_0 = const()[name = string("out_25_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_25_gamma_0_to_fp16 = const()[name = string("out_25_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1309376832)))];
            fp16 var_2845_to_fp16 = const()[name = string("op_2845_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_25_cast_fp16 = layer_norm(axes = out_25_axes_0, epsilon = var_2845_to_fp16, gamma = out_25_gamma_0_to_fp16, x = doubled_49_cast_fp16)[name = string("out_25_cast_fp16")];
            tensor<int32, [2]> var_2856_split_sizes_0 = const()[name = string("op_2856_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2856_axis_0 = const()[name = string("op_2856_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_2856_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_2856_cast_fp16_1 = split(axis = var_2856_axis_0, split_sizes = var_2856_split_sizes_0, x = out_25_cast_fp16)[name = string("op_2856_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_6_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_6_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1309385088)))];
            tensor<int32, [2]> query_states_37_strides_0 = const()[name = string("query_states_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_37_pad_type_0 = const()[name = string("query_states_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_37_pad_0 = const()[name = string("query_states_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_37_dilations_0 = const()[name = string("query_states_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_37_groups_0 = const()[name = string("query_states_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_37_cast_fp16 = conv(dilations = query_states_37_dilations_0, groups = query_states_37_groups_0, pad = query_states_37_pad_0, pad_type = query_states_37_pad_type_0, strides = query_states_37_strides_0, weight = layers_6_self_attn_q_proj_weight_to_fp16, x = var_2856_cast_fp16_0)[name = string("query_states_37_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_6_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_6_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1317773760)))];
            tensor<int32, [2]> key_states_61_strides_0 = const()[name = string("key_states_61_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_61_pad_type_0 = const()[name = string("key_states_61_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_61_pad_0 = const()[name = string("key_states_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_61_dilations_0 = const()[name = string("key_states_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_61_groups_0 = const()[name = string("key_states_61_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_61_cast_fp16 = conv(dilations = key_states_61_dilations_0, groups = key_states_61_groups_0, pad = key_states_61_pad_0, pad_type = key_states_61_pad_type_0, strides = key_states_61_strides_0, weight = layers_6_self_attn_k_proj_weight_to_fp16, x = var_2856_cast_fp16_0)[name = string("key_states_61_cast_fp16")];
            tensor<int32, [2]> value_states_37_strides_0 = const()[name = string("value_states_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_37_pad_type_0 = const()[name = string("value_states_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_37_pad_0 = const()[name = string("value_states_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_37_dilations_0 = const()[name = string("value_states_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_37_groups_0 = const()[name = string("value_states_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_37_cast_fp16 = conv(dilations = value_states_37_dilations_0, groups = value_states_37_groups_0, pad = value_states_37_pad_0, pad_type = value_states_37_pad_type_0, strides = value_states_37_strides_0, weight = layers_6_self_attn_v_proj_weight_cast_fp16, x = var_2856_cast_fp16_0)[name = string("value_states_37_cast_fp16")];
            tensor<int32, [4]> concat_72x = const()[name = string("concat_72x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_61_cast_fp16 = reshape(shape = concat_72x, x = query_states_37_cast_fp16)[name = string("x_61_cast_fp16")];
            tensor<int32, [4]> concat_73x = const()[name = string("concat_73x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_2913_cast_fp16 = reshape(shape = concat_73x, x = key_states_61_cast_fp16)[name = string("op_2913_cast_fp16")];
            tensor<int32, [4]> concat_74x = const()[name = string("concat_74x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_2920_cast_fp16 = reshape(shape = concat_74x, x = value_states_37_cast_fp16)[name = string("op_2920_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_2924_cast_fp16 = mul(x = x_61_cast_fp16, y = var_859_cast_fp16)[name = string("op_2924_cast_fp16")];
            tensor<int32, [2]> var_2925_split_sizes_0 = const()[name = string("op_2925_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2925_axis_0 = const()[name = string("op_2925_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_2925_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_2925_cast_fp16_1 = split(axis = var_2925_axis_0, split_sizes = var_2925_split_sizes_0, x = x_61_cast_fp16)[name = string("op_2925_cast_fp16")];
            fp16 const_64_promoted_to_fp16 = const()[name = string("const_64_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_2927_cast_fp16 = mul(x = var_2925_cast_fp16_1, y = const_64_promoted_to_fp16)[name = string("op_2927_cast_fp16")];
            int32 var_2929 = const()[name = string("op_2929"), val = int32(-2)];
            bool var_2930_interleave_0 = const()[name = string("op_2930_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_2930_cast_fp16 = concat(axis = var_2929, interleave = var_2930_interleave_0, values = (var_2927_cast_fp16, var_2925_cast_fp16_0))[name = string("op_2930_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_2931_cast_fp16 = mul(x = var_2930_cast_fp16, y = var_866_cast_fp16)[name = string("op_2931_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_39_cast_fp16 = add(x = var_2924_cast_fp16, y = var_2931_cast_fp16)[name = string("query_states_39_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_2937_cast_fp16 = mul(x = var_2913_cast_fp16, y = var_859_cast_fp16)[name = string("op_2937_cast_fp16")];
            tensor<int32, [2]> var_2938_split_sizes_0 = const()[name = string("op_2938_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2938_axis_0 = const()[name = string("op_2938_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_2938_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_2938_cast_fp16_1 = split(axis = var_2938_axis_0, split_sizes = var_2938_split_sizes_0, x = var_2913_cast_fp16)[name = string("op_2938_cast_fp16")];
            fp16 const_65_promoted_to_fp16 = const()[name = string("const_65_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_2940_cast_fp16 = mul(x = var_2938_cast_fp16_1, y = const_65_promoted_to_fp16)[name = string("op_2940_cast_fp16")];
            int32 var_2942 = const()[name = string("op_2942"), val = int32(-2)];
            bool var_2943_interleave_0 = const()[name = string("op_2943_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_2943_cast_fp16 = concat(axis = var_2942, interleave = var_2943_interleave_0, values = (var_2940_cast_fp16, var_2938_cast_fp16_0))[name = string("op_2943_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_2944_cast_fp16 = mul(x = var_2943_cast_fp16, y = var_866_cast_fp16)[name = string("op_2944_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_65_cast_fp16 = add(x = var_2937_cast_fp16, y = var_2944_cast_fp16)[name = string("key_states_65_cast_fp16")];
            tensor<int32, [1]> expand_dims_72 = const()[name = string("expand_dims_72"), val = tensor<int32, [1]>([6])];
            tensor<int32, [1]> expand_dims_73 = const()[name = string("expand_dims_73"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_75 = const()[name = string("expand_dims_75"), val = tensor<int32, [1]>([0])];
            int32 concat_77_axis_0 = const()[name = string("concat_77_axis_0"), val = int32(0)];
            bool concat_77_interleave_0 = const()[name = string("concat_77_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_77 = concat(axis = concat_77_axis_0, interleave = concat_77_interleave_0, values = (expand_dims_72, expand_dims_73, position_id, expand_dims_75))[name = string("concat_77")];
            tensor<int32, [1]> expand_dims_76 = const()[name = string("expand_dims_76"), val = tensor<int32, [1]>([7])];
            tensor<int32, [1]> concat_78_values1_0 = const()[name = string("concat_78_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_78_values3_0 = const()[name = string("concat_78_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_78_axis_0 = const()[name = string("concat_78_axis_0"), val = int32(0)];
            bool concat_78_interleave_0 = const()[name = string("concat_78_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_78 = concat(axis = concat_78_axis_0, interleave = concat_78_interleave_0, values = (expand_dims_76, concat_78_values1_0, cache_position_end, concat_78_values3_0))[name = string("concat_78")];
            tensor<int32, [4]> key_states_67_perm_0 = const()[name = string("key_states_67_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_7_stride_0 = const()[name = string("key_cache_internal_tensor_assign_7_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_7_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_7_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_7_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_7_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_7_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_7_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_67_cast_fp16 = transpose(perm = key_states_67_perm_0, x = key_states_65_cast_fp16)[name = string("transpose_326")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_7_cast_fp16 = slice_update(begin = concat_77, begin_mask = key_cache_internal_tensor_assign_7_begin_mask_0, end = concat_78, end_mask = key_cache_internal_tensor_assign_7_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_7_squeeze_mask_0, stride = key_cache_internal_tensor_assign_7_stride_0, update = key_states_67_cast_fp16, x = coreml_update_state_178)[name = string("key_cache_internal_tensor_assign_7_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_7_cast_fp16, input = key_cache)[name = string("coreml_update_state_180_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_180 = read_state(input = key_cache)[name = string("coreml_update_state_180")];
            tensor<int32, [4]> value_states_39_perm_0 = const()[name = string("value_states_39_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_7_stride_0 = const()[name = string("value_cache_internal_tensor_assign_7_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_7_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_7_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_7_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_7_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_7_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_7_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_39_cast_fp16 = transpose(perm = value_states_39_perm_0, x = var_2920_cast_fp16)[name = string("transpose_325")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_7_cast_fp16 = slice_update(begin = concat_77, begin_mask = value_cache_internal_tensor_assign_7_begin_mask_0, end = concat_78, end_mask = value_cache_internal_tensor_assign_7_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_7_squeeze_mask_0, stride = value_cache_internal_tensor_assign_7_stride_0, update = value_states_39_cast_fp16, x = coreml_update_state_179)[name = string("value_cache_internal_tensor_assign_7_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_7_cast_fp16, input = value_cache)[name = string("coreml_update_state_181_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_181 = read_state(input = value_cache)[name = string("coreml_update_state_181")];
            tensor<int32, [4]> var_3014_begin_0 = const()[name = string("op_3014_begin_0"), val = tensor<int32, [4]>([6, 0, 0, 0])];
            tensor<int32, [4]> var_3014_end_0 = const()[name = string("op_3014_end_0"), val = tensor<int32, [4]>([7, 2, 2048, 128])];
            tensor<bool, [4]> var_3014_end_mask_0 = const()[name = string("op_3014_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3014_cast_fp16 = slice_by_index(begin = var_3014_begin_0, end = var_3014_end_0, end_mask = var_3014_end_mask_0, x = coreml_update_state_180)[name = string("op_3014_cast_fp16")];
            tensor<int32, [2]> tile_12 = const()[name = string("tile_12"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3017_axis_0 = const()[name = string("op_3017_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3017_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3017_cast_fp16_1 = split(axis = var_3017_axis_0, split_sizes = tile_12, x = var_3014_cast_fp16)[name = string("op_3017_cast_fp16")];
            tensor<int32, [4]> var_3024_begin_0 = const()[name = string("op_3024_begin_0"), val = tensor<int32, [4]>([6, 0, 0, 0])];
            tensor<int32, [4]> var_3024_end_0 = const()[name = string("op_3024_end_0"), val = tensor<int32, [4]>([7, 2, 2048, 128])];
            tensor<bool, [4]> var_3024_end_mask_0 = const()[name = string("op_3024_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3024_cast_fp16 = slice_by_index(begin = var_3024_begin_0, end = var_3024_end_0, end_mask = var_3024_end_mask_0, x = coreml_update_state_181)[name = string("op_3024_cast_fp16")];
            tensor<int32, [2]> tile_13 = const()[name = string("tile_13"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3027_axis_0 = const()[name = string("op_3027_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3027_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3027_cast_fp16_1 = split(axis = var_3027_axis_0, split_sizes = tile_13, x = var_3024_cast_fp16)[name = string("op_3027_cast_fp16")];
            tensor<int32, [2]> var_3030_split_sizes_0 = const()[name = string("op_3030_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3030_axis_0 = const()[name = string("op_3030_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_3030_0, tensor<fp16, [1, 8, 128, 16]> var_3030_1 = split(axis = var_3030_axis_0, split_sizes = var_3030_split_sizes_0, x = query_states_39_cast_fp16)[name = string("op_3030")];
            bool attn_weights_97_transpose_x_0 = const()[name = string("attn_weights_97_transpose_x_0"), val = bool(false)];
            bool attn_weights_97_transpose_y_0 = const()[name = string("attn_weights_97_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_97_cast_fp16 = matmul(transpose_x = attn_weights_97_transpose_x_0, transpose_y = attn_weights_97_transpose_y_0, x = var_3017_cast_fp16_0, y = var_3030_0)[name = string("attn_weights_97_cast_fp16")];
            fp16 var_3033_to_fp16 = const()[name = string("op_3033_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_99_cast_fp16 = mul(x = attn_weights_97_cast_fp16, y = var_3033_to_fp16)[name = string("attn_weights_99_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_101_cast_fp16 = add(x = attn_weights_99_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_101_cast_fp16")];
            int32 var_3037 = const()[name = string("op_3037"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_103_cast_fp16 = softmax(axis = var_3037, x = attn_weights_101_cast_fp16)[name = string("attn_weights_103_cast_fp16")];
            bool var_3043_transpose_x_1 = const()[name = string("op_3043_transpose_x_1"), val = bool(true)];
            bool var_3043_transpose_y_1 = const()[name = string("op_3043_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_3043_cast_fp16 = matmul(transpose_x = var_3043_transpose_x_1, transpose_y = var_3043_transpose_y_1, x = attn_weights_103_cast_fp16, y = var_3027_cast_fp16_0)[name = string("op_3043_cast_fp16")];
            bool attn_weights_105_transpose_x_0 = const()[name = string("attn_weights_105_transpose_x_0"), val = bool(false)];
            bool attn_weights_105_transpose_y_0 = const()[name = string("attn_weights_105_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_105_cast_fp16 = matmul(transpose_x = attn_weights_105_transpose_x_0, transpose_y = attn_weights_105_transpose_y_0, x = var_3017_cast_fp16_1, y = var_3030_1)[name = string("attn_weights_105_cast_fp16")];
            fp16 var_3045_to_fp16 = const()[name = string("op_3045_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_107_cast_fp16 = mul(x = attn_weights_105_cast_fp16, y = var_3045_to_fp16)[name = string("attn_weights_107_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_109_cast_fp16 = add(x = attn_weights_107_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_109_cast_fp16")];
            int32 var_3049 = const()[name = string("op_3049"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_111_cast_fp16 = softmax(axis = var_3049, x = attn_weights_109_cast_fp16)[name = string("attn_weights_111_cast_fp16")];
            bool attn_output_49_transpose_x_1 = const()[name = string("attn_output_49_transpose_x_1"), val = bool(true)];
            bool attn_output_49_transpose_y_1 = const()[name = string("attn_output_49_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_49_cast_fp16 = matmul(transpose_x = attn_output_49_transpose_x_1, transpose_y = attn_output_49_transpose_y_1, x = attn_weights_111_cast_fp16, y = var_3027_cast_fp16_1)[name = string("attn_output_49_cast_fp16")];
            int32 var_3057 = const()[name = string("op_3057"), val = int32(1)];
            bool attn_output_51_interleave_0 = const()[name = string("attn_output_51_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_51_cast_fp16 = concat(axis = var_3057, interleave = attn_output_51_interleave_0, values = (var_3043_cast_fp16, attn_output_49_cast_fp16))[name = string("attn_output_51_cast_fp16")];
            tensor<int32, [4]> var_3061_perm_0 = const()[name = string("op_3061_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_83x = const()[name = string("concat_83x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_3061_cast_fp16 = transpose(perm = var_3061_perm_0, x = attn_output_51_cast_fp16)[name = string("transpose_324")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_55_cast_fp16 = reshape(shape = concat_83x, x = var_3061_cast_fp16)[name = string("attn_output_55_cast_fp16")];
            tensor<int32, [2]> hidden_states_63_strides_0 = const()[name = string("hidden_states_63_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_63_pad_type_0 = const()[name = string("hidden_states_63_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_63_pad_0 = const()[name = string("hidden_states_63_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_63_dilations_0 = const()[name = string("hidden_states_63_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_63_groups_0 = const()[name = string("hidden_states_63_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_63_cast_fp16 = conv(dilations = hidden_states_63_dilations_0, groups = hidden_states_63_groups_0, pad = hidden_states_63_pad_0, pad_type = hidden_states_63_pad_type_0, strides = hidden_states_63_strides_0, weight = layers_6_self_attn_o_proj_weight_cast_fp16, x = attn_output_55_cast_fp16)[name = string("hidden_states_63_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_65_cast_fp16 = add(x = hidden_states_59_cast_fp16, y = hidden_states_63_cast_fp16)[name = string("hidden_states_65_cast_fp16")];
            fp16 const_70_promoted_to_fp16 = const()[name = string("const_70_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_3094_cast_fp16 = mul(x = hidden_states_65_cast_fp16, y = const_70_promoted_to_fp16)[name = string("op_3094_cast_fp16")];
            int32 var_3092 = const()[name = string("op_3092"), val = int32(1)];
            bool doubled_53_interleave_0 = const()[name = string("doubled_53_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_53_cast_fp16 = concat(axis = var_3092, interleave = doubled_53_interleave_0, values = (hidden_states_65_cast_fp16, var_3094_cast_fp16))[name = string("doubled_53_cast_fp16")];
            tensor<int32, [1]> out_27_axes_0 = const()[name = string("out_27_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_27_gamma_0_to_fp16 = const()[name = string("out_27_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1318822400)))];
            fp16 var_3104_to_fp16 = const()[name = string("op_3104_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_27_cast_fp16 = layer_norm(axes = out_27_axes_0, epsilon = var_3104_to_fp16, gamma = out_27_gamma_0_to_fp16, x = doubled_53_cast_fp16)[name = string("out_27_cast_fp16")];
            tensor<int32, [2]> var_3115_split_sizes_0 = const()[name = string("op_3115_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3115_axis_0 = const()[name = string("op_3115_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_3115_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_3115_cast_fp16_1 = split(axis = var_3115_axis_0, split_sizes = var_3115_split_sizes_0, x = out_27_cast_fp16)[name = string("op_3115_cast_fp16")];
            tensor<int32, [2]> input_13_strides_0 = const()[name = string("input_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_13_pad_type_0 = const()[name = string("input_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_13_pad_0 = const()[name = string("input_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_13_dilations_0 = const()[name = string("input_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_13_groups_0 = const()[name = string("input_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_13_cast_fp16 = conv(dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = layers_6_mlp_gate_proj_weight_cast_fp16, x = var_3115_cast_fp16_0)[name = string("input_13_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_3132_cast_fp16 = silu(x = input_13_cast_fp16)[name = string("op_3132_cast_fp16")];
            tensor<int32, [2]> var_3138_strides_0 = const()[name = string("op_3138_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_3138_pad_type_0 = const()[name = string("op_3138_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_3138_pad_0 = const()[name = string("op_3138_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_3138_dilations_0 = const()[name = string("op_3138_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3138_groups_0 = const()[name = string("op_3138_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_3138_cast_fp16 = conv(dilations = var_3138_dilations_0, groups = var_3138_groups_0, pad = var_3138_pad_0, pad_type = var_3138_pad_type_0, strides = var_3138_strides_0, weight = layers_6_mlp_up_proj_weight_cast_fp16, x = var_3115_cast_fp16_0)[name = string("op_3138_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_69_cast_fp16 = mul(x = var_3132_cast_fp16, y = var_3138_cast_fp16)[name = string("x_69_cast_fp16")];
            tensor<int32, [2]> hidden_states_67_strides_0 = const()[name = string("hidden_states_67_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_67_pad_type_0 = const()[name = string("hidden_states_67_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_67_pad_0 = const()[name = string("hidden_states_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_67_dilations_0 = const()[name = string("hidden_states_67_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_67_groups_0 = const()[name = string("hidden_states_67_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_67_cast_fp16 = conv(dilations = hidden_states_67_dilations_0, groups = hidden_states_67_groups_0, pad = hidden_states_67_pad_0, pad_type = hidden_states_67_pad_type_0, strides = hidden_states_67_strides_0, weight = layers_6_mlp_down_proj_weight_cast_fp16, x = x_69_cast_fp16)[name = string("hidden_states_67_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_69_cast_fp16 = add(x = hidden_states_65_cast_fp16, y = hidden_states_67_cast_fp16)[name = string("hidden_states_69_cast_fp16")];
            fp16 const_72_promoted_to_fp16 = const()[name = string("const_72_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_3156_cast_fp16 = mul(x = hidden_states_69_cast_fp16, y = const_72_promoted_to_fp16)[name = string("op_3156_cast_fp16")];
            int32 var_3154 = const()[name = string("op_3154"), val = int32(1)];
            bool doubled_57_interleave_0 = const()[name = string("doubled_57_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_57_cast_fp16 = concat(axis = var_3154, interleave = doubled_57_interleave_0, values = (hidden_states_69_cast_fp16, var_3156_cast_fp16))[name = string("doubled_57_cast_fp16")];
            tensor<int32, [1]> out_29_axes_0 = const()[name = string("out_29_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_29_gamma_0_to_fp16 = const()[name = string("out_29_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1318830656)))];
            fp16 var_3166_to_fp16 = const()[name = string("op_3166_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_29_cast_fp16 = layer_norm(axes = out_29_axes_0, epsilon = var_3166_to_fp16, gamma = out_29_gamma_0_to_fp16, x = doubled_57_cast_fp16)[name = string("out_29_cast_fp16")];
            tensor<int32, [2]> var_3177_split_sizes_0 = const()[name = string("op_3177_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3177_axis_0 = const()[name = string("op_3177_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_3177_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_3177_cast_fp16_1 = split(axis = var_3177_axis_0, split_sizes = var_3177_split_sizes_0, x = out_29_cast_fp16)[name = string("op_3177_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_7_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_7_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1318838912)))];
            tensor<int32, [2]> query_states_43_strides_0 = const()[name = string("query_states_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_43_pad_type_0 = const()[name = string("query_states_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_43_pad_0 = const()[name = string("query_states_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_43_dilations_0 = const()[name = string("query_states_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_43_groups_0 = const()[name = string("query_states_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_43_cast_fp16 = conv(dilations = query_states_43_dilations_0, groups = query_states_43_groups_0, pad = query_states_43_pad_0, pad_type = query_states_43_pad_type_0, strides = query_states_43_strides_0, weight = layers_7_self_attn_q_proj_weight_to_fp16, x = var_3177_cast_fp16_0)[name = string("query_states_43_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_7_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_7_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1327227584)))];
            tensor<int32, [2]> key_states_71_strides_0 = const()[name = string("key_states_71_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_71_pad_type_0 = const()[name = string("key_states_71_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_71_pad_0 = const()[name = string("key_states_71_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_71_dilations_0 = const()[name = string("key_states_71_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_71_groups_0 = const()[name = string("key_states_71_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_71_cast_fp16 = conv(dilations = key_states_71_dilations_0, groups = key_states_71_groups_0, pad = key_states_71_pad_0, pad_type = key_states_71_pad_type_0, strides = key_states_71_strides_0, weight = layers_7_self_attn_k_proj_weight_to_fp16, x = var_3177_cast_fp16_0)[name = string("key_states_71_cast_fp16")];
            tensor<int32, [2]> value_states_43_strides_0 = const()[name = string("value_states_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_43_pad_type_0 = const()[name = string("value_states_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_43_pad_0 = const()[name = string("value_states_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_43_dilations_0 = const()[name = string("value_states_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_43_groups_0 = const()[name = string("value_states_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_43_cast_fp16 = conv(dilations = value_states_43_dilations_0, groups = value_states_43_groups_0, pad = value_states_43_pad_0, pad_type = value_states_43_pad_type_0, strides = value_states_43_strides_0, weight = layers_7_self_attn_v_proj_weight_cast_fp16, x = var_3177_cast_fp16_0)[name = string("value_states_43_cast_fp16")];
            tensor<int32, [4]> concat_84x = const()[name = string("concat_84x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_71_cast_fp16 = reshape(shape = concat_84x, x = query_states_43_cast_fp16)[name = string("x_71_cast_fp16")];
            tensor<int32, [4]> concat_85x = const()[name = string("concat_85x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_3234_cast_fp16 = reshape(shape = concat_85x, x = key_states_71_cast_fp16)[name = string("op_3234_cast_fp16")];
            tensor<int32, [4]> concat_86x = const()[name = string("concat_86x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_3241_cast_fp16 = reshape(shape = concat_86x, x = value_states_43_cast_fp16)[name = string("op_3241_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_3245_cast_fp16 = mul(x = x_71_cast_fp16, y = var_859_cast_fp16)[name = string("op_3245_cast_fp16")];
            tensor<int32, [2]> var_3246_split_sizes_0 = const()[name = string("op_3246_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3246_axis_0 = const()[name = string("op_3246_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_3246_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_3246_cast_fp16_1 = split(axis = var_3246_axis_0, split_sizes = var_3246_split_sizes_0, x = x_71_cast_fp16)[name = string("op_3246_cast_fp16")];
            fp16 const_74_promoted_to_fp16 = const()[name = string("const_74_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_3248_cast_fp16 = mul(x = var_3246_cast_fp16_1, y = const_74_promoted_to_fp16)[name = string("op_3248_cast_fp16")];
            int32 var_3250 = const()[name = string("op_3250"), val = int32(-2)];
            bool var_3251_interleave_0 = const()[name = string("op_3251_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_3251_cast_fp16 = concat(axis = var_3250, interleave = var_3251_interleave_0, values = (var_3248_cast_fp16, var_3246_cast_fp16_0))[name = string("op_3251_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_3252_cast_fp16 = mul(x = var_3251_cast_fp16, y = var_866_cast_fp16)[name = string("op_3252_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_45_cast_fp16 = add(x = var_3245_cast_fp16, y = var_3252_cast_fp16)[name = string("query_states_45_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_3258_cast_fp16 = mul(x = var_3234_cast_fp16, y = var_859_cast_fp16)[name = string("op_3258_cast_fp16")];
            tensor<int32, [2]> var_3259_split_sizes_0 = const()[name = string("op_3259_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3259_axis_0 = const()[name = string("op_3259_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_3259_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_3259_cast_fp16_1 = split(axis = var_3259_axis_0, split_sizes = var_3259_split_sizes_0, x = var_3234_cast_fp16)[name = string("op_3259_cast_fp16")];
            fp16 const_75_promoted_to_fp16 = const()[name = string("const_75_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_3261_cast_fp16 = mul(x = var_3259_cast_fp16_1, y = const_75_promoted_to_fp16)[name = string("op_3261_cast_fp16")];
            int32 var_3263 = const()[name = string("op_3263"), val = int32(-2)];
            bool var_3264_interleave_0 = const()[name = string("op_3264_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_3264_cast_fp16 = concat(axis = var_3263, interleave = var_3264_interleave_0, values = (var_3261_cast_fp16, var_3259_cast_fp16_0))[name = string("op_3264_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_3265_cast_fp16 = mul(x = var_3264_cast_fp16, y = var_866_cast_fp16)[name = string("op_3265_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_75_cast_fp16 = add(x = var_3258_cast_fp16, y = var_3265_cast_fp16)[name = string("key_states_75_cast_fp16")];
            tensor<int32, [1]> expand_dims_84 = const()[name = string("expand_dims_84"), val = tensor<int32, [1]>([7])];
            tensor<int32, [1]> expand_dims_85 = const()[name = string("expand_dims_85"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_87 = const()[name = string("expand_dims_87"), val = tensor<int32, [1]>([0])];
            int32 concat_89_axis_0 = const()[name = string("concat_89_axis_0"), val = int32(0)];
            bool concat_89_interleave_0 = const()[name = string("concat_89_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_89 = concat(axis = concat_89_axis_0, interleave = concat_89_interleave_0, values = (expand_dims_84, expand_dims_85, position_id, expand_dims_87))[name = string("concat_89")];
            tensor<int32, [1]> expand_dims_88 = const()[name = string("expand_dims_88"), val = tensor<int32, [1]>([8])];
            tensor<int32, [1]> concat_90_values1_0 = const()[name = string("concat_90_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_90_values3_0 = const()[name = string("concat_90_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_90_axis_0 = const()[name = string("concat_90_axis_0"), val = int32(0)];
            bool concat_90_interleave_0 = const()[name = string("concat_90_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_90 = concat(axis = concat_90_axis_0, interleave = concat_90_interleave_0, values = (expand_dims_88, concat_90_values1_0, cache_position_end, concat_90_values3_0))[name = string("concat_90")];
            tensor<int32, [4]> key_states_77_perm_0 = const()[name = string("key_states_77_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_8_stride_0 = const()[name = string("key_cache_internal_tensor_assign_8_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_8_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_8_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_8_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_8_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_8_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_8_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_77_cast_fp16 = transpose(perm = key_states_77_perm_0, x = key_states_75_cast_fp16)[name = string("transpose_323")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_8_cast_fp16 = slice_update(begin = concat_89, begin_mask = key_cache_internal_tensor_assign_8_begin_mask_0, end = concat_90, end_mask = key_cache_internal_tensor_assign_8_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_8_squeeze_mask_0, stride = key_cache_internal_tensor_assign_8_stride_0, update = key_states_77_cast_fp16, x = coreml_update_state_180)[name = string("key_cache_internal_tensor_assign_8_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_8_cast_fp16, input = key_cache)[name = string("coreml_update_state_182_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_182 = read_state(input = key_cache)[name = string("coreml_update_state_182")];
            tensor<int32, [4]> value_states_45_perm_0 = const()[name = string("value_states_45_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_8_stride_0 = const()[name = string("value_cache_internal_tensor_assign_8_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_8_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_8_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_8_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_8_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_8_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_8_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_45_cast_fp16 = transpose(perm = value_states_45_perm_0, x = var_3241_cast_fp16)[name = string("transpose_322")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_8_cast_fp16 = slice_update(begin = concat_89, begin_mask = value_cache_internal_tensor_assign_8_begin_mask_0, end = concat_90, end_mask = value_cache_internal_tensor_assign_8_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_8_squeeze_mask_0, stride = value_cache_internal_tensor_assign_8_stride_0, update = value_states_45_cast_fp16, x = coreml_update_state_181)[name = string("value_cache_internal_tensor_assign_8_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_8_cast_fp16, input = value_cache)[name = string("coreml_update_state_183_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_183 = read_state(input = value_cache)[name = string("coreml_update_state_183")];
            tensor<int32, [4]> var_3335_begin_0 = const()[name = string("op_3335_begin_0"), val = tensor<int32, [4]>([7, 0, 0, 0])];
            tensor<int32, [4]> var_3335_end_0 = const()[name = string("op_3335_end_0"), val = tensor<int32, [4]>([8, 2, 2048, 128])];
            tensor<bool, [4]> var_3335_end_mask_0 = const()[name = string("op_3335_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3335_cast_fp16 = slice_by_index(begin = var_3335_begin_0, end = var_3335_end_0, end_mask = var_3335_end_mask_0, x = coreml_update_state_182)[name = string("op_3335_cast_fp16")];
            tensor<int32, [2]> tile_14 = const()[name = string("tile_14"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3338_axis_0 = const()[name = string("op_3338_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3338_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3338_cast_fp16_1 = split(axis = var_3338_axis_0, split_sizes = tile_14, x = var_3335_cast_fp16)[name = string("op_3338_cast_fp16")];
            tensor<int32, [4]> var_3345_begin_0 = const()[name = string("op_3345_begin_0"), val = tensor<int32, [4]>([7, 0, 0, 0])];
            tensor<int32, [4]> var_3345_end_0 = const()[name = string("op_3345_end_0"), val = tensor<int32, [4]>([8, 2, 2048, 128])];
            tensor<bool, [4]> var_3345_end_mask_0 = const()[name = string("op_3345_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3345_cast_fp16 = slice_by_index(begin = var_3345_begin_0, end = var_3345_end_0, end_mask = var_3345_end_mask_0, x = coreml_update_state_183)[name = string("op_3345_cast_fp16")];
            tensor<int32, [2]> tile_15 = const()[name = string("tile_15"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3348_axis_0 = const()[name = string("op_3348_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3348_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3348_cast_fp16_1 = split(axis = var_3348_axis_0, split_sizes = tile_15, x = var_3345_cast_fp16)[name = string("op_3348_cast_fp16")];
            tensor<int32, [2]> var_3351_split_sizes_0 = const()[name = string("op_3351_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3351_axis_0 = const()[name = string("op_3351_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_3351_0, tensor<fp16, [1, 8, 128, 16]> var_3351_1 = split(axis = var_3351_axis_0, split_sizes = var_3351_split_sizes_0, x = query_states_45_cast_fp16)[name = string("op_3351")];
            bool attn_weights_113_transpose_x_0 = const()[name = string("attn_weights_113_transpose_x_0"), val = bool(false)];
            bool attn_weights_113_transpose_y_0 = const()[name = string("attn_weights_113_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_113_cast_fp16 = matmul(transpose_x = attn_weights_113_transpose_x_0, transpose_y = attn_weights_113_transpose_y_0, x = var_3338_cast_fp16_0, y = var_3351_0)[name = string("attn_weights_113_cast_fp16")];
            fp16 var_3354_to_fp16 = const()[name = string("op_3354_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_115_cast_fp16 = mul(x = attn_weights_113_cast_fp16, y = var_3354_to_fp16)[name = string("attn_weights_115_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_117_cast_fp16 = add(x = attn_weights_115_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_117_cast_fp16")];
            int32 var_3358 = const()[name = string("op_3358"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_119_cast_fp16 = softmax(axis = var_3358, x = attn_weights_117_cast_fp16)[name = string("attn_weights_119_cast_fp16")];
            bool var_3364_transpose_x_1 = const()[name = string("op_3364_transpose_x_1"), val = bool(true)];
            bool var_3364_transpose_y_1 = const()[name = string("op_3364_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_3364_cast_fp16 = matmul(transpose_x = var_3364_transpose_x_1, transpose_y = var_3364_transpose_y_1, x = attn_weights_119_cast_fp16, y = var_3348_cast_fp16_0)[name = string("op_3364_cast_fp16")];
            bool attn_weights_121_transpose_x_0 = const()[name = string("attn_weights_121_transpose_x_0"), val = bool(false)];
            bool attn_weights_121_transpose_y_0 = const()[name = string("attn_weights_121_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_121_cast_fp16 = matmul(transpose_x = attn_weights_121_transpose_x_0, transpose_y = attn_weights_121_transpose_y_0, x = var_3338_cast_fp16_1, y = var_3351_1)[name = string("attn_weights_121_cast_fp16")];
            fp16 var_3366_to_fp16 = const()[name = string("op_3366_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_123_cast_fp16 = mul(x = attn_weights_121_cast_fp16, y = var_3366_to_fp16)[name = string("attn_weights_123_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_125_cast_fp16 = add(x = attn_weights_123_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_125_cast_fp16")];
            int32 var_3370 = const()[name = string("op_3370"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_127_cast_fp16 = softmax(axis = var_3370, x = attn_weights_125_cast_fp16)[name = string("attn_weights_127_cast_fp16")];
            bool attn_output_57_transpose_x_1 = const()[name = string("attn_output_57_transpose_x_1"), val = bool(true)];
            bool attn_output_57_transpose_y_1 = const()[name = string("attn_output_57_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_57_cast_fp16 = matmul(transpose_x = attn_output_57_transpose_x_1, transpose_y = attn_output_57_transpose_y_1, x = attn_weights_127_cast_fp16, y = var_3348_cast_fp16_1)[name = string("attn_output_57_cast_fp16")];
            int32 var_3378 = const()[name = string("op_3378"), val = int32(1)];
            bool attn_output_59_interleave_0 = const()[name = string("attn_output_59_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_59_cast_fp16 = concat(axis = var_3378, interleave = attn_output_59_interleave_0, values = (var_3364_cast_fp16, attn_output_57_cast_fp16))[name = string("attn_output_59_cast_fp16")];
            tensor<int32, [4]> var_3382_perm_0 = const()[name = string("op_3382_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_95x = const()[name = string("concat_95x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_3382_cast_fp16 = transpose(perm = var_3382_perm_0, x = attn_output_59_cast_fp16)[name = string("transpose_321")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_63_cast_fp16 = reshape(shape = concat_95x, x = var_3382_cast_fp16)[name = string("attn_output_63_cast_fp16")];
            tensor<int32, [2]> hidden_states_73_strides_0 = const()[name = string("hidden_states_73_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_73_pad_type_0 = const()[name = string("hidden_states_73_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_73_pad_0 = const()[name = string("hidden_states_73_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_73_dilations_0 = const()[name = string("hidden_states_73_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_73_groups_0 = const()[name = string("hidden_states_73_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_73_cast_fp16 = conv(dilations = hidden_states_73_dilations_0, groups = hidden_states_73_groups_0, pad = hidden_states_73_pad_0, pad_type = hidden_states_73_pad_type_0, strides = hidden_states_73_strides_0, weight = layers_7_self_attn_o_proj_weight_cast_fp16, x = attn_output_63_cast_fp16)[name = string("hidden_states_73_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_75_cast_fp16 = add(x = hidden_states_69_cast_fp16, y = hidden_states_73_cast_fp16)[name = string("hidden_states_75_cast_fp16")];
            fp16 const_80_promoted_to_fp16 = const()[name = string("const_80_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_3415_cast_fp16 = mul(x = hidden_states_75_cast_fp16, y = const_80_promoted_to_fp16)[name = string("op_3415_cast_fp16")];
            int32 var_3413 = const()[name = string("op_3413"), val = int32(1)];
            bool doubled_61_interleave_0 = const()[name = string("doubled_61_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_61_cast_fp16 = concat(axis = var_3413, interleave = doubled_61_interleave_0, values = (hidden_states_75_cast_fp16, var_3415_cast_fp16))[name = string("doubled_61_cast_fp16")];
            tensor<int32, [1]> out_31_axes_0 = const()[name = string("out_31_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_31_gamma_0_to_fp16 = const()[name = string("out_31_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1328276224)))];
            fp16 var_3425_to_fp16 = const()[name = string("op_3425_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_31_cast_fp16 = layer_norm(axes = out_31_axes_0, epsilon = var_3425_to_fp16, gamma = out_31_gamma_0_to_fp16, x = doubled_61_cast_fp16)[name = string("out_31_cast_fp16")];
            tensor<int32, [2]> var_3436_split_sizes_0 = const()[name = string("op_3436_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3436_axis_0 = const()[name = string("op_3436_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_3436_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_3436_cast_fp16_1 = split(axis = var_3436_axis_0, split_sizes = var_3436_split_sizes_0, x = out_31_cast_fp16)[name = string("op_3436_cast_fp16")];
            tensor<int32, [2]> input_15_strides_0 = const()[name = string("input_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_15_pad_type_0 = const()[name = string("input_15_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_15_dilations_0 = const()[name = string("input_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_15_groups_0 = const()[name = string("input_15_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_15_cast_fp16 = conv(dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = layers_7_mlp_gate_proj_weight_cast_fp16, x = var_3436_cast_fp16_0)[name = string("input_15_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_3453_cast_fp16 = silu(x = input_15_cast_fp16)[name = string("op_3453_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_7_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_7_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1328284480)))];
            tensor<int32, [2]> var_3459_strides_0 = const()[name = string("op_3459_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_3459_pad_type_0 = const()[name = string("op_3459_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_3459_pad_0 = const()[name = string("op_3459_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_3459_dilations_0 = const()[name = string("op_3459_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3459_groups_0 = const()[name = string("op_3459_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_3459_cast_fp16 = conv(dilations = var_3459_dilations_0, groups = var_3459_groups_0, pad = var_3459_pad_0, pad_type = var_3459_pad_type_0, strides = var_3459_strides_0, weight = layers_7_mlp_up_proj_weight_to_fp16, x = var_3436_cast_fp16_0)[name = string("op_3459_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_79_cast_fp16 = mul(x = var_3453_cast_fp16, y = var_3459_cast_fp16)[name = string("x_79_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_7_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_7_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353450368)))];
            tensor<int32, [2]> hidden_states_77_strides_0 = const()[name = string("hidden_states_77_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_77_pad_type_0 = const()[name = string("hidden_states_77_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_77_pad_0 = const()[name = string("hidden_states_77_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_77_dilations_0 = const()[name = string("hidden_states_77_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_77_groups_0 = const()[name = string("hidden_states_77_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_77_cast_fp16 = conv(dilations = hidden_states_77_dilations_0, groups = hidden_states_77_groups_0, pad = hidden_states_77_pad_0, pad_type = hidden_states_77_pad_type_0, strides = hidden_states_77_strides_0, weight = layers_7_mlp_down_proj_weight_to_fp16, x = x_79_cast_fp16)[name = string("hidden_states_77_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_79_cast_fp16 = add(x = hidden_states_75_cast_fp16, y = hidden_states_77_cast_fp16)[name = string("hidden_states_79_cast_fp16")];
            fp16 const_82_promoted_to_fp16 = const()[name = string("const_82_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_3477_cast_fp16 = mul(x = hidden_states_79_cast_fp16, y = const_82_promoted_to_fp16)[name = string("op_3477_cast_fp16")];
            int32 var_3475 = const()[name = string("op_3475"), val = int32(1)];
            bool doubled_65_interleave_0 = const()[name = string("doubled_65_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_65_cast_fp16 = concat(axis = var_3475, interleave = doubled_65_interleave_0, values = (hidden_states_79_cast_fp16, var_3477_cast_fp16))[name = string("doubled_65_cast_fp16")];
            tensor<int32, [1]> out_33_axes_0 = const()[name = string("out_33_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_33_gamma_0_to_fp16 = const()[name = string("out_33_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1378616256)))];
            fp16 var_3487_to_fp16 = const()[name = string("op_3487_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_33_cast_fp16 = layer_norm(axes = out_33_axes_0, epsilon = var_3487_to_fp16, gamma = out_33_gamma_0_to_fp16, x = doubled_65_cast_fp16)[name = string("out_33_cast_fp16")];
            tensor<int32, [2]> var_3498_split_sizes_0 = const()[name = string("op_3498_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3498_axis_0 = const()[name = string("op_3498_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_3498_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_3498_cast_fp16_1 = split(axis = var_3498_axis_0, split_sizes = var_3498_split_sizes_0, x = out_33_cast_fp16)[name = string("op_3498_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_8_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_8_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1378624512)))];
            tensor<int32, [2]> query_states_49_strides_0 = const()[name = string("query_states_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_49_pad_type_0 = const()[name = string("query_states_49_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_49_pad_0 = const()[name = string("query_states_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_49_dilations_0 = const()[name = string("query_states_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_49_groups_0 = const()[name = string("query_states_49_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_49_cast_fp16 = conv(dilations = query_states_49_dilations_0, groups = query_states_49_groups_0, pad = query_states_49_pad_0, pad_type = query_states_49_pad_type_0, strides = query_states_49_strides_0, weight = layers_8_self_attn_q_proj_weight_to_fp16, x = var_3498_cast_fp16_0)[name = string("query_states_49_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_8_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_8_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1387013184)))];
            tensor<int32, [2]> key_states_81_strides_0 = const()[name = string("key_states_81_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_81_pad_type_0 = const()[name = string("key_states_81_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_81_pad_0 = const()[name = string("key_states_81_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_81_dilations_0 = const()[name = string("key_states_81_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_81_groups_0 = const()[name = string("key_states_81_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_81_cast_fp16 = conv(dilations = key_states_81_dilations_0, groups = key_states_81_groups_0, pad = key_states_81_pad_0, pad_type = key_states_81_pad_type_0, strides = key_states_81_strides_0, weight = layers_8_self_attn_k_proj_weight_to_fp16, x = var_3498_cast_fp16_0)[name = string("key_states_81_cast_fp16")];
            tensor<int32, [2]> value_states_49_strides_0 = const()[name = string("value_states_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_49_pad_type_0 = const()[name = string("value_states_49_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_49_pad_0 = const()[name = string("value_states_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_49_dilations_0 = const()[name = string("value_states_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_49_groups_0 = const()[name = string("value_states_49_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_49_cast_fp16 = conv(dilations = value_states_49_dilations_0, groups = value_states_49_groups_0, pad = value_states_49_pad_0, pad_type = value_states_49_pad_type_0, strides = value_states_49_strides_0, weight = layers_8_self_attn_v_proj_weight_cast_fp16, x = var_3498_cast_fp16_0)[name = string("value_states_49_cast_fp16")];
            tensor<int32, [4]> concat_96x = const()[name = string("concat_96x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_81_cast_fp16 = reshape(shape = concat_96x, x = query_states_49_cast_fp16)[name = string("x_81_cast_fp16")];
            tensor<int32, [4]> concat_97x = const()[name = string("concat_97x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_3555_cast_fp16 = reshape(shape = concat_97x, x = key_states_81_cast_fp16)[name = string("op_3555_cast_fp16")];
            tensor<int32, [4]> concat_98x = const()[name = string("concat_98x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_3562_cast_fp16 = reshape(shape = concat_98x, x = value_states_49_cast_fp16)[name = string("op_3562_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_3566_cast_fp16 = mul(x = x_81_cast_fp16, y = var_859_cast_fp16)[name = string("op_3566_cast_fp16")];
            tensor<int32, [2]> var_3567_split_sizes_0 = const()[name = string("op_3567_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3567_axis_0 = const()[name = string("op_3567_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_3567_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_3567_cast_fp16_1 = split(axis = var_3567_axis_0, split_sizes = var_3567_split_sizes_0, x = x_81_cast_fp16)[name = string("op_3567_cast_fp16")];
            fp16 const_84_promoted_to_fp16 = const()[name = string("const_84_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_3569_cast_fp16 = mul(x = var_3567_cast_fp16_1, y = const_84_promoted_to_fp16)[name = string("op_3569_cast_fp16")];
            int32 var_3571 = const()[name = string("op_3571"), val = int32(-2)];
            bool var_3572_interleave_0 = const()[name = string("op_3572_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_3572_cast_fp16 = concat(axis = var_3571, interleave = var_3572_interleave_0, values = (var_3569_cast_fp16, var_3567_cast_fp16_0))[name = string("op_3572_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_3573_cast_fp16 = mul(x = var_3572_cast_fp16, y = var_866_cast_fp16)[name = string("op_3573_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_51_cast_fp16 = add(x = var_3566_cast_fp16, y = var_3573_cast_fp16)[name = string("query_states_51_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_3579_cast_fp16 = mul(x = var_3555_cast_fp16, y = var_859_cast_fp16)[name = string("op_3579_cast_fp16")];
            tensor<int32, [2]> var_3580_split_sizes_0 = const()[name = string("op_3580_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3580_axis_0 = const()[name = string("op_3580_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_3580_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_3580_cast_fp16_1 = split(axis = var_3580_axis_0, split_sizes = var_3580_split_sizes_0, x = var_3555_cast_fp16)[name = string("op_3580_cast_fp16")];
            fp16 const_85_promoted_to_fp16 = const()[name = string("const_85_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_3582_cast_fp16 = mul(x = var_3580_cast_fp16_1, y = const_85_promoted_to_fp16)[name = string("op_3582_cast_fp16")];
            int32 var_3584 = const()[name = string("op_3584"), val = int32(-2)];
            bool var_3585_interleave_0 = const()[name = string("op_3585_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_3585_cast_fp16 = concat(axis = var_3584, interleave = var_3585_interleave_0, values = (var_3582_cast_fp16, var_3580_cast_fp16_0))[name = string("op_3585_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_3586_cast_fp16 = mul(x = var_3585_cast_fp16, y = var_866_cast_fp16)[name = string("op_3586_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_85_cast_fp16 = add(x = var_3579_cast_fp16, y = var_3586_cast_fp16)[name = string("key_states_85_cast_fp16")];
            tensor<int32, [1]> expand_dims_96 = const()[name = string("expand_dims_96"), val = tensor<int32, [1]>([8])];
            tensor<int32, [1]> expand_dims_97 = const()[name = string("expand_dims_97"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_99 = const()[name = string("expand_dims_99"), val = tensor<int32, [1]>([0])];
            int32 concat_101_axis_0 = const()[name = string("concat_101_axis_0"), val = int32(0)];
            bool concat_101_interleave_0 = const()[name = string("concat_101_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_101 = concat(axis = concat_101_axis_0, interleave = concat_101_interleave_0, values = (expand_dims_96, expand_dims_97, position_id, expand_dims_99))[name = string("concat_101")];
            tensor<int32, [1]> expand_dims_100 = const()[name = string("expand_dims_100"), val = tensor<int32, [1]>([9])];
            tensor<int32, [1]> concat_102_values1_0 = const()[name = string("concat_102_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_102_values3_0 = const()[name = string("concat_102_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_102_axis_0 = const()[name = string("concat_102_axis_0"), val = int32(0)];
            bool concat_102_interleave_0 = const()[name = string("concat_102_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_102 = concat(axis = concat_102_axis_0, interleave = concat_102_interleave_0, values = (expand_dims_100, concat_102_values1_0, cache_position_end, concat_102_values3_0))[name = string("concat_102")];
            tensor<int32, [4]> key_states_87_perm_0 = const()[name = string("key_states_87_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_9_stride_0 = const()[name = string("key_cache_internal_tensor_assign_9_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_9_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_9_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_9_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_9_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_9_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_9_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_87_cast_fp16 = transpose(perm = key_states_87_perm_0, x = key_states_85_cast_fp16)[name = string("transpose_320")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_9_cast_fp16 = slice_update(begin = concat_101, begin_mask = key_cache_internal_tensor_assign_9_begin_mask_0, end = concat_102, end_mask = key_cache_internal_tensor_assign_9_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_9_squeeze_mask_0, stride = key_cache_internal_tensor_assign_9_stride_0, update = key_states_87_cast_fp16, x = coreml_update_state_182)[name = string("key_cache_internal_tensor_assign_9_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_9_cast_fp16, input = key_cache)[name = string("coreml_update_state_184_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_184 = read_state(input = key_cache)[name = string("coreml_update_state_184")];
            tensor<int32, [4]> value_states_51_perm_0 = const()[name = string("value_states_51_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_9_stride_0 = const()[name = string("value_cache_internal_tensor_assign_9_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_9_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_9_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_9_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_9_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_9_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_9_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_51_cast_fp16 = transpose(perm = value_states_51_perm_0, x = var_3562_cast_fp16)[name = string("transpose_319")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_9_cast_fp16 = slice_update(begin = concat_101, begin_mask = value_cache_internal_tensor_assign_9_begin_mask_0, end = concat_102, end_mask = value_cache_internal_tensor_assign_9_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_9_squeeze_mask_0, stride = value_cache_internal_tensor_assign_9_stride_0, update = value_states_51_cast_fp16, x = coreml_update_state_183)[name = string("value_cache_internal_tensor_assign_9_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_9_cast_fp16, input = value_cache)[name = string("coreml_update_state_185_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_185 = read_state(input = value_cache)[name = string("coreml_update_state_185")];
            tensor<int32, [4]> var_3656_begin_0 = const()[name = string("op_3656_begin_0"), val = tensor<int32, [4]>([8, 0, 0, 0])];
            tensor<int32, [4]> var_3656_end_0 = const()[name = string("op_3656_end_0"), val = tensor<int32, [4]>([9, 2, 2048, 128])];
            tensor<bool, [4]> var_3656_end_mask_0 = const()[name = string("op_3656_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3656_cast_fp16 = slice_by_index(begin = var_3656_begin_0, end = var_3656_end_0, end_mask = var_3656_end_mask_0, x = coreml_update_state_184)[name = string("op_3656_cast_fp16")];
            tensor<int32, [2]> tile_16 = const()[name = string("tile_16"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3659_axis_0 = const()[name = string("op_3659_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3659_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3659_cast_fp16_1 = split(axis = var_3659_axis_0, split_sizes = tile_16, x = var_3656_cast_fp16)[name = string("op_3659_cast_fp16")];
            tensor<int32, [4]> var_3666_begin_0 = const()[name = string("op_3666_begin_0"), val = tensor<int32, [4]>([8, 0, 0, 0])];
            tensor<int32, [4]> var_3666_end_0 = const()[name = string("op_3666_end_0"), val = tensor<int32, [4]>([9, 2, 2048, 128])];
            tensor<bool, [4]> var_3666_end_mask_0 = const()[name = string("op_3666_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3666_cast_fp16 = slice_by_index(begin = var_3666_begin_0, end = var_3666_end_0, end_mask = var_3666_end_mask_0, x = coreml_update_state_185)[name = string("op_3666_cast_fp16")];
            tensor<int32, [2]> tile_17 = const()[name = string("tile_17"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3669_axis_0 = const()[name = string("op_3669_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3669_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3669_cast_fp16_1 = split(axis = var_3669_axis_0, split_sizes = tile_17, x = var_3666_cast_fp16)[name = string("op_3669_cast_fp16")];
            tensor<int32, [2]> var_3672_split_sizes_0 = const()[name = string("op_3672_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3672_axis_0 = const()[name = string("op_3672_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_3672_0, tensor<fp16, [1, 8, 128, 16]> var_3672_1 = split(axis = var_3672_axis_0, split_sizes = var_3672_split_sizes_0, x = query_states_51_cast_fp16)[name = string("op_3672")];
            bool attn_weights_129_transpose_x_0 = const()[name = string("attn_weights_129_transpose_x_0"), val = bool(false)];
            bool attn_weights_129_transpose_y_0 = const()[name = string("attn_weights_129_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_129_cast_fp16 = matmul(transpose_x = attn_weights_129_transpose_x_0, transpose_y = attn_weights_129_transpose_y_0, x = var_3659_cast_fp16_0, y = var_3672_0)[name = string("attn_weights_129_cast_fp16")];
            fp16 var_3675_to_fp16 = const()[name = string("op_3675_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_131_cast_fp16 = mul(x = attn_weights_129_cast_fp16, y = var_3675_to_fp16)[name = string("attn_weights_131_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_133_cast_fp16 = add(x = attn_weights_131_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_133_cast_fp16")];
            int32 var_3679 = const()[name = string("op_3679"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_135_cast_fp16 = softmax(axis = var_3679, x = attn_weights_133_cast_fp16)[name = string("attn_weights_135_cast_fp16")];
            bool var_3685_transpose_x_1 = const()[name = string("op_3685_transpose_x_1"), val = bool(true)];
            bool var_3685_transpose_y_1 = const()[name = string("op_3685_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_3685_cast_fp16 = matmul(transpose_x = var_3685_transpose_x_1, transpose_y = var_3685_transpose_y_1, x = attn_weights_135_cast_fp16, y = var_3669_cast_fp16_0)[name = string("op_3685_cast_fp16")];
            bool attn_weights_137_transpose_x_0 = const()[name = string("attn_weights_137_transpose_x_0"), val = bool(false)];
            bool attn_weights_137_transpose_y_0 = const()[name = string("attn_weights_137_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_137_cast_fp16 = matmul(transpose_x = attn_weights_137_transpose_x_0, transpose_y = attn_weights_137_transpose_y_0, x = var_3659_cast_fp16_1, y = var_3672_1)[name = string("attn_weights_137_cast_fp16")];
            fp16 var_3687_to_fp16 = const()[name = string("op_3687_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_139_cast_fp16 = mul(x = attn_weights_137_cast_fp16, y = var_3687_to_fp16)[name = string("attn_weights_139_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_141_cast_fp16 = add(x = attn_weights_139_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_141_cast_fp16")];
            int32 var_3691 = const()[name = string("op_3691"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_143_cast_fp16 = softmax(axis = var_3691, x = attn_weights_141_cast_fp16)[name = string("attn_weights_143_cast_fp16")];
            bool attn_output_65_transpose_x_1 = const()[name = string("attn_output_65_transpose_x_1"), val = bool(true)];
            bool attn_output_65_transpose_y_1 = const()[name = string("attn_output_65_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_65_cast_fp16 = matmul(transpose_x = attn_output_65_transpose_x_1, transpose_y = attn_output_65_transpose_y_1, x = attn_weights_143_cast_fp16, y = var_3669_cast_fp16_1)[name = string("attn_output_65_cast_fp16")];
            int32 var_3699 = const()[name = string("op_3699"), val = int32(1)];
            bool attn_output_67_interleave_0 = const()[name = string("attn_output_67_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_67_cast_fp16 = concat(axis = var_3699, interleave = attn_output_67_interleave_0, values = (var_3685_cast_fp16, attn_output_65_cast_fp16))[name = string("attn_output_67_cast_fp16")];
            tensor<int32, [4]> var_3703_perm_0 = const()[name = string("op_3703_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_107x = const()[name = string("concat_107x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_3703_cast_fp16 = transpose(perm = var_3703_perm_0, x = attn_output_67_cast_fp16)[name = string("transpose_318")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_71_cast_fp16 = reshape(shape = concat_107x, x = var_3703_cast_fp16)[name = string("attn_output_71_cast_fp16")];
            tensor<int32, [2]> hidden_states_83_strides_0 = const()[name = string("hidden_states_83_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_83_pad_type_0 = const()[name = string("hidden_states_83_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_83_pad_0 = const()[name = string("hidden_states_83_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_83_dilations_0 = const()[name = string("hidden_states_83_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_83_groups_0 = const()[name = string("hidden_states_83_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_83_cast_fp16 = conv(dilations = hidden_states_83_dilations_0, groups = hidden_states_83_groups_0, pad = hidden_states_83_pad_0, pad_type = hidden_states_83_pad_type_0, strides = hidden_states_83_strides_0, weight = layers_8_self_attn_o_proj_weight_cast_fp16, x = attn_output_71_cast_fp16)[name = string("hidden_states_83_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_85_cast_fp16 = add(x = hidden_states_79_cast_fp16, y = hidden_states_83_cast_fp16)[name = string("hidden_states_85_cast_fp16")];
            fp16 const_90_promoted_to_fp16 = const()[name = string("const_90_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_3736_cast_fp16 = mul(x = hidden_states_85_cast_fp16, y = const_90_promoted_to_fp16)[name = string("op_3736_cast_fp16")];
            int32 var_3734 = const()[name = string("op_3734"), val = int32(1)];
            bool doubled_69_interleave_0 = const()[name = string("doubled_69_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_69_cast_fp16 = concat(axis = var_3734, interleave = doubled_69_interleave_0, values = (hidden_states_85_cast_fp16, var_3736_cast_fp16))[name = string("doubled_69_cast_fp16")];
            tensor<int32, [1]> out_35_axes_0 = const()[name = string("out_35_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_35_gamma_0_to_fp16 = const()[name = string("out_35_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1388061824)))];
            fp16 var_3746_to_fp16 = const()[name = string("op_3746_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_35_cast_fp16 = layer_norm(axes = out_35_axes_0, epsilon = var_3746_to_fp16, gamma = out_35_gamma_0_to_fp16, x = doubled_69_cast_fp16)[name = string("out_35_cast_fp16")];
            tensor<int32, [2]> var_3757_split_sizes_0 = const()[name = string("op_3757_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3757_axis_0 = const()[name = string("op_3757_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_3757_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_3757_cast_fp16_1 = split(axis = var_3757_axis_0, split_sizes = var_3757_split_sizes_0, x = out_35_cast_fp16)[name = string("op_3757_cast_fp16")];
            tensor<int32, [2]> input_17_strides_0 = const()[name = string("input_17_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_17_pad_type_0 = const()[name = string("input_17_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_17_pad_0 = const()[name = string("input_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_17_dilations_0 = const()[name = string("input_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_17_groups_0 = const()[name = string("input_17_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_17_cast_fp16 = conv(dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = layers_8_mlp_gate_proj_weight_cast_fp16, x = var_3757_cast_fp16_0)[name = string("input_17_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_3774_cast_fp16 = silu(x = input_17_cast_fp16)[name = string("op_3774_cast_fp16")];
            tensor<int32, [2]> var_3780_strides_0 = const()[name = string("op_3780_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_3780_pad_type_0 = const()[name = string("op_3780_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_3780_pad_0 = const()[name = string("op_3780_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_3780_dilations_0 = const()[name = string("op_3780_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3780_groups_0 = const()[name = string("op_3780_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_3780_cast_fp16 = conv(dilations = var_3780_dilations_0, groups = var_3780_groups_0, pad = var_3780_pad_0, pad_type = var_3780_pad_type_0, strides = var_3780_strides_0, weight = layers_8_mlp_up_proj_weight_cast_fp16, x = var_3757_cast_fp16_0)[name = string("op_3780_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_89_cast_fp16 = mul(x = var_3774_cast_fp16, y = var_3780_cast_fp16)[name = string("x_89_cast_fp16")];
            tensor<int32, [2]> hidden_states_87_strides_0 = const()[name = string("hidden_states_87_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_87_pad_type_0 = const()[name = string("hidden_states_87_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_87_pad_0 = const()[name = string("hidden_states_87_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_87_dilations_0 = const()[name = string("hidden_states_87_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_87_groups_0 = const()[name = string("hidden_states_87_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_87_cast_fp16 = conv(dilations = hidden_states_87_dilations_0, groups = hidden_states_87_groups_0, pad = hidden_states_87_pad_0, pad_type = hidden_states_87_pad_type_0, strides = hidden_states_87_strides_0, weight = layers_8_mlp_down_proj_weight_cast_fp16, x = x_89_cast_fp16)[name = string("hidden_states_87_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_89_cast_fp16 = add(x = hidden_states_85_cast_fp16, y = hidden_states_87_cast_fp16)[name = string("hidden_states_89_cast_fp16")];
            fp16 const_92_promoted_to_fp16 = const()[name = string("const_92_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_3798_cast_fp16 = mul(x = hidden_states_89_cast_fp16, y = const_92_promoted_to_fp16)[name = string("op_3798_cast_fp16")];
            int32 var_3796 = const()[name = string("op_3796"), val = int32(1)];
            bool doubled_73_interleave_0 = const()[name = string("doubled_73_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_73_cast_fp16 = concat(axis = var_3796, interleave = doubled_73_interleave_0, values = (hidden_states_89_cast_fp16, var_3798_cast_fp16))[name = string("doubled_73_cast_fp16")];
            tensor<int32, [1]> out_37_axes_0 = const()[name = string("out_37_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_37_gamma_0_to_fp16 = const()[name = string("out_37_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1388070080)))];
            fp16 var_3808_to_fp16 = const()[name = string("op_3808_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_37_cast_fp16 = layer_norm(axes = out_37_axes_0, epsilon = var_3808_to_fp16, gamma = out_37_gamma_0_to_fp16, x = doubled_73_cast_fp16)[name = string("out_37_cast_fp16")];
            tensor<int32, [2]> var_3819_split_sizes_0 = const()[name = string("op_3819_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3819_axis_0 = const()[name = string("op_3819_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_3819_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_3819_cast_fp16_1 = split(axis = var_3819_axis_0, split_sizes = var_3819_split_sizes_0, x = out_37_cast_fp16)[name = string("op_3819_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_9_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_9_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1388078336)))];
            tensor<int32, [2]> query_states_55_strides_0 = const()[name = string("query_states_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_55_pad_type_0 = const()[name = string("query_states_55_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_55_pad_0 = const()[name = string("query_states_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_55_dilations_0 = const()[name = string("query_states_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_55_groups_0 = const()[name = string("query_states_55_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_55_cast_fp16 = conv(dilations = query_states_55_dilations_0, groups = query_states_55_groups_0, pad = query_states_55_pad_0, pad_type = query_states_55_pad_type_0, strides = query_states_55_strides_0, weight = layers_9_self_attn_q_proj_weight_to_fp16, x = var_3819_cast_fp16_0)[name = string("query_states_55_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_9_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_9_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1396467008)))];
            tensor<int32, [2]> key_states_91_strides_0 = const()[name = string("key_states_91_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_91_pad_type_0 = const()[name = string("key_states_91_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_91_pad_0 = const()[name = string("key_states_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_91_dilations_0 = const()[name = string("key_states_91_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_91_groups_0 = const()[name = string("key_states_91_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_91_cast_fp16 = conv(dilations = key_states_91_dilations_0, groups = key_states_91_groups_0, pad = key_states_91_pad_0, pad_type = key_states_91_pad_type_0, strides = key_states_91_strides_0, weight = layers_9_self_attn_k_proj_weight_to_fp16, x = var_3819_cast_fp16_0)[name = string("key_states_91_cast_fp16")];
            tensor<int32, [2]> value_states_55_strides_0 = const()[name = string("value_states_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_55_pad_type_0 = const()[name = string("value_states_55_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_55_pad_0 = const()[name = string("value_states_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_55_dilations_0 = const()[name = string("value_states_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_55_groups_0 = const()[name = string("value_states_55_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_55_cast_fp16 = conv(dilations = value_states_55_dilations_0, groups = value_states_55_groups_0, pad = value_states_55_pad_0, pad_type = value_states_55_pad_type_0, strides = value_states_55_strides_0, weight = layers_9_self_attn_v_proj_weight_cast_fp16, x = var_3819_cast_fp16_0)[name = string("value_states_55_cast_fp16")];
            tensor<int32, [4]> concat_108x = const()[name = string("concat_108x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_91_cast_fp16 = reshape(shape = concat_108x, x = query_states_55_cast_fp16)[name = string("x_91_cast_fp16")];
            tensor<int32, [4]> concat_109x = const()[name = string("concat_109x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_3876_cast_fp16 = reshape(shape = concat_109x, x = key_states_91_cast_fp16)[name = string("op_3876_cast_fp16")];
            tensor<int32, [4]> concat_110x = const()[name = string("concat_110x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_3883_cast_fp16 = reshape(shape = concat_110x, x = value_states_55_cast_fp16)[name = string("op_3883_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_3887_cast_fp16 = mul(x = x_91_cast_fp16, y = var_859_cast_fp16)[name = string("op_3887_cast_fp16")];
            tensor<int32, [2]> var_3888_split_sizes_0 = const()[name = string("op_3888_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3888_axis_0 = const()[name = string("op_3888_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_3888_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_3888_cast_fp16_1 = split(axis = var_3888_axis_0, split_sizes = var_3888_split_sizes_0, x = x_91_cast_fp16)[name = string("op_3888_cast_fp16")];
            fp16 const_94_promoted_to_fp16 = const()[name = string("const_94_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_3890_cast_fp16 = mul(x = var_3888_cast_fp16_1, y = const_94_promoted_to_fp16)[name = string("op_3890_cast_fp16")];
            int32 var_3892 = const()[name = string("op_3892"), val = int32(-2)];
            bool var_3893_interleave_0 = const()[name = string("op_3893_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_3893_cast_fp16 = concat(axis = var_3892, interleave = var_3893_interleave_0, values = (var_3890_cast_fp16, var_3888_cast_fp16_0))[name = string("op_3893_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_3894_cast_fp16 = mul(x = var_3893_cast_fp16, y = var_866_cast_fp16)[name = string("op_3894_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_57_cast_fp16 = add(x = var_3887_cast_fp16, y = var_3894_cast_fp16)[name = string("query_states_57_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_3900_cast_fp16 = mul(x = var_3876_cast_fp16, y = var_859_cast_fp16)[name = string("op_3900_cast_fp16")];
            tensor<int32, [2]> var_3901_split_sizes_0 = const()[name = string("op_3901_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3901_axis_0 = const()[name = string("op_3901_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_3901_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_3901_cast_fp16_1 = split(axis = var_3901_axis_0, split_sizes = var_3901_split_sizes_0, x = var_3876_cast_fp16)[name = string("op_3901_cast_fp16")];
            fp16 const_95_promoted_to_fp16 = const()[name = string("const_95_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_3903_cast_fp16 = mul(x = var_3901_cast_fp16_1, y = const_95_promoted_to_fp16)[name = string("op_3903_cast_fp16")];
            int32 var_3905 = const()[name = string("op_3905"), val = int32(-2)];
            bool var_3906_interleave_0 = const()[name = string("op_3906_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_3906_cast_fp16 = concat(axis = var_3905, interleave = var_3906_interleave_0, values = (var_3903_cast_fp16, var_3901_cast_fp16_0))[name = string("op_3906_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_3907_cast_fp16 = mul(x = var_3906_cast_fp16, y = var_866_cast_fp16)[name = string("op_3907_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_95_cast_fp16 = add(x = var_3900_cast_fp16, y = var_3907_cast_fp16)[name = string("key_states_95_cast_fp16")];
            tensor<int32, [1]> expand_dims_108 = const()[name = string("expand_dims_108"), val = tensor<int32, [1]>([9])];
            tensor<int32, [1]> expand_dims_109 = const()[name = string("expand_dims_109"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_111 = const()[name = string("expand_dims_111"), val = tensor<int32, [1]>([0])];
            int32 concat_113_axis_0 = const()[name = string("concat_113_axis_0"), val = int32(0)];
            bool concat_113_interleave_0 = const()[name = string("concat_113_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_113 = concat(axis = concat_113_axis_0, interleave = concat_113_interleave_0, values = (expand_dims_108, expand_dims_109, position_id, expand_dims_111))[name = string("concat_113")];
            tensor<int32, [1]> expand_dims_112 = const()[name = string("expand_dims_112"), val = tensor<int32, [1]>([10])];
            tensor<int32, [1]> concat_114_values1_0 = const()[name = string("concat_114_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_114_values3_0 = const()[name = string("concat_114_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_114_axis_0 = const()[name = string("concat_114_axis_0"), val = int32(0)];
            bool concat_114_interleave_0 = const()[name = string("concat_114_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_114 = concat(axis = concat_114_axis_0, interleave = concat_114_interleave_0, values = (expand_dims_112, concat_114_values1_0, cache_position_end, concat_114_values3_0))[name = string("concat_114")];
            tensor<int32, [4]> key_states_97_perm_0 = const()[name = string("key_states_97_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_10_stride_0 = const()[name = string("key_cache_internal_tensor_assign_10_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_10_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_10_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_10_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_10_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_10_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_10_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_97_cast_fp16 = transpose(perm = key_states_97_perm_0, x = key_states_95_cast_fp16)[name = string("transpose_317")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_10_cast_fp16 = slice_update(begin = concat_113, begin_mask = key_cache_internal_tensor_assign_10_begin_mask_0, end = concat_114, end_mask = key_cache_internal_tensor_assign_10_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_10_squeeze_mask_0, stride = key_cache_internal_tensor_assign_10_stride_0, update = key_states_97_cast_fp16, x = coreml_update_state_184)[name = string("key_cache_internal_tensor_assign_10_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_10_cast_fp16, input = key_cache)[name = string("coreml_update_state_186_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_186 = read_state(input = key_cache)[name = string("coreml_update_state_186")];
            tensor<int32, [4]> value_states_57_perm_0 = const()[name = string("value_states_57_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_10_stride_0 = const()[name = string("value_cache_internal_tensor_assign_10_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_10_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_10_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_10_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_10_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_10_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_10_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_57_cast_fp16 = transpose(perm = value_states_57_perm_0, x = var_3883_cast_fp16)[name = string("transpose_316")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_10_cast_fp16 = slice_update(begin = concat_113, begin_mask = value_cache_internal_tensor_assign_10_begin_mask_0, end = concat_114, end_mask = value_cache_internal_tensor_assign_10_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_10_squeeze_mask_0, stride = value_cache_internal_tensor_assign_10_stride_0, update = value_states_57_cast_fp16, x = coreml_update_state_185)[name = string("value_cache_internal_tensor_assign_10_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_10_cast_fp16, input = value_cache)[name = string("coreml_update_state_187_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_187 = read_state(input = value_cache)[name = string("coreml_update_state_187")];
            tensor<int32, [4]> var_3977_begin_0 = const()[name = string("op_3977_begin_0"), val = tensor<int32, [4]>([9, 0, 0, 0])];
            tensor<int32, [4]> var_3977_end_0 = const()[name = string("op_3977_end_0"), val = tensor<int32, [4]>([10, 2, 2048, 128])];
            tensor<bool, [4]> var_3977_end_mask_0 = const()[name = string("op_3977_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3977_cast_fp16 = slice_by_index(begin = var_3977_begin_0, end = var_3977_end_0, end_mask = var_3977_end_mask_0, x = coreml_update_state_186)[name = string("op_3977_cast_fp16")];
            tensor<int32, [2]> tile_18 = const()[name = string("tile_18"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3980_axis_0 = const()[name = string("op_3980_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3980_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3980_cast_fp16_1 = split(axis = var_3980_axis_0, split_sizes = tile_18, x = var_3977_cast_fp16)[name = string("op_3980_cast_fp16")];
            tensor<int32, [4]> var_3987_begin_0 = const()[name = string("op_3987_begin_0"), val = tensor<int32, [4]>([9, 0, 0, 0])];
            tensor<int32, [4]> var_3987_end_0 = const()[name = string("op_3987_end_0"), val = tensor<int32, [4]>([10, 2, 2048, 128])];
            tensor<bool, [4]> var_3987_end_mask_0 = const()[name = string("op_3987_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3987_cast_fp16 = slice_by_index(begin = var_3987_begin_0, end = var_3987_end_0, end_mask = var_3987_end_mask_0, x = coreml_update_state_187)[name = string("op_3987_cast_fp16")];
            tensor<int32, [2]> tile_19 = const()[name = string("tile_19"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3990_axis_0 = const()[name = string("op_3990_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3990_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3990_cast_fp16_1 = split(axis = var_3990_axis_0, split_sizes = tile_19, x = var_3987_cast_fp16)[name = string("op_3990_cast_fp16")];
            tensor<int32, [2]> var_3993_split_sizes_0 = const()[name = string("op_3993_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3993_axis_0 = const()[name = string("op_3993_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_3993_0, tensor<fp16, [1, 8, 128, 16]> var_3993_1 = split(axis = var_3993_axis_0, split_sizes = var_3993_split_sizes_0, x = query_states_57_cast_fp16)[name = string("op_3993")];
            bool attn_weights_145_transpose_x_0 = const()[name = string("attn_weights_145_transpose_x_0"), val = bool(false)];
            bool attn_weights_145_transpose_y_0 = const()[name = string("attn_weights_145_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_145_cast_fp16 = matmul(transpose_x = attn_weights_145_transpose_x_0, transpose_y = attn_weights_145_transpose_y_0, x = var_3980_cast_fp16_0, y = var_3993_0)[name = string("attn_weights_145_cast_fp16")];
            fp16 var_3996_to_fp16 = const()[name = string("op_3996_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_147_cast_fp16 = mul(x = attn_weights_145_cast_fp16, y = var_3996_to_fp16)[name = string("attn_weights_147_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_149_cast_fp16 = add(x = attn_weights_147_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_149_cast_fp16")];
            int32 var_4000 = const()[name = string("op_4000"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_151_cast_fp16 = softmax(axis = var_4000, x = attn_weights_149_cast_fp16)[name = string("attn_weights_151_cast_fp16")];
            bool var_4006_transpose_x_1 = const()[name = string("op_4006_transpose_x_1"), val = bool(true)];
            bool var_4006_transpose_y_1 = const()[name = string("op_4006_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_4006_cast_fp16 = matmul(transpose_x = var_4006_transpose_x_1, transpose_y = var_4006_transpose_y_1, x = attn_weights_151_cast_fp16, y = var_3990_cast_fp16_0)[name = string("op_4006_cast_fp16")];
            bool attn_weights_153_transpose_x_0 = const()[name = string("attn_weights_153_transpose_x_0"), val = bool(false)];
            bool attn_weights_153_transpose_y_0 = const()[name = string("attn_weights_153_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_153_cast_fp16 = matmul(transpose_x = attn_weights_153_transpose_x_0, transpose_y = attn_weights_153_transpose_y_0, x = var_3980_cast_fp16_1, y = var_3993_1)[name = string("attn_weights_153_cast_fp16")];
            fp16 var_4008_to_fp16 = const()[name = string("op_4008_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_155_cast_fp16 = mul(x = attn_weights_153_cast_fp16, y = var_4008_to_fp16)[name = string("attn_weights_155_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_157_cast_fp16 = add(x = attn_weights_155_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_157_cast_fp16")];
            int32 var_4012 = const()[name = string("op_4012"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_159_cast_fp16 = softmax(axis = var_4012, x = attn_weights_157_cast_fp16)[name = string("attn_weights_159_cast_fp16")];
            bool attn_output_73_transpose_x_1 = const()[name = string("attn_output_73_transpose_x_1"), val = bool(true)];
            bool attn_output_73_transpose_y_1 = const()[name = string("attn_output_73_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_73_cast_fp16 = matmul(transpose_x = attn_output_73_transpose_x_1, transpose_y = attn_output_73_transpose_y_1, x = attn_weights_159_cast_fp16, y = var_3990_cast_fp16_1)[name = string("attn_output_73_cast_fp16")];
            int32 var_4020 = const()[name = string("op_4020"), val = int32(1)];
            bool attn_output_75_interleave_0 = const()[name = string("attn_output_75_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_75_cast_fp16 = concat(axis = var_4020, interleave = attn_output_75_interleave_0, values = (var_4006_cast_fp16, attn_output_73_cast_fp16))[name = string("attn_output_75_cast_fp16")];
            tensor<int32, [4]> var_4024_perm_0 = const()[name = string("op_4024_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_119x = const()[name = string("concat_119x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_4024_cast_fp16 = transpose(perm = var_4024_perm_0, x = attn_output_75_cast_fp16)[name = string("transpose_315")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_79_cast_fp16 = reshape(shape = concat_119x, x = var_4024_cast_fp16)[name = string("attn_output_79_cast_fp16")];
            tensor<int32, [2]> hidden_states_93_strides_0 = const()[name = string("hidden_states_93_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_93_pad_type_0 = const()[name = string("hidden_states_93_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_93_pad_0 = const()[name = string("hidden_states_93_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_93_dilations_0 = const()[name = string("hidden_states_93_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_93_groups_0 = const()[name = string("hidden_states_93_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_93_cast_fp16 = conv(dilations = hidden_states_93_dilations_0, groups = hidden_states_93_groups_0, pad = hidden_states_93_pad_0, pad_type = hidden_states_93_pad_type_0, strides = hidden_states_93_strides_0, weight = layers_9_self_attn_o_proj_weight_cast_fp16, x = attn_output_79_cast_fp16)[name = string("hidden_states_93_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_95_cast_fp16 = add(x = hidden_states_89_cast_fp16, y = hidden_states_93_cast_fp16)[name = string("hidden_states_95_cast_fp16")];
            fp16 const_100_promoted_to_fp16 = const()[name = string("const_100_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_4057_cast_fp16 = mul(x = hidden_states_95_cast_fp16, y = const_100_promoted_to_fp16)[name = string("op_4057_cast_fp16")];
            int32 var_4055 = const()[name = string("op_4055"), val = int32(1)];
            bool doubled_77_interleave_0 = const()[name = string("doubled_77_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_77_cast_fp16 = concat(axis = var_4055, interleave = doubled_77_interleave_0, values = (hidden_states_95_cast_fp16, var_4057_cast_fp16))[name = string("doubled_77_cast_fp16")];
            tensor<int32, [1]> out_39_axes_0 = const()[name = string("out_39_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_39_gamma_0_to_fp16 = const()[name = string("out_39_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1397515648)))];
            fp16 var_4067_to_fp16 = const()[name = string("op_4067_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_39_cast_fp16 = layer_norm(axes = out_39_axes_0, epsilon = var_4067_to_fp16, gamma = out_39_gamma_0_to_fp16, x = doubled_77_cast_fp16)[name = string("out_39_cast_fp16")];
            tensor<int32, [2]> var_4078_split_sizes_0 = const()[name = string("op_4078_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4078_axis_0 = const()[name = string("op_4078_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_4078_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_4078_cast_fp16_1 = split(axis = var_4078_axis_0, split_sizes = var_4078_split_sizes_0, x = out_39_cast_fp16)[name = string("op_4078_cast_fp16")];
            tensor<int32, [2]> input_19_strides_0 = const()[name = string("input_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_19_pad_type_0 = const()[name = string("input_19_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_19_pad_0 = const()[name = string("input_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_19_dilations_0 = const()[name = string("input_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_19_groups_0 = const()[name = string("input_19_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_19_cast_fp16 = conv(dilations = input_19_dilations_0, groups = input_19_groups_0, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = input_19_strides_0, weight = layers_9_mlp_gate_proj_weight_cast_fp16, x = var_4078_cast_fp16_0)[name = string("input_19_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_4095_cast_fp16 = silu(x = input_19_cast_fp16)[name = string("op_4095_cast_fp16")];
            tensor<int32, [2]> var_4101_strides_0 = const()[name = string("op_4101_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_4101_pad_type_0 = const()[name = string("op_4101_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_4101_pad_0 = const()[name = string("op_4101_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_4101_dilations_0 = const()[name = string("op_4101_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4101_groups_0 = const()[name = string("op_4101_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_4101_cast_fp16 = conv(dilations = var_4101_dilations_0, groups = var_4101_groups_0, pad = var_4101_pad_0, pad_type = var_4101_pad_type_0, strides = var_4101_strides_0, weight = layers_9_mlp_up_proj_weight_cast_fp16, x = var_4078_cast_fp16_0)[name = string("op_4101_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_99_cast_fp16 = mul(x = var_4095_cast_fp16, y = var_4101_cast_fp16)[name = string("x_99_cast_fp16")];
            tensor<int32, [2]> hidden_states_97_strides_0 = const()[name = string("hidden_states_97_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_97_pad_type_0 = const()[name = string("hidden_states_97_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_97_pad_0 = const()[name = string("hidden_states_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_97_dilations_0 = const()[name = string("hidden_states_97_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_97_groups_0 = const()[name = string("hidden_states_97_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_97_cast_fp16 = conv(dilations = hidden_states_97_dilations_0, groups = hidden_states_97_groups_0, pad = hidden_states_97_pad_0, pad_type = hidden_states_97_pad_type_0, strides = hidden_states_97_strides_0, weight = layers_9_mlp_down_proj_weight_cast_fp16, x = x_99_cast_fp16)[name = string("hidden_states_97_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_99_cast_fp16 = add(x = hidden_states_95_cast_fp16, y = hidden_states_97_cast_fp16)[name = string("hidden_states_99_cast_fp16")];
            fp16 const_102_promoted_to_fp16 = const()[name = string("const_102_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_4119_cast_fp16 = mul(x = hidden_states_99_cast_fp16, y = const_102_promoted_to_fp16)[name = string("op_4119_cast_fp16")];
            int32 var_4117 = const()[name = string("op_4117"), val = int32(1)];
            bool doubled_81_interleave_0 = const()[name = string("doubled_81_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_81_cast_fp16 = concat(axis = var_4117, interleave = doubled_81_interleave_0, values = (hidden_states_99_cast_fp16, var_4119_cast_fp16))[name = string("doubled_81_cast_fp16")];
            tensor<int32, [1]> out_41_axes_0 = const()[name = string("out_41_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_41_gamma_0_to_fp16 = const()[name = string("out_41_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1397523904)))];
            fp16 var_4129_to_fp16 = const()[name = string("op_4129_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_41_cast_fp16 = layer_norm(axes = out_41_axes_0, epsilon = var_4129_to_fp16, gamma = out_41_gamma_0_to_fp16, x = doubled_81_cast_fp16)[name = string("out_41_cast_fp16")];
            tensor<int32, [2]> var_4140_split_sizes_0 = const()[name = string("op_4140_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4140_axis_0 = const()[name = string("op_4140_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_4140_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_4140_cast_fp16_1 = split(axis = var_4140_axis_0, split_sizes = var_4140_split_sizes_0, x = out_41_cast_fp16)[name = string("op_4140_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_10_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_10_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1397532160)))];
            tensor<int32, [2]> query_states_61_strides_0 = const()[name = string("query_states_61_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_61_pad_type_0 = const()[name = string("query_states_61_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_61_pad_0 = const()[name = string("query_states_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_61_dilations_0 = const()[name = string("query_states_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_61_groups_0 = const()[name = string("query_states_61_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_61_cast_fp16 = conv(dilations = query_states_61_dilations_0, groups = query_states_61_groups_0, pad = query_states_61_pad_0, pad_type = query_states_61_pad_type_0, strides = query_states_61_strides_0, weight = layers_10_self_attn_q_proj_weight_to_fp16, x = var_4140_cast_fp16_0)[name = string("query_states_61_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_10_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_10_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1405920832)))];
            tensor<int32, [2]> key_states_101_strides_0 = const()[name = string("key_states_101_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_101_pad_type_0 = const()[name = string("key_states_101_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_101_pad_0 = const()[name = string("key_states_101_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_101_dilations_0 = const()[name = string("key_states_101_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_101_groups_0 = const()[name = string("key_states_101_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_101_cast_fp16 = conv(dilations = key_states_101_dilations_0, groups = key_states_101_groups_0, pad = key_states_101_pad_0, pad_type = key_states_101_pad_type_0, strides = key_states_101_strides_0, weight = layers_10_self_attn_k_proj_weight_to_fp16, x = var_4140_cast_fp16_0)[name = string("key_states_101_cast_fp16")];
            tensor<int32, [2]> value_states_61_strides_0 = const()[name = string("value_states_61_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_61_pad_type_0 = const()[name = string("value_states_61_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_61_pad_0 = const()[name = string("value_states_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_61_dilations_0 = const()[name = string("value_states_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_61_groups_0 = const()[name = string("value_states_61_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_61_cast_fp16 = conv(dilations = value_states_61_dilations_0, groups = value_states_61_groups_0, pad = value_states_61_pad_0, pad_type = value_states_61_pad_type_0, strides = value_states_61_strides_0, weight = layers_10_self_attn_v_proj_weight_cast_fp16, x = var_4140_cast_fp16_0)[name = string("value_states_61_cast_fp16")];
            tensor<int32, [4]> concat_120x = const()[name = string("concat_120x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_101_cast_fp16 = reshape(shape = concat_120x, x = query_states_61_cast_fp16)[name = string("x_101_cast_fp16")];
            tensor<int32, [4]> concat_121x = const()[name = string("concat_121x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_4197_cast_fp16 = reshape(shape = concat_121x, x = key_states_101_cast_fp16)[name = string("op_4197_cast_fp16")];
            tensor<int32, [4]> concat_122x = const()[name = string("concat_122x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_4204_cast_fp16 = reshape(shape = concat_122x, x = value_states_61_cast_fp16)[name = string("op_4204_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_4208_cast_fp16 = mul(x = x_101_cast_fp16, y = var_859_cast_fp16)[name = string("op_4208_cast_fp16")];
            tensor<int32, [2]> var_4209_split_sizes_0 = const()[name = string("op_4209_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4209_axis_0 = const()[name = string("op_4209_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_4209_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_4209_cast_fp16_1 = split(axis = var_4209_axis_0, split_sizes = var_4209_split_sizes_0, x = x_101_cast_fp16)[name = string("op_4209_cast_fp16")];
            fp16 const_104_promoted_to_fp16 = const()[name = string("const_104_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_4211_cast_fp16 = mul(x = var_4209_cast_fp16_1, y = const_104_promoted_to_fp16)[name = string("op_4211_cast_fp16")];
            int32 var_4213 = const()[name = string("op_4213"), val = int32(-2)];
            bool var_4214_interleave_0 = const()[name = string("op_4214_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_4214_cast_fp16 = concat(axis = var_4213, interleave = var_4214_interleave_0, values = (var_4211_cast_fp16, var_4209_cast_fp16_0))[name = string("op_4214_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_4215_cast_fp16 = mul(x = var_4214_cast_fp16, y = var_866_cast_fp16)[name = string("op_4215_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_63_cast_fp16 = add(x = var_4208_cast_fp16, y = var_4215_cast_fp16)[name = string("query_states_63_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_4221_cast_fp16 = mul(x = var_4197_cast_fp16, y = var_859_cast_fp16)[name = string("op_4221_cast_fp16")];
            tensor<int32, [2]> var_4222_split_sizes_0 = const()[name = string("op_4222_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4222_axis_0 = const()[name = string("op_4222_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_4222_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_4222_cast_fp16_1 = split(axis = var_4222_axis_0, split_sizes = var_4222_split_sizes_0, x = var_4197_cast_fp16)[name = string("op_4222_cast_fp16")];
            fp16 const_105_promoted_to_fp16 = const()[name = string("const_105_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_4224_cast_fp16 = mul(x = var_4222_cast_fp16_1, y = const_105_promoted_to_fp16)[name = string("op_4224_cast_fp16")];
            int32 var_4226 = const()[name = string("op_4226"), val = int32(-2)];
            bool var_4227_interleave_0 = const()[name = string("op_4227_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_4227_cast_fp16 = concat(axis = var_4226, interleave = var_4227_interleave_0, values = (var_4224_cast_fp16, var_4222_cast_fp16_0))[name = string("op_4227_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_4228_cast_fp16 = mul(x = var_4227_cast_fp16, y = var_866_cast_fp16)[name = string("op_4228_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_105_cast_fp16 = add(x = var_4221_cast_fp16, y = var_4228_cast_fp16)[name = string("key_states_105_cast_fp16")];
            tensor<int32, [1]> expand_dims_120 = const()[name = string("expand_dims_120"), val = tensor<int32, [1]>([10])];
            tensor<int32, [1]> expand_dims_121 = const()[name = string("expand_dims_121"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_123 = const()[name = string("expand_dims_123"), val = tensor<int32, [1]>([0])];
            int32 concat_125_axis_0 = const()[name = string("concat_125_axis_0"), val = int32(0)];
            bool concat_125_interleave_0 = const()[name = string("concat_125_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_125 = concat(axis = concat_125_axis_0, interleave = concat_125_interleave_0, values = (expand_dims_120, expand_dims_121, position_id, expand_dims_123))[name = string("concat_125")];
            tensor<int32, [1]> expand_dims_124 = const()[name = string("expand_dims_124"), val = tensor<int32, [1]>([11])];
            tensor<int32, [1]> concat_126_values1_0 = const()[name = string("concat_126_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_126_values3_0 = const()[name = string("concat_126_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_126_axis_0 = const()[name = string("concat_126_axis_0"), val = int32(0)];
            bool concat_126_interleave_0 = const()[name = string("concat_126_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_126 = concat(axis = concat_126_axis_0, interleave = concat_126_interleave_0, values = (expand_dims_124, concat_126_values1_0, cache_position_end, concat_126_values3_0))[name = string("concat_126")];
            tensor<int32, [4]> key_states_107_perm_0 = const()[name = string("key_states_107_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_11_stride_0 = const()[name = string("key_cache_internal_tensor_assign_11_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_11_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_11_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_11_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_11_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_11_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_11_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_107_cast_fp16 = transpose(perm = key_states_107_perm_0, x = key_states_105_cast_fp16)[name = string("transpose_314")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_11_cast_fp16 = slice_update(begin = concat_125, begin_mask = key_cache_internal_tensor_assign_11_begin_mask_0, end = concat_126, end_mask = key_cache_internal_tensor_assign_11_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_11_squeeze_mask_0, stride = key_cache_internal_tensor_assign_11_stride_0, update = key_states_107_cast_fp16, x = coreml_update_state_186)[name = string("key_cache_internal_tensor_assign_11_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_11_cast_fp16, input = key_cache)[name = string("coreml_update_state_188_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_188 = read_state(input = key_cache)[name = string("coreml_update_state_188")];
            tensor<int32, [4]> value_states_63_perm_0 = const()[name = string("value_states_63_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_11_stride_0 = const()[name = string("value_cache_internal_tensor_assign_11_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_11_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_11_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_11_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_11_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_11_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_11_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_63_cast_fp16 = transpose(perm = value_states_63_perm_0, x = var_4204_cast_fp16)[name = string("transpose_313")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_11_cast_fp16 = slice_update(begin = concat_125, begin_mask = value_cache_internal_tensor_assign_11_begin_mask_0, end = concat_126, end_mask = value_cache_internal_tensor_assign_11_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_11_squeeze_mask_0, stride = value_cache_internal_tensor_assign_11_stride_0, update = value_states_63_cast_fp16, x = coreml_update_state_187)[name = string("value_cache_internal_tensor_assign_11_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_11_cast_fp16, input = value_cache)[name = string("coreml_update_state_189_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_189 = read_state(input = value_cache)[name = string("coreml_update_state_189")];
            tensor<int32, [4]> var_4298_begin_0 = const()[name = string("op_4298_begin_0"), val = tensor<int32, [4]>([10, 0, 0, 0])];
            tensor<int32, [4]> var_4298_end_0 = const()[name = string("op_4298_end_0"), val = tensor<int32, [4]>([11, 2, 2048, 128])];
            tensor<bool, [4]> var_4298_end_mask_0 = const()[name = string("op_4298_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4298_cast_fp16 = slice_by_index(begin = var_4298_begin_0, end = var_4298_end_0, end_mask = var_4298_end_mask_0, x = coreml_update_state_188)[name = string("op_4298_cast_fp16")];
            tensor<int32, [2]> tile_20 = const()[name = string("tile_20"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4301_axis_0 = const()[name = string("op_4301_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4301_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4301_cast_fp16_1 = split(axis = var_4301_axis_0, split_sizes = tile_20, x = var_4298_cast_fp16)[name = string("op_4301_cast_fp16")];
            tensor<int32, [4]> var_4308_begin_0 = const()[name = string("op_4308_begin_0"), val = tensor<int32, [4]>([10, 0, 0, 0])];
            tensor<int32, [4]> var_4308_end_0 = const()[name = string("op_4308_end_0"), val = tensor<int32, [4]>([11, 2, 2048, 128])];
            tensor<bool, [4]> var_4308_end_mask_0 = const()[name = string("op_4308_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4308_cast_fp16 = slice_by_index(begin = var_4308_begin_0, end = var_4308_end_0, end_mask = var_4308_end_mask_0, x = coreml_update_state_189)[name = string("op_4308_cast_fp16")];
            tensor<int32, [2]> tile_21 = const()[name = string("tile_21"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4311_axis_0 = const()[name = string("op_4311_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4311_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4311_cast_fp16_1 = split(axis = var_4311_axis_0, split_sizes = tile_21, x = var_4308_cast_fp16)[name = string("op_4311_cast_fp16")];
            tensor<int32, [2]> var_4314_split_sizes_0 = const()[name = string("op_4314_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_4314_axis_0 = const()[name = string("op_4314_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_4314_0, tensor<fp16, [1, 8, 128, 16]> var_4314_1 = split(axis = var_4314_axis_0, split_sizes = var_4314_split_sizes_0, x = query_states_63_cast_fp16)[name = string("op_4314")];
            bool attn_weights_161_transpose_x_0 = const()[name = string("attn_weights_161_transpose_x_0"), val = bool(false)];
            bool attn_weights_161_transpose_y_0 = const()[name = string("attn_weights_161_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_161_cast_fp16 = matmul(transpose_x = attn_weights_161_transpose_x_0, transpose_y = attn_weights_161_transpose_y_0, x = var_4301_cast_fp16_0, y = var_4314_0)[name = string("attn_weights_161_cast_fp16")];
            fp16 var_4317_to_fp16 = const()[name = string("op_4317_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_163_cast_fp16 = mul(x = attn_weights_161_cast_fp16, y = var_4317_to_fp16)[name = string("attn_weights_163_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_165_cast_fp16 = add(x = attn_weights_163_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_165_cast_fp16")];
            int32 var_4321 = const()[name = string("op_4321"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_167_cast_fp16 = softmax(axis = var_4321, x = attn_weights_165_cast_fp16)[name = string("attn_weights_167_cast_fp16")];
            bool var_4327_transpose_x_1 = const()[name = string("op_4327_transpose_x_1"), val = bool(true)];
            bool var_4327_transpose_y_1 = const()[name = string("op_4327_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_4327_cast_fp16 = matmul(transpose_x = var_4327_transpose_x_1, transpose_y = var_4327_transpose_y_1, x = attn_weights_167_cast_fp16, y = var_4311_cast_fp16_0)[name = string("op_4327_cast_fp16")];
            bool attn_weights_169_transpose_x_0 = const()[name = string("attn_weights_169_transpose_x_0"), val = bool(false)];
            bool attn_weights_169_transpose_y_0 = const()[name = string("attn_weights_169_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_169_cast_fp16 = matmul(transpose_x = attn_weights_169_transpose_x_0, transpose_y = attn_weights_169_transpose_y_0, x = var_4301_cast_fp16_1, y = var_4314_1)[name = string("attn_weights_169_cast_fp16")];
            fp16 var_4329_to_fp16 = const()[name = string("op_4329_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_171_cast_fp16 = mul(x = attn_weights_169_cast_fp16, y = var_4329_to_fp16)[name = string("attn_weights_171_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_173_cast_fp16 = add(x = attn_weights_171_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_173_cast_fp16")];
            int32 var_4333 = const()[name = string("op_4333"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_175_cast_fp16 = softmax(axis = var_4333, x = attn_weights_173_cast_fp16)[name = string("attn_weights_175_cast_fp16")];
            bool attn_output_81_transpose_x_1 = const()[name = string("attn_output_81_transpose_x_1"), val = bool(true)];
            bool attn_output_81_transpose_y_1 = const()[name = string("attn_output_81_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_81_cast_fp16 = matmul(transpose_x = attn_output_81_transpose_x_1, transpose_y = attn_output_81_transpose_y_1, x = attn_weights_175_cast_fp16, y = var_4311_cast_fp16_1)[name = string("attn_output_81_cast_fp16")];
            int32 var_4341 = const()[name = string("op_4341"), val = int32(1)];
            bool attn_output_83_interleave_0 = const()[name = string("attn_output_83_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_83_cast_fp16 = concat(axis = var_4341, interleave = attn_output_83_interleave_0, values = (var_4327_cast_fp16, attn_output_81_cast_fp16))[name = string("attn_output_83_cast_fp16")];
            tensor<int32, [4]> var_4345_perm_0 = const()[name = string("op_4345_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_131x = const()[name = string("concat_131x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_4345_cast_fp16 = transpose(perm = var_4345_perm_0, x = attn_output_83_cast_fp16)[name = string("transpose_312")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_87_cast_fp16 = reshape(shape = concat_131x, x = var_4345_cast_fp16)[name = string("attn_output_87_cast_fp16")];
            tensor<int32, [2]> hidden_states_103_strides_0 = const()[name = string("hidden_states_103_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_103_pad_type_0 = const()[name = string("hidden_states_103_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_103_pad_0 = const()[name = string("hidden_states_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_103_dilations_0 = const()[name = string("hidden_states_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_103_groups_0 = const()[name = string("hidden_states_103_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_103_cast_fp16 = conv(dilations = hidden_states_103_dilations_0, groups = hidden_states_103_groups_0, pad = hidden_states_103_pad_0, pad_type = hidden_states_103_pad_type_0, strides = hidden_states_103_strides_0, weight = layers_10_self_attn_o_proj_weight_cast_fp16, x = attn_output_87_cast_fp16)[name = string("hidden_states_103_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_105_cast_fp16 = add(x = hidden_states_99_cast_fp16, y = hidden_states_103_cast_fp16)[name = string("hidden_states_105_cast_fp16")];
            fp16 const_110_promoted_to_fp16 = const()[name = string("const_110_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_4378_cast_fp16 = mul(x = hidden_states_105_cast_fp16, y = const_110_promoted_to_fp16)[name = string("op_4378_cast_fp16")];
            int32 var_4376 = const()[name = string("op_4376"), val = int32(1)];
            bool doubled_85_interleave_0 = const()[name = string("doubled_85_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_85_cast_fp16 = concat(axis = var_4376, interleave = doubled_85_interleave_0, values = (hidden_states_105_cast_fp16, var_4378_cast_fp16))[name = string("doubled_85_cast_fp16")];
            tensor<int32, [1]> out_43_axes_0 = const()[name = string("out_43_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_43_gamma_0_to_fp16 = const()[name = string("out_43_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406969472)))];
            fp16 var_4388_to_fp16 = const()[name = string("op_4388_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_43_cast_fp16 = layer_norm(axes = out_43_axes_0, epsilon = var_4388_to_fp16, gamma = out_43_gamma_0_to_fp16, x = doubled_85_cast_fp16)[name = string("out_43_cast_fp16")];
            tensor<int32, [2]> var_4399_split_sizes_0 = const()[name = string("op_4399_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4399_axis_0 = const()[name = string("op_4399_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_4399_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_4399_cast_fp16_1 = split(axis = var_4399_axis_0, split_sizes = var_4399_split_sizes_0, x = out_43_cast_fp16)[name = string("op_4399_cast_fp16")];
            tensor<int32, [2]> input_21_strides_0 = const()[name = string("input_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_21_pad_type_0 = const()[name = string("input_21_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_21_pad_0 = const()[name = string("input_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_21_dilations_0 = const()[name = string("input_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_21_groups_0 = const()[name = string("input_21_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_21_cast_fp16 = conv(dilations = input_21_dilations_0, groups = input_21_groups_0, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = input_21_strides_0, weight = layers_10_mlp_gate_proj_weight_cast_fp16, x = var_4399_cast_fp16_0)[name = string("input_21_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_4416_cast_fp16 = silu(x = input_21_cast_fp16)[name = string("op_4416_cast_fp16")];
            tensor<int32, [2]> var_4422_strides_0 = const()[name = string("op_4422_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_4422_pad_type_0 = const()[name = string("op_4422_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_4422_pad_0 = const()[name = string("op_4422_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_4422_dilations_0 = const()[name = string("op_4422_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4422_groups_0 = const()[name = string("op_4422_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_4422_cast_fp16 = conv(dilations = var_4422_dilations_0, groups = var_4422_groups_0, pad = var_4422_pad_0, pad_type = var_4422_pad_type_0, strides = var_4422_strides_0, weight = layers_10_mlp_up_proj_weight_cast_fp16, x = var_4399_cast_fp16_0)[name = string("op_4422_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_109_cast_fp16 = mul(x = var_4416_cast_fp16, y = var_4422_cast_fp16)[name = string("x_109_cast_fp16")];
            tensor<int32, [2]> hidden_states_107_strides_0 = const()[name = string("hidden_states_107_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_107_pad_type_0 = const()[name = string("hidden_states_107_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_107_pad_0 = const()[name = string("hidden_states_107_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_107_dilations_0 = const()[name = string("hidden_states_107_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_107_groups_0 = const()[name = string("hidden_states_107_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_107_cast_fp16 = conv(dilations = hidden_states_107_dilations_0, groups = hidden_states_107_groups_0, pad = hidden_states_107_pad_0, pad_type = hidden_states_107_pad_type_0, strides = hidden_states_107_strides_0, weight = layers_10_mlp_down_proj_weight_cast_fp16, x = x_109_cast_fp16)[name = string("hidden_states_107_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_109_cast_fp16 = add(x = hidden_states_105_cast_fp16, y = hidden_states_107_cast_fp16)[name = string("hidden_states_109_cast_fp16")];
            fp16 const_112_promoted_to_fp16 = const()[name = string("const_112_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_4440_cast_fp16 = mul(x = hidden_states_109_cast_fp16, y = const_112_promoted_to_fp16)[name = string("op_4440_cast_fp16")];
            int32 var_4438 = const()[name = string("op_4438"), val = int32(1)];
            bool doubled_89_interleave_0 = const()[name = string("doubled_89_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_89_cast_fp16 = concat(axis = var_4438, interleave = doubled_89_interleave_0, values = (hidden_states_109_cast_fp16, var_4440_cast_fp16))[name = string("doubled_89_cast_fp16")];
            tensor<int32, [1]> out_45_axes_0 = const()[name = string("out_45_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_45_gamma_0_to_fp16 = const()[name = string("out_45_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406977728)))];
            fp16 var_4450_to_fp16 = const()[name = string("op_4450_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_45_cast_fp16 = layer_norm(axes = out_45_axes_0, epsilon = var_4450_to_fp16, gamma = out_45_gamma_0_to_fp16, x = doubled_89_cast_fp16)[name = string("out_45_cast_fp16")];
            tensor<int32, [2]> var_4461_split_sizes_0 = const()[name = string("op_4461_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4461_axis_0 = const()[name = string("op_4461_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_4461_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_4461_cast_fp16_1 = split(axis = var_4461_axis_0, split_sizes = var_4461_split_sizes_0, x = out_45_cast_fp16)[name = string("op_4461_cast_fp16")];
            tensor<int32, [2]> query_states_67_strides_0 = const()[name = string("query_states_67_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_67_pad_type_0 = const()[name = string("query_states_67_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_67_pad_0 = const()[name = string("query_states_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_67_dilations_0 = const()[name = string("query_states_67_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_67_groups_0 = const()[name = string("query_states_67_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_67_cast_fp16 = conv(dilations = query_states_67_dilations_0, groups = query_states_67_groups_0, pad = query_states_67_pad_0, pad_type = query_states_67_pad_type_0, strides = query_states_67_strides_0, weight = layers_11_self_attn_q_proj_weight_cast_fp16, x = var_4461_cast_fp16_0)[name = string("query_states_67_cast_fp16")];
            tensor<int32, [2]> key_states_111_strides_0 = const()[name = string("key_states_111_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_111_pad_type_0 = const()[name = string("key_states_111_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_111_pad_0 = const()[name = string("key_states_111_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_111_dilations_0 = const()[name = string("key_states_111_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_111_groups_0 = const()[name = string("key_states_111_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_111_cast_fp16 = conv(dilations = key_states_111_dilations_0, groups = key_states_111_groups_0, pad = key_states_111_pad_0, pad_type = key_states_111_pad_type_0, strides = key_states_111_strides_0, weight = layers_11_self_attn_k_proj_weight_cast_fp16, x = var_4461_cast_fp16_0)[name = string("key_states_111_cast_fp16")];
            tensor<int32, [2]> value_states_67_strides_0 = const()[name = string("value_states_67_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_67_pad_type_0 = const()[name = string("value_states_67_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_67_pad_0 = const()[name = string("value_states_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_67_dilations_0 = const()[name = string("value_states_67_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_67_groups_0 = const()[name = string("value_states_67_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_67_cast_fp16 = conv(dilations = value_states_67_dilations_0, groups = value_states_67_groups_0, pad = value_states_67_pad_0, pad_type = value_states_67_pad_type_0, strides = value_states_67_strides_0, weight = layers_11_self_attn_v_proj_weight_cast_fp16, x = var_4461_cast_fp16_0)[name = string("value_states_67_cast_fp16")];
            tensor<int32, [4]> concat_132x = const()[name = string("concat_132x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_111_cast_fp16 = reshape(shape = concat_132x, x = query_states_67_cast_fp16)[name = string("x_111_cast_fp16")];
            tensor<int32, [4]> concat_133x = const()[name = string("concat_133x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_4518_cast_fp16 = reshape(shape = concat_133x, x = key_states_111_cast_fp16)[name = string("op_4518_cast_fp16")];
            tensor<int32, [4]> concat_134x = const()[name = string("concat_134x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_4525_cast_fp16 = reshape(shape = concat_134x, x = value_states_67_cast_fp16)[name = string("op_4525_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_4529_cast_fp16 = mul(x = x_111_cast_fp16, y = var_859_cast_fp16)[name = string("op_4529_cast_fp16")];
            tensor<int32, [2]> var_4530_split_sizes_0 = const()[name = string("op_4530_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4530_axis_0 = const()[name = string("op_4530_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_4530_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_4530_cast_fp16_1 = split(axis = var_4530_axis_0, split_sizes = var_4530_split_sizes_0, x = x_111_cast_fp16)[name = string("op_4530_cast_fp16")];
            fp16 const_114_promoted_to_fp16 = const()[name = string("const_114_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_4532_cast_fp16 = mul(x = var_4530_cast_fp16_1, y = const_114_promoted_to_fp16)[name = string("op_4532_cast_fp16")];
            int32 var_4534 = const()[name = string("op_4534"), val = int32(-2)];
            bool var_4535_interleave_0 = const()[name = string("op_4535_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_4535_cast_fp16 = concat(axis = var_4534, interleave = var_4535_interleave_0, values = (var_4532_cast_fp16, var_4530_cast_fp16_0))[name = string("op_4535_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_4536_cast_fp16 = mul(x = var_4535_cast_fp16, y = var_866_cast_fp16)[name = string("op_4536_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_69_cast_fp16 = add(x = var_4529_cast_fp16, y = var_4536_cast_fp16)[name = string("query_states_69_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_4542_cast_fp16 = mul(x = var_4518_cast_fp16, y = var_859_cast_fp16)[name = string("op_4542_cast_fp16")];
            tensor<int32, [2]> var_4543_split_sizes_0 = const()[name = string("op_4543_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4543_axis_0 = const()[name = string("op_4543_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_4543_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_4543_cast_fp16_1 = split(axis = var_4543_axis_0, split_sizes = var_4543_split_sizes_0, x = var_4518_cast_fp16)[name = string("op_4543_cast_fp16")];
            fp16 const_115_promoted_to_fp16 = const()[name = string("const_115_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_4545_cast_fp16 = mul(x = var_4543_cast_fp16_1, y = const_115_promoted_to_fp16)[name = string("op_4545_cast_fp16")];
            int32 var_4547 = const()[name = string("op_4547"), val = int32(-2)];
            bool var_4548_interleave_0 = const()[name = string("op_4548_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_4548_cast_fp16 = concat(axis = var_4547, interleave = var_4548_interleave_0, values = (var_4545_cast_fp16, var_4543_cast_fp16_0))[name = string("op_4548_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_4549_cast_fp16 = mul(x = var_4548_cast_fp16, y = var_866_cast_fp16)[name = string("op_4549_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_115_cast_fp16 = add(x = var_4542_cast_fp16, y = var_4549_cast_fp16)[name = string("key_states_115_cast_fp16")];
            tensor<int32, [1]> expand_dims_132 = const()[name = string("expand_dims_132"), val = tensor<int32, [1]>([11])];
            tensor<int32, [1]> expand_dims_133 = const()[name = string("expand_dims_133"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_135 = const()[name = string("expand_dims_135"), val = tensor<int32, [1]>([0])];
            int32 concat_137_axis_0 = const()[name = string("concat_137_axis_0"), val = int32(0)];
            bool concat_137_interleave_0 = const()[name = string("concat_137_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_137 = concat(axis = concat_137_axis_0, interleave = concat_137_interleave_0, values = (expand_dims_132, expand_dims_133, position_id, expand_dims_135))[name = string("concat_137")];
            tensor<int32, [1]> expand_dims_136 = const()[name = string("expand_dims_136"), val = tensor<int32, [1]>([12])];
            tensor<int32, [1]> concat_138_values1_0 = const()[name = string("concat_138_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_138_values3_0 = const()[name = string("concat_138_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_138_axis_0 = const()[name = string("concat_138_axis_0"), val = int32(0)];
            bool concat_138_interleave_0 = const()[name = string("concat_138_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_138 = concat(axis = concat_138_axis_0, interleave = concat_138_interleave_0, values = (expand_dims_136, concat_138_values1_0, cache_position_end, concat_138_values3_0))[name = string("concat_138")];
            tensor<int32, [4]> key_states_117_perm_0 = const()[name = string("key_states_117_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_12_stride_0 = const()[name = string("key_cache_internal_tensor_assign_12_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_12_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_12_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_12_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_12_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_12_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_12_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_117_cast_fp16 = transpose(perm = key_states_117_perm_0, x = key_states_115_cast_fp16)[name = string("transpose_311")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_12_cast_fp16 = slice_update(begin = concat_137, begin_mask = key_cache_internal_tensor_assign_12_begin_mask_0, end = concat_138, end_mask = key_cache_internal_tensor_assign_12_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_12_squeeze_mask_0, stride = key_cache_internal_tensor_assign_12_stride_0, update = key_states_117_cast_fp16, x = coreml_update_state_188)[name = string("key_cache_internal_tensor_assign_12_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_12_cast_fp16, input = key_cache)[name = string("coreml_update_state_190_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_190 = read_state(input = key_cache)[name = string("coreml_update_state_190")];
            tensor<int32, [4]> value_states_69_perm_0 = const()[name = string("value_states_69_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_12_stride_0 = const()[name = string("value_cache_internal_tensor_assign_12_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_12_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_12_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_12_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_12_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_12_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_12_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_69_cast_fp16 = transpose(perm = value_states_69_perm_0, x = var_4525_cast_fp16)[name = string("transpose_310")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_12_cast_fp16 = slice_update(begin = concat_137, begin_mask = value_cache_internal_tensor_assign_12_begin_mask_0, end = concat_138, end_mask = value_cache_internal_tensor_assign_12_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_12_squeeze_mask_0, stride = value_cache_internal_tensor_assign_12_stride_0, update = value_states_69_cast_fp16, x = coreml_update_state_189)[name = string("value_cache_internal_tensor_assign_12_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_12_cast_fp16, input = value_cache)[name = string("coreml_update_state_191_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_191 = read_state(input = value_cache)[name = string("coreml_update_state_191")];
            tensor<int32, [4]> var_4619_begin_0 = const()[name = string("op_4619_begin_0"), val = tensor<int32, [4]>([11, 0, 0, 0])];
            tensor<int32, [4]> var_4619_end_0 = const()[name = string("op_4619_end_0"), val = tensor<int32, [4]>([12, 2, 2048, 128])];
            tensor<bool, [4]> var_4619_end_mask_0 = const()[name = string("op_4619_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4619_cast_fp16 = slice_by_index(begin = var_4619_begin_0, end = var_4619_end_0, end_mask = var_4619_end_mask_0, x = coreml_update_state_190)[name = string("op_4619_cast_fp16")];
            tensor<int32, [2]> tile_22 = const()[name = string("tile_22"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4622_axis_0 = const()[name = string("op_4622_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4622_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4622_cast_fp16_1 = split(axis = var_4622_axis_0, split_sizes = tile_22, x = var_4619_cast_fp16)[name = string("op_4622_cast_fp16")];
            tensor<int32, [4]> var_4629_begin_0 = const()[name = string("op_4629_begin_0"), val = tensor<int32, [4]>([11, 0, 0, 0])];
            tensor<int32, [4]> var_4629_end_0 = const()[name = string("op_4629_end_0"), val = tensor<int32, [4]>([12, 2, 2048, 128])];
            tensor<bool, [4]> var_4629_end_mask_0 = const()[name = string("op_4629_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4629_cast_fp16 = slice_by_index(begin = var_4629_begin_0, end = var_4629_end_0, end_mask = var_4629_end_mask_0, x = coreml_update_state_191)[name = string("op_4629_cast_fp16")];
            tensor<int32, [2]> tile_23 = const()[name = string("tile_23"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4632_axis_0 = const()[name = string("op_4632_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4632_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4632_cast_fp16_1 = split(axis = var_4632_axis_0, split_sizes = tile_23, x = var_4629_cast_fp16)[name = string("op_4632_cast_fp16")];
            tensor<int32, [2]> var_4635_split_sizes_0 = const()[name = string("op_4635_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_4635_axis_0 = const()[name = string("op_4635_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_4635_0, tensor<fp16, [1, 8, 128, 16]> var_4635_1 = split(axis = var_4635_axis_0, split_sizes = var_4635_split_sizes_0, x = query_states_69_cast_fp16)[name = string("op_4635")];
            bool attn_weights_177_transpose_x_0 = const()[name = string("attn_weights_177_transpose_x_0"), val = bool(false)];
            bool attn_weights_177_transpose_y_0 = const()[name = string("attn_weights_177_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_177_cast_fp16 = matmul(transpose_x = attn_weights_177_transpose_x_0, transpose_y = attn_weights_177_transpose_y_0, x = var_4622_cast_fp16_0, y = var_4635_0)[name = string("attn_weights_177_cast_fp16")];
            fp16 var_4638_to_fp16 = const()[name = string("op_4638_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_179_cast_fp16 = mul(x = attn_weights_177_cast_fp16, y = var_4638_to_fp16)[name = string("attn_weights_179_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_181_cast_fp16 = add(x = attn_weights_179_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_181_cast_fp16")];
            int32 var_4642 = const()[name = string("op_4642"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_183_cast_fp16 = softmax(axis = var_4642, x = attn_weights_181_cast_fp16)[name = string("attn_weights_183_cast_fp16")];
            bool var_4648_transpose_x_1 = const()[name = string("op_4648_transpose_x_1"), val = bool(true)];
            bool var_4648_transpose_y_1 = const()[name = string("op_4648_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_4648_cast_fp16 = matmul(transpose_x = var_4648_transpose_x_1, transpose_y = var_4648_transpose_y_1, x = attn_weights_183_cast_fp16, y = var_4632_cast_fp16_0)[name = string("op_4648_cast_fp16")];
            bool attn_weights_185_transpose_x_0 = const()[name = string("attn_weights_185_transpose_x_0"), val = bool(false)];
            bool attn_weights_185_transpose_y_0 = const()[name = string("attn_weights_185_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_185_cast_fp16 = matmul(transpose_x = attn_weights_185_transpose_x_0, transpose_y = attn_weights_185_transpose_y_0, x = var_4622_cast_fp16_1, y = var_4635_1)[name = string("attn_weights_185_cast_fp16")];
            fp16 var_4650_to_fp16 = const()[name = string("op_4650_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_187_cast_fp16 = mul(x = attn_weights_185_cast_fp16, y = var_4650_to_fp16)[name = string("attn_weights_187_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_189_cast_fp16 = add(x = attn_weights_187_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_189_cast_fp16")];
            int32 var_4654 = const()[name = string("op_4654"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_191_cast_fp16 = softmax(axis = var_4654, x = attn_weights_189_cast_fp16)[name = string("attn_weights_191_cast_fp16")];
            bool attn_output_89_transpose_x_1 = const()[name = string("attn_output_89_transpose_x_1"), val = bool(true)];
            bool attn_output_89_transpose_y_1 = const()[name = string("attn_output_89_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_89_cast_fp16 = matmul(transpose_x = attn_output_89_transpose_x_1, transpose_y = attn_output_89_transpose_y_1, x = attn_weights_191_cast_fp16, y = var_4632_cast_fp16_1)[name = string("attn_output_89_cast_fp16")];
            int32 var_4662 = const()[name = string("op_4662"), val = int32(1)];
            bool attn_output_91_interleave_0 = const()[name = string("attn_output_91_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_91_cast_fp16 = concat(axis = var_4662, interleave = attn_output_91_interleave_0, values = (var_4648_cast_fp16, attn_output_89_cast_fp16))[name = string("attn_output_91_cast_fp16")];
            tensor<int32, [4]> var_4666_perm_0 = const()[name = string("op_4666_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_143x = const()[name = string("concat_143x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_4666_cast_fp16 = transpose(perm = var_4666_perm_0, x = attn_output_91_cast_fp16)[name = string("transpose_309")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_95_cast_fp16 = reshape(shape = concat_143x, x = var_4666_cast_fp16)[name = string("attn_output_95_cast_fp16")];
            tensor<int32, [2]> hidden_states_113_strides_0 = const()[name = string("hidden_states_113_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_113_pad_type_0 = const()[name = string("hidden_states_113_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_113_pad_0 = const()[name = string("hidden_states_113_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_113_dilations_0 = const()[name = string("hidden_states_113_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_113_groups_0 = const()[name = string("hidden_states_113_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_113_cast_fp16 = conv(dilations = hidden_states_113_dilations_0, groups = hidden_states_113_groups_0, pad = hidden_states_113_pad_0, pad_type = hidden_states_113_pad_type_0, strides = hidden_states_113_strides_0, weight = layers_11_self_attn_o_proj_weight_cast_fp16, x = attn_output_95_cast_fp16)[name = string("hidden_states_113_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_115_cast_fp16 = add(x = hidden_states_109_cast_fp16, y = hidden_states_113_cast_fp16)[name = string("hidden_states_115_cast_fp16")];
            fp16 const_120_promoted_to_fp16 = const()[name = string("const_120_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_4699_cast_fp16 = mul(x = hidden_states_115_cast_fp16, y = const_120_promoted_to_fp16)[name = string("op_4699_cast_fp16")];
            int32 var_4697 = const()[name = string("op_4697"), val = int32(1)];
            bool doubled_93_interleave_0 = const()[name = string("doubled_93_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_93_cast_fp16 = concat(axis = var_4697, interleave = doubled_93_interleave_0, values = (hidden_states_115_cast_fp16, var_4699_cast_fp16))[name = string("doubled_93_cast_fp16")];
            tensor<int32, [1]> out_47_axes_0 = const()[name = string("out_47_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_47_gamma_0_to_fp16 = const()[name = string("out_47_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406985984)))];
            fp16 var_4709_to_fp16 = const()[name = string("op_4709_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_47_cast_fp16 = layer_norm(axes = out_47_axes_0, epsilon = var_4709_to_fp16, gamma = out_47_gamma_0_to_fp16, x = doubled_93_cast_fp16)[name = string("out_47_cast_fp16")];
            tensor<int32, [2]> var_4720_split_sizes_0 = const()[name = string("op_4720_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4720_axis_0 = const()[name = string("op_4720_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_4720_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_4720_cast_fp16_1 = split(axis = var_4720_axis_0, split_sizes = var_4720_split_sizes_0, x = out_47_cast_fp16)[name = string("op_4720_cast_fp16")];
            tensor<int32, [2]> input_23_strides_0 = const()[name = string("input_23_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_23_pad_type_0 = const()[name = string("input_23_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_23_pad_0 = const()[name = string("input_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_23_dilations_0 = const()[name = string("input_23_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_23_groups_0 = const()[name = string("input_23_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_23_cast_fp16 = conv(dilations = input_23_dilations_0, groups = input_23_groups_0, pad = input_23_pad_0, pad_type = input_23_pad_type_0, strides = input_23_strides_0, weight = layers_11_mlp_gate_proj_weight_cast_fp16, x = var_4720_cast_fp16_0)[name = string("input_23_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_4737_cast_fp16 = silu(x = input_23_cast_fp16)[name = string("op_4737_cast_fp16")];
            tensor<int32, [2]> var_4743_strides_0 = const()[name = string("op_4743_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_4743_pad_type_0 = const()[name = string("op_4743_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_4743_pad_0 = const()[name = string("op_4743_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_4743_dilations_0 = const()[name = string("op_4743_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4743_groups_0 = const()[name = string("op_4743_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_4743_cast_fp16 = conv(dilations = var_4743_dilations_0, groups = var_4743_groups_0, pad = var_4743_pad_0, pad_type = var_4743_pad_type_0, strides = var_4743_strides_0, weight = layers_11_mlp_up_proj_weight_cast_fp16, x = var_4720_cast_fp16_0)[name = string("op_4743_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_119_cast_fp16 = mul(x = var_4737_cast_fp16, y = var_4743_cast_fp16)[name = string("x_119_cast_fp16")];
            tensor<int32, [2]> hidden_states_117_strides_0 = const()[name = string("hidden_states_117_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_117_pad_type_0 = const()[name = string("hidden_states_117_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_117_pad_0 = const()[name = string("hidden_states_117_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_117_dilations_0 = const()[name = string("hidden_states_117_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_117_groups_0 = const()[name = string("hidden_states_117_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_117_cast_fp16 = conv(dilations = hidden_states_117_dilations_0, groups = hidden_states_117_groups_0, pad = hidden_states_117_pad_0, pad_type = hidden_states_117_pad_type_0, strides = hidden_states_117_strides_0, weight = layers_11_mlp_down_proj_weight_cast_fp16, x = x_119_cast_fp16)[name = string("hidden_states_117_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_119_cast_fp16 = add(x = hidden_states_115_cast_fp16, y = hidden_states_117_cast_fp16)[name = string("hidden_states_119_cast_fp16")];
            fp16 const_122_promoted_to_fp16 = const()[name = string("const_122_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_4761_cast_fp16 = mul(x = hidden_states_119_cast_fp16, y = const_122_promoted_to_fp16)[name = string("op_4761_cast_fp16")];
            int32 var_4759 = const()[name = string("op_4759"), val = int32(1)];
            bool doubled_97_interleave_0 = const()[name = string("doubled_97_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_97_cast_fp16 = concat(axis = var_4759, interleave = doubled_97_interleave_0, values = (hidden_states_119_cast_fp16, var_4761_cast_fp16))[name = string("doubled_97_cast_fp16")];
            tensor<int32, [1]> out_49_axes_0 = const()[name = string("out_49_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_49_gamma_0_to_fp16 = const()[name = string("out_49_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406994240)))];
            fp16 var_4771_to_fp16 = const()[name = string("op_4771_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_49_cast_fp16 = layer_norm(axes = out_49_axes_0, epsilon = var_4771_to_fp16, gamma = out_49_gamma_0_to_fp16, x = doubled_97_cast_fp16)[name = string("out_49_cast_fp16")];
            tensor<int32, [2]> var_4782_split_sizes_0 = const()[name = string("op_4782_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4782_axis_0 = const()[name = string("op_4782_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_4782_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_4782_cast_fp16_1 = split(axis = var_4782_axis_0, split_sizes = var_4782_split_sizes_0, x = out_49_cast_fp16)[name = string("op_4782_cast_fp16")];
            tensor<int32, [2]> query_states_73_strides_0 = const()[name = string("query_states_73_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_73_pad_type_0 = const()[name = string("query_states_73_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_73_pad_0 = const()[name = string("query_states_73_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_73_dilations_0 = const()[name = string("query_states_73_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_73_groups_0 = const()[name = string("query_states_73_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_73_cast_fp16 = conv(dilations = query_states_73_dilations_0, groups = query_states_73_groups_0, pad = query_states_73_pad_0, pad_type = query_states_73_pad_type_0, strides = query_states_73_strides_0, weight = layers_12_self_attn_q_proj_weight_cast_fp16, x = var_4782_cast_fp16_0)[name = string("query_states_73_cast_fp16")];
            tensor<int32, [2]> key_states_121_strides_0 = const()[name = string("key_states_121_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_121_pad_type_0 = const()[name = string("key_states_121_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_121_pad_0 = const()[name = string("key_states_121_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_121_dilations_0 = const()[name = string("key_states_121_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_121_groups_0 = const()[name = string("key_states_121_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_121_cast_fp16 = conv(dilations = key_states_121_dilations_0, groups = key_states_121_groups_0, pad = key_states_121_pad_0, pad_type = key_states_121_pad_type_0, strides = key_states_121_strides_0, weight = layers_12_self_attn_k_proj_weight_cast_fp16, x = var_4782_cast_fp16_0)[name = string("key_states_121_cast_fp16")];
            tensor<int32, [2]> value_states_73_strides_0 = const()[name = string("value_states_73_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_73_pad_type_0 = const()[name = string("value_states_73_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_73_pad_0 = const()[name = string("value_states_73_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_73_dilations_0 = const()[name = string("value_states_73_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_73_groups_0 = const()[name = string("value_states_73_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_73_cast_fp16 = conv(dilations = value_states_73_dilations_0, groups = value_states_73_groups_0, pad = value_states_73_pad_0, pad_type = value_states_73_pad_type_0, strides = value_states_73_strides_0, weight = layers_12_self_attn_v_proj_weight_cast_fp16, x = var_4782_cast_fp16_0)[name = string("value_states_73_cast_fp16")];
            tensor<int32, [4]> concat_144x = const()[name = string("concat_144x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_121_cast_fp16 = reshape(shape = concat_144x, x = query_states_73_cast_fp16)[name = string("x_121_cast_fp16")];
            tensor<int32, [4]> concat_145x = const()[name = string("concat_145x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_4839_cast_fp16 = reshape(shape = concat_145x, x = key_states_121_cast_fp16)[name = string("op_4839_cast_fp16")];
            tensor<int32, [4]> concat_146x = const()[name = string("concat_146x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_4846_cast_fp16 = reshape(shape = concat_146x, x = value_states_73_cast_fp16)[name = string("op_4846_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_4850_cast_fp16 = mul(x = x_121_cast_fp16, y = var_859_cast_fp16)[name = string("op_4850_cast_fp16")];
            tensor<int32, [2]> var_4851_split_sizes_0 = const()[name = string("op_4851_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4851_axis_0 = const()[name = string("op_4851_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_4851_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_4851_cast_fp16_1 = split(axis = var_4851_axis_0, split_sizes = var_4851_split_sizes_0, x = x_121_cast_fp16)[name = string("op_4851_cast_fp16")];
            fp16 const_124_promoted_to_fp16 = const()[name = string("const_124_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_4853_cast_fp16 = mul(x = var_4851_cast_fp16_1, y = const_124_promoted_to_fp16)[name = string("op_4853_cast_fp16")];
            int32 var_4855 = const()[name = string("op_4855"), val = int32(-2)];
            bool var_4856_interleave_0 = const()[name = string("op_4856_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_4856_cast_fp16 = concat(axis = var_4855, interleave = var_4856_interleave_0, values = (var_4853_cast_fp16, var_4851_cast_fp16_0))[name = string("op_4856_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_4857_cast_fp16 = mul(x = var_4856_cast_fp16, y = var_866_cast_fp16)[name = string("op_4857_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_75_cast_fp16 = add(x = var_4850_cast_fp16, y = var_4857_cast_fp16)[name = string("query_states_75_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_4863_cast_fp16 = mul(x = var_4839_cast_fp16, y = var_859_cast_fp16)[name = string("op_4863_cast_fp16")];
            tensor<int32, [2]> var_4864_split_sizes_0 = const()[name = string("op_4864_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4864_axis_0 = const()[name = string("op_4864_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_4864_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_4864_cast_fp16_1 = split(axis = var_4864_axis_0, split_sizes = var_4864_split_sizes_0, x = var_4839_cast_fp16)[name = string("op_4864_cast_fp16")];
            fp16 const_125_promoted_to_fp16 = const()[name = string("const_125_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_4866_cast_fp16 = mul(x = var_4864_cast_fp16_1, y = const_125_promoted_to_fp16)[name = string("op_4866_cast_fp16")];
            int32 var_4868 = const()[name = string("op_4868"), val = int32(-2)];
            bool var_4869_interleave_0 = const()[name = string("op_4869_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_4869_cast_fp16 = concat(axis = var_4868, interleave = var_4869_interleave_0, values = (var_4866_cast_fp16, var_4864_cast_fp16_0))[name = string("op_4869_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_4870_cast_fp16 = mul(x = var_4869_cast_fp16, y = var_866_cast_fp16)[name = string("op_4870_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_125_cast_fp16 = add(x = var_4863_cast_fp16, y = var_4870_cast_fp16)[name = string("key_states_125_cast_fp16")];
            tensor<int32, [1]> expand_dims_144 = const()[name = string("expand_dims_144"), val = tensor<int32, [1]>([12])];
            tensor<int32, [1]> expand_dims_145 = const()[name = string("expand_dims_145"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_147 = const()[name = string("expand_dims_147"), val = tensor<int32, [1]>([0])];
            int32 concat_149_axis_0 = const()[name = string("concat_149_axis_0"), val = int32(0)];
            bool concat_149_interleave_0 = const()[name = string("concat_149_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_149 = concat(axis = concat_149_axis_0, interleave = concat_149_interleave_0, values = (expand_dims_144, expand_dims_145, position_id, expand_dims_147))[name = string("concat_149")];
            tensor<int32, [1]> expand_dims_148 = const()[name = string("expand_dims_148"), val = tensor<int32, [1]>([13])];
            tensor<int32, [1]> concat_150_values1_0 = const()[name = string("concat_150_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_150_values3_0 = const()[name = string("concat_150_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_150_axis_0 = const()[name = string("concat_150_axis_0"), val = int32(0)];
            bool concat_150_interleave_0 = const()[name = string("concat_150_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_150 = concat(axis = concat_150_axis_0, interleave = concat_150_interleave_0, values = (expand_dims_148, concat_150_values1_0, cache_position_end, concat_150_values3_0))[name = string("concat_150")];
            tensor<int32, [4]> key_states_127_perm_0 = const()[name = string("key_states_127_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_13_stride_0 = const()[name = string("key_cache_internal_tensor_assign_13_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_13_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_13_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_13_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_13_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_13_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_13_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_127_cast_fp16 = transpose(perm = key_states_127_perm_0, x = key_states_125_cast_fp16)[name = string("transpose_308")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_13_cast_fp16 = slice_update(begin = concat_149, begin_mask = key_cache_internal_tensor_assign_13_begin_mask_0, end = concat_150, end_mask = key_cache_internal_tensor_assign_13_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_13_squeeze_mask_0, stride = key_cache_internal_tensor_assign_13_stride_0, update = key_states_127_cast_fp16, x = coreml_update_state_190)[name = string("key_cache_internal_tensor_assign_13_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_13_cast_fp16, input = key_cache)[name = string("coreml_update_state_192_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_192 = read_state(input = key_cache)[name = string("coreml_update_state_192")];
            tensor<int32, [4]> value_states_75_perm_0 = const()[name = string("value_states_75_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_13_stride_0 = const()[name = string("value_cache_internal_tensor_assign_13_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_13_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_13_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_13_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_13_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_13_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_13_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_75_cast_fp16 = transpose(perm = value_states_75_perm_0, x = var_4846_cast_fp16)[name = string("transpose_307")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_13_cast_fp16 = slice_update(begin = concat_149, begin_mask = value_cache_internal_tensor_assign_13_begin_mask_0, end = concat_150, end_mask = value_cache_internal_tensor_assign_13_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_13_squeeze_mask_0, stride = value_cache_internal_tensor_assign_13_stride_0, update = value_states_75_cast_fp16, x = coreml_update_state_191)[name = string("value_cache_internal_tensor_assign_13_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_13_cast_fp16, input = value_cache)[name = string("coreml_update_state_193_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_193 = read_state(input = value_cache)[name = string("coreml_update_state_193")];
            tensor<int32, [4]> var_4940_begin_0 = const()[name = string("op_4940_begin_0"), val = tensor<int32, [4]>([12, 0, 0, 0])];
            tensor<int32, [4]> var_4940_end_0 = const()[name = string("op_4940_end_0"), val = tensor<int32, [4]>([13, 2, 2048, 128])];
            tensor<bool, [4]> var_4940_end_mask_0 = const()[name = string("op_4940_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4940_cast_fp16 = slice_by_index(begin = var_4940_begin_0, end = var_4940_end_0, end_mask = var_4940_end_mask_0, x = coreml_update_state_192)[name = string("op_4940_cast_fp16")];
            tensor<int32, [2]> tile_24 = const()[name = string("tile_24"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4943_axis_0 = const()[name = string("op_4943_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4943_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4943_cast_fp16_1 = split(axis = var_4943_axis_0, split_sizes = tile_24, x = var_4940_cast_fp16)[name = string("op_4943_cast_fp16")];
            tensor<int32, [4]> var_4950_begin_0 = const()[name = string("op_4950_begin_0"), val = tensor<int32, [4]>([12, 0, 0, 0])];
            tensor<int32, [4]> var_4950_end_0 = const()[name = string("op_4950_end_0"), val = tensor<int32, [4]>([13, 2, 2048, 128])];
            tensor<bool, [4]> var_4950_end_mask_0 = const()[name = string("op_4950_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4950_cast_fp16 = slice_by_index(begin = var_4950_begin_0, end = var_4950_end_0, end_mask = var_4950_end_mask_0, x = coreml_update_state_193)[name = string("op_4950_cast_fp16")];
            tensor<int32, [2]> tile_25 = const()[name = string("tile_25"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4953_axis_0 = const()[name = string("op_4953_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4953_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4953_cast_fp16_1 = split(axis = var_4953_axis_0, split_sizes = tile_25, x = var_4950_cast_fp16)[name = string("op_4953_cast_fp16")];
            tensor<int32, [2]> var_4956_split_sizes_0 = const()[name = string("op_4956_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_4956_axis_0 = const()[name = string("op_4956_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_4956_0, tensor<fp16, [1, 8, 128, 16]> var_4956_1 = split(axis = var_4956_axis_0, split_sizes = var_4956_split_sizes_0, x = query_states_75_cast_fp16)[name = string("op_4956")];
            bool attn_weights_193_transpose_x_0 = const()[name = string("attn_weights_193_transpose_x_0"), val = bool(false)];
            bool attn_weights_193_transpose_y_0 = const()[name = string("attn_weights_193_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_193_cast_fp16 = matmul(transpose_x = attn_weights_193_transpose_x_0, transpose_y = attn_weights_193_transpose_y_0, x = var_4943_cast_fp16_0, y = var_4956_0)[name = string("attn_weights_193_cast_fp16")];
            fp16 var_4959_to_fp16 = const()[name = string("op_4959_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_195_cast_fp16 = mul(x = attn_weights_193_cast_fp16, y = var_4959_to_fp16)[name = string("attn_weights_195_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_197_cast_fp16 = add(x = attn_weights_195_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_197_cast_fp16")];
            int32 var_4963 = const()[name = string("op_4963"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_199_cast_fp16 = softmax(axis = var_4963, x = attn_weights_197_cast_fp16)[name = string("attn_weights_199_cast_fp16")];
            bool var_4969_transpose_x_1 = const()[name = string("op_4969_transpose_x_1"), val = bool(true)];
            bool var_4969_transpose_y_1 = const()[name = string("op_4969_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_4969_cast_fp16 = matmul(transpose_x = var_4969_transpose_x_1, transpose_y = var_4969_transpose_y_1, x = attn_weights_199_cast_fp16, y = var_4953_cast_fp16_0)[name = string("op_4969_cast_fp16")];
            bool attn_weights_201_transpose_x_0 = const()[name = string("attn_weights_201_transpose_x_0"), val = bool(false)];
            bool attn_weights_201_transpose_y_0 = const()[name = string("attn_weights_201_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_201_cast_fp16 = matmul(transpose_x = attn_weights_201_transpose_x_0, transpose_y = attn_weights_201_transpose_y_0, x = var_4943_cast_fp16_1, y = var_4956_1)[name = string("attn_weights_201_cast_fp16")];
            fp16 var_4971_to_fp16 = const()[name = string("op_4971_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_203_cast_fp16 = mul(x = attn_weights_201_cast_fp16, y = var_4971_to_fp16)[name = string("attn_weights_203_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_205_cast_fp16 = add(x = attn_weights_203_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_205_cast_fp16")];
            int32 var_4975 = const()[name = string("op_4975"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_207_cast_fp16 = softmax(axis = var_4975, x = attn_weights_205_cast_fp16)[name = string("attn_weights_207_cast_fp16")];
            bool attn_output_97_transpose_x_1 = const()[name = string("attn_output_97_transpose_x_1"), val = bool(true)];
            bool attn_output_97_transpose_y_1 = const()[name = string("attn_output_97_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_97_cast_fp16 = matmul(transpose_x = attn_output_97_transpose_x_1, transpose_y = attn_output_97_transpose_y_1, x = attn_weights_207_cast_fp16, y = var_4953_cast_fp16_1)[name = string("attn_output_97_cast_fp16")];
            int32 var_4983 = const()[name = string("op_4983"), val = int32(1)];
            bool attn_output_99_interleave_0 = const()[name = string("attn_output_99_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_99_cast_fp16 = concat(axis = var_4983, interleave = attn_output_99_interleave_0, values = (var_4969_cast_fp16, attn_output_97_cast_fp16))[name = string("attn_output_99_cast_fp16")];
            tensor<int32, [4]> var_4987_perm_0 = const()[name = string("op_4987_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_155x = const()[name = string("concat_155x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_4987_cast_fp16 = transpose(perm = var_4987_perm_0, x = attn_output_99_cast_fp16)[name = string("transpose_306")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_103_cast_fp16 = reshape(shape = concat_155x, x = var_4987_cast_fp16)[name = string("attn_output_103_cast_fp16")];
            tensor<int32, [2]> hidden_states_123_strides_0 = const()[name = string("hidden_states_123_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_123_pad_type_0 = const()[name = string("hidden_states_123_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_123_pad_0 = const()[name = string("hidden_states_123_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_123_dilations_0 = const()[name = string("hidden_states_123_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_123_groups_0 = const()[name = string("hidden_states_123_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_123_cast_fp16 = conv(dilations = hidden_states_123_dilations_0, groups = hidden_states_123_groups_0, pad = hidden_states_123_pad_0, pad_type = hidden_states_123_pad_type_0, strides = hidden_states_123_strides_0, weight = layers_12_self_attn_o_proj_weight_cast_fp16, x = attn_output_103_cast_fp16)[name = string("hidden_states_123_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_125_cast_fp16 = add(x = hidden_states_119_cast_fp16, y = hidden_states_123_cast_fp16)[name = string("hidden_states_125_cast_fp16")];
            fp16 const_130_promoted_to_fp16 = const()[name = string("const_130_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_5020_cast_fp16 = mul(x = hidden_states_125_cast_fp16, y = const_130_promoted_to_fp16)[name = string("op_5020_cast_fp16")];
            int32 var_5018 = const()[name = string("op_5018"), val = int32(1)];
            bool doubled_101_interleave_0 = const()[name = string("doubled_101_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_101_cast_fp16 = concat(axis = var_5018, interleave = doubled_101_interleave_0, values = (hidden_states_125_cast_fp16, var_5020_cast_fp16))[name = string("doubled_101_cast_fp16")];
            tensor<int32, [1]> out_51_axes_0 = const()[name = string("out_51_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_51_gamma_0_to_fp16 = const()[name = string("out_51_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407002496)))];
            fp16 var_5030_to_fp16 = const()[name = string("op_5030_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_51_cast_fp16 = layer_norm(axes = out_51_axes_0, epsilon = var_5030_to_fp16, gamma = out_51_gamma_0_to_fp16, x = doubled_101_cast_fp16)[name = string("out_51_cast_fp16")];
            tensor<int32, [2]> var_5041_split_sizes_0 = const()[name = string("op_5041_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5041_axis_0 = const()[name = string("op_5041_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_5041_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_5041_cast_fp16_1 = split(axis = var_5041_axis_0, split_sizes = var_5041_split_sizes_0, x = out_51_cast_fp16)[name = string("op_5041_cast_fp16")];
            tensor<int32, [2]> input_25_strides_0 = const()[name = string("input_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_25_pad_type_0 = const()[name = string("input_25_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_25_pad_0 = const()[name = string("input_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_25_dilations_0 = const()[name = string("input_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_25_groups_0 = const()[name = string("input_25_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_25_cast_fp16 = conv(dilations = input_25_dilations_0, groups = input_25_groups_0, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = input_25_strides_0, weight = layers_12_mlp_gate_proj_weight_cast_fp16, x = var_5041_cast_fp16_0)[name = string("input_25_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_5058_cast_fp16 = silu(x = input_25_cast_fp16)[name = string("op_5058_cast_fp16")];
            tensor<int32, [2]> var_5064_strides_0 = const()[name = string("op_5064_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_5064_pad_type_0 = const()[name = string("op_5064_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_5064_pad_0 = const()[name = string("op_5064_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_5064_dilations_0 = const()[name = string("op_5064_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5064_groups_0 = const()[name = string("op_5064_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_5064_cast_fp16 = conv(dilations = var_5064_dilations_0, groups = var_5064_groups_0, pad = var_5064_pad_0, pad_type = var_5064_pad_type_0, strides = var_5064_strides_0, weight = layers_12_mlp_up_proj_weight_cast_fp16, x = var_5041_cast_fp16_0)[name = string("op_5064_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_129_cast_fp16 = mul(x = var_5058_cast_fp16, y = var_5064_cast_fp16)[name = string("x_129_cast_fp16")];
            tensor<int32, [2]> hidden_states_127_strides_0 = const()[name = string("hidden_states_127_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_127_pad_type_0 = const()[name = string("hidden_states_127_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_127_pad_0 = const()[name = string("hidden_states_127_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_127_dilations_0 = const()[name = string("hidden_states_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_127_groups_0 = const()[name = string("hidden_states_127_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_127_cast_fp16 = conv(dilations = hidden_states_127_dilations_0, groups = hidden_states_127_groups_0, pad = hidden_states_127_pad_0, pad_type = hidden_states_127_pad_type_0, strides = hidden_states_127_strides_0, weight = layers_12_mlp_down_proj_weight_cast_fp16, x = x_129_cast_fp16)[name = string("hidden_states_127_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_129_cast_fp16 = add(x = hidden_states_125_cast_fp16, y = hidden_states_127_cast_fp16)[name = string("hidden_states_129_cast_fp16")];
            fp16 const_132_promoted_to_fp16 = const()[name = string("const_132_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_5082_cast_fp16 = mul(x = hidden_states_129_cast_fp16, y = const_132_promoted_to_fp16)[name = string("op_5082_cast_fp16")];
            int32 var_5080 = const()[name = string("op_5080"), val = int32(1)];
            bool doubled_105_interleave_0 = const()[name = string("doubled_105_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_105_cast_fp16 = concat(axis = var_5080, interleave = doubled_105_interleave_0, values = (hidden_states_129_cast_fp16, var_5082_cast_fp16))[name = string("doubled_105_cast_fp16")];
            tensor<int32, [1]> out_53_axes_0 = const()[name = string("out_53_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_53_gamma_0_to_fp16 = const()[name = string("out_53_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407010752)))];
            fp16 var_5092_to_fp16 = const()[name = string("op_5092_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_53_cast_fp16 = layer_norm(axes = out_53_axes_0, epsilon = var_5092_to_fp16, gamma = out_53_gamma_0_to_fp16, x = doubled_105_cast_fp16)[name = string("out_53_cast_fp16")];
            tensor<int32, [2]> var_5103_split_sizes_0 = const()[name = string("op_5103_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5103_axis_0 = const()[name = string("op_5103_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_5103_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_5103_cast_fp16_1 = split(axis = var_5103_axis_0, split_sizes = var_5103_split_sizes_0, x = out_53_cast_fp16)[name = string("op_5103_cast_fp16")];
            tensor<int32, [2]> query_states_79_strides_0 = const()[name = string("query_states_79_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_79_pad_type_0 = const()[name = string("query_states_79_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_79_pad_0 = const()[name = string("query_states_79_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_79_dilations_0 = const()[name = string("query_states_79_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_79_groups_0 = const()[name = string("query_states_79_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_79_cast_fp16 = conv(dilations = query_states_79_dilations_0, groups = query_states_79_groups_0, pad = query_states_79_pad_0, pad_type = query_states_79_pad_type_0, strides = query_states_79_strides_0, weight = layers_13_self_attn_q_proj_weight_cast_fp16, x = var_5103_cast_fp16_0)[name = string("query_states_79_cast_fp16")];
            tensor<int32, [2]> key_states_131_strides_0 = const()[name = string("key_states_131_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_131_pad_type_0 = const()[name = string("key_states_131_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_131_pad_0 = const()[name = string("key_states_131_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_131_dilations_0 = const()[name = string("key_states_131_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_131_groups_0 = const()[name = string("key_states_131_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_131_cast_fp16 = conv(dilations = key_states_131_dilations_0, groups = key_states_131_groups_0, pad = key_states_131_pad_0, pad_type = key_states_131_pad_type_0, strides = key_states_131_strides_0, weight = layers_13_self_attn_k_proj_weight_cast_fp16, x = var_5103_cast_fp16_0)[name = string("key_states_131_cast_fp16")];
            tensor<int32, [2]> value_states_79_strides_0 = const()[name = string("value_states_79_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_79_pad_type_0 = const()[name = string("value_states_79_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_79_pad_0 = const()[name = string("value_states_79_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_79_dilations_0 = const()[name = string("value_states_79_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_79_groups_0 = const()[name = string("value_states_79_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_79_cast_fp16 = conv(dilations = value_states_79_dilations_0, groups = value_states_79_groups_0, pad = value_states_79_pad_0, pad_type = value_states_79_pad_type_0, strides = value_states_79_strides_0, weight = layers_13_self_attn_v_proj_weight_cast_fp16, x = var_5103_cast_fp16_0)[name = string("value_states_79_cast_fp16")];
            tensor<int32, [4]> concat_156x = const()[name = string("concat_156x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_131_cast_fp16 = reshape(shape = concat_156x, x = query_states_79_cast_fp16)[name = string("x_131_cast_fp16")];
            tensor<int32, [4]> concat_157x = const()[name = string("concat_157x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_5160_cast_fp16 = reshape(shape = concat_157x, x = key_states_131_cast_fp16)[name = string("op_5160_cast_fp16")];
            tensor<int32, [4]> concat_158x = const()[name = string("concat_158x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_5167_cast_fp16 = reshape(shape = concat_158x, x = value_states_79_cast_fp16)[name = string("op_5167_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_5171_cast_fp16 = mul(x = x_131_cast_fp16, y = var_859_cast_fp16)[name = string("op_5171_cast_fp16")];
            tensor<int32, [2]> var_5172_split_sizes_0 = const()[name = string("op_5172_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5172_axis_0 = const()[name = string("op_5172_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_5172_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_5172_cast_fp16_1 = split(axis = var_5172_axis_0, split_sizes = var_5172_split_sizes_0, x = x_131_cast_fp16)[name = string("op_5172_cast_fp16")];
            fp16 const_134_promoted_to_fp16 = const()[name = string("const_134_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_5174_cast_fp16 = mul(x = var_5172_cast_fp16_1, y = const_134_promoted_to_fp16)[name = string("op_5174_cast_fp16")];
            int32 var_5176 = const()[name = string("op_5176"), val = int32(-2)];
            bool var_5177_interleave_0 = const()[name = string("op_5177_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_5177_cast_fp16 = concat(axis = var_5176, interleave = var_5177_interleave_0, values = (var_5174_cast_fp16, var_5172_cast_fp16_0))[name = string("op_5177_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_5178_cast_fp16 = mul(x = var_5177_cast_fp16, y = var_866_cast_fp16)[name = string("op_5178_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_81_cast_fp16 = add(x = var_5171_cast_fp16, y = var_5178_cast_fp16)[name = string("query_states_81_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_5184_cast_fp16 = mul(x = var_5160_cast_fp16, y = var_859_cast_fp16)[name = string("op_5184_cast_fp16")];
            tensor<int32, [2]> var_5185_split_sizes_0 = const()[name = string("op_5185_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5185_axis_0 = const()[name = string("op_5185_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_5185_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_5185_cast_fp16_1 = split(axis = var_5185_axis_0, split_sizes = var_5185_split_sizes_0, x = var_5160_cast_fp16)[name = string("op_5185_cast_fp16")];
            fp16 const_135_promoted_to_fp16 = const()[name = string("const_135_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_5187_cast_fp16 = mul(x = var_5185_cast_fp16_1, y = const_135_promoted_to_fp16)[name = string("op_5187_cast_fp16")];
            int32 var_5189 = const()[name = string("op_5189"), val = int32(-2)];
            bool var_5190_interleave_0 = const()[name = string("op_5190_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_5190_cast_fp16 = concat(axis = var_5189, interleave = var_5190_interleave_0, values = (var_5187_cast_fp16, var_5185_cast_fp16_0))[name = string("op_5190_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_5191_cast_fp16 = mul(x = var_5190_cast_fp16, y = var_866_cast_fp16)[name = string("op_5191_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_135_cast_fp16 = add(x = var_5184_cast_fp16, y = var_5191_cast_fp16)[name = string("key_states_135_cast_fp16")];
            tensor<int32, [1]> expand_dims_156 = const()[name = string("expand_dims_156"), val = tensor<int32, [1]>([13])];
            tensor<int32, [1]> expand_dims_157 = const()[name = string("expand_dims_157"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_159 = const()[name = string("expand_dims_159"), val = tensor<int32, [1]>([0])];
            int32 concat_161_axis_0 = const()[name = string("concat_161_axis_0"), val = int32(0)];
            bool concat_161_interleave_0 = const()[name = string("concat_161_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_161 = concat(axis = concat_161_axis_0, interleave = concat_161_interleave_0, values = (expand_dims_156, expand_dims_157, position_id, expand_dims_159))[name = string("concat_161")];
            tensor<int32, [1]> expand_dims_160 = const()[name = string("expand_dims_160"), val = tensor<int32, [1]>([14])];
            tensor<int32, [1]> concat_162_values1_0 = const()[name = string("concat_162_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_162_values3_0 = const()[name = string("concat_162_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_162_axis_0 = const()[name = string("concat_162_axis_0"), val = int32(0)];
            bool concat_162_interleave_0 = const()[name = string("concat_162_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_162 = concat(axis = concat_162_axis_0, interleave = concat_162_interleave_0, values = (expand_dims_160, concat_162_values1_0, cache_position_end, concat_162_values3_0))[name = string("concat_162")];
            tensor<int32, [4]> key_states_137_perm_0 = const()[name = string("key_states_137_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_14_stride_0 = const()[name = string("key_cache_internal_tensor_assign_14_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_14_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_14_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_14_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_14_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_14_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_14_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_137_cast_fp16 = transpose(perm = key_states_137_perm_0, x = key_states_135_cast_fp16)[name = string("transpose_305")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_14_cast_fp16 = slice_update(begin = concat_161, begin_mask = key_cache_internal_tensor_assign_14_begin_mask_0, end = concat_162, end_mask = key_cache_internal_tensor_assign_14_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_14_squeeze_mask_0, stride = key_cache_internal_tensor_assign_14_stride_0, update = key_states_137_cast_fp16, x = coreml_update_state_192)[name = string("key_cache_internal_tensor_assign_14_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_14_cast_fp16, input = key_cache)[name = string("coreml_update_state_194_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_194 = read_state(input = key_cache)[name = string("coreml_update_state_194")];
            tensor<int32, [4]> value_states_81_perm_0 = const()[name = string("value_states_81_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_14_stride_0 = const()[name = string("value_cache_internal_tensor_assign_14_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_14_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_14_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_14_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_14_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_14_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_14_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_81_cast_fp16 = transpose(perm = value_states_81_perm_0, x = var_5167_cast_fp16)[name = string("transpose_304")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_14_cast_fp16 = slice_update(begin = concat_161, begin_mask = value_cache_internal_tensor_assign_14_begin_mask_0, end = concat_162, end_mask = value_cache_internal_tensor_assign_14_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_14_squeeze_mask_0, stride = value_cache_internal_tensor_assign_14_stride_0, update = value_states_81_cast_fp16, x = coreml_update_state_193)[name = string("value_cache_internal_tensor_assign_14_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_14_cast_fp16, input = value_cache)[name = string("coreml_update_state_195_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_195 = read_state(input = value_cache)[name = string("coreml_update_state_195")];
            tensor<int32, [4]> var_5261_begin_0 = const()[name = string("op_5261_begin_0"), val = tensor<int32, [4]>([13, 0, 0, 0])];
            tensor<int32, [4]> var_5261_end_0 = const()[name = string("op_5261_end_0"), val = tensor<int32, [4]>([14, 2, 2048, 128])];
            tensor<bool, [4]> var_5261_end_mask_0 = const()[name = string("op_5261_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5261_cast_fp16 = slice_by_index(begin = var_5261_begin_0, end = var_5261_end_0, end_mask = var_5261_end_mask_0, x = coreml_update_state_194)[name = string("op_5261_cast_fp16")];
            tensor<int32, [2]> tile_26 = const()[name = string("tile_26"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5264_axis_0 = const()[name = string("op_5264_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5264_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5264_cast_fp16_1 = split(axis = var_5264_axis_0, split_sizes = tile_26, x = var_5261_cast_fp16)[name = string("op_5264_cast_fp16")];
            tensor<int32, [4]> var_5271_begin_0 = const()[name = string("op_5271_begin_0"), val = tensor<int32, [4]>([13, 0, 0, 0])];
            tensor<int32, [4]> var_5271_end_0 = const()[name = string("op_5271_end_0"), val = tensor<int32, [4]>([14, 2, 2048, 128])];
            tensor<bool, [4]> var_5271_end_mask_0 = const()[name = string("op_5271_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5271_cast_fp16 = slice_by_index(begin = var_5271_begin_0, end = var_5271_end_0, end_mask = var_5271_end_mask_0, x = coreml_update_state_195)[name = string("op_5271_cast_fp16")];
            tensor<int32, [2]> tile_27 = const()[name = string("tile_27"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5274_axis_0 = const()[name = string("op_5274_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5274_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5274_cast_fp16_1 = split(axis = var_5274_axis_0, split_sizes = tile_27, x = var_5271_cast_fp16)[name = string("op_5274_cast_fp16")];
            tensor<int32, [2]> var_5277_split_sizes_0 = const()[name = string("op_5277_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_5277_axis_0 = const()[name = string("op_5277_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_5277_0, tensor<fp16, [1, 8, 128, 16]> var_5277_1 = split(axis = var_5277_axis_0, split_sizes = var_5277_split_sizes_0, x = query_states_81_cast_fp16)[name = string("op_5277")];
            bool attn_weights_209_transpose_x_0 = const()[name = string("attn_weights_209_transpose_x_0"), val = bool(false)];
            bool attn_weights_209_transpose_y_0 = const()[name = string("attn_weights_209_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_209_cast_fp16 = matmul(transpose_x = attn_weights_209_transpose_x_0, transpose_y = attn_weights_209_transpose_y_0, x = var_5264_cast_fp16_0, y = var_5277_0)[name = string("attn_weights_209_cast_fp16")];
            fp16 var_5280_to_fp16 = const()[name = string("op_5280_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_211_cast_fp16 = mul(x = attn_weights_209_cast_fp16, y = var_5280_to_fp16)[name = string("attn_weights_211_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_213_cast_fp16 = add(x = attn_weights_211_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_213_cast_fp16")];
            int32 var_5284 = const()[name = string("op_5284"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_215_cast_fp16 = softmax(axis = var_5284, x = attn_weights_213_cast_fp16)[name = string("attn_weights_215_cast_fp16")];
            bool var_5290_transpose_x_1 = const()[name = string("op_5290_transpose_x_1"), val = bool(true)];
            bool var_5290_transpose_y_1 = const()[name = string("op_5290_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_5290_cast_fp16 = matmul(transpose_x = var_5290_transpose_x_1, transpose_y = var_5290_transpose_y_1, x = attn_weights_215_cast_fp16, y = var_5274_cast_fp16_0)[name = string("op_5290_cast_fp16")];
            bool attn_weights_217_transpose_x_0 = const()[name = string("attn_weights_217_transpose_x_0"), val = bool(false)];
            bool attn_weights_217_transpose_y_0 = const()[name = string("attn_weights_217_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_217_cast_fp16 = matmul(transpose_x = attn_weights_217_transpose_x_0, transpose_y = attn_weights_217_transpose_y_0, x = var_5264_cast_fp16_1, y = var_5277_1)[name = string("attn_weights_217_cast_fp16")];
            fp16 var_5292_to_fp16 = const()[name = string("op_5292_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_219_cast_fp16 = mul(x = attn_weights_217_cast_fp16, y = var_5292_to_fp16)[name = string("attn_weights_219_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_221_cast_fp16 = add(x = attn_weights_219_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_221_cast_fp16")];
            int32 var_5296 = const()[name = string("op_5296"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_223_cast_fp16 = softmax(axis = var_5296, x = attn_weights_221_cast_fp16)[name = string("attn_weights_223_cast_fp16")];
            bool attn_output_105_transpose_x_1 = const()[name = string("attn_output_105_transpose_x_1"), val = bool(true)];
            bool attn_output_105_transpose_y_1 = const()[name = string("attn_output_105_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_105_cast_fp16 = matmul(transpose_x = attn_output_105_transpose_x_1, transpose_y = attn_output_105_transpose_y_1, x = attn_weights_223_cast_fp16, y = var_5274_cast_fp16_1)[name = string("attn_output_105_cast_fp16")];
            int32 var_5304 = const()[name = string("op_5304"), val = int32(1)];
            bool attn_output_107_interleave_0 = const()[name = string("attn_output_107_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_107_cast_fp16 = concat(axis = var_5304, interleave = attn_output_107_interleave_0, values = (var_5290_cast_fp16, attn_output_105_cast_fp16))[name = string("attn_output_107_cast_fp16")];
            tensor<int32, [4]> var_5308_perm_0 = const()[name = string("op_5308_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_167x = const()[name = string("concat_167x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_5308_cast_fp16 = transpose(perm = var_5308_perm_0, x = attn_output_107_cast_fp16)[name = string("transpose_303")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_111_cast_fp16 = reshape(shape = concat_167x, x = var_5308_cast_fp16)[name = string("attn_output_111_cast_fp16")];
            tensor<int32, [2]> hidden_states_133_strides_0 = const()[name = string("hidden_states_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_133_pad_type_0 = const()[name = string("hidden_states_133_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_133_pad_0 = const()[name = string("hidden_states_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_133_dilations_0 = const()[name = string("hidden_states_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_133_groups_0 = const()[name = string("hidden_states_133_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_133_cast_fp16 = conv(dilations = hidden_states_133_dilations_0, groups = hidden_states_133_groups_0, pad = hidden_states_133_pad_0, pad_type = hidden_states_133_pad_type_0, strides = hidden_states_133_strides_0, weight = layers_13_self_attn_o_proj_weight_cast_fp16, x = attn_output_111_cast_fp16)[name = string("hidden_states_133_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_135_cast_fp16 = add(x = hidden_states_129_cast_fp16, y = hidden_states_133_cast_fp16)[name = string("hidden_states_135_cast_fp16")];
            fp16 const_140_promoted_to_fp16 = const()[name = string("const_140_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_5341_cast_fp16 = mul(x = hidden_states_135_cast_fp16, y = const_140_promoted_to_fp16)[name = string("op_5341_cast_fp16")];
            int32 var_5339 = const()[name = string("op_5339"), val = int32(1)];
            bool doubled_109_interleave_0 = const()[name = string("doubled_109_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_109_cast_fp16 = concat(axis = var_5339, interleave = doubled_109_interleave_0, values = (hidden_states_135_cast_fp16, var_5341_cast_fp16))[name = string("doubled_109_cast_fp16")];
            tensor<int32, [1]> out_55_axes_0 = const()[name = string("out_55_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_55_gamma_0_to_fp16 = const()[name = string("out_55_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407019008)))];
            fp16 var_5351_to_fp16 = const()[name = string("op_5351_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_55_cast_fp16 = layer_norm(axes = out_55_axes_0, epsilon = var_5351_to_fp16, gamma = out_55_gamma_0_to_fp16, x = doubled_109_cast_fp16)[name = string("out_55_cast_fp16")];
            tensor<int32, [2]> var_5362_split_sizes_0 = const()[name = string("op_5362_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5362_axis_0 = const()[name = string("op_5362_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_5362_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_5362_cast_fp16_1 = split(axis = var_5362_axis_0, split_sizes = var_5362_split_sizes_0, x = out_55_cast_fp16)[name = string("op_5362_cast_fp16")];
            tensor<int32, [2]> input_27_strides_0 = const()[name = string("input_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_27_pad_type_0 = const()[name = string("input_27_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_27_pad_0 = const()[name = string("input_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_27_dilations_0 = const()[name = string("input_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_27_groups_0 = const()[name = string("input_27_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_27_cast_fp16 = conv(dilations = input_27_dilations_0, groups = input_27_groups_0, pad = input_27_pad_0, pad_type = input_27_pad_type_0, strides = input_27_strides_0, weight = layers_13_mlp_gate_proj_weight_cast_fp16, x = var_5362_cast_fp16_0)[name = string("input_27_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_5379_cast_fp16 = silu(x = input_27_cast_fp16)[name = string("op_5379_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_13_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_13_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407027264)))];
            tensor<int32, [2]> var_5385_strides_0 = const()[name = string("op_5385_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_5385_pad_type_0 = const()[name = string("op_5385_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_5385_pad_0 = const()[name = string("op_5385_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_5385_dilations_0 = const()[name = string("op_5385_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5385_groups_0 = const()[name = string("op_5385_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_5385_cast_fp16 = conv(dilations = var_5385_dilations_0, groups = var_5385_groups_0, pad = var_5385_pad_0, pad_type = var_5385_pad_type_0, strides = var_5385_strides_0, weight = layers_13_mlp_up_proj_weight_to_fp16, x = var_5362_cast_fp16_0)[name = string("op_5385_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_139_cast_fp16 = mul(x = var_5379_cast_fp16, y = var_5385_cast_fp16)[name = string("x_139_cast_fp16")];
            tensor<int32, [2]> hidden_states_137_strides_0 = const()[name = string("hidden_states_137_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_137_pad_type_0 = const()[name = string("hidden_states_137_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_137_pad_0 = const()[name = string("hidden_states_137_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_137_dilations_0 = const()[name = string("hidden_states_137_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_137_groups_0 = const()[name = string("hidden_states_137_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_137_cast_fp16 = conv(dilations = hidden_states_137_dilations_0, groups = hidden_states_137_groups_0, pad = hidden_states_137_pad_0, pad_type = hidden_states_137_pad_type_0, strides = hidden_states_137_strides_0, weight = layers_13_mlp_down_proj_weight_cast_fp16, x = x_139_cast_fp16)[name = string("hidden_states_137_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_139_cast_fp16 = add(x = hidden_states_135_cast_fp16, y = hidden_states_137_cast_fp16)[name = string("hidden_states_139_cast_fp16")];
            fp16 const_142_promoted_to_fp16 = const()[name = string("const_142_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_5403_cast_fp16 = mul(x = hidden_states_139_cast_fp16, y = const_142_promoted_to_fp16)[name = string("op_5403_cast_fp16")];
            int32 var_5401 = const()[name = string("op_5401"), val = int32(1)];
            bool doubled_113_interleave_0 = const()[name = string("doubled_113_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_113_cast_fp16 = concat(axis = var_5401, interleave = doubled_113_interleave_0, values = (hidden_states_139_cast_fp16, var_5403_cast_fp16))[name = string("doubled_113_cast_fp16")];
            tensor<int32, [1]> out_57_axes_0 = const()[name = string("out_57_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_57_gamma_0_to_fp16 = const()[name = string("out_57_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1432193152)))];
            fp16 var_5413_to_fp16 = const()[name = string("op_5413_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_57_cast_fp16 = layer_norm(axes = out_57_axes_0, epsilon = var_5413_to_fp16, gamma = out_57_gamma_0_to_fp16, x = doubled_113_cast_fp16)[name = string("out_57_cast_fp16")];
            tensor<int32, [2]> var_5424_split_sizes_0 = const()[name = string("op_5424_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5424_axis_0 = const()[name = string("op_5424_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_5424_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_5424_cast_fp16_1 = split(axis = var_5424_axis_0, split_sizes = var_5424_split_sizes_0, x = out_57_cast_fp16)[name = string("op_5424_cast_fp16")];
            tensor<int32, [2]> query_states_85_strides_0 = const()[name = string("query_states_85_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_85_pad_type_0 = const()[name = string("query_states_85_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_85_pad_0 = const()[name = string("query_states_85_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_85_dilations_0 = const()[name = string("query_states_85_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_85_groups_0 = const()[name = string("query_states_85_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_85_cast_fp16 = conv(dilations = query_states_85_dilations_0, groups = query_states_85_groups_0, pad = query_states_85_pad_0, pad_type = query_states_85_pad_type_0, strides = query_states_85_strides_0, weight = layers_14_self_attn_q_proj_weight_cast_fp16, x = var_5424_cast_fp16_0)[name = string("query_states_85_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_14_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_14_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1432201408)))];
            tensor<int32, [2]> key_states_141_strides_0 = const()[name = string("key_states_141_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_141_pad_type_0 = const()[name = string("key_states_141_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_141_pad_0 = const()[name = string("key_states_141_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_141_dilations_0 = const()[name = string("key_states_141_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_141_groups_0 = const()[name = string("key_states_141_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_141_cast_fp16 = conv(dilations = key_states_141_dilations_0, groups = key_states_141_groups_0, pad = key_states_141_pad_0, pad_type = key_states_141_pad_type_0, strides = key_states_141_strides_0, weight = layers_14_self_attn_k_proj_weight_to_fp16, x = var_5424_cast_fp16_0)[name = string("key_states_141_cast_fp16")];
            tensor<int32, [2]> value_states_85_strides_0 = const()[name = string("value_states_85_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_85_pad_type_0 = const()[name = string("value_states_85_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_85_pad_0 = const()[name = string("value_states_85_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_85_dilations_0 = const()[name = string("value_states_85_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_85_groups_0 = const()[name = string("value_states_85_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_85_cast_fp16 = conv(dilations = value_states_85_dilations_0, groups = value_states_85_groups_0, pad = value_states_85_pad_0, pad_type = value_states_85_pad_type_0, strides = value_states_85_strides_0, weight = layers_14_self_attn_v_proj_weight_cast_fp16, x = var_5424_cast_fp16_0)[name = string("value_states_85_cast_fp16")];
            tensor<int32, [4]> concat_168x = const()[name = string("concat_168x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_141_cast_fp16 = reshape(shape = concat_168x, x = query_states_85_cast_fp16)[name = string("x_141_cast_fp16")];
            tensor<int32, [4]> concat_169x = const()[name = string("concat_169x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_5481_cast_fp16 = reshape(shape = concat_169x, x = key_states_141_cast_fp16)[name = string("op_5481_cast_fp16")];
            tensor<int32, [4]> concat_170x = const()[name = string("concat_170x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_5488_cast_fp16 = reshape(shape = concat_170x, x = value_states_85_cast_fp16)[name = string("op_5488_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_5492_cast_fp16 = mul(x = x_141_cast_fp16, y = var_859_cast_fp16)[name = string("op_5492_cast_fp16")];
            tensor<int32, [2]> var_5493_split_sizes_0 = const()[name = string("op_5493_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5493_axis_0 = const()[name = string("op_5493_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_5493_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_5493_cast_fp16_1 = split(axis = var_5493_axis_0, split_sizes = var_5493_split_sizes_0, x = x_141_cast_fp16)[name = string("op_5493_cast_fp16")];
            fp16 const_144_promoted_to_fp16 = const()[name = string("const_144_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_5495_cast_fp16 = mul(x = var_5493_cast_fp16_1, y = const_144_promoted_to_fp16)[name = string("op_5495_cast_fp16")];
            int32 var_5497 = const()[name = string("op_5497"), val = int32(-2)];
            bool var_5498_interleave_0 = const()[name = string("op_5498_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_5498_cast_fp16 = concat(axis = var_5497, interleave = var_5498_interleave_0, values = (var_5495_cast_fp16, var_5493_cast_fp16_0))[name = string("op_5498_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_5499_cast_fp16 = mul(x = var_5498_cast_fp16, y = var_866_cast_fp16)[name = string("op_5499_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_87_cast_fp16 = add(x = var_5492_cast_fp16, y = var_5499_cast_fp16)[name = string("query_states_87_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_5505_cast_fp16 = mul(x = var_5481_cast_fp16, y = var_859_cast_fp16)[name = string("op_5505_cast_fp16")];
            tensor<int32, [2]> var_5506_split_sizes_0 = const()[name = string("op_5506_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5506_axis_0 = const()[name = string("op_5506_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_5506_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_5506_cast_fp16_1 = split(axis = var_5506_axis_0, split_sizes = var_5506_split_sizes_0, x = var_5481_cast_fp16)[name = string("op_5506_cast_fp16")];
            fp16 const_145_promoted_to_fp16 = const()[name = string("const_145_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_5508_cast_fp16 = mul(x = var_5506_cast_fp16_1, y = const_145_promoted_to_fp16)[name = string("op_5508_cast_fp16")];
            int32 var_5510 = const()[name = string("op_5510"), val = int32(-2)];
            bool var_5511_interleave_0 = const()[name = string("op_5511_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_5511_cast_fp16 = concat(axis = var_5510, interleave = var_5511_interleave_0, values = (var_5508_cast_fp16, var_5506_cast_fp16_0))[name = string("op_5511_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_5512_cast_fp16 = mul(x = var_5511_cast_fp16, y = var_866_cast_fp16)[name = string("op_5512_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_145_cast_fp16 = add(x = var_5505_cast_fp16, y = var_5512_cast_fp16)[name = string("key_states_145_cast_fp16")];
            tensor<int32, [1]> expand_dims_168 = const()[name = string("expand_dims_168"), val = tensor<int32, [1]>([14])];
            tensor<int32, [1]> expand_dims_169 = const()[name = string("expand_dims_169"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_171 = const()[name = string("expand_dims_171"), val = tensor<int32, [1]>([0])];
            int32 concat_173_axis_0 = const()[name = string("concat_173_axis_0"), val = int32(0)];
            bool concat_173_interleave_0 = const()[name = string("concat_173_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_173 = concat(axis = concat_173_axis_0, interleave = concat_173_interleave_0, values = (expand_dims_168, expand_dims_169, position_id, expand_dims_171))[name = string("concat_173")];
            tensor<int32, [1]> expand_dims_172 = const()[name = string("expand_dims_172"), val = tensor<int32, [1]>([15])];
            tensor<int32, [1]> concat_174_values1_0 = const()[name = string("concat_174_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_174_values3_0 = const()[name = string("concat_174_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_174_axis_0 = const()[name = string("concat_174_axis_0"), val = int32(0)];
            bool concat_174_interleave_0 = const()[name = string("concat_174_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_174 = concat(axis = concat_174_axis_0, interleave = concat_174_interleave_0, values = (expand_dims_172, concat_174_values1_0, cache_position_end, concat_174_values3_0))[name = string("concat_174")];
            tensor<int32, [4]> key_states_147_perm_0 = const()[name = string("key_states_147_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_15_stride_0 = const()[name = string("key_cache_internal_tensor_assign_15_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_15_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_15_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_15_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_15_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_15_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_15_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_147_cast_fp16 = transpose(perm = key_states_147_perm_0, x = key_states_145_cast_fp16)[name = string("transpose_302")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_15_cast_fp16 = slice_update(begin = concat_173, begin_mask = key_cache_internal_tensor_assign_15_begin_mask_0, end = concat_174, end_mask = key_cache_internal_tensor_assign_15_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_15_squeeze_mask_0, stride = key_cache_internal_tensor_assign_15_stride_0, update = key_states_147_cast_fp16, x = coreml_update_state_194)[name = string("key_cache_internal_tensor_assign_15_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_15_cast_fp16, input = key_cache)[name = string("coreml_update_state_196_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_196 = read_state(input = key_cache)[name = string("coreml_update_state_196")];
            tensor<int32, [4]> value_states_87_perm_0 = const()[name = string("value_states_87_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_15_stride_0 = const()[name = string("value_cache_internal_tensor_assign_15_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_15_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_15_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_15_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_15_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_15_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_15_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_87_cast_fp16 = transpose(perm = value_states_87_perm_0, x = var_5488_cast_fp16)[name = string("transpose_301")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_15_cast_fp16 = slice_update(begin = concat_173, begin_mask = value_cache_internal_tensor_assign_15_begin_mask_0, end = concat_174, end_mask = value_cache_internal_tensor_assign_15_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_15_squeeze_mask_0, stride = value_cache_internal_tensor_assign_15_stride_0, update = value_states_87_cast_fp16, x = coreml_update_state_195)[name = string("value_cache_internal_tensor_assign_15_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_15_cast_fp16, input = value_cache)[name = string("coreml_update_state_197_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_197 = read_state(input = value_cache)[name = string("coreml_update_state_197")];
            tensor<int32, [4]> var_5582_begin_0 = const()[name = string("op_5582_begin_0"), val = tensor<int32, [4]>([14, 0, 0, 0])];
            tensor<int32, [4]> var_5582_end_0 = const()[name = string("op_5582_end_0"), val = tensor<int32, [4]>([15, 2, 2048, 128])];
            tensor<bool, [4]> var_5582_end_mask_0 = const()[name = string("op_5582_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5582_cast_fp16 = slice_by_index(begin = var_5582_begin_0, end = var_5582_end_0, end_mask = var_5582_end_mask_0, x = coreml_update_state_196)[name = string("op_5582_cast_fp16")];
            tensor<int32, [2]> tile_28 = const()[name = string("tile_28"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5585_axis_0 = const()[name = string("op_5585_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5585_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5585_cast_fp16_1 = split(axis = var_5585_axis_0, split_sizes = tile_28, x = var_5582_cast_fp16)[name = string("op_5585_cast_fp16")];
            tensor<int32, [4]> var_5592_begin_0 = const()[name = string("op_5592_begin_0"), val = tensor<int32, [4]>([14, 0, 0, 0])];
            tensor<int32, [4]> var_5592_end_0 = const()[name = string("op_5592_end_0"), val = tensor<int32, [4]>([15, 2, 2048, 128])];
            tensor<bool, [4]> var_5592_end_mask_0 = const()[name = string("op_5592_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5592_cast_fp16 = slice_by_index(begin = var_5592_begin_0, end = var_5592_end_0, end_mask = var_5592_end_mask_0, x = coreml_update_state_197)[name = string("op_5592_cast_fp16")];
            tensor<int32, [2]> tile_29 = const()[name = string("tile_29"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5595_axis_0 = const()[name = string("op_5595_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5595_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5595_cast_fp16_1 = split(axis = var_5595_axis_0, split_sizes = tile_29, x = var_5592_cast_fp16)[name = string("op_5595_cast_fp16")];
            tensor<int32, [2]> var_5598_split_sizes_0 = const()[name = string("op_5598_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_5598_axis_0 = const()[name = string("op_5598_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_5598_0, tensor<fp16, [1, 8, 128, 16]> var_5598_1 = split(axis = var_5598_axis_0, split_sizes = var_5598_split_sizes_0, x = query_states_87_cast_fp16)[name = string("op_5598")];
            bool attn_weights_225_transpose_x_0 = const()[name = string("attn_weights_225_transpose_x_0"), val = bool(false)];
            bool attn_weights_225_transpose_y_0 = const()[name = string("attn_weights_225_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_225_cast_fp16 = matmul(transpose_x = attn_weights_225_transpose_x_0, transpose_y = attn_weights_225_transpose_y_0, x = var_5585_cast_fp16_0, y = var_5598_0)[name = string("attn_weights_225_cast_fp16")];
            fp16 var_5601_to_fp16 = const()[name = string("op_5601_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_227_cast_fp16 = mul(x = attn_weights_225_cast_fp16, y = var_5601_to_fp16)[name = string("attn_weights_227_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_229_cast_fp16 = add(x = attn_weights_227_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_229_cast_fp16")];
            int32 var_5605 = const()[name = string("op_5605"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_231_cast_fp16 = softmax(axis = var_5605, x = attn_weights_229_cast_fp16)[name = string("attn_weights_231_cast_fp16")];
            bool var_5611_transpose_x_1 = const()[name = string("op_5611_transpose_x_1"), val = bool(true)];
            bool var_5611_transpose_y_1 = const()[name = string("op_5611_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_5611_cast_fp16 = matmul(transpose_x = var_5611_transpose_x_1, transpose_y = var_5611_transpose_y_1, x = attn_weights_231_cast_fp16, y = var_5595_cast_fp16_0)[name = string("op_5611_cast_fp16")];
            bool attn_weights_233_transpose_x_0 = const()[name = string("attn_weights_233_transpose_x_0"), val = bool(false)];
            bool attn_weights_233_transpose_y_0 = const()[name = string("attn_weights_233_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_233_cast_fp16 = matmul(transpose_x = attn_weights_233_transpose_x_0, transpose_y = attn_weights_233_transpose_y_0, x = var_5585_cast_fp16_1, y = var_5598_1)[name = string("attn_weights_233_cast_fp16")];
            fp16 var_5613_to_fp16 = const()[name = string("op_5613_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_235_cast_fp16 = mul(x = attn_weights_233_cast_fp16, y = var_5613_to_fp16)[name = string("attn_weights_235_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_237_cast_fp16 = add(x = attn_weights_235_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_237_cast_fp16")];
            int32 var_5617 = const()[name = string("op_5617"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_239_cast_fp16 = softmax(axis = var_5617, x = attn_weights_237_cast_fp16)[name = string("attn_weights_239_cast_fp16")];
            bool attn_output_113_transpose_x_1 = const()[name = string("attn_output_113_transpose_x_1"), val = bool(true)];
            bool attn_output_113_transpose_y_1 = const()[name = string("attn_output_113_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_113_cast_fp16 = matmul(transpose_x = attn_output_113_transpose_x_1, transpose_y = attn_output_113_transpose_y_1, x = attn_weights_239_cast_fp16, y = var_5595_cast_fp16_1)[name = string("attn_output_113_cast_fp16")];
            int32 var_5625 = const()[name = string("op_5625"), val = int32(1)];
            bool attn_output_115_interleave_0 = const()[name = string("attn_output_115_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_115_cast_fp16 = concat(axis = var_5625, interleave = attn_output_115_interleave_0, values = (var_5611_cast_fp16, attn_output_113_cast_fp16))[name = string("attn_output_115_cast_fp16")];
            tensor<int32, [4]> var_5629_perm_0 = const()[name = string("op_5629_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_179x = const()[name = string("concat_179x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_5629_cast_fp16 = transpose(perm = var_5629_perm_0, x = attn_output_115_cast_fp16)[name = string("transpose_300")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_119_cast_fp16 = reshape(shape = concat_179x, x = var_5629_cast_fp16)[name = string("attn_output_119_cast_fp16")];
            tensor<int32, [2]> hidden_states_143_strides_0 = const()[name = string("hidden_states_143_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_143_pad_type_0 = const()[name = string("hidden_states_143_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_143_pad_0 = const()[name = string("hidden_states_143_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_143_dilations_0 = const()[name = string("hidden_states_143_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_143_groups_0 = const()[name = string("hidden_states_143_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_143_cast_fp16 = conv(dilations = hidden_states_143_dilations_0, groups = hidden_states_143_groups_0, pad = hidden_states_143_pad_0, pad_type = hidden_states_143_pad_type_0, strides = hidden_states_143_strides_0, weight = layers_14_self_attn_o_proj_weight_cast_fp16, x = attn_output_119_cast_fp16)[name = string("hidden_states_143_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_145_cast_fp16 = add(x = hidden_states_139_cast_fp16, y = hidden_states_143_cast_fp16)[name = string("hidden_states_145_cast_fp16")];
            fp16 const_150_promoted_to_fp16 = const()[name = string("const_150_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_5662_cast_fp16 = mul(x = hidden_states_145_cast_fp16, y = const_150_promoted_to_fp16)[name = string("op_5662_cast_fp16")];
            int32 var_5660 = const()[name = string("op_5660"), val = int32(1)];
            bool doubled_117_interleave_0 = const()[name = string("doubled_117_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_117_cast_fp16 = concat(axis = var_5660, interleave = doubled_117_interleave_0, values = (hidden_states_145_cast_fp16, var_5662_cast_fp16))[name = string("doubled_117_cast_fp16")];
            tensor<int32, [1]> out_59_axes_0 = const()[name = string("out_59_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_59_gamma_0_to_fp16 = const()[name = string("out_59_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433250048)))];
            fp16 var_5672_to_fp16 = const()[name = string("op_5672_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_59_cast_fp16 = layer_norm(axes = out_59_axes_0, epsilon = var_5672_to_fp16, gamma = out_59_gamma_0_to_fp16, x = doubled_117_cast_fp16)[name = string("out_59_cast_fp16")];
            tensor<int32, [2]> var_5683_split_sizes_0 = const()[name = string("op_5683_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5683_axis_0 = const()[name = string("op_5683_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_5683_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_5683_cast_fp16_1 = split(axis = var_5683_axis_0, split_sizes = var_5683_split_sizes_0, x = out_59_cast_fp16)[name = string("op_5683_cast_fp16")];
            tensor<int32, [2]> input_29_strides_0 = const()[name = string("input_29_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_29_pad_type_0 = const()[name = string("input_29_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_29_pad_0 = const()[name = string("input_29_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_29_dilations_0 = const()[name = string("input_29_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_29_groups_0 = const()[name = string("input_29_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_29_cast_fp16 = conv(dilations = input_29_dilations_0, groups = input_29_groups_0, pad = input_29_pad_0, pad_type = input_29_pad_type_0, strides = input_29_strides_0, weight = layers_14_mlp_gate_proj_weight_cast_fp16, x = var_5683_cast_fp16_0)[name = string("input_29_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_5700_cast_fp16 = silu(x = input_29_cast_fp16)[name = string("op_5700_cast_fp16")];
            tensor<int32, [2]> var_5706_strides_0 = const()[name = string("op_5706_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_5706_pad_type_0 = const()[name = string("op_5706_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_5706_pad_0 = const()[name = string("op_5706_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_5706_dilations_0 = const()[name = string("op_5706_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5706_groups_0 = const()[name = string("op_5706_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_5706_cast_fp16 = conv(dilations = var_5706_dilations_0, groups = var_5706_groups_0, pad = var_5706_pad_0, pad_type = var_5706_pad_type_0, strides = var_5706_strides_0, weight = layers_14_mlp_up_proj_weight_cast_fp16, x = var_5683_cast_fp16_0)[name = string("op_5706_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_149_cast_fp16 = mul(x = var_5700_cast_fp16, y = var_5706_cast_fp16)[name = string("x_149_cast_fp16")];
            tensor<int32, [2]> hidden_states_147_strides_0 = const()[name = string("hidden_states_147_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_147_pad_type_0 = const()[name = string("hidden_states_147_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_147_pad_0 = const()[name = string("hidden_states_147_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_147_dilations_0 = const()[name = string("hidden_states_147_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_147_groups_0 = const()[name = string("hidden_states_147_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_147_cast_fp16 = conv(dilations = hidden_states_147_dilations_0, groups = hidden_states_147_groups_0, pad = hidden_states_147_pad_0, pad_type = hidden_states_147_pad_type_0, strides = hidden_states_147_strides_0, weight = layers_14_mlp_down_proj_weight_cast_fp16, x = x_149_cast_fp16)[name = string("hidden_states_147_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_149_cast_fp16 = add(x = hidden_states_145_cast_fp16, y = hidden_states_147_cast_fp16)[name = string("hidden_states_149_cast_fp16")];
            fp16 const_152_promoted_to_fp16 = const()[name = string("const_152_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_5724_cast_fp16 = mul(x = hidden_states_149_cast_fp16, y = const_152_promoted_to_fp16)[name = string("op_5724_cast_fp16")];
            int32 var_5722 = const()[name = string("op_5722"), val = int32(1)];
            bool doubled_121_interleave_0 = const()[name = string("doubled_121_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_121_cast_fp16 = concat(axis = var_5722, interleave = doubled_121_interleave_0, values = (hidden_states_149_cast_fp16, var_5724_cast_fp16))[name = string("doubled_121_cast_fp16")];
            tensor<int32, [1]> out_61_axes_0 = const()[name = string("out_61_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_61_gamma_0_to_fp16 = const()[name = string("out_61_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433258304)))];
            fp16 var_5734_to_fp16 = const()[name = string("op_5734_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_61_cast_fp16 = layer_norm(axes = out_61_axes_0, epsilon = var_5734_to_fp16, gamma = out_61_gamma_0_to_fp16, x = doubled_121_cast_fp16)[name = string("out_61_cast_fp16")];
            tensor<int32, [2]> var_5745_split_sizes_0 = const()[name = string("op_5745_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5745_axis_0 = const()[name = string("op_5745_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_5745_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_5745_cast_fp16_1 = split(axis = var_5745_axis_0, split_sizes = var_5745_split_sizes_0, x = out_61_cast_fp16)[name = string("op_5745_cast_fp16")];
            tensor<int32, [2]> query_states_91_strides_0 = const()[name = string("query_states_91_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_91_pad_type_0 = const()[name = string("query_states_91_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_91_pad_0 = const()[name = string("query_states_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_91_dilations_0 = const()[name = string("query_states_91_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_91_groups_0 = const()[name = string("query_states_91_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_91_cast_fp16 = conv(dilations = query_states_91_dilations_0, groups = query_states_91_groups_0, pad = query_states_91_pad_0, pad_type = query_states_91_pad_type_0, strides = query_states_91_strides_0, weight = layers_15_self_attn_q_proj_weight_cast_fp16, x = var_5745_cast_fp16_0)[name = string("query_states_91_cast_fp16")];
            tensor<int32, [2]> key_states_151_strides_0 = const()[name = string("key_states_151_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_151_pad_type_0 = const()[name = string("key_states_151_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_151_pad_0 = const()[name = string("key_states_151_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_151_dilations_0 = const()[name = string("key_states_151_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_151_groups_0 = const()[name = string("key_states_151_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_151_cast_fp16 = conv(dilations = key_states_151_dilations_0, groups = key_states_151_groups_0, pad = key_states_151_pad_0, pad_type = key_states_151_pad_type_0, strides = key_states_151_strides_0, weight = layers_15_self_attn_k_proj_weight_cast_fp16, x = var_5745_cast_fp16_0)[name = string("key_states_151_cast_fp16")];
            tensor<int32, [2]> value_states_91_strides_0 = const()[name = string("value_states_91_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_91_pad_type_0 = const()[name = string("value_states_91_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_91_pad_0 = const()[name = string("value_states_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_91_dilations_0 = const()[name = string("value_states_91_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_91_groups_0 = const()[name = string("value_states_91_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_91_cast_fp16 = conv(dilations = value_states_91_dilations_0, groups = value_states_91_groups_0, pad = value_states_91_pad_0, pad_type = value_states_91_pad_type_0, strides = value_states_91_strides_0, weight = layers_15_self_attn_v_proj_weight_cast_fp16, x = var_5745_cast_fp16_0)[name = string("value_states_91_cast_fp16")];
            tensor<int32, [4]> concat_180x = const()[name = string("concat_180x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_151_cast_fp16 = reshape(shape = concat_180x, x = query_states_91_cast_fp16)[name = string("x_151_cast_fp16")];
            tensor<int32, [4]> concat_181x = const()[name = string("concat_181x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_5802_cast_fp16 = reshape(shape = concat_181x, x = key_states_151_cast_fp16)[name = string("op_5802_cast_fp16")];
            tensor<int32, [4]> concat_182x = const()[name = string("concat_182x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_5809_cast_fp16 = reshape(shape = concat_182x, x = value_states_91_cast_fp16)[name = string("op_5809_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_5813_cast_fp16 = mul(x = x_151_cast_fp16, y = var_859_cast_fp16)[name = string("op_5813_cast_fp16")];
            tensor<int32, [2]> var_5814_split_sizes_0 = const()[name = string("op_5814_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5814_axis_0 = const()[name = string("op_5814_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_5814_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_5814_cast_fp16_1 = split(axis = var_5814_axis_0, split_sizes = var_5814_split_sizes_0, x = x_151_cast_fp16)[name = string("op_5814_cast_fp16")];
            fp16 const_154_promoted_to_fp16 = const()[name = string("const_154_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_5816_cast_fp16 = mul(x = var_5814_cast_fp16_1, y = const_154_promoted_to_fp16)[name = string("op_5816_cast_fp16")];
            int32 var_5818 = const()[name = string("op_5818"), val = int32(-2)];
            bool var_5819_interleave_0 = const()[name = string("op_5819_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_5819_cast_fp16 = concat(axis = var_5818, interleave = var_5819_interleave_0, values = (var_5816_cast_fp16, var_5814_cast_fp16_0))[name = string("op_5819_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_5820_cast_fp16 = mul(x = var_5819_cast_fp16, y = var_866_cast_fp16)[name = string("op_5820_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_93_cast_fp16 = add(x = var_5813_cast_fp16, y = var_5820_cast_fp16)[name = string("query_states_93_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_5826_cast_fp16 = mul(x = var_5802_cast_fp16, y = var_859_cast_fp16)[name = string("op_5826_cast_fp16")];
            tensor<int32, [2]> var_5827_split_sizes_0 = const()[name = string("op_5827_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5827_axis_0 = const()[name = string("op_5827_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_5827_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_5827_cast_fp16_1 = split(axis = var_5827_axis_0, split_sizes = var_5827_split_sizes_0, x = var_5802_cast_fp16)[name = string("op_5827_cast_fp16")];
            fp16 const_155_promoted_to_fp16 = const()[name = string("const_155_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_5829_cast_fp16 = mul(x = var_5827_cast_fp16_1, y = const_155_promoted_to_fp16)[name = string("op_5829_cast_fp16")];
            int32 var_5831 = const()[name = string("op_5831"), val = int32(-2)];
            bool var_5832_interleave_0 = const()[name = string("op_5832_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_5832_cast_fp16 = concat(axis = var_5831, interleave = var_5832_interleave_0, values = (var_5829_cast_fp16, var_5827_cast_fp16_0))[name = string("op_5832_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_5833_cast_fp16 = mul(x = var_5832_cast_fp16, y = var_866_cast_fp16)[name = string("op_5833_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_155_cast_fp16 = add(x = var_5826_cast_fp16, y = var_5833_cast_fp16)[name = string("key_states_155_cast_fp16")];
            tensor<int32, [1]> expand_dims_180 = const()[name = string("expand_dims_180"), val = tensor<int32, [1]>([15])];
            tensor<int32, [1]> expand_dims_181 = const()[name = string("expand_dims_181"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_183 = const()[name = string("expand_dims_183"), val = tensor<int32, [1]>([0])];
            int32 concat_185_axis_0 = const()[name = string("concat_185_axis_0"), val = int32(0)];
            bool concat_185_interleave_0 = const()[name = string("concat_185_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_185 = concat(axis = concat_185_axis_0, interleave = concat_185_interleave_0, values = (expand_dims_180, expand_dims_181, position_id, expand_dims_183))[name = string("concat_185")];
            tensor<int32, [1]> expand_dims_184 = const()[name = string("expand_dims_184"), val = tensor<int32, [1]>([16])];
            tensor<int32, [1]> concat_186_values1_0 = const()[name = string("concat_186_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_186_values3_0 = const()[name = string("concat_186_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_186_axis_0 = const()[name = string("concat_186_axis_0"), val = int32(0)];
            bool concat_186_interleave_0 = const()[name = string("concat_186_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_186 = concat(axis = concat_186_axis_0, interleave = concat_186_interleave_0, values = (expand_dims_184, concat_186_values1_0, cache_position_end, concat_186_values3_0))[name = string("concat_186")];
            tensor<int32, [4]> key_states_157_perm_0 = const()[name = string("key_states_157_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_16_stride_0 = const()[name = string("key_cache_internal_tensor_assign_16_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_16_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_16_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_16_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_16_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_16_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_16_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_157_cast_fp16 = transpose(perm = key_states_157_perm_0, x = key_states_155_cast_fp16)[name = string("transpose_299")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_16_cast_fp16 = slice_update(begin = concat_185, begin_mask = key_cache_internal_tensor_assign_16_begin_mask_0, end = concat_186, end_mask = key_cache_internal_tensor_assign_16_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_16_squeeze_mask_0, stride = key_cache_internal_tensor_assign_16_stride_0, update = key_states_157_cast_fp16, x = coreml_update_state_196)[name = string("key_cache_internal_tensor_assign_16_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_16_cast_fp16, input = key_cache)[name = string("coreml_update_state_198_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_198 = read_state(input = key_cache)[name = string("coreml_update_state_198")];
            tensor<int32, [4]> value_states_93_perm_0 = const()[name = string("value_states_93_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_16_stride_0 = const()[name = string("value_cache_internal_tensor_assign_16_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_16_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_16_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_16_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_16_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_16_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_16_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_93_cast_fp16 = transpose(perm = value_states_93_perm_0, x = var_5809_cast_fp16)[name = string("transpose_298")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_16_cast_fp16 = slice_update(begin = concat_185, begin_mask = value_cache_internal_tensor_assign_16_begin_mask_0, end = concat_186, end_mask = value_cache_internal_tensor_assign_16_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_16_squeeze_mask_0, stride = value_cache_internal_tensor_assign_16_stride_0, update = value_states_93_cast_fp16, x = coreml_update_state_197)[name = string("value_cache_internal_tensor_assign_16_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_16_cast_fp16, input = value_cache)[name = string("coreml_update_state_199_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_199 = read_state(input = value_cache)[name = string("coreml_update_state_199")];
            tensor<int32, [4]> var_5903_begin_0 = const()[name = string("op_5903_begin_0"), val = tensor<int32, [4]>([15, 0, 0, 0])];
            tensor<int32, [4]> var_5903_end_0 = const()[name = string("op_5903_end_0"), val = tensor<int32, [4]>([16, 2, 2048, 128])];
            tensor<bool, [4]> var_5903_end_mask_0 = const()[name = string("op_5903_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5903_cast_fp16 = slice_by_index(begin = var_5903_begin_0, end = var_5903_end_0, end_mask = var_5903_end_mask_0, x = coreml_update_state_198)[name = string("op_5903_cast_fp16")];
            tensor<int32, [2]> tile_30 = const()[name = string("tile_30"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5906_axis_0 = const()[name = string("op_5906_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5906_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5906_cast_fp16_1 = split(axis = var_5906_axis_0, split_sizes = tile_30, x = var_5903_cast_fp16)[name = string("op_5906_cast_fp16")];
            tensor<int32, [4]> var_5913_begin_0 = const()[name = string("op_5913_begin_0"), val = tensor<int32, [4]>([15, 0, 0, 0])];
            tensor<int32, [4]> var_5913_end_0 = const()[name = string("op_5913_end_0"), val = tensor<int32, [4]>([16, 2, 2048, 128])];
            tensor<bool, [4]> var_5913_end_mask_0 = const()[name = string("op_5913_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5913_cast_fp16 = slice_by_index(begin = var_5913_begin_0, end = var_5913_end_0, end_mask = var_5913_end_mask_0, x = coreml_update_state_199)[name = string("op_5913_cast_fp16")];
            tensor<int32, [2]> tile_31 = const()[name = string("tile_31"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5916_axis_0 = const()[name = string("op_5916_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5916_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5916_cast_fp16_1 = split(axis = var_5916_axis_0, split_sizes = tile_31, x = var_5913_cast_fp16)[name = string("op_5916_cast_fp16")];
            tensor<int32, [2]> var_5919_split_sizes_0 = const()[name = string("op_5919_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_5919_axis_0 = const()[name = string("op_5919_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_5919_0, tensor<fp16, [1, 8, 128, 16]> var_5919_1 = split(axis = var_5919_axis_0, split_sizes = var_5919_split_sizes_0, x = query_states_93_cast_fp16)[name = string("op_5919")];
            bool attn_weights_241_transpose_x_0 = const()[name = string("attn_weights_241_transpose_x_0"), val = bool(false)];
            bool attn_weights_241_transpose_y_0 = const()[name = string("attn_weights_241_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_241_cast_fp16 = matmul(transpose_x = attn_weights_241_transpose_x_0, transpose_y = attn_weights_241_transpose_y_0, x = var_5906_cast_fp16_0, y = var_5919_0)[name = string("attn_weights_241_cast_fp16")];
            fp16 var_5922_to_fp16 = const()[name = string("op_5922_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_243_cast_fp16 = mul(x = attn_weights_241_cast_fp16, y = var_5922_to_fp16)[name = string("attn_weights_243_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_245_cast_fp16 = add(x = attn_weights_243_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_245_cast_fp16")];
            int32 var_5926 = const()[name = string("op_5926"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_247_cast_fp16 = softmax(axis = var_5926, x = attn_weights_245_cast_fp16)[name = string("attn_weights_247_cast_fp16")];
            bool var_5932_transpose_x_1 = const()[name = string("op_5932_transpose_x_1"), val = bool(true)];
            bool var_5932_transpose_y_1 = const()[name = string("op_5932_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_5932_cast_fp16 = matmul(transpose_x = var_5932_transpose_x_1, transpose_y = var_5932_transpose_y_1, x = attn_weights_247_cast_fp16, y = var_5916_cast_fp16_0)[name = string("op_5932_cast_fp16")];
            bool attn_weights_249_transpose_x_0 = const()[name = string("attn_weights_249_transpose_x_0"), val = bool(false)];
            bool attn_weights_249_transpose_y_0 = const()[name = string("attn_weights_249_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_249_cast_fp16 = matmul(transpose_x = attn_weights_249_transpose_x_0, transpose_y = attn_weights_249_transpose_y_0, x = var_5906_cast_fp16_1, y = var_5919_1)[name = string("attn_weights_249_cast_fp16")];
            fp16 var_5934_to_fp16 = const()[name = string("op_5934_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_251_cast_fp16 = mul(x = attn_weights_249_cast_fp16, y = var_5934_to_fp16)[name = string("attn_weights_251_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_253_cast_fp16 = add(x = attn_weights_251_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_253_cast_fp16")];
            int32 var_5938 = const()[name = string("op_5938"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_255_cast_fp16 = softmax(axis = var_5938, x = attn_weights_253_cast_fp16)[name = string("attn_weights_255_cast_fp16")];
            bool attn_output_121_transpose_x_1 = const()[name = string("attn_output_121_transpose_x_1"), val = bool(true)];
            bool attn_output_121_transpose_y_1 = const()[name = string("attn_output_121_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_121_cast_fp16 = matmul(transpose_x = attn_output_121_transpose_x_1, transpose_y = attn_output_121_transpose_y_1, x = attn_weights_255_cast_fp16, y = var_5916_cast_fp16_1)[name = string("attn_output_121_cast_fp16")];
            int32 var_5946 = const()[name = string("op_5946"), val = int32(1)];
            bool attn_output_123_interleave_0 = const()[name = string("attn_output_123_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_123_cast_fp16 = concat(axis = var_5946, interleave = attn_output_123_interleave_0, values = (var_5932_cast_fp16, attn_output_121_cast_fp16))[name = string("attn_output_123_cast_fp16")];
            tensor<int32, [4]> var_5950_perm_0 = const()[name = string("op_5950_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_191x = const()[name = string("concat_191x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_5950_cast_fp16 = transpose(perm = var_5950_perm_0, x = attn_output_123_cast_fp16)[name = string("transpose_297")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_127_cast_fp16 = reshape(shape = concat_191x, x = var_5950_cast_fp16)[name = string("attn_output_127_cast_fp16")];
            tensor<int32, [2]> hidden_states_153_strides_0 = const()[name = string("hidden_states_153_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_153_pad_type_0 = const()[name = string("hidden_states_153_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_153_pad_0 = const()[name = string("hidden_states_153_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_153_dilations_0 = const()[name = string("hidden_states_153_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_153_groups_0 = const()[name = string("hidden_states_153_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_153_cast_fp16 = conv(dilations = hidden_states_153_dilations_0, groups = hidden_states_153_groups_0, pad = hidden_states_153_pad_0, pad_type = hidden_states_153_pad_type_0, strides = hidden_states_153_strides_0, weight = layers_15_self_attn_o_proj_weight_cast_fp16, x = attn_output_127_cast_fp16)[name = string("hidden_states_153_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_155_cast_fp16 = add(x = hidden_states_149_cast_fp16, y = hidden_states_153_cast_fp16)[name = string("hidden_states_155_cast_fp16")];
            fp16 const_160_promoted_to_fp16 = const()[name = string("const_160_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_5983_cast_fp16 = mul(x = hidden_states_155_cast_fp16, y = const_160_promoted_to_fp16)[name = string("op_5983_cast_fp16")];
            int32 var_5981 = const()[name = string("op_5981"), val = int32(1)];
            bool doubled_125_interleave_0 = const()[name = string("doubled_125_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_125_cast_fp16 = concat(axis = var_5981, interleave = doubled_125_interleave_0, values = (hidden_states_155_cast_fp16, var_5983_cast_fp16))[name = string("doubled_125_cast_fp16")];
            tensor<int32, [1]> out_63_axes_0 = const()[name = string("out_63_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_63_gamma_0_to_fp16 = const()[name = string("out_63_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433266560)))];
            fp16 var_5993_to_fp16 = const()[name = string("op_5993_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_63_cast_fp16 = layer_norm(axes = out_63_axes_0, epsilon = var_5993_to_fp16, gamma = out_63_gamma_0_to_fp16, x = doubled_125_cast_fp16)[name = string("out_63_cast_fp16")];
            tensor<int32, [2]> var_6004_split_sizes_0 = const()[name = string("op_6004_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6004_axis_0 = const()[name = string("op_6004_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_6004_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_6004_cast_fp16_1 = split(axis = var_6004_axis_0, split_sizes = var_6004_split_sizes_0, x = out_63_cast_fp16)[name = string("op_6004_cast_fp16")];
            tensor<int32, [2]> input_31_strides_0 = const()[name = string("input_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_31_pad_type_0 = const()[name = string("input_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_31_pad_0 = const()[name = string("input_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_31_dilations_0 = const()[name = string("input_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_31_groups_0 = const()[name = string("input_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_31_cast_fp16 = conv(dilations = input_31_dilations_0, groups = input_31_groups_0, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = input_31_strides_0, weight = layers_15_mlp_gate_proj_weight_cast_fp16, x = var_6004_cast_fp16_0)[name = string("input_31_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_6021_cast_fp16 = silu(x = input_31_cast_fp16)[name = string("op_6021_cast_fp16")];
            tensor<int32, [2]> var_6027_strides_0 = const()[name = string("op_6027_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6027_pad_type_0 = const()[name = string("op_6027_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6027_pad_0 = const()[name = string("op_6027_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6027_dilations_0 = const()[name = string("op_6027_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6027_groups_0 = const()[name = string("op_6027_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_6027_cast_fp16 = conv(dilations = var_6027_dilations_0, groups = var_6027_groups_0, pad = var_6027_pad_0, pad_type = var_6027_pad_type_0, strides = var_6027_strides_0, weight = layers_15_mlp_up_proj_weight_cast_fp16, x = var_6004_cast_fp16_0)[name = string("op_6027_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_159_cast_fp16 = mul(x = var_6021_cast_fp16, y = var_6027_cast_fp16)[name = string("x_159_cast_fp16")];
            tensor<int32, [2]> hidden_states_157_strides_0 = const()[name = string("hidden_states_157_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_157_pad_type_0 = const()[name = string("hidden_states_157_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_157_pad_0 = const()[name = string("hidden_states_157_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_157_dilations_0 = const()[name = string("hidden_states_157_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_157_groups_0 = const()[name = string("hidden_states_157_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_157_cast_fp16 = conv(dilations = hidden_states_157_dilations_0, groups = hidden_states_157_groups_0, pad = hidden_states_157_pad_0, pad_type = hidden_states_157_pad_type_0, strides = hidden_states_157_strides_0, weight = layers_15_mlp_down_proj_weight_cast_fp16, x = x_159_cast_fp16)[name = string("hidden_states_157_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_159_cast_fp16 = add(x = hidden_states_155_cast_fp16, y = hidden_states_157_cast_fp16)[name = string("hidden_states_159_cast_fp16")];
            fp16 const_162_promoted_to_fp16 = const()[name = string("const_162_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_6045_cast_fp16 = mul(x = hidden_states_159_cast_fp16, y = const_162_promoted_to_fp16)[name = string("op_6045_cast_fp16")];
            int32 var_6043 = const()[name = string("op_6043"), val = int32(1)];
            bool doubled_129_interleave_0 = const()[name = string("doubled_129_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_129_cast_fp16 = concat(axis = var_6043, interleave = doubled_129_interleave_0, values = (hidden_states_159_cast_fp16, var_6045_cast_fp16))[name = string("doubled_129_cast_fp16")];
            tensor<int32, [1]> out_65_axes_0 = const()[name = string("out_65_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_65_gamma_0_to_fp16 = const()[name = string("out_65_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433274816)))];
            fp16 var_6055_to_fp16 = const()[name = string("op_6055_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_65_cast_fp16 = layer_norm(axes = out_65_axes_0, epsilon = var_6055_to_fp16, gamma = out_65_gamma_0_to_fp16, x = doubled_129_cast_fp16)[name = string("out_65_cast_fp16")];
            tensor<int32, [2]> var_6066_split_sizes_0 = const()[name = string("op_6066_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6066_axis_0 = const()[name = string("op_6066_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_6066_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_6066_cast_fp16_1 = split(axis = var_6066_axis_0, split_sizes = var_6066_split_sizes_0, x = out_65_cast_fp16)[name = string("op_6066_cast_fp16")];
            tensor<int32, [2]> query_states_97_strides_0 = const()[name = string("query_states_97_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_97_pad_type_0 = const()[name = string("query_states_97_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_97_pad_0 = const()[name = string("query_states_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_97_dilations_0 = const()[name = string("query_states_97_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_97_groups_0 = const()[name = string("query_states_97_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_97_cast_fp16 = conv(dilations = query_states_97_dilations_0, groups = query_states_97_groups_0, pad = query_states_97_pad_0, pad_type = query_states_97_pad_type_0, strides = query_states_97_strides_0, weight = layers_16_self_attn_q_proj_weight_cast_fp16, x = var_6066_cast_fp16_0)[name = string("query_states_97_cast_fp16")];
            tensor<int32, [2]> key_states_161_strides_0 = const()[name = string("key_states_161_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_161_pad_type_0 = const()[name = string("key_states_161_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_161_pad_0 = const()[name = string("key_states_161_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_161_dilations_0 = const()[name = string("key_states_161_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_161_groups_0 = const()[name = string("key_states_161_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_161_cast_fp16 = conv(dilations = key_states_161_dilations_0, groups = key_states_161_groups_0, pad = key_states_161_pad_0, pad_type = key_states_161_pad_type_0, strides = key_states_161_strides_0, weight = layers_16_self_attn_k_proj_weight_cast_fp16, x = var_6066_cast_fp16_0)[name = string("key_states_161_cast_fp16")];
            tensor<int32, [2]> value_states_97_strides_0 = const()[name = string("value_states_97_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_97_pad_type_0 = const()[name = string("value_states_97_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_97_pad_0 = const()[name = string("value_states_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_97_dilations_0 = const()[name = string("value_states_97_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_97_groups_0 = const()[name = string("value_states_97_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_97_cast_fp16 = conv(dilations = value_states_97_dilations_0, groups = value_states_97_groups_0, pad = value_states_97_pad_0, pad_type = value_states_97_pad_type_0, strides = value_states_97_strides_0, weight = layers_16_self_attn_v_proj_weight_cast_fp16, x = var_6066_cast_fp16_0)[name = string("value_states_97_cast_fp16")];
            tensor<int32, [4]> concat_192x = const()[name = string("concat_192x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_161_cast_fp16 = reshape(shape = concat_192x, x = query_states_97_cast_fp16)[name = string("x_161_cast_fp16")];
            tensor<int32, [4]> concat_193x = const()[name = string("concat_193x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_6123_cast_fp16 = reshape(shape = concat_193x, x = key_states_161_cast_fp16)[name = string("op_6123_cast_fp16")];
            tensor<int32, [4]> concat_194x = const()[name = string("concat_194x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_6130_cast_fp16 = reshape(shape = concat_194x, x = value_states_97_cast_fp16)[name = string("op_6130_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_6134_cast_fp16 = mul(x = x_161_cast_fp16, y = var_859_cast_fp16)[name = string("op_6134_cast_fp16")];
            tensor<int32, [2]> var_6135_split_sizes_0 = const()[name = string("op_6135_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6135_axis_0 = const()[name = string("op_6135_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_6135_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_6135_cast_fp16_1 = split(axis = var_6135_axis_0, split_sizes = var_6135_split_sizes_0, x = x_161_cast_fp16)[name = string("op_6135_cast_fp16")];
            fp16 const_164_promoted_to_fp16 = const()[name = string("const_164_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_6137_cast_fp16 = mul(x = var_6135_cast_fp16_1, y = const_164_promoted_to_fp16)[name = string("op_6137_cast_fp16")];
            int32 var_6139 = const()[name = string("op_6139"), val = int32(-2)];
            bool var_6140_interleave_0 = const()[name = string("op_6140_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_6140_cast_fp16 = concat(axis = var_6139, interleave = var_6140_interleave_0, values = (var_6137_cast_fp16, var_6135_cast_fp16_0))[name = string("op_6140_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_6141_cast_fp16 = mul(x = var_6140_cast_fp16, y = var_866_cast_fp16)[name = string("op_6141_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_99_cast_fp16 = add(x = var_6134_cast_fp16, y = var_6141_cast_fp16)[name = string("query_states_99_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_6147_cast_fp16 = mul(x = var_6123_cast_fp16, y = var_859_cast_fp16)[name = string("op_6147_cast_fp16")];
            tensor<int32, [2]> var_6148_split_sizes_0 = const()[name = string("op_6148_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6148_axis_0 = const()[name = string("op_6148_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_6148_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_6148_cast_fp16_1 = split(axis = var_6148_axis_0, split_sizes = var_6148_split_sizes_0, x = var_6123_cast_fp16)[name = string("op_6148_cast_fp16")];
            fp16 const_165_promoted_to_fp16 = const()[name = string("const_165_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_6150_cast_fp16 = mul(x = var_6148_cast_fp16_1, y = const_165_promoted_to_fp16)[name = string("op_6150_cast_fp16")];
            int32 var_6152 = const()[name = string("op_6152"), val = int32(-2)];
            bool var_6153_interleave_0 = const()[name = string("op_6153_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_6153_cast_fp16 = concat(axis = var_6152, interleave = var_6153_interleave_0, values = (var_6150_cast_fp16, var_6148_cast_fp16_0))[name = string("op_6153_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_6154_cast_fp16 = mul(x = var_6153_cast_fp16, y = var_866_cast_fp16)[name = string("op_6154_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_165_cast_fp16 = add(x = var_6147_cast_fp16, y = var_6154_cast_fp16)[name = string("key_states_165_cast_fp16")];
            tensor<int32, [1]> expand_dims_192 = const()[name = string("expand_dims_192"), val = tensor<int32, [1]>([16])];
            tensor<int32, [1]> expand_dims_193 = const()[name = string("expand_dims_193"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_195 = const()[name = string("expand_dims_195"), val = tensor<int32, [1]>([0])];
            int32 concat_197_axis_0 = const()[name = string("concat_197_axis_0"), val = int32(0)];
            bool concat_197_interleave_0 = const()[name = string("concat_197_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_197 = concat(axis = concat_197_axis_0, interleave = concat_197_interleave_0, values = (expand_dims_192, expand_dims_193, position_id, expand_dims_195))[name = string("concat_197")];
            tensor<int32, [1]> expand_dims_196 = const()[name = string("expand_dims_196"), val = tensor<int32, [1]>([17])];
            tensor<int32, [1]> concat_198_values1_0 = const()[name = string("concat_198_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_198_values3_0 = const()[name = string("concat_198_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_198_axis_0 = const()[name = string("concat_198_axis_0"), val = int32(0)];
            bool concat_198_interleave_0 = const()[name = string("concat_198_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_198 = concat(axis = concat_198_axis_0, interleave = concat_198_interleave_0, values = (expand_dims_196, concat_198_values1_0, cache_position_end, concat_198_values3_0))[name = string("concat_198")];
            tensor<int32, [4]> key_states_167_perm_0 = const()[name = string("key_states_167_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_17_stride_0 = const()[name = string("key_cache_internal_tensor_assign_17_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_17_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_17_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_17_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_17_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_17_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_17_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_167_cast_fp16 = transpose(perm = key_states_167_perm_0, x = key_states_165_cast_fp16)[name = string("transpose_296")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_17_cast_fp16 = slice_update(begin = concat_197, begin_mask = key_cache_internal_tensor_assign_17_begin_mask_0, end = concat_198, end_mask = key_cache_internal_tensor_assign_17_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_17_squeeze_mask_0, stride = key_cache_internal_tensor_assign_17_stride_0, update = key_states_167_cast_fp16, x = coreml_update_state_198)[name = string("key_cache_internal_tensor_assign_17_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_17_cast_fp16, input = key_cache)[name = string("coreml_update_state_200_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_200 = read_state(input = key_cache)[name = string("coreml_update_state_200")];
            tensor<int32, [4]> value_states_99_perm_0 = const()[name = string("value_states_99_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_17_stride_0 = const()[name = string("value_cache_internal_tensor_assign_17_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_17_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_17_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_17_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_17_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_17_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_17_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_99_cast_fp16 = transpose(perm = value_states_99_perm_0, x = var_6130_cast_fp16)[name = string("transpose_295")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_17_cast_fp16 = slice_update(begin = concat_197, begin_mask = value_cache_internal_tensor_assign_17_begin_mask_0, end = concat_198, end_mask = value_cache_internal_tensor_assign_17_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_17_squeeze_mask_0, stride = value_cache_internal_tensor_assign_17_stride_0, update = value_states_99_cast_fp16, x = coreml_update_state_199)[name = string("value_cache_internal_tensor_assign_17_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_17_cast_fp16, input = value_cache)[name = string("coreml_update_state_201_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_201 = read_state(input = value_cache)[name = string("coreml_update_state_201")];
            tensor<int32, [4]> var_6224_begin_0 = const()[name = string("op_6224_begin_0"), val = tensor<int32, [4]>([16, 0, 0, 0])];
            tensor<int32, [4]> var_6224_end_0 = const()[name = string("op_6224_end_0"), val = tensor<int32, [4]>([17, 2, 2048, 128])];
            tensor<bool, [4]> var_6224_end_mask_0 = const()[name = string("op_6224_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6224_cast_fp16 = slice_by_index(begin = var_6224_begin_0, end = var_6224_end_0, end_mask = var_6224_end_mask_0, x = coreml_update_state_200)[name = string("op_6224_cast_fp16")];
            tensor<int32, [2]> tile_32 = const()[name = string("tile_32"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6227_axis_0 = const()[name = string("op_6227_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6227_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6227_cast_fp16_1 = split(axis = var_6227_axis_0, split_sizes = tile_32, x = var_6224_cast_fp16)[name = string("op_6227_cast_fp16")];
            tensor<int32, [4]> var_6234_begin_0 = const()[name = string("op_6234_begin_0"), val = tensor<int32, [4]>([16, 0, 0, 0])];
            tensor<int32, [4]> var_6234_end_0 = const()[name = string("op_6234_end_0"), val = tensor<int32, [4]>([17, 2, 2048, 128])];
            tensor<bool, [4]> var_6234_end_mask_0 = const()[name = string("op_6234_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6234_cast_fp16 = slice_by_index(begin = var_6234_begin_0, end = var_6234_end_0, end_mask = var_6234_end_mask_0, x = coreml_update_state_201)[name = string("op_6234_cast_fp16")];
            tensor<int32, [2]> tile_33 = const()[name = string("tile_33"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6237_axis_0 = const()[name = string("op_6237_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6237_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6237_cast_fp16_1 = split(axis = var_6237_axis_0, split_sizes = tile_33, x = var_6234_cast_fp16)[name = string("op_6237_cast_fp16")];
            tensor<int32, [2]> var_6240_split_sizes_0 = const()[name = string("op_6240_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_6240_axis_0 = const()[name = string("op_6240_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_6240_0, tensor<fp16, [1, 8, 128, 16]> var_6240_1 = split(axis = var_6240_axis_0, split_sizes = var_6240_split_sizes_0, x = query_states_99_cast_fp16)[name = string("op_6240")];
            bool attn_weights_257_transpose_x_0 = const()[name = string("attn_weights_257_transpose_x_0"), val = bool(false)];
            bool attn_weights_257_transpose_y_0 = const()[name = string("attn_weights_257_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_257_cast_fp16 = matmul(transpose_x = attn_weights_257_transpose_x_0, transpose_y = attn_weights_257_transpose_y_0, x = var_6227_cast_fp16_0, y = var_6240_0)[name = string("attn_weights_257_cast_fp16")];
            fp16 var_6243_to_fp16 = const()[name = string("op_6243_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_259_cast_fp16 = mul(x = attn_weights_257_cast_fp16, y = var_6243_to_fp16)[name = string("attn_weights_259_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_261_cast_fp16 = add(x = attn_weights_259_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_261_cast_fp16")];
            int32 var_6247 = const()[name = string("op_6247"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_263_cast_fp16 = softmax(axis = var_6247, x = attn_weights_261_cast_fp16)[name = string("attn_weights_263_cast_fp16")];
            bool var_6253_transpose_x_1 = const()[name = string("op_6253_transpose_x_1"), val = bool(true)];
            bool var_6253_transpose_y_1 = const()[name = string("op_6253_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_6253_cast_fp16 = matmul(transpose_x = var_6253_transpose_x_1, transpose_y = var_6253_transpose_y_1, x = attn_weights_263_cast_fp16, y = var_6237_cast_fp16_0)[name = string("op_6253_cast_fp16")];
            bool attn_weights_265_transpose_x_0 = const()[name = string("attn_weights_265_transpose_x_0"), val = bool(false)];
            bool attn_weights_265_transpose_y_0 = const()[name = string("attn_weights_265_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_265_cast_fp16 = matmul(transpose_x = attn_weights_265_transpose_x_0, transpose_y = attn_weights_265_transpose_y_0, x = var_6227_cast_fp16_1, y = var_6240_1)[name = string("attn_weights_265_cast_fp16")];
            fp16 var_6255_to_fp16 = const()[name = string("op_6255_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_267_cast_fp16 = mul(x = attn_weights_265_cast_fp16, y = var_6255_to_fp16)[name = string("attn_weights_267_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_269_cast_fp16 = add(x = attn_weights_267_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_269_cast_fp16")];
            int32 var_6259 = const()[name = string("op_6259"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_271_cast_fp16 = softmax(axis = var_6259, x = attn_weights_269_cast_fp16)[name = string("attn_weights_271_cast_fp16")];
            bool attn_output_129_transpose_x_1 = const()[name = string("attn_output_129_transpose_x_1"), val = bool(true)];
            bool attn_output_129_transpose_y_1 = const()[name = string("attn_output_129_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_129_cast_fp16 = matmul(transpose_x = attn_output_129_transpose_x_1, transpose_y = attn_output_129_transpose_y_1, x = attn_weights_271_cast_fp16, y = var_6237_cast_fp16_1)[name = string("attn_output_129_cast_fp16")];
            int32 var_6267 = const()[name = string("op_6267"), val = int32(1)];
            bool attn_output_131_interleave_0 = const()[name = string("attn_output_131_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_131_cast_fp16 = concat(axis = var_6267, interleave = attn_output_131_interleave_0, values = (var_6253_cast_fp16, attn_output_129_cast_fp16))[name = string("attn_output_131_cast_fp16")];
            tensor<int32, [4]> var_6271_perm_0 = const()[name = string("op_6271_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_203x = const()[name = string("concat_203x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_6271_cast_fp16 = transpose(perm = var_6271_perm_0, x = attn_output_131_cast_fp16)[name = string("transpose_294")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_135_cast_fp16 = reshape(shape = concat_203x, x = var_6271_cast_fp16)[name = string("attn_output_135_cast_fp16")];
            tensor<int32, [2]> hidden_states_163_strides_0 = const()[name = string("hidden_states_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_163_pad_type_0 = const()[name = string("hidden_states_163_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_163_pad_0 = const()[name = string("hidden_states_163_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_163_dilations_0 = const()[name = string("hidden_states_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_163_groups_0 = const()[name = string("hidden_states_163_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_163_cast_fp16 = conv(dilations = hidden_states_163_dilations_0, groups = hidden_states_163_groups_0, pad = hidden_states_163_pad_0, pad_type = hidden_states_163_pad_type_0, strides = hidden_states_163_strides_0, weight = layers_16_self_attn_o_proj_weight_cast_fp16, x = attn_output_135_cast_fp16)[name = string("hidden_states_163_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_165_cast_fp16 = add(x = hidden_states_159_cast_fp16, y = hidden_states_163_cast_fp16)[name = string("hidden_states_165_cast_fp16")];
            fp16 const_170_promoted_to_fp16 = const()[name = string("const_170_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_6304_cast_fp16 = mul(x = hidden_states_165_cast_fp16, y = const_170_promoted_to_fp16)[name = string("op_6304_cast_fp16")];
            int32 var_6302 = const()[name = string("op_6302"), val = int32(1)];
            bool doubled_133_interleave_0 = const()[name = string("doubled_133_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_133_cast_fp16 = concat(axis = var_6302, interleave = doubled_133_interleave_0, values = (hidden_states_165_cast_fp16, var_6304_cast_fp16))[name = string("doubled_133_cast_fp16")];
            tensor<int32, [1]> out_67_axes_0 = const()[name = string("out_67_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_67_gamma_0_to_fp16 = const()[name = string("out_67_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433283072)))];
            fp16 var_6314_to_fp16 = const()[name = string("op_6314_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_67_cast_fp16 = layer_norm(axes = out_67_axes_0, epsilon = var_6314_to_fp16, gamma = out_67_gamma_0_to_fp16, x = doubled_133_cast_fp16)[name = string("out_67_cast_fp16")];
            tensor<int32, [2]> var_6325_split_sizes_0 = const()[name = string("op_6325_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6325_axis_0 = const()[name = string("op_6325_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_6325_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_6325_cast_fp16_1 = split(axis = var_6325_axis_0, split_sizes = var_6325_split_sizes_0, x = out_67_cast_fp16)[name = string("op_6325_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_16_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_16_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433291328)))];
            tensor<int32, [2]> input_33_strides_0 = const()[name = string("input_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_33_pad_type_0 = const()[name = string("input_33_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_33_pad_0 = const()[name = string("input_33_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_33_dilations_0 = const()[name = string("input_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_33_groups_0 = const()[name = string("input_33_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_33_cast_fp16 = conv(dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = layers_16_mlp_gate_proj_weight_to_fp16, x = var_6325_cast_fp16_0)[name = string("input_33_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_6342_cast_fp16 = silu(x = input_33_cast_fp16)[name = string("op_6342_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_16_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_16_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1458457216)))];
            tensor<int32, [2]> var_6348_strides_0 = const()[name = string("op_6348_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6348_pad_type_0 = const()[name = string("op_6348_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6348_pad_0 = const()[name = string("op_6348_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6348_dilations_0 = const()[name = string("op_6348_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6348_groups_0 = const()[name = string("op_6348_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_6348_cast_fp16 = conv(dilations = var_6348_dilations_0, groups = var_6348_groups_0, pad = var_6348_pad_0, pad_type = var_6348_pad_type_0, strides = var_6348_strides_0, weight = layers_16_mlp_up_proj_weight_to_fp16, x = var_6325_cast_fp16_0)[name = string("op_6348_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_169_cast_fp16 = mul(x = var_6342_cast_fp16, y = var_6348_cast_fp16)[name = string("x_169_cast_fp16")];
            tensor<int32, [2]> hidden_states_167_strides_0 = const()[name = string("hidden_states_167_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_167_pad_type_0 = const()[name = string("hidden_states_167_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_167_pad_0 = const()[name = string("hidden_states_167_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_167_dilations_0 = const()[name = string("hidden_states_167_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_167_groups_0 = const()[name = string("hidden_states_167_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_167_cast_fp16 = conv(dilations = hidden_states_167_dilations_0, groups = hidden_states_167_groups_0, pad = hidden_states_167_pad_0, pad_type = hidden_states_167_pad_type_0, strides = hidden_states_167_strides_0, weight = layers_16_mlp_down_proj_weight_cast_fp16, x = x_169_cast_fp16)[name = string("hidden_states_167_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_169_cast_fp16 = add(x = hidden_states_165_cast_fp16, y = hidden_states_167_cast_fp16)[name = string("hidden_states_169_cast_fp16")];
            fp16 const_172_promoted_to_fp16 = const()[name = string("const_172_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_6366_cast_fp16 = mul(x = hidden_states_169_cast_fp16, y = const_172_promoted_to_fp16)[name = string("op_6366_cast_fp16")];
            int32 var_6364 = const()[name = string("op_6364"), val = int32(1)];
            bool doubled_137_interleave_0 = const()[name = string("doubled_137_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_137_cast_fp16 = concat(axis = var_6364, interleave = doubled_137_interleave_0, values = (hidden_states_169_cast_fp16, var_6366_cast_fp16))[name = string("doubled_137_cast_fp16")];
            tensor<int32, [1]> out_69_axes_0 = const()[name = string("out_69_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_69_gamma_0_to_fp16 = const()[name = string("out_69_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483623104)))];
            fp16 var_6376_to_fp16 = const()[name = string("op_6376_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_69_cast_fp16 = layer_norm(axes = out_69_axes_0, epsilon = var_6376_to_fp16, gamma = out_69_gamma_0_to_fp16, x = doubled_137_cast_fp16)[name = string("out_69_cast_fp16")];
            tensor<int32, [2]> var_6387_split_sizes_0 = const()[name = string("op_6387_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6387_axis_0 = const()[name = string("op_6387_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_6387_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_6387_cast_fp16_1 = split(axis = var_6387_axis_0, split_sizes = var_6387_split_sizes_0, x = out_69_cast_fp16)[name = string("op_6387_cast_fp16")];
            tensor<int32, [2]> query_states_103_strides_0 = const()[name = string("query_states_103_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_103_pad_type_0 = const()[name = string("query_states_103_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_103_pad_0 = const()[name = string("query_states_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_103_dilations_0 = const()[name = string("query_states_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_103_groups_0 = const()[name = string("query_states_103_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_103_cast_fp16 = conv(dilations = query_states_103_dilations_0, groups = query_states_103_groups_0, pad = query_states_103_pad_0, pad_type = query_states_103_pad_type_0, strides = query_states_103_strides_0, weight = layers_17_self_attn_q_proj_weight_cast_fp16, x = var_6387_cast_fp16_0)[name = string("query_states_103_cast_fp16")];
            tensor<int32, [2]> key_states_171_strides_0 = const()[name = string("key_states_171_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_171_pad_type_0 = const()[name = string("key_states_171_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_171_pad_0 = const()[name = string("key_states_171_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_171_dilations_0 = const()[name = string("key_states_171_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_171_groups_0 = const()[name = string("key_states_171_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_171_cast_fp16 = conv(dilations = key_states_171_dilations_0, groups = key_states_171_groups_0, pad = key_states_171_pad_0, pad_type = key_states_171_pad_type_0, strides = key_states_171_strides_0, weight = layers_17_self_attn_k_proj_weight_cast_fp16, x = var_6387_cast_fp16_0)[name = string("key_states_171_cast_fp16")];
            tensor<int32, [2]> value_states_103_strides_0 = const()[name = string("value_states_103_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_103_pad_type_0 = const()[name = string("value_states_103_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_103_pad_0 = const()[name = string("value_states_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_103_dilations_0 = const()[name = string("value_states_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_103_groups_0 = const()[name = string("value_states_103_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_103_cast_fp16 = conv(dilations = value_states_103_dilations_0, groups = value_states_103_groups_0, pad = value_states_103_pad_0, pad_type = value_states_103_pad_type_0, strides = value_states_103_strides_0, weight = layers_17_self_attn_v_proj_weight_cast_fp16, x = var_6387_cast_fp16_0)[name = string("value_states_103_cast_fp16")];
            tensor<int32, [4]> concat_204x = const()[name = string("concat_204x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_171_cast_fp16 = reshape(shape = concat_204x, x = query_states_103_cast_fp16)[name = string("x_171_cast_fp16")];
            tensor<int32, [4]> concat_205x = const()[name = string("concat_205x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_6444_cast_fp16 = reshape(shape = concat_205x, x = key_states_171_cast_fp16)[name = string("op_6444_cast_fp16")];
            tensor<int32, [4]> concat_206x = const()[name = string("concat_206x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_6451_cast_fp16 = reshape(shape = concat_206x, x = value_states_103_cast_fp16)[name = string("op_6451_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_6455_cast_fp16 = mul(x = x_171_cast_fp16, y = var_859_cast_fp16)[name = string("op_6455_cast_fp16")];
            tensor<int32, [2]> var_6456_split_sizes_0 = const()[name = string("op_6456_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6456_axis_0 = const()[name = string("op_6456_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_6456_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_6456_cast_fp16_1 = split(axis = var_6456_axis_0, split_sizes = var_6456_split_sizes_0, x = x_171_cast_fp16)[name = string("op_6456_cast_fp16")];
            fp16 const_174_promoted_to_fp16 = const()[name = string("const_174_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_6458_cast_fp16 = mul(x = var_6456_cast_fp16_1, y = const_174_promoted_to_fp16)[name = string("op_6458_cast_fp16")];
            int32 var_6460 = const()[name = string("op_6460"), val = int32(-2)];
            bool var_6461_interleave_0 = const()[name = string("op_6461_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_6461_cast_fp16 = concat(axis = var_6460, interleave = var_6461_interleave_0, values = (var_6458_cast_fp16, var_6456_cast_fp16_0))[name = string("op_6461_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_6462_cast_fp16 = mul(x = var_6461_cast_fp16, y = var_866_cast_fp16)[name = string("op_6462_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_105_cast_fp16 = add(x = var_6455_cast_fp16, y = var_6462_cast_fp16)[name = string("query_states_105_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_6468_cast_fp16 = mul(x = var_6444_cast_fp16, y = var_859_cast_fp16)[name = string("op_6468_cast_fp16")];
            tensor<int32, [2]> var_6469_split_sizes_0 = const()[name = string("op_6469_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6469_axis_0 = const()[name = string("op_6469_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_6469_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_6469_cast_fp16_1 = split(axis = var_6469_axis_0, split_sizes = var_6469_split_sizes_0, x = var_6444_cast_fp16)[name = string("op_6469_cast_fp16")];
            fp16 const_175_promoted_to_fp16 = const()[name = string("const_175_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_6471_cast_fp16 = mul(x = var_6469_cast_fp16_1, y = const_175_promoted_to_fp16)[name = string("op_6471_cast_fp16")];
            int32 var_6473 = const()[name = string("op_6473"), val = int32(-2)];
            bool var_6474_interleave_0 = const()[name = string("op_6474_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_6474_cast_fp16 = concat(axis = var_6473, interleave = var_6474_interleave_0, values = (var_6471_cast_fp16, var_6469_cast_fp16_0))[name = string("op_6474_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_6475_cast_fp16 = mul(x = var_6474_cast_fp16, y = var_866_cast_fp16)[name = string("op_6475_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_175_cast_fp16 = add(x = var_6468_cast_fp16, y = var_6475_cast_fp16)[name = string("key_states_175_cast_fp16")];
            tensor<int32, [1]> expand_dims_204 = const()[name = string("expand_dims_204"), val = tensor<int32, [1]>([17])];
            tensor<int32, [1]> expand_dims_205 = const()[name = string("expand_dims_205"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_207 = const()[name = string("expand_dims_207"), val = tensor<int32, [1]>([0])];
            int32 concat_209_axis_0 = const()[name = string("concat_209_axis_0"), val = int32(0)];
            bool concat_209_interleave_0 = const()[name = string("concat_209_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_209 = concat(axis = concat_209_axis_0, interleave = concat_209_interleave_0, values = (expand_dims_204, expand_dims_205, position_id, expand_dims_207))[name = string("concat_209")];
            tensor<int32, [1]> expand_dims_208 = const()[name = string("expand_dims_208"), val = tensor<int32, [1]>([18])];
            tensor<int32, [1]> concat_210_values1_0 = const()[name = string("concat_210_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_210_values3_0 = const()[name = string("concat_210_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_210_axis_0 = const()[name = string("concat_210_axis_0"), val = int32(0)];
            bool concat_210_interleave_0 = const()[name = string("concat_210_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_210 = concat(axis = concat_210_axis_0, interleave = concat_210_interleave_0, values = (expand_dims_208, concat_210_values1_0, cache_position_end, concat_210_values3_0))[name = string("concat_210")];
            tensor<int32, [4]> key_states_177_perm_0 = const()[name = string("key_states_177_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_18_stride_0 = const()[name = string("key_cache_internal_tensor_assign_18_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_18_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_18_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_18_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_18_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_18_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_18_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_177_cast_fp16 = transpose(perm = key_states_177_perm_0, x = key_states_175_cast_fp16)[name = string("transpose_293")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_18_cast_fp16 = slice_update(begin = concat_209, begin_mask = key_cache_internal_tensor_assign_18_begin_mask_0, end = concat_210, end_mask = key_cache_internal_tensor_assign_18_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_18_squeeze_mask_0, stride = key_cache_internal_tensor_assign_18_stride_0, update = key_states_177_cast_fp16, x = coreml_update_state_200)[name = string("key_cache_internal_tensor_assign_18_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_18_cast_fp16, input = key_cache)[name = string("coreml_update_state_202_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_202 = read_state(input = key_cache)[name = string("coreml_update_state_202")];
            tensor<int32, [4]> value_states_105_perm_0 = const()[name = string("value_states_105_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_18_stride_0 = const()[name = string("value_cache_internal_tensor_assign_18_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_18_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_18_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_18_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_18_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_18_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_18_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_105_cast_fp16 = transpose(perm = value_states_105_perm_0, x = var_6451_cast_fp16)[name = string("transpose_292")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_18_cast_fp16 = slice_update(begin = concat_209, begin_mask = value_cache_internal_tensor_assign_18_begin_mask_0, end = concat_210, end_mask = value_cache_internal_tensor_assign_18_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_18_squeeze_mask_0, stride = value_cache_internal_tensor_assign_18_stride_0, update = value_states_105_cast_fp16, x = coreml_update_state_201)[name = string("value_cache_internal_tensor_assign_18_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_18_cast_fp16, input = value_cache)[name = string("coreml_update_state_203_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_203 = read_state(input = value_cache)[name = string("coreml_update_state_203")];
            tensor<int32, [4]> var_6545_begin_0 = const()[name = string("op_6545_begin_0"), val = tensor<int32, [4]>([17, 0, 0, 0])];
            tensor<int32, [4]> var_6545_end_0 = const()[name = string("op_6545_end_0"), val = tensor<int32, [4]>([18, 2, 2048, 128])];
            tensor<bool, [4]> var_6545_end_mask_0 = const()[name = string("op_6545_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6545_cast_fp16 = slice_by_index(begin = var_6545_begin_0, end = var_6545_end_0, end_mask = var_6545_end_mask_0, x = coreml_update_state_202)[name = string("op_6545_cast_fp16")];
            tensor<int32, [2]> tile_34 = const()[name = string("tile_34"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6548_axis_0 = const()[name = string("op_6548_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6548_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6548_cast_fp16_1 = split(axis = var_6548_axis_0, split_sizes = tile_34, x = var_6545_cast_fp16)[name = string("op_6548_cast_fp16")];
            tensor<int32, [4]> var_6555_begin_0 = const()[name = string("op_6555_begin_0"), val = tensor<int32, [4]>([17, 0, 0, 0])];
            tensor<int32, [4]> var_6555_end_0 = const()[name = string("op_6555_end_0"), val = tensor<int32, [4]>([18, 2, 2048, 128])];
            tensor<bool, [4]> var_6555_end_mask_0 = const()[name = string("op_6555_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6555_cast_fp16 = slice_by_index(begin = var_6555_begin_0, end = var_6555_end_0, end_mask = var_6555_end_mask_0, x = coreml_update_state_203)[name = string("op_6555_cast_fp16")];
            tensor<int32, [2]> tile_35 = const()[name = string("tile_35"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6558_axis_0 = const()[name = string("op_6558_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6558_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6558_cast_fp16_1 = split(axis = var_6558_axis_0, split_sizes = tile_35, x = var_6555_cast_fp16)[name = string("op_6558_cast_fp16")];
            tensor<int32, [2]> var_6561_split_sizes_0 = const()[name = string("op_6561_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_6561_axis_0 = const()[name = string("op_6561_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_6561_0, tensor<fp16, [1, 8, 128, 16]> var_6561_1 = split(axis = var_6561_axis_0, split_sizes = var_6561_split_sizes_0, x = query_states_105_cast_fp16)[name = string("op_6561")];
            bool attn_weights_273_transpose_x_0 = const()[name = string("attn_weights_273_transpose_x_0"), val = bool(false)];
            bool attn_weights_273_transpose_y_0 = const()[name = string("attn_weights_273_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_273_cast_fp16 = matmul(transpose_x = attn_weights_273_transpose_x_0, transpose_y = attn_weights_273_transpose_y_0, x = var_6548_cast_fp16_0, y = var_6561_0)[name = string("attn_weights_273_cast_fp16")];
            fp16 var_6564_to_fp16 = const()[name = string("op_6564_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_275_cast_fp16 = mul(x = attn_weights_273_cast_fp16, y = var_6564_to_fp16)[name = string("attn_weights_275_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_277_cast_fp16 = add(x = attn_weights_275_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_277_cast_fp16")];
            int32 var_6568 = const()[name = string("op_6568"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_279_cast_fp16 = softmax(axis = var_6568, x = attn_weights_277_cast_fp16)[name = string("attn_weights_279_cast_fp16")];
            bool var_6574_transpose_x_1 = const()[name = string("op_6574_transpose_x_1"), val = bool(true)];
            bool var_6574_transpose_y_1 = const()[name = string("op_6574_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_6574_cast_fp16 = matmul(transpose_x = var_6574_transpose_x_1, transpose_y = var_6574_transpose_y_1, x = attn_weights_279_cast_fp16, y = var_6558_cast_fp16_0)[name = string("op_6574_cast_fp16")];
            bool attn_weights_281_transpose_x_0 = const()[name = string("attn_weights_281_transpose_x_0"), val = bool(false)];
            bool attn_weights_281_transpose_y_0 = const()[name = string("attn_weights_281_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_281_cast_fp16 = matmul(transpose_x = attn_weights_281_transpose_x_0, transpose_y = attn_weights_281_transpose_y_0, x = var_6548_cast_fp16_1, y = var_6561_1)[name = string("attn_weights_281_cast_fp16")];
            fp16 var_6576_to_fp16 = const()[name = string("op_6576_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_283_cast_fp16 = mul(x = attn_weights_281_cast_fp16, y = var_6576_to_fp16)[name = string("attn_weights_283_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_285_cast_fp16 = add(x = attn_weights_283_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_285_cast_fp16")];
            int32 var_6580 = const()[name = string("op_6580"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_287_cast_fp16 = softmax(axis = var_6580, x = attn_weights_285_cast_fp16)[name = string("attn_weights_287_cast_fp16")];
            bool attn_output_137_transpose_x_1 = const()[name = string("attn_output_137_transpose_x_1"), val = bool(true)];
            bool attn_output_137_transpose_y_1 = const()[name = string("attn_output_137_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_137_cast_fp16 = matmul(transpose_x = attn_output_137_transpose_x_1, transpose_y = attn_output_137_transpose_y_1, x = attn_weights_287_cast_fp16, y = var_6558_cast_fp16_1)[name = string("attn_output_137_cast_fp16")];
            int32 var_6588 = const()[name = string("op_6588"), val = int32(1)];
            bool attn_output_139_interleave_0 = const()[name = string("attn_output_139_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_139_cast_fp16 = concat(axis = var_6588, interleave = attn_output_139_interleave_0, values = (var_6574_cast_fp16, attn_output_137_cast_fp16))[name = string("attn_output_139_cast_fp16")];
            tensor<int32, [4]> var_6592_perm_0 = const()[name = string("op_6592_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_215x = const()[name = string("concat_215x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_6592_cast_fp16 = transpose(perm = var_6592_perm_0, x = attn_output_139_cast_fp16)[name = string("transpose_291")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_143_cast_fp16 = reshape(shape = concat_215x, x = var_6592_cast_fp16)[name = string("attn_output_143_cast_fp16")];
            tensor<int32, [2]> hidden_states_173_strides_0 = const()[name = string("hidden_states_173_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_173_pad_type_0 = const()[name = string("hidden_states_173_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_173_pad_0 = const()[name = string("hidden_states_173_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_173_dilations_0 = const()[name = string("hidden_states_173_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_173_groups_0 = const()[name = string("hidden_states_173_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_173_cast_fp16 = conv(dilations = hidden_states_173_dilations_0, groups = hidden_states_173_groups_0, pad = hidden_states_173_pad_0, pad_type = hidden_states_173_pad_type_0, strides = hidden_states_173_strides_0, weight = layers_17_self_attn_o_proj_weight_cast_fp16, x = attn_output_143_cast_fp16)[name = string("hidden_states_173_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_175_cast_fp16 = add(x = hidden_states_169_cast_fp16, y = hidden_states_173_cast_fp16)[name = string("hidden_states_175_cast_fp16")];
            fp16 const_180_promoted_to_fp16 = const()[name = string("const_180_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_6625_cast_fp16 = mul(x = hidden_states_175_cast_fp16, y = const_180_promoted_to_fp16)[name = string("op_6625_cast_fp16")];
            int32 var_6623 = const()[name = string("op_6623"), val = int32(1)];
            bool doubled_141_interleave_0 = const()[name = string("doubled_141_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_141_cast_fp16 = concat(axis = var_6623, interleave = doubled_141_interleave_0, values = (hidden_states_175_cast_fp16, var_6625_cast_fp16))[name = string("doubled_141_cast_fp16")];
            tensor<int32, [1]> out_71_axes_0 = const()[name = string("out_71_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_71_gamma_0_to_fp16 = const()[name = string("out_71_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483631360)))];
            fp16 var_6635_to_fp16 = const()[name = string("op_6635_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_71_cast_fp16 = layer_norm(axes = out_71_axes_0, epsilon = var_6635_to_fp16, gamma = out_71_gamma_0_to_fp16, x = doubled_141_cast_fp16)[name = string("out_71_cast_fp16")];
            tensor<int32, [2]> var_6646_split_sizes_0 = const()[name = string("op_6646_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6646_axis_0 = const()[name = string("op_6646_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_6646_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_6646_cast_fp16_1 = split(axis = var_6646_axis_0, split_sizes = var_6646_split_sizes_0, x = out_71_cast_fp16)[name = string("op_6646_cast_fp16")];
            tensor<int32, [2]> input_35_strides_0 = const()[name = string("input_35_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_35_pad_type_0 = const()[name = string("input_35_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_35_pad_0 = const()[name = string("input_35_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_35_dilations_0 = const()[name = string("input_35_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_35_groups_0 = const()[name = string("input_35_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_35_cast_fp16 = conv(dilations = input_35_dilations_0, groups = input_35_groups_0, pad = input_35_pad_0, pad_type = input_35_pad_type_0, strides = input_35_strides_0, weight = layers_17_mlp_gate_proj_weight_cast_fp16, x = var_6646_cast_fp16_0)[name = string("input_35_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_6663_cast_fp16 = silu(x = input_35_cast_fp16)[name = string("op_6663_cast_fp16")];
            tensor<int32, [2]> var_6669_strides_0 = const()[name = string("op_6669_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6669_pad_type_0 = const()[name = string("op_6669_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6669_pad_0 = const()[name = string("op_6669_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6669_dilations_0 = const()[name = string("op_6669_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6669_groups_0 = const()[name = string("op_6669_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_6669_cast_fp16 = conv(dilations = var_6669_dilations_0, groups = var_6669_groups_0, pad = var_6669_pad_0, pad_type = var_6669_pad_type_0, strides = var_6669_strides_0, weight = layers_17_mlp_up_proj_weight_cast_fp16, x = var_6646_cast_fp16_0)[name = string("op_6669_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_179_cast_fp16 = mul(x = var_6663_cast_fp16, y = var_6669_cast_fp16)[name = string("x_179_cast_fp16")];
            tensor<int32, [2]> hidden_states_177_strides_0 = const()[name = string("hidden_states_177_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_177_pad_type_0 = const()[name = string("hidden_states_177_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_177_pad_0 = const()[name = string("hidden_states_177_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_177_dilations_0 = const()[name = string("hidden_states_177_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_177_groups_0 = const()[name = string("hidden_states_177_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_177_cast_fp16 = conv(dilations = hidden_states_177_dilations_0, groups = hidden_states_177_groups_0, pad = hidden_states_177_pad_0, pad_type = hidden_states_177_pad_type_0, strides = hidden_states_177_strides_0, weight = layers_17_mlp_down_proj_weight_cast_fp16, x = x_179_cast_fp16)[name = string("hidden_states_177_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_179_cast_fp16 = add(x = hidden_states_175_cast_fp16, y = hidden_states_177_cast_fp16)[name = string("hidden_states_179_cast_fp16")];
            fp16 const_182_promoted_to_fp16 = const()[name = string("const_182_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_6687_cast_fp16 = mul(x = hidden_states_179_cast_fp16, y = const_182_promoted_to_fp16)[name = string("op_6687_cast_fp16")];
            int32 var_6685 = const()[name = string("op_6685"), val = int32(1)];
            bool doubled_145_interleave_0 = const()[name = string("doubled_145_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_145_cast_fp16 = concat(axis = var_6685, interleave = doubled_145_interleave_0, values = (hidden_states_179_cast_fp16, var_6687_cast_fp16))[name = string("doubled_145_cast_fp16")];
            tensor<int32, [1]> out_73_axes_0 = const()[name = string("out_73_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_73_gamma_0_to_fp16 = const()[name = string("out_73_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483639616)))];
            fp16 var_6697_to_fp16 = const()[name = string("op_6697_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_73_cast_fp16 = layer_norm(axes = out_73_axes_0, epsilon = var_6697_to_fp16, gamma = out_73_gamma_0_to_fp16, x = doubled_145_cast_fp16)[name = string("out_73_cast_fp16")];
            tensor<int32, [2]> var_6708_split_sizes_0 = const()[name = string("op_6708_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6708_axis_0 = const()[name = string("op_6708_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_6708_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_6708_cast_fp16_1 = split(axis = var_6708_axis_0, split_sizes = var_6708_split_sizes_0, x = out_73_cast_fp16)[name = string("op_6708_cast_fp16")];
            tensor<int32, [2]> query_states_109_strides_0 = const()[name = string("query_states_109_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_109_pad_type_0 = const()[name = string("query_states_109_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_109_pad_0 = const()[name = string("query_states_109_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_109_dilations_0 = const()[name = string("query_states_109_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_109_groups_0 = const()[name = string("query_states_109_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_109_cast_fp16 = conv(dilations = query_states_109_dilations_0, groups = query_states_109_groups_0, pad = query_states_109_pad_0, pad_type = query_states_109_pad_type_0, strides = query_states_109_strides_0, weight = layers_18_self_attn_q_proj_weight_cast_fp16, x = var_6708_cast_fp16_0)[name = string("query_states_109_cast_fp16")];
            tensor<int32, [2]> key_states_181_strides_0 = const()[name = string("key_states_181_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_181_pad_type_0 = const()[name = string("key_states_181_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_181_pad_0 = const()[name = string("key_states_181_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_181_dilations_0 = const()[name = string("key_states_181_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_181_groups_0 = const()[name = string("key_states_181_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_181_cast_fp16 = conv(dilations = key_states_181_dilations_0, groups = key_states_181_groups_0, pad = key_states_181_pad_0, pad_type = key_states_181_pad_type_0, strides = key_states_181_strides_0, weight = layers_18_self_attn_k_proj_weight_cast_fp16, x = var_6708_cast_fp16_0)[name = string("key_states_181_cast_fp16")];
            tensor<int32, [2]> value_states_109_strides_0 = const()[name = string("value_states_109_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_109_pad_type_0 = const()[name = string("value_states_109_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_109_pad_0 = const()[name = string("value_states_109_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_109_dilations_0 = const()[name = string("value_states_109_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_109_groups_0 = const()[name = string("value_states_109_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_109_cast_fp16 = conv(dilations = value_states_109_dilations_0, groups = value_states_109_groups_0, pad = value_states_109_pad_0, pad_type = value_states_109_pad_type_0, strides = value_states_109_strides_0, weight = layers_18_self_attn_v_proj_weight_cast_fp16, x = var_6708_cast_fp16_0)[name = string("value_states_109_cast_fp16")];
            tensor<int32, [4]> concat_216x = const()[name = string("concat_216x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_181_cast_fp16 = reshape(shape = concat_216x, x = query_states_109_cast_fp16)[name = string("x_181_cast_fp16")];
            tensor<int32, [4]> concat_217x = const()[name = string("concat_217x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_6765_cast_fp16 = reshape(shape = concat_217x, x = key_states_181_cast_fp16)[name = string("op_6765_cast_fp16")];
            tensor<int32, [4]> concat_218x = const()[name = string("concat_218x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_6772_cast_fp16 = reshape(shape = concat_218x, x = value_states_109_cast_fp16)[name = string("op_6772_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_6776_cast_fp16 = mul(x = x_181_cast_fp16, y = var_859_cast_fp16)[name = string("op_6776_cast_fp16")];
            tensor<int32, [2]> var_6777_split_sizes_0 = const()[name = string("op_6777_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6777_axis_0 = const()[name = string("op_6777_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_6777_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_6777_cast_fp16_1 = split(axis = var_6777_axis_0, split_sizes = var_6777_split_sizes_0, x = x_181_cast_fp16)[name = string("op_6777_cast_fp16")];
            fp16 const_184_promoted_to_fp16 = const()[name = string("const_184_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_6779_cast_fp16 = mul(x = var_6777_cast_fp16_1, y = const_184_promoted_to_fp16)[name = string("op_6779_cast_fp16")];
            int32 var_6781 = const()[name = string("op_6781"), val = int32(-2)];
            bool var_6782_interleave_0 = const()[name = string("op_6782_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_6782_cast_fp16 = concat(axis = var_6781, interleave = var_6782_interleave_0, values = (var_6779_cast_fp16, var_6777_cast_fp16_0))[name = string("op_6782_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_6783_cast_fp16 = mul(x = var_6782_cast_fp16, y = var_866_cast_fp16)[name = string("op_6783_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_111_cast_fp16 = add(x = var_6776_cast_fp16, y = var_6783_cast_fp16)[name = string("query_states_111_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_6789_cast_fp16 = mul(x = var_6765_cast_fp16, y = var_859_cast_fp16)[name = string("op_6789_cast_fp16")];
            tensor<int32, [2]> var_6790_split_sizes_0 = const()[name = string("op_6790_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6790_axis_0 = const()[name = string("op_6790_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_6790_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_6790_cast_fp16_1 = split(axis = var_6790_axis_0, split_sizes = var_6790_split_sizes_0, x = var_6765_cast_fp16)[name = string("op_6790_cast_fp16")];
            fp16 const_185_promoted_to_fp16 = const()[name = string("const_185_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_6792_cast_fp16 = mul(x = var_6790_cast_fp16_1, y = const_185_promoted_to_fp16)[name = string("op_6792_cast_fp16")];
            int32 var_6794 = const()[name = string("op_6794"), val = int32(-2)];
            bool var_6795_interleave_0 = const()[name = string("op_6795_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_6795_cast_fp16 = concat(axis = var_6794, interleave = var_6795_interleave_0, values = (var_6792_cast_fp16, var_6790_cast_fp16_0))[name = string("op_6795_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_6796_cast_fp16 = mul(x = var_6795_cast_fp16, y = var_866_cast_fp16)[name = string("op_6796_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_185_cast_fp16 = add(x = var_6789_cast_fp16, y = var_6796_cast_fp16)[name = string("key_states_185_cast_fp16")];
            tensor<int32, [1]> expand_dims_216 = const()[name = string("expand_dims_216"), val = tensor<int32, [1]>([18])];
            tensor<int32, [1]> expand_dims_217 = const()[name = string("expand_dims_217"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_219 = const()[name = string("expand_dims_219"), val = tensor<int32, [1]>([0])];
            int32 concat_221_axis_0 = const()[name = string("concat_221_axis_0"), val = int32(0)];
            bool concat_221_interleave_0 = const()[name = string("concat_221_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_221 = concat(axis = concat_221_axis_0, interleave = concat_221_interleave_0, values = (expand_dims_216, expand_dims_217, position_id, expand_dims_219))[name = string("concat_221")];
            tensor<int32, [1]> expand_dims_220 = const()[name = string("expand_dims_220"), val = tensor<int32, [1]>([19])];
            tensor<int32, [1]> concat_222_values1_0 = const()[name = string("concat_222_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_222_values3_0 = const()[name = string("concat_222_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_222_axis_0 = const()[name = string("concat_222_axis_0"), val = int32(0)];
            bool concat_222_interleave_0 = const()[name = string("concat_222_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_222 = concat(axis = concat_222_axis_0, interleave = concat_222_interleave_0, values = (expand_dims_220, concat_222_values1_0, cache_position_end, concat_222_values3_0))[name = string("concat_222")];
            tensor<int32, [4]> key_states_187_perm_0 = const()[name = string("key_states_187_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_19_stride_0 = const()[name = string("key_cache_internal_tensor_assign_19_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_19_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_19_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_19_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_19_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_19_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_19_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_187_cast_fp16 = transpose(perm = key_states_187_perm_0, x = key_states_185_cast_fp16)[name = string("transpose_290")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_19_cast_fp16 = slice_update(begin = concat_221, begin_mask = key_cache_internal_tensor_assign_19_begin_mask_0, end = concat_222, end_mask = key_cache_internal_tensor_assign_19_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_19_squeeze_mask_0, stride = key_cache_internal_tensor_assign_19_stride_0, update = key_states_187_cast_fp16, x = coreml_update_state_202)[name = string("key_cache_internal_tensor_assign_19_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_19_cast_fp16, input = key_cache)[name = string("coreml_update_state_204_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_204 = read_state(input = key_cache)[name = string("coreml_update_state_204")];
            tensor<int32, [4]> value_states_111_perm_0 = const()[name = string("value_states_111_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_19_stride_0 = const()[name = string("value_cache_internal_tensor_assign_19_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_19_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_19_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_19_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_19_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_19_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_19_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_111_cast_fp16 = transpose(perm = value_states_111_perm_0, x = var_6772_cast_fp16)[name = string("transpose_289")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_19_cast_fp16 = slice_update(begin = concat_221, begin_mask = value_cache_internal_tensor_assign_19_begin_mask_0, end = concat_222, end_mask = value_cache_internal_tensor_assign_19_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_19_squeeze_mask_0, stride = value_cache_internal_tensor_assign_19_stride_0, update = value_states_111_cast_fp16, x = coreml_update_state_203)[name = string("value_cache_internal_tensor_assign_19_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_19_cast_fp16, input = value_cache)[name = string("coreml_update_state_205_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_205 = read_state(input = value_cache)[name = string("coreml_update_state_205")];
            tensor<int32, [4]> var_6866_begin_0 = const()[name = string("op_6866_begin_0"), val = tensor<int32, [4]>([18, 0, 0, 0])];
            tensor<int32, [4]> var_6866_end_0 = const()[name = string("op_6866_end_0"), val = tensor<int32, [4]>([19, 2, 2048, 128])];
            tensor<bool, [4]> var_6866_end_mask_0 = const()[name = string("op_6866_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6866_cast_fp16 = slice_by_index(begin = var_6866_begin_0, end = var_6866_end_0, end_mask = var_6866_end_mask_0, x = coreml_update_state_204)[name = string("op_6866_cast_fp16")];
            tensor<int32, [2]> tile_36 = const()[name = string("tile_36"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6869_axis_0 = const()[name = string("op_6869_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6869_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6869_cast_fp16_1 = split(axis = var_6869_axis_0, split_sizes = tile_36, x = var_6866_cast_fp16)[name = string("op_6869_cast_fp16")];
            tensor<int32, [4]> var_6876_begin_0 = const()[name = string("op_6876_begin_0"), val = tensor<int32, [4]>([18, 0, 0, 0])];
            tensor<int32, [4]> var_6876_end_0 = const()[name = string("op_6876_end_0"), val = tensor<int32, [4]>([19, 2, 2048, 128])];
            tensor<bool, [4]> var_6876_end_mask_0 = const()[name = string("op_6876_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6876_cast_fp16 = slice_by_index(begin = var_6876_begin_0, end = var_6876_end_0, end_mask = var_6876_end_mask_0, x = coreml_update_state_205)[name = string("op_6876_cast_fp16")];
            tensor<int32, [2]> tile_37 = const()[name = string("tile_37"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6879_axis_0 = const()[name = string("op_6879_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6879_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6879_cast_fp16_1 = split(axis = var_6879_axis_0, split_sizes = tile_37, x = var_6876_cast_fp16)[name = string("op_6879_cast_fp16")];
            tensor<int32, [2]> var_6882_split_sizes_0 = const()[name = string("op_6882_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_6882_axis_0 = const()[name = string("op_6882_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_6882_0, tensor<fp16, [1, 8, 128, 16]> var_6882_1 = split(axis = var_6882_axis_0, split_sizes = var_6882_split_sizes_0, x = query_states_111_cast_fp16)[name = string("op_6882")];
            bool attn_weights_289_transpose_x_0 = const()[name = string("attn_weights_289_transpose_x_0"), val = bool(false)];
            bool attn_weights_289_transpose_y_0 = const()[name = string("attn_weights_289_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_289_cast_fp16 = matmul(transpose_x = attn_weights_289_transpose_x_0, transpose_y = attn_weights_289_transpose_y_0, x = var_6869_cast_fp16_0, y = var_6882_0)[name = string("attn_weights_289_cast_fp16")];
            fp16 var_6885_to_fp16 = const()[name = string("op_6885_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_291_cast_fp16 = mul(x = attn_weights_289_cast_fp16, y = var_6885_to_fp16)[name = string("attn_weights_291_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_293_cast_fp16 = add(x = attn_weights_291_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_293_cast_fp16")];
            int32 var_6889 = const()[name = string("op_6889"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_295_cast_fp16 = softmax(axis = var_6889, x = attn_weights_293_cast_fp16)[name = string("attn_weights_295_cast_fp16")];
            bool var_6895_transpose_x_1 = const()[name = string("op_6895_transpose_x_1"), val = bool(true)];
            bool var_6895_transpose_y_1 = const()[name = string("op_6895_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_6895_cast_fp16 = matmul(transpose_x = var_6895_transpose_x_1, transpose_y = var_6895_transpose_y_1, x = attn_weights_295_cast_fp16, y = var_6879_cast_fp16_0)[name = string("op_6895_cast_fp16")];
            bool attn_weights_297_transpose_x_0 = const()[name = string("attn_weights_297_transpose_x_0"), val = bool(false)];
            bool attn_weights_297_transpose_y_0 = const()[name = string("attn_weights_297_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_297_cast_fp16 = matmul(transpose_x = attn_weights_297_transpose_x_0, transpose_y = attn_weights_297_transpose_y_0, x = var_6869_cast_fp16_1, y = var_6882_1)[name = string("attn_weights_297_cast_fp16")];
            fp16 var_6897_to_fp16 = const()[name = string("op_6897_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_299_cast_fp16 = mul(x = attn_weights_297_cast_fp16, y = var_6897_to_fp16)[name = string("attn_weights_299_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_301_cast_fp16 = add(x = attn_weights_299_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_301_cast_fp16")];
            int32 var_6901 = const()[name = string("op_6901"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_303_cast_fp16 = softmax(axis = var_6901, x = attn_weights_301_cast_fp16)[name = string("attn_weights_303_cast_fp16")];
            bool attn_output_145_transpose_x_1 = const()[name = string("attn_output_145_transpose_x_1"), val = bool(true)];
            bool attn_output_145_transpose_y_1 = const()[name = string("attn_output_145_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_145_cast_fp16 = matmul(transpose_x = attn_output_145_transpose_x_1, transpose_y = attn_output_145_transpose_y_1, x = attn_weights_303_cast_fp16, y = var_6879_cast_fp16_1)[name = string("attn_output_145_cast_fp16")];
            int32 var_6909 = const()[name = string("op_6909"), val = int32(1)];
            bool attn_output_147_interleave_0 = const()[name = string("attn_output_147_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_147_cast_fp16 = concat(axis = var_6909, interleave = attn_output_147_interleave_0, values = (var_6895_cast_fp16, attn_output_145_cast_fp16))[name = string("attn_output_147_cast_fp16")];
            tensor<int32, [4]> var_6913_perm_0 = const()[name = string("op_6913_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_227x = const()[name = string("concat_227x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_6913_cast_fp16 = transpose(perm = var_6913_perm_0, x = attn_output_147_cast_fp16)[name = string("transpose_288")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_151_cast_fp16 = reshape(shape = concat_227x, x = var_6913_cast_fp16)[name = string("attn_output_151_cast_fp16")];
            tensor<int32, [2]> hidden_states_183_strides_0 = const()[name = string("hidden_states_183_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_183_pad_type_0 = const()[name = string("hidden_states_183_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_183_pad_0 = const()[name = string("hidden_states_183_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_183_dilations_0 = const()[name = string("hidden_states_183_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_183_groups_0 = const()[name = string("hidden_states_183_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_183_cast_fp16 = conv(dilations = hidden_states_183_dilations_0, groups = hidden_states_183_groups_0, pad = hidden_states_183_pad_0, pad_type = hidden_states_183_pad_type_0, strides = hidden_states_183_strides_0, weight = layers_18_self_attn_o_proj_weight_cast_fp16, x = attn_output_151_cast_fp16)[name = string("hidden_states_183_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_185_cast_fp16 = add(x = hidden_states_179_cast_fp16, y = hidden_states_183_cast_fp16)[name = string("hidden_states_185_cast_fp16")];
            fp16 const_190_promoted_to_fp16 = const()[name = string("const_190_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_6946_cast_fp16 = mul(x = hidden_states_185_cast_fp16, y = const_190_promoted_to_fp16)[name = string("op_6946_cast_fp16")];
            int32 var_6944 = const()[name = string("op_6944"), val = int32(1)];
            bool doubled_149_interleave_0 = const()[name = string("doubled_149_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_149_cast_fp16 = concat(axis = var_6944, interleave = doubled_149_interleave_0, values = (hidden_states_185_cast_fp16, var_6946_cast_fp16))[name = string("doubled_149_cast_fp16")];
            tensor<int32, [1]> out_75_axes_0 = const()[name = string("out_75_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_75_gamma_0_to_fp16 = const()[name = string("out_75_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483647872)))];
            fp16 var_6956_to_fp16 = const()[name = string("op_6956_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_75_cast_fp16 = layer_norm(axes = out_75_axes_0, epsilon = var_6956_to_fp16, gamma = out_75_gamma_0_to_fp16, x = doubled_149_cast_fp16)[name = string("out_75_cast_fp16")];
            tensor<int32, [2]> var_6967_split_sizes_0 = const()[name = string("op_6967_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6967_axis_0 = const()[name = string("op_6967_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_6967_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_6967_cast_fp16_1 = split(axis = var_6967_axis_0, split_sizes = var_6967_split_sizes_0, x = out_75_cast_fp16)[name = string("op_6967_cast_fp16")];
            tensor<int32, [2]> input_37_strides_0 = const()[name = string("input_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_37_pad_type_0 = const()[name = string("input_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_37_pad_0 = const()[name = string("input_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_37_dilations_0 = const()[name = string("input_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_37_groups_0 = const()[name = string("input_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_37_cast_fp16 = conv(dilations = input_37_dilations_0, groups = input_37_groups_0, pad = input_37_pad_0, pad_type = input_37_pad_type_0, strides = input_37_strides_0, weight = layers_18_mlp_gate_proj_weight_cast_fp16, x = var_6967_cast_fp16_0)[name = string("input_37_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_6984_cast_fp16 = silu(x = input_37_cast_fp16)[name = string("op_6984_cast_fp16")];
            tensor<int32, [2]> var_6990_strides_0 = const()[name = string("op_6990_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6990_pad_type_0 = const()[name = string("op_6990_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6990_pad_0 = const()[name = string("op_6990_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6990_dilations_0 = const()[name = string("op_6990_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6990_groups_0 = const()[name = string("op_6990_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_6990_cast_fp16 = conv(dilations = var_6990_dilations_0, groups = var_6990_groups_0, pad = var_6990_pad_0, pad_type = var_6990_pad_type_0, strides = var_6990_strides_0, weight = layers_18_mlp_up_proj_weight_cast_fp16, x = var_6967_cast_fp16_0)[name = string("op_6990_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_189_cast_fp16 = mul(x = var_6984_cast_fp16, y = var_6990_cast_fp16)[name = string("x_189_cast_fp16")];
            tensor<int32, [2]> hidden_states_187_strides_0 = const()[name = string("hidden_states_187_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_187_pad_type_0 = const()[name = string("hidden_states_187_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_187_pad_0 = const()[name = string("hidden_states_187_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_187_dilations_0 = const()[name = string("hidden_states_187_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_187_groups_0 = const()[name = string("hidden_states_187_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_187_cast_fp16 = conv(dilations = hidden_states_187_dilations_0, groups = hidden_states_187_groups_0, pad = hidden_states_187_pad_0, pad_type = hidden_states_187_pad_type_0, strides = hidden_states_187_strides_0, weight = layers_18_mlp_down_proj_weight_cast_fp16, x = x_189_cast_fp16)[name = string("hidden_states_187_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_189_cast_fp16 = add(x = hidden_states_185_cast_fp16, y = hidden_states_187_cast_fp16)[name = string("hidden_states_189_cast_fp16")];
            fp16 const_192_promoted_to_fp16 = const()[name = string("const_192_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_7008_cast_fp16 = mul(x = hidden_states_189_cast_fp16, y = const_192_promoted_to_fp16)[name = string("op_7008_cast_fp16")];
            int32 var_7006 = const()[name = string("op_7006"), val = int32(1)];
            bool doubled_153_interleave_0 = const()[name = string("doubled_153_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_153_cast_fp16 = concat(axis = var_7006, interleave = doubled_153_interleave_0, values = (hidden_states_189_cast_fp16, var_7008_cast_fp16))[name = string("doubled_153_cast_fp16")];
            tensor<int32, [1]> out_77_axes_0 = const()[name = string("out_77_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_77_gamma_0_to_fp16 = const()[name = string("out_77_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483656128)))];
            fp16 var_7018_to_fp16 = const()[name = string("op_7018_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_77_cast_fp16 = layer_norm(axes = out_77_axes_0, epsilon = var_7018_to_fp16, gamma = out_77_gamma_0_to_fp16, x = doubled_153_cast_fp16)[name = string("out_77_cast_fp16")];
            tensor<int32, [2]> var_7029_split_sizes_0 = const()[name = string("op_7029_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7029_axis_0 = const()[name = string("op_7029_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_7029_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_7029_cast_fp16_1 = split(axis = var_7029_axis_0, split_sizes = var_7029_split_sizes_0, x = out_77_cast_fp16)[name = string("op_7029_cast_fp16")];
            tensor<int32, [2]> query_states_115_strides_0 = const()[name = string("query_states_115_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_115_pad_type_0 = const()[name = string("query_states_115_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_115_pad_0 = const()[name = string("query_states_115_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_115_dilations_0 = const()[name = string("query_states_115_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_115_groups_0 = const()[name = string("query_states_115_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_115_cast_fp16 = conv(dilations = query_states_115_dilations_0, groups = query_states_115_groups_0, pad = query_states_115_pad_0, pad_type = query_states_115_pad_type_0, strides = query_states_115_strides_0, weight = layers_19_self_attn_q_proj_weight_cast_fp16, x = var_7029_cast_fp16_0)[name = string("query_states_115_cast_fp16")];
            tensor<int32, [2]> key_states_191_strides_0 = const()[name = string("key_states_191_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_191_pad_type_0 = const()[name = string("key_states_191_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_191_pad_0 = const()[name = string("key_states_191_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_191_dilations_0 = const()[name = string("key_states_191_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_191_groups_0 = const()[name = string("key_states_191_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_191_cast_fp16 = conv(dilations = key_states_191_dilations_0, groups = key_states_191_groups_0, pad = key_states_191_pad_0, pad_type = key_states_191_pad_type_0, strides = key_states_191_strides_0, weight = layers_19_self_attn_k_proj_weight_cast_fp16, x = var_7029_cast_fp16_0)[name = string("key_states_191_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_19_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_19_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483664384)))];
            tensor<int32, [2]> value_states_115_strides_0 = const()[name = string("value_states_115_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_115_pad_type_0 = const()[name = string("value_states_115_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_115_pad_0 = const()[name = string("value_states_115_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_115_dilations_0 = const()[name = string("value_states_115_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_115_groups_0 = const()[name = string("value_states_115_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_115_cast_fp16 = conv(dilations = value_states_115_dilations_0, groups = value_states_115_groups_0, pad = value_states_115_pad_0, pad_type = value_states_115_pad_type_0, strides = value_states_115_strides_0, weight = layers_19_self_attn_v_proj_weight_to_fp16, x = var_7029_cast_fp16_0)[name = string("value_states_115_cast_fp16")];
            tensor<int32, [4]> concat_228x = const()[name = string("concat_228x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_191_cast_fp16 = reshape(shape = concat_228x, x = query_states_115_cast_fp16)[name = string("x_191_cast_fp16")];
            tensor<int32, [4]> concat_229x = const()[name = string("concat_229x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_7086_cast_fp16 = reshape(shape = concat_229x, x = key_states_191_cast_fp16)[name = string("op_7086_cast_fp16")];
            tensor<int32, [4]> concat_230x = const()[name = string("concat_230x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_7093_cast_fp16 = reshape(shape = concat_230x, x = value_states_115_cast_fp16)[name = string("op_7093_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_7097_cast_fp16 = mul(x = x_191_cast_fp16, y = var_859_cast_fp16)[name = string("op_7097_cast_fp16")];
            tensor<int32, [2]> var_7098_split_sizes_0 = const()[name = string("op_7098_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7098_axis_0 = const()[name = string("op_7098_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_7098_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_7098_cast_fp16_1 = split(axis = var_7098_axis_0, split_sizes = var_7098_split_sizes_0, x = x_191_cast_fp16)[name = string("op_7098_cast_fp16")];
            fp16 const_194_promoted_to_fp16 = const()[name = string("const_194_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_7100_cast_fp16 = mul(x = var_7098_cast_fp16_1, y = const_194_promoted_to_fp16)[name = string("op_7100_cast_fp16")];
            int32 var_7102 = const()[name = string("op_7102"), val = int32(-2)];
            bool var_7103_interleave_0 = const()[name = string("op_7103_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_7103_cast_fp16 = concat(axis = var_7102, interleave = var_7103_interleave_0, values = (var_7100_cast_fp16, var_7098_cast_fp16_0))[name = string("op_7103_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_7104_cast_fp16 = mul(x = var_7103_cast_fp16, y = var_866_cast_fp16)[name = string("op_7104_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_117_cast_fp16 = add(x = var_7097_cast_fp16, y = var_7104_cast_fp16)[name = string("query_states_117_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_7110_cast_fp16 = mul(x = var_7086_cast_fp16, y = var_859_cast_fp16)[name = string("op_7110_cast_fp16")];
            tensor<int32, [2]> var_7111_split_sizes_0 = const()[name = string("op_7111_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7111_axis_0 = const()[name = string("op_7111_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_7111_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_7111_cast_fp16_1 = split(axis = var_7111_axis_0, split_sizes = var_7111_split_sizes_0, x = var_7086_cast_fp16)[name = string("op_7111_cast_fp16")];
            fp16 const_195_promoted_to_fp16 = const()[name = string("const_195_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_7113_cast_fp16 = mul(x = var_7111_cast_fp16_1, y = const_195_promoted_to_fp16)[name = string("op_7113_cast_fp16")];
            int32 var_7115 = const()[name = string("op_7115"), val = int32(-2)];
            bool var_7116_interleave_0 = const()[name = string("op_7116_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_7116_cast_fp16 = concat(axis = var_7115, interleave = var_7116_interleave_0, values = (var_7113_cast_fp16, var_7111_cast_fp16_0))[name = string("op_7116_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_7117_cast_fp16 = mul(x = var_7116_cast_fp16, y = var_866_cast_fp16)[name = string("op_7117_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_195_cast_fp16 = add(x = var_7110_cast_fp16, y = var_7117_cast_fp16)[name = string("key_states_195_cast_fp16")];
            tensor<int32, [1]> expand_dims_228 = const()[name = string("expand_dims_228"), val = tensor<int32, [1]>([19])];
            tensor<int32, [1]> expand_dims_229 = const()[name = string("expand_dims_229"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_231 = const()[name = string("expand_dims_231"), val = tensor<int32, [1]>([0])];
            int32 concat_233_axis_0 = const()[name = string("concat_233_axis_0"), val = int32(0)];
            bool concat_233_interleave_0 = const()[name = string("concat_233_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_233 = concat(axis = concat_233_axis_0, interleave = concat_233_interleave_0, values = (expand_dims_228, expand_dims_229, position_id, expand_dims_231))[name = string("concat_233")];
            tensor<int32, [1]> expand_dims_232 = const()[name = string("expand_dims_232"), val = tensor<int32, [1]>([20])];
            tensor<int32, [1]> concat_234_values1_0 = const()[name = string("concat_234_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_234_values3_0 = const()[name = string("concat_234_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_234_axis_0 = const()[name = string("concat_234_axis_0"), val = int32(0)];
            bool concat_234_interleave_0 = const()[name = string("concat_234_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_234 = concat(axis = concat_234_axis_0, interleave = concat_234_interleave_0, values = (expand_dims_232, concat_234_values1_0, cache_position_end, concat_234_values3_0))[name = string("concat_234")];
            tensor<int32, [4]> key_states_197_perm_0 = const()[name = string("key_states_197_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_20_stride_0 = const()[name = string("key_cache_internal_tensor_assign_20_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_20_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_20_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_20_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_20_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_20_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_20_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_197_cast_fp16 = transpose(perm = key_states_197_perm_0, x = key_states_195_cast_fp16)[name = string("transpose_287")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_20_cast_fp16 = slice_update(begin = concat_233, begin_mask = key_cache_internal_tensor_assign_20_begin_mask_0, end = concat_234, end_mask = key_cache_internal_tensor_assign_20_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_20_squeeze_mask_0, stride = key_cache_internal_tensor_assign_20_stride_0, update = key_states_197_cast_fp16, x = coreml_update_state_204)[name = string("key_cache_internal_tensor_assign_20_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_20_cast_fp16, input = key_cache)[name = string("coreml_update_state_206_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_206 = read_state(input = key_cache)[name = string("coreml_update_state_206")];
            tensor<int32, [4]> value_states_117_perm_0 = const()[name = string("value_states_117_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_20_stride_0 = const()[name = string("value_cache_internal_tensor_assign_20_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_20_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_20_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_20_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_20_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_20_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_20_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_117_cast_fp16 = transpose(perm = value_states_117_perm_0, x = var_7093_cast_fp16)[name = string("transpose_286")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_20_cast_fp16 = slice_update(begin = concat_233, begin_mask = value_cache_internal_tensor_assign_20_begin_mask_0, end = concat_234, end_mask = value_cache_internal_tensor_assign_20_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_20_squeeze_mask_0, stride = value_cache_internal_tensor_assign_20_stride_0, update = value_states_117_cast_fp16, x = coreml_update_state_205)[name = string("value_cache_internal_tensor_assign_20_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_20_cast_fp16, input = value_cache)[name = string("coreml_update_state_207_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_207 = read_state(input = value_cache)[name = string("coreml_update_state_207")];
            tensor<int32, [4]> var_7187_begin_0 = const()[name = string("op_7187_begin_0"), val = tensor<int32, [4]>([19, 0, 0, 0])];
            tensor<int32, [4]> var_7187_end_0 = const()[name = string("op_7187_end_0"), val = tensor<int32, [4]>([20, 2, 2048, 128])];
            tensor<bool, [4]> var_7187_end_mask_0 = const()[name = string("op_7187_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7187_cast_fp16 = slice_by_index(begin = var_7187_begin_0, end = var_7187_end_0, end_mask = var_7187_end_mask_0, x = coreml_update_state_206)[name = string("op_7187_cast_fp16")];
            tensor<int32, [2]> tile_38 = const()[name = string("tile_38"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7190_axis_0 = const()[name = string("op_7190_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7190_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7190_cast_fp16_1 = split(axis = var_7190_axis_0, split_sizes = tile_38, x = var_7187_cast_fp16)[name = string("op_7190_cast_fp16")];
            tensor<int32, [4]> var_7197_begin_0 = const()[name = string("op_7197_begin_0"), val = tensor<int32, [4]>([19, 0, 0, 0])];
            tensor<int32, [4]> var_7197_end_0 = const()[name = string("op_7197_end_0"), val = tensor<int32, [4]>([20, 2, 2048, 128])];
            tensor<bool, [4]> var_7197_end_mask_0 = const()[name = string("op_7197_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7197_cast_fp16 = slice_by_index(begin = var_7197_begin_0, end = var_7197_end_0, end_mask = var_7197_end_mask_0, x = coreml_update_state_207)[name = string("op_7197_cast_fp16")];
            tensor<int32, [2]> tile_39 = const()[name = string("tile_39"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7200_axis_0 = const()[name = string("op_7200_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7200_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7200_cast_fp16_1 = split(axis = var_7200_axis_0, split_sizes = tile_39, x = var_7197_cast_fp16)[name = string("op_7200_cast_fp16")];
            tensor<int32, [2]> var_7203_split_sizes_0 = const()[name = string("op_7203_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_7203_axis_0 = const()[name = string("op_7203_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_7203_0, tensor<fp16, [1, 8, 128, 16]> var_7203_1 = split(axis = var_7203_axis_0, split_sizes = var_7203_split_sizes_0, x = query_states_117_cast_fp16)[name = string("op_7203")];
            bool attn_weights_305_transpose_x_0 = const()[name = string("attn_weights_305_transpose_x_0"), val = bool(false)];
            bool attn_weights_305_transpose_y_0 = const()[name = string("attn_weights_305_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_305_cast_fp16 = matmul(transpose_x = attn_weights_305_transpose_x_0, transpose_y = attn_weights_305_transpose_y_0, x = var_7190_cast_fp16_0, y = var_7203_0)[name = string("attn_weights_305_cast_fp16")];
            fp16 var_7206_to_fp16 = const()[name = string("op_7206_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_307_cast_fp16 = mul(x = attn_weights_305_cast_fp16, y = var_7206_to_fp16)[name = string("attn_weights_307_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_309_cast_fp16 = add(x = attn_weights_307_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_309_cast_fp16")];
            int32 var_7210 = const()[name = string("op_7210"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_311_cast_fp16 = softmax(axis = var_7210, x = attn_weights_309_cast_fp16)[name = string("attn_weights_311_cast_fp16")];
            bool var_7216_transpose_x_1 = const()[name = string("op_7216_transpose_x_1"), val = bool(true)];
            bool var_7216_transpose_y_1 = const()[name = string("op_7216_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_7216_cast_fp16 = matmul(transpose_x = var_7216_transpose_x_1, transpose_y = var_7216_transpose_y_1, x = attn_weights_311_cast_fp16, y = var_7200_cast_fp16_0)[name = string("op_7216_cast_fp16")];
            bool attn_weights_313_transpose_x_0 = const()[name = string("attn_weights_313_transpose_x_0"), val = bool(false)];
            bool attn_weights_313_transpose_y_0 = const()[name = string("attn_weights_313_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_313_cast_fp16 = matmul(transpose_x = attn_weights_313_transpose_x_0, transpose_y = attn_weights_313_transpose_y_0, x = var_7190_cast_fp16_1, y = var_7203_1)[name = string("attn_weights_313_cast_fp16")];
            fp16 var_7218_to_fp16 = const()[name = string("op_7218_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_315_cast_fp16 = mul(x = attn_weights_313_cast_fp16, y = var_7218_to_fp16)[name = string("attn_weights_315_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_317_cast_fp16 = add(x = attn_weights_315_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_317_cast_fp16")];
            int32 var_7222 = const()[name = string("op_7222"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_319_cast_fp16 = softmax(axis = var_7222, x = attn_weights_317_cast_fp16)[name = string("attn_weights_319_cast_fp16")];
            bool attn_output_153_transpose_x_1 = const()[name = string("attn_output_153_transpose_x_1"), val = bool(true)];
            bool attn_output_153_transpose_y_1 = const()[name = string("attn_output_153_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_153_cast_fp16 = matmul(transpose_x = attn_output_153_transpose_x_1, transpose_y = attn_output_153_transpose_y_1, x = attn_weights_319_cast_fp16, y = var_7200_cast_fp16_1)[name = string("attn_output_153_cast_fp16")];
            int32 var_7230 = const()[name = string("op_7230"), val = int32(1)];
            bool attn_output_155_interleave_0 = const()[name = string("attn_output_155_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_155_cast_fp16 = concat(axis = var_7230, interleave = attn_output_155_interleave_0, values = (var_7216_cast_fp16, attn_output_153_cast_fp16))[name = string("attn_output_155_cast_fp16")];
            tensor<int32, [4]> var_7234_perm_0 = const()[name = string("op_7234_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_239x = const()[name = string("concat_239x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_7234_cast_fp16 = transpose(perm = var_7234_perm_0, x = attn_output_155_cast_fp16)[name = string("transpose_285")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_159_cast_fp16 = reshape(shape = concat_239x, x = var_7234_cast_fp16)[name = string("attn_output_159_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_19_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_19_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1484713024)))];
            tensor<int32, [2]> hidden_states_193_strides_0 = const()[name = string("hidden_states_193_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_193_pad_type_0 = const()[name = string("hidden_states_193_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_193_pad_0 = const()[name = string("hidden_states_193_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_193_dilations_0 = const()[name = string("hidden_states_193_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_193_groups_0 = const()[name = string("hidden_states_193_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_193_cast_fp16 = conv(dilations = hidden_states_193_dilations_0, groups = hidden_states_193_groups_0, pad = hidden_states_193_pad_0, pad_type = hidden_states_193_pad_type_0, strides = hidden_states_193_strides_0, weight = layers_19_self_attn_o_proj_weight_to_fp16, x = attn_output_159_cast_fp16)[name = string("hidden_states_193_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_195_cast_fp16 = add(x = hidden_states_189_cast_fp16, y = hidden_states_193_cast_fp16)[name = string("hidden_states_195_cast_fp16")];
            fp16 const_200_promoted_to_fp16 = const()[name = string("const_200_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_7267_cast_fp16 = mul(x = hidden_states_195_cast_fp16, y = const_200_promoted_to_fp16)[name = string("op_7267_cast_fp16")];
            int32 var_7265 = const()[name = string("op_7265"), val = int32(1)];
            bool doubled_157_interleave_0 = const()[name = string("doubled_157_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_157_cast_fp16 = concat(axis = var_7265, interleave = doubled_157_interleave_0, values = (hidden_states_195_cast_fp16, var_7267_cast_fp16))[name = string("doubled_157_cast_fp16")];
            tensor<int32, [1]> out_79_axes_0 = const()[name = string("out_79_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_79_gamma_0_to_fp16 = const()[name = string("out_79_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1493101696)))];
            fp16 var_7277_to_fp16 = const()[name = string("op_7277_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_79_cast_fp16 = layer_norm(axes = out_79_axes_0, epsilon = var_7277_to_fp16, gamma = out_79_gamma_0_to_fp16, x = doubled_157_cast_fp16)[name = string("out_79_cast_fp16")];
            tensor<int32, [2]> var_7288_split_sizes_0 = const()[name = string("op_7288_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7288_axis_0 = const()[name = string("op_7288_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_7288_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_7288_cast_fp16_1 = split(axis = var_7288_axis_0, split_sizes = var_7288_split_sizes_0, x = out_79_cast_fp16)[name = string("op_7288_cast_fp16")];
            tensor<int32, [2]> input_39_strides_0 = const()[name = string("input_39_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_39_pad_type_0 = const()[name = string("input_39_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_39_pad_0 = const()[name = string("input_39_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_39_dilations_0 = const()[name = string("input_39_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_39_groups_0 = const()[name = string("input_39_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_39_cast_fp16 = conv(dilations = input_39_dilations_0, groups = input_39_groups_0, pad = input_39_pad_0, pad_type = input_39_pad_type_0, strides = input_39_strides_0, weight = layers_19_mlp_gate_proj_weight_cast_fp16, x = var_7288_cast_fp16_0)[name = string("input_39_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_7305_cast_fp16 = silu(x = input_39_cast_fp16)[name = string("op_7305_cast_fp16")];
            tensor<int32, [2]> var_7311_strides_0 = const()[name = string("op_7311_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_7311_pad_type_0 = const()[name = string("op_7311_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_7311_pad_0 = const()[name = string("op_7311_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_7311_dilations_0 = const()[name = string("op_7311_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7311_groups_0 = const()[name = string("op_7311_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_7311_cast_fp16 = conv(dilations = var_7311_dilations_0, groups = var_7311_groups_0, pad = var_7311_pad_0, pad_type = var_7311_pad_type_0, strides = var_7311_strides_0, weight = layers_19_mlp_up_proj_weight_cast_fp16, x = var_7288_cast_fp16_0)[name = string("op_7311_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_199_cast_fp16 = mul(x = var_7305_cast_fp16, y = var_7311_cast_fp16)[name = string("x_199_cast_fp16")];
            tensor<int32, [2]> hidden_states_197_strides_0 = const()[name = string("hidden_states_197_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_197_pad_type_0 = const()[name = string("hidden_states_197_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_197_pad_0 = const()[name = string("hidden_states_197_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_197_dilations_0 = const()[name = string("hidden_states_197_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_197_groups_0 = const()[name = string("hidden_states_197_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_197_cast_fp16 = conv(dilations = hidden_states_197_dilations_0, groups = hidden_states_197_groups_0, pad = hidden_states_197_pad_0, pad_type = hidden_states_197_pad_type_0, strides = hidden_states_197_strides_0, weight = layers_19_mlp_down_proj_weight_cast_fp16, x = x_199_cast_fp16)[name = string("hidden_states_197_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_199_cast_fp16 = add(x = hidden_states_195_cast_fp16, y = hidden_states_197_cast_fp16)[name = string("hidden_states_199_cast_fp16")];
            fp16 const_202_promoted_to_fp16 = const()[name = string("const_202_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_7329_cast_fp16 = mul(x = hidden_states_199_cast_fp16, y = const_202_promoted_to_fp16)[name = string("op_7329_cast_fp16")];
            int32 var_7327 = const()[name = string("op_7327"), val = int32(1)];
            bool doubled_161_interleave_0 = const()[name = string("doubled_161_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_161_cast_fp16 = concat(axis = var_7327, interleave = doubled_161_interleave_0, values = (hidden_states_199_cast_fp16, var_7329_cast_fp16))[name = string("doubled_161_cast_fp16")];
            tensor<int32, [1]> out_81_axes_0 = const()[name = string("out_81_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_81_gamma_0_to_fp16 = const()[name = string("out_81_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1493109952)))];
            fp16 var_7339_to_fp16 = const()[name = string("op_7339_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_81_cast_fp16 = layer_norm(axes = out_81_axes_0, epsilon = var_7339_to_fp16, gamma = out_81_gamma_0_to_fp16, x = doubled_161_cast_fp16)[name = string("out_81_cast_fp16")];
            tensor<int32, [2]> var_7350_split_sizes_0 = const()[name = string("op_7350_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7350_axis_0 = const()[name = string("op_7350_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_7350_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_7350_cast_fp16_1 = split(axis = var_7350_axis_0, split_sizes = var_7350_split_sizes_0, x = out_81_cast_fp16)[name = string("op_7350_cast_fp16")];
            tensor<int32, [2]> query_states_121_strides_0 = const()[name = string("query_states_121_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_121_pad_type_0 = const()[name = string("query_states_121_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_121_pad_0 = const()[name = string("query_states_121_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_121_dilations_0 = const()[name = string("query_states_121_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_121_groups_0 = const()[name = string("query_states_121_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_121_cast_fp16 = conv(dilations = query_states_121_dilations_0, groups = query_states_121_groups_0, pad = query_states_121_pad_0, pad_type = query_states_121_pad_type_0, strides = query_states_121_strides_0, weight = layers_20_self_attn_q_proj_weight_cast_fp16, x = var_7350_cast_fp16_0)[name = string("query_states_121_cast_fp16")];
            tensor<int32, [2]> key_states_201_strides_0 = const()[name = string("key_states_201_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_201_pad_type_0 = const()[name = string("key_states_201_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_201_pad_0 = const()[name = string("key_states_201_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_201_dilations_0 = const()[name = string("key_states_201_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_201_groups_0 = const()[name = string("key_states_201_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_201_cast_fp16 = conv(dilations = key_states_201_dilations_0, groups = key_states_201_groups_0, pad = key_states_201_pad_0, pad_type = key_states_201_pad_type_0, strides = key_states_201_strides_0, weight = layers_20_self_attn_k_proj_weight_cast_fp16, x = var_7350_cast_fp16_0)[name = string("key_states_201_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_20_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_20_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1493118208)))];
            tensor<int32, [2]> value_states_121_strides_0 = const()[name = string("value_states_121_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_121_pad_type_0 = const()[name = string("value_states_121_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_121_pad_0 = const()[name = string("value_states_121_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_121_dilations_0 = const()[name = string("value_states_121_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_121_groups_0 = const()[name = string("value_states_121_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_121_cast_fp16 = conv(dilations = value_states_121_dilations_0, groups = value_states_121_groups_0, pad = value_states_121_pad_0, pad_type = value_states_121_pad_type_0, strides = value_states_121_strides_0, weight = layers_20_self_attn_v_proj_weight_to_fp16, x = var_7350_cast_fp16_0)[name = string("value_states_121_cast_fp16")];
            tensor<int32, [4]> concat_240x = const()[name = string("concat_240x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_201_cast_fp16 = reshape(shape = concat_240x, x = query_states_121_cast_fp16)[name = string("x_201_cast_fp16")];
            tensor<int32, [4]> concat_241x = const()[name = string("concat_241x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_7407_cast_fp16 = reshape(shape = concat_241x, x = key_states_201_cast_fp16)[name = string("op_7407_cast_fp16")];
            tensor<int32, [4]> concat_242x = const()[name = string("concat_242x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_7414_cast_fp16 = reshape(shape = concat_242x, x = value_states_121_cast_fp16)[name = string("op_7414_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_7418_cast_fp16 = mul(x = x_201_cast_fp16, y = var_859_cast_fp16)[name = string("op_7418_cast_fp16")];
            tensor<int32, [2]> var_7419_split_sizes_0 = const()[name = string("op_7419_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7419_axis_0 = const()[name = string("op_7419_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_7419_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_7419_cast_fp16_1 = split(axis = var_7419_axis_0, split_sizes = var_7419_split_sizes_0, x = x_201_cast_fp16)[name = string("op_7419_cast_fp16")];
            fp16 const_204_promoted_to_fp16 = const()[name = string("const_204_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_7421_cast_fp16 = mul(x = var_7419_cast_fp16_1, y = const_204_promoted_to_fp16)[name = string("op_7421_cast_fp16")];
            int32 var_7423 = const()[name = string("op_7423"), val = int32(-2)];
            bool var_7424_interleave_0 = const()[name = string("op_7424_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_7424_cast_fp16 = concat(axis = var_7423, interleave = var_7424_interleave_0, values = (var_7421_cast_fp16, var_7419_cast_fp16_0))[name = string("op_7424_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_7425_cast_fp16 = mul(x = var_7424_cast_fp16, y = var_866_cast_fp16)[name = string("op_7425_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_123_cast_fp16 = add(x = var_7418_cast_fp16, y = var_7425_cast_fp16)[name = string("query_states_123_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_7431_cast_fp16 = mul(x = var_7407_cast_fp16, y = var_859_cast_fp16)[name = string("op_7431_cast_fp16")];
            tensor<int32, [2]> var_7432_split_sizes_0 = const()[name = string("op_7432_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7432_axis_0 = const()[name = string("op_7432_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_7432_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_7432_cast_fp16_1 = split(axis = var_7432_axis_0, split_sizes = var_7432_split_sizes_0, x = var_7407_cast_fp16)[name = string("op_7432_cast_fp16")];
            fp16 const_205_promoted_to_fp16 = const()[name = string("const_205_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_7434_cast_fp16 = mul(x = var_7432_cast_fp16_1, y = const_205_promoted_to_fp16)[name = string("op_7434_cast_fp16")];
            int32 var_7436 = const()[name = string("op_7436"), val = int32(-2)];
            bool var_7437_interleave_0 = const()[name = string("op_7437_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_7437_cast_fp16 = concat(axis = var_7436, interleave = var_7437_interleave_0, values = (var_7434_cast_fp16, var_7432_cast_fp16_0))[name = string("op_7437_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_7438_cast_fp16 = mul(x = var_7437_cast_fp16, y = var_866_cast_fp16)[name = string("op_7438_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_205_cast_fp16 = add(x = var_7431_cast_fp16, y = var_7438_cast_fp16)[name = string("key_states_205_cast_fp16")];
            tensor<int32, [1]> expand_dims_240 = const()[name = string("expand_dims_240"), val = tensor<int32, [1]>([20])];
            tensor<int32, [1]> expand_dims_241 = const()[name = string("expand_dims_241"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_243 = const()[name = string("expand_dims_243"), val = tensor<int32, [1]>([0])];
            int32 concat_245_axis_0 = const()[name = string("concat_245_axis_0"), val = int32(0)];
            bool concat_245_interleave_0 = const()[name = string("concat_245_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_245 = concat(axis = concat_245_axis_0, interleave = concat_245_interleave_0, values = (expand_dims_240, expand_dims_241, position_id, expand_dims_243))[name = string("concat_245")];
            tensor<int32, [1]> expand_dims_244 = const()[name = string("expand_dims_244"), val = tensor<int32, [1]>([21])];
            tensor<int32, [1]> concat_246_values1_0 = const()[name = string("concat_246_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_246_values3_0 = const()[name = string("concat_246_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_246_axis_0 = const()[name = string("concat_246_axis_0"), val = int32(0)];
            bool concat_246_interleave_0 = const()[name = string("concat_246_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_246 = concat(axis = concat_246_axis_0, interleave = concat_246_interleave_0, values = (expand_dims_244, concat_246_values1_0, cache_position_end, concat_246_values3_0))[name = string("concat_246")];
            tensor<int32, [4]> key_states_207_perm_0 = const()[name = string("key_states_207_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_21_stride_0 = const()[name = string("key_cache_internal_tensor_assign_21_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_21_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_21_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_21_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_21_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_21_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_21_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_207_cast_fp16 = transpose(perm = key_states_207_perm_0, x = key_states_205_cast_fp16)[name = string("transpose_284")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_21_cast_fp16 = slice_update(begin = concat_245, begin_mask = key_cache_internal_tensor_assign_21_begin_mask_0, end = concat_246, end_mask = key_cache_internal_tensor_assign_21_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_21_squeeze_mask_0, stride = key_cache_internal_tensor_assign_21_stride_0, update = key_states_207_cast_fp16, x = coreml_update_state_206)[name = string("key_cache_internal_tensor_assign_21_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_21_cast_fp16, input = key_cache)[name = string("coreml_update_state_208_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_208 = read_state(input = key_cache)[name = string("coreml_update_state_208")];
            tensor<int32, [4]> value_states_123_perm_0 = const()[name = string("value_states_123_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_21_stride_0 = const()[name = string("value_cache_internal_tensor_assign_21_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_21_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_21_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_21_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_21_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_21_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_21_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_123_cast_fp16 = transpose(perm = value_states_123_perm_0, x = var_7414_cast_fp16)[name = string("transpose_283")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_21_cast_fp16 = slice_update(begin = concat_245, begin_mask = value_cache_internal_tensor_assign_21_begin_mask_0, end = concat_246, end_mask = value_cache_internal_tensor_assign_21_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_21_squeeze_mask_0, stride = value_cache_internal_tensor_assign_21_stride_0, update = value_states_123_cast_fp16, x = coreml_update_state_207)[name = string("value_cache_internal_tensor_assign_21_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_21_cast_fp16, input = value_cache)[name = string("coreml_update_state_209_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_209 = read_state(input = value_cache)[name = string("coreml_update_state_209")];
            tensor<int32, [4]> var_7508_begin_0 = const()[name = string("op_7508_begin_0"), val = tensor<int32, [4]>([20, 0, 0, 0])];
            tensor<int32, [4]> var_7508_end_0 = const()[name = string("op_7508_end_0"), val = tensor<int32, [4]>([21, 2, 2048, 128])];
            tensor<bool, [4]> var_7508_end_mask_0 = const()[name = string("op_7508_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7508_cast_fp16 = slice_by_index(begin = var_7508_begin_0, end = var_7508_end_0, end_mask = var_7508_end_mask_0, x = coreml_update_state_208)[name = string("op_7508_cast_fp16")];
            tensor<int32, [2]> tile_40 = const()[name = string("tile_40"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7511_axis_0 = const()[name = string("op_7511_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7511_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7511_cast_fp16_1 = split(axis = var_7511_axis_0, split_sizes = tile_40, x = var_7508_cast_fp16)[name = string("op_7511_cast_fp16")];
            tensor<int32, [4]> var_7518_begin_0 = const()[name = string("op_7518_begin_0"), val = tensor<int32, [4]>([20, 0, 0, 0])];
            tensor<int32, [4]> var_7518_end_0 = const()[name = string("op_7518_end_0"), val = tensor<int32, [4]>([21, 2, 2048, 128])];
            tensor<bool, [4]> var_7518_end_mask_0 = const()[name = string("op_7518_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7518_cast_fp16 = slice_by_index(begin = var_7518_begin_0, end = var_7518_end_0, end_mask = var_7518_end_mask_0, x = coreml_update_state_209)[name = string("op_7518_cast_fp16")];
            tensor<int32, [2]> tile_41 = const()[name = string("tile_41"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7521_axis_0 = const()[name = string("op_7521_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7521_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7521_cast_fp16_1 = split(axis = var_7521_axis_0, split_sizes = tile_41, x = var_7518_cast_fp16)[name = string("op_7521_cast_fp16")];
            tensor<int32, [2]> var_7524_split_sizes_0 = const()[name = string("op_7524_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_7524_axis_0 = const()[name = string("op_7524_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_7524_0, tensor<fp16, [1, 8, 128, 16]> var_7524_1 = split(axis = var_7524_axis_0, split_sizes = var_7524_split_sizes_0, x = query_states_123_cast_fp16)[name = string("op_7524")];
            bool attn_weights_321_transpose_x_0 = const()[name = string("attn_weights_321_transpose_x_0"), val = bool(false)];
            bool attn_weights_321_transpose_y_0 = const()[name = string("attn_weights_321_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_321_cast_fp16 = matmul(transpose_x = attn_weights_321_transpose_x_0, transpose_y = attn_weights_321_transpose_y_0, x = var_7511_cast_fp16_0, y = var_7524_0)[name = string("attn_weights_321_cast_fp16")];
            fp16 var_7527_to_fp16 = const()[name = string("op_7527_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_323_cast_fp16 = mul(x = attn_weights_321_cast_fp16, y = var_7527_to_fp16)[name = string("attn_weights_323_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_325_cast_fp16 = add(x = attn_weights_323_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_325_cast_fp16")];
            int32 var_7531 = const()[name = string("op_7531"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_327_cast_fp16 = softmax(axis = var_7531, x = attn_weights_325_cast_fp16)[name = string("attn_weights_327_cast_fp16")];
            bool var_7537_transpose_x_1 = const()[name = string("op_7537_transpose_x_1"), val = bool(true)];
            bool var_7537_transpose_y_1 = const()[name = string("op_7537_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_7537_cast_fp16 = matmul(transpose_x = var_7537_transpose_x_1, transpose_y = var_7537_transpose_y_1, x = attn_weights_327_cast_fp16, y = var_7521_cast_fp16_0)[name = string("op_7537_cast_fp16")];
            bool attn_weights_329_transpose_x_0 = const()[name = string("attn_weights_329_transpose_x_0"), val = bool(false)];
            bool attn_weights_329_transpose_y_0 = const()[name = string("attn_weights_329_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_329_cast_fp16 = matmul(transpose_x = attn_weights_329_transpose_x_0, transpose_y = attn_weights_329_transpose_y_0, x = var_7511_cast_fp16_1, y = var_7524_1)[name = string("attn_weights_329_cast_fp16")];
            fp16 var_7539_to_fp16 = const()[name = string("op_7539_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_331_cast_fp16 = mul(x = attn_weights_329_cast_fp16, y = var_7539_to_fp16)[name = string("attn_weights_331_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_333_cast_fp16 = add(x = attn_weights_331_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_333_cast_fp16")];
            int32 var_7543 = const()[name = string("op_7543"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_335_cast_fp16 = softmax(axis = var_7543, x = attn_weights_333_cast_fp16)[name = string("attn_weights_335_cast_fp16")];
            bool attn_output_161_transpose_x_1 = const()[name = string("attn_output_161_transpose_x_1"), val = bool(true)];
            bool attn_output_161_transpose_y_1 = const()[name = string("attn_output_161_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_161_cast_fp16 = matmul(transpose_x = attn_output_161_transpose_x_1, transpose_y = attn_output_161_transpose_y_1, x = attn_weights_335_cast_fp16, y = var_7521_cast_fp16_1)[name = string("attn_output_161_cast_fp16")];
            int32 var_7551 = const()[name = string("op_7551"), val = int32(1)];
            bool attn_output_163_interleave_0 = const()[name = string("attn_output_163_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_163_cast_fp16 = concat(axis = var_7551, interleave = attn_output_163_interleave_0, values = (var_7537_cast_fp16, attn_output_161_cast_fp16))[name = string("attn_output_163_cast_fp16")];
            tensor<int32, [4]> var_7555_perm_0 = const()[name = string("op_7555_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_251x = const()[name = string("concat_251x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_7555_cast_fp16 = transpose(perm = var_7555_perm_0, x = attn_output_163_cast_fp16)[name = string("transpose_282")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_167_cast_fp16 = reshape(shape = concat_251x, x = var_7555_cast_fp16)[name = string("attn_output_167_cast_fp16")];
            tensor<int32, [2]> hidden_states_203_strides_0 = const()[name = string("hidden_states_203_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_203_pad_type_0 = const()[name = string("hidden_states_203_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_203_pad_0 = const()[name = string("hidden_states_203_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_203_dilations_0 = const()[name = string("hidden_states_203_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_203_groups_0 = const()[name = string("hidden_states_203_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_203_cast_fp16 = conv(dilations = hidden_states_203_dilations_0, groups = hidden_states_203_groups_0, pad = hidden_states_203_pad_0, pad_type = hidden_states_203_pad_type_0, strides = hidden_states_203_strides_0, weight = layers_20_self_attn_o_proj_weight_cast_fp16, x = attn_output_167_cast_fp16)[name = string("hidden_states_203_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_205_cast_fp16 = add(x = hidden_states_199_cast_fp16, y = hidden_states_203_cast_fp16)[name = string("hidden_states_205_cast_fp16")];
            fp16 const_210_promoted_to_fp16 = const()[name = string("const_210_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_7588_cast_fp16 = mul(x = hidden_states_205_cast_fp16, y = const_210_promoted_to_fp16)[name = string("op_7588_cast_fp16")];
            int32 var_7586 = const()[name = string("op_7586"), val = int32(1)];
            bool doubled_165_interleave_0 = const()[name = string("doubled_165_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_165_cast_fp16 = concat(axis = var_7586, interleave = doubled_165_interleave_0, values = (hidden_states_205_cast_fp16, var_7588_cast_fp16))[name = string("doubled_165_cast_fp16")];
            tensor<int32, [1]> out_83_axes_0 = const()[name = string("out_83_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_83_gamma_0_to_fp16 = const()[name = string("out_83_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1494166848)))];
            fp16 var_7598_to_fp16 = const()[name = string("op_7598_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_83_cast_fp16 = layer_norm(axes = out_83_axes_0, epsilon = var_7598_to_fp16, gamma = out_83_gamma_0_to_fp16, x = doubled_165_cast_fp16)[name = string("out_83_cast_fp16")];
            tensor<int32, [2]> var_7609_split_sizes_0 = const()[name = string("op_7609_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7609_axis_0 = const()[name = string("op_7609_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_7609_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_7609_cast_fp16_1 = split(axis = var_7609_axis_0, split_sizes = var_7609_split_sizes_0, x = out_83_cast_fp16)[name = string("op_7609_cast_fp16")];
            tensor<int32, [2]> input_41_strides_0 = const()[name = string("input_41_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_41_pad_type_0 = const()[name = string("input_41_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_41_pad_0 = const()[name = string("input_41_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_41_dilations_0 = const()[name = string("input_41_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_41_groups_0 = const()[name = string("input_41_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_41_cast_fp16 = conv(dilations = input_41_dilations_0, groups = input_41_groups_0, pad = input_41_pad_0, pad_type = input_41_pad_type_0, strides = input_41_strides_0, weight = layers_20_mlp_gate_proj_weight_cast_fp16, x = var_7609_cast_fp16_0)[name = string("input_41_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_7626_cast_fp16 = silu(x = input_41_cast_fp16)[name = string("op_7626_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_20_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_20_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1494175104)))];
            tensor<int32, [2]> var_7632_strides_0 = const()[name = string("op_7632_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_7632_pad_type_0 = const()[name = string("op_7632_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_7632_pad_0 = const()[name = string("op_7632_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_7632_dilations_0 = const()[name = string("op_7632_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7632_groups_0 = const()[name = string("op_7632_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_7632_cast_fp16 = conv(dilations = var_7632_dilations_0, groups = var_7632_groups_0, pad = var_7632_pad_0, pad_type = var_7632_pad_type_0, strides = var_7632_strides_0, weight = layers_20_mlp_up_proj_weight_to_fp16, x = var_7609_cast_fp16_0)[name = string("op_7632_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_209_cast_fp16 = mul(x = var_7626_cast_fp16, y = var_7632_cast_fp16)[name = string("x_209_cast_fp16")];
            tensor<int32, [2]> hidden_states_207_strides_0 = const()[name = string("hidden_states_207_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_207_pad_type_0 = const()[name = string("hidden_states_207_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_207_pad_0 = const()[name = string("hidden_states_207_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_207_dilations_0 = const()[name = string("hidden_states_207_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_207_groups_0 = const()[name = string("hidden_states_207_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_207_cast_fp16 = conv(dilations = hidden_states_207_dilations_0, groups = hidden_states_207_groups_0, pad = hidden_states_207_pad_0, pad_type = hidden_states_207_pad_type_0, strides = hidden_states_207_strides_0, weight = layers_20_mlp_down_proj_weight_cast_fp16, x = x_209_cast_fp16)[name = string("hidden_states_207_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_209_cast_fp16 = add(x = hidden_states_205_cast_fp16, y = hidden_states_207_cast_fp16)[name = string("hidden_states_209_cast_fp16")];
            fp16 const_212_promoted_to_fp16 = const()[name = string("const_212_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_7650_cast_fp16 = mul(x = hidden_states_209_cast_fp16, y = const_212_promoted_to_fp16)[name = string("op_7650_cast_fp16")];
            int32 var_7648 = const()[name = string("op_7648"), val = int32(1)];
            bool doubled_169_interleave_0 = const()[name = string("doubled_169_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_169_cast_fp16 = concat(axis = var_7648, interleave = doubled_169_interleave_0, values = (hidden_states_209_cast_fp16, var_7650_cast_fp16))[name = string("doubled_169_cast_fp16")];
            tensor<int32, [1]> out_85_axes_0 = const()[name = string("out_85_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_85_gamma_0_to_fp16 = const()[name = string("out_85_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1519340992)))];
            fp16 var_7660_to_fp16 = const()[name = string("op_7660_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_85_cast_fp16 = layer_norm(axes = out_85_axes_0, epsilon = var_7660_to_fp16, gamma = out_85_gamma_0_to_fp16, x = doubled_169_cast_fp16)[name = string("out_85_cast_fp16")];
            tensor<int32, [2]> var_7671_split_sizes_0 = const()[name = string("op_7671_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7671_axis_0 = const()[name = string("op_7671_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_7671_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_7671_cast_fp16_1 = split(axis = var_7671_axis_0, split_sizes = var_7671_split_sizes_0, x = out_85_cast_fp16)[name = string("op_7671_cast_fp16")];
            tensor<int32, [2]> query_states_127_strides_0 = const()[name = string("query_states_127_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_127_pad_type_0 = const()[name = string("query_states_127_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_127_pad_0 = const()[name = string("query_states_127_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_127_dilations_0 = const()[name = string("query_states_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_127_groups_0 = const()[name = string("query_states_127_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_127_cast_fp16 = conv(dilations = query_states_127_dilations_0, groups = query_states_127_groups_0, pad = query_states_127_pad_0, pad_type = query_states_127_pad_type_0, strides = query_states_127_strides_0, weight = layers_21_self_attn_q_proj_weight_cast_fp16, x = var_7671_cast_fp16_0)[name = string("query_states_127_cast_fp16")];
            tensor<int32, [2]> key_states_211_strides_0 = const()[name = string("key_states_211_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_211_pad_type_0 = const()[name = string("key_states_211_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_211_pad_0 = const()[name = string("key_states_211_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_211_dilations_0 = const()[name = string("key_states_211_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_211_groups_0 = const()[name = string("key_states_211_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_211_cast_fp16 = conv(dilations = key_states_211_dilations_0, groups = key_states_211_groups_0, pad = key_states_211_pad_0, pad_type = key_states_211_pad_type_0, strides = key_states_211_strides_0, weight = layers_21_self_attn_k_proj_weight_cast_fp16, x = var_7671_cast_fp16_0)[name = string("key_states_211_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_21_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_21_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1519349248)))];
            tensor<int32, [2]> value_states_127_strides_0 = const()[name = string("value_states_127_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_127_pad_type_0 = const()[name = string("value_states_127_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_127_pad_0 = const()[name = string("value_states_127_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_127_dilations_0 = const()[name = string("value_states_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_127_groups_0 = const()[name = string("value_states_127_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_127_cast_fp16 = conv(dilations = value_states_127_dilations_0, groups = value_states_127_groups_0, pad = value_states_127_pad_0, pad_type = value_states_127_pad_type_0, strides = value_states_127_strides_0, weight = layers_21_self_attn_v_proj_weight_to_fp16, x = var_7671_cast_fp16_0)[name = string("value_states_127_cast_fp16")];
            tensor<int32, [4]> concat_252x = const()[name = string("concat_252x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_211_cast_fp16 = reshape(shape = concat_252x, x = query_states_127_cast_fp16)[name = string("x_211_cast_fp16")];
            tensor<int32, [4]> concat_253x = const()[name = string("concat_253x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_7728_cast_fp16 = reshape(shape = concat_253x, x = key_states_211_cast_fp16)[name = string("op_7728_cast_fp16")];
            tensor<int32, [4]> concat_254x = const()[name = string("concat_254x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_7735_cast_fp16 = reshape(shape = concat_254x, x = value_states_127_cast_fp16)[name = string("op_7735_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_7739_cast_fp16 = mul(x = x_211_cast_fp16, y = var_859_cast_fp16)[name = string("op_7739_cast_fp16")];
            tensor<int32, [2]> var_7740_split_sizes_0 = const()[name = string("op_7740_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7740_axis_0 = const()[name = string("op_7740_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_7740_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_7740_cast_fp16_1 = split(axis = var_7740_axis_0, split_sizes = var_7740_split_sizes_0, x = x_211_cast_fp16)[name = string("op_7740_cast_fp16")];
            fp16 const_214_promoted_to_fp16 = const()[name = string("const_214_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_7742_cast_fp16 = mul(x = var_7740_cast_fp16_1, y = const_214_promoted_to_fp16)[name = string("op_7742_cast_fp16")];
            int32 var_7744 = const()[name = string("op_7744"), val = int32(-2)];
            bool var_7745_interleave_0 = const()[name = string("op_7745_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_7745_cast_fp16 = concat(axis = var_7744, interleave = var_7745_interleave_0, values = (var_7742_cast_fp16, var_7740_cast_fp16_0))[name = string("op_7745_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_7746_cast_fp16 = mul(x = var_7745_cast_fp16, y = var_866_cast_fp16)[name = string("op_7746_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_129_cast_fp16 = add(x = var_7739_cast_fp16, y = var_7746_cast_fp16)[name = string("query_states_129_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_7752_cast_fp16 = mul(x = var_7728_cast_fp16, y = var_859_cast_fp16)[name = string("op_7752_cast_fp16")];
            tensor<int32, [2]> var_7753_split_sizes_0 = const()[name = string("op_7753_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7753_axis_0 = const()[name = string("op_7753_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_7753_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_7753_cast_fp16_1 = split(axis = var_7753_axis_0, split_sizes = var_7753_split_sizes_0, x = var_7728_cast_fp16)[name = string("op_7753_cast_fp16")];
            fp16 const_215_promoted_to_fp16 = const()[name = string("const_215_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_7755_cast_fp16 = mul(x = var_7753_cast_fp16_1, y = const_215_promoted_to_fp16)[name = string("op_7755_cast_fp16")];
            int32 var_7757 = const()[name = string("op_7757"), val = int32(-2)];
            bool var_7758_interleave_0 = const()[name = string("op_7758_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_7758_cast_fp16 = concat(axis = var_7757, interleave = var_7758_interleave_0, values = (var_7755_cast_fp16, var_7753_cast_fp16_0))[name = string("op_7758_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_7759_cast_fp16 = mul(x = var_7758_cast_fp16, y = var_866_cast_fp16)[name = string("op_7759_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_215_cast_fp16 = add(x = var_7752_cast_fp16, y = var_7759_cast_fp16)[name = string("key_states_215_cast_fp16")];
            tensor<int32, [1]> expand_dims_252 = const()[name = string("expand_dims_252"), val = tensor<int32, [1]>([21])];
            tensor<int32, [1]> expand_dims_253 = const()[name = string("expand_dims_253"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_255 = const()[name = string("expand_dims_255"), val = tensor<int32, [1]>([0])];
            int32 concat_257_axis_0 = const()[name = string("concat_257_axis_0"), val = int32(0)];
            bool concat_257_interleave_0 = const()[name = string("concat_257_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_257 = concat(axis = concat_257_axis_0, interleave = concat_257_interleave_0, values = (expand_dims_252, expand_dims_253, position_id, expand_dims_255))[name = string("concat_257")];
            tensor<int32, [1]> expand_dims_256 = const()[name = string("expand_dims_256"), val = tensor<int32, [1]>([22])];
            tensor<int32, [1]> concat_258_values1_0 = const()[name = string("concat_258_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_258_values3_0 = const()[name = string("concat_258_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_258_axis_0 = const()[name = string("concat_258_axis_0"), val = int32(0)];
            bool concat_258_interleave_0 = const()[name = string("concat_258_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_258 = concat(axis = concat_258_axis_0, interleave = concat_258_interleave_0, values = (expand_dims_256, concat_258_values1_0, cache_position_end, concat_258_values3_0))[name = string("concat_258")];
            tensor<int32, [4]> key_states_217_perm_0 = const()[name = string("key_states_217_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_22_stride_0 = const()[name = string("key_cache_internal_tensor_assign_22_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_22_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_22_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_22_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_22_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_22_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_22_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_217_cast_fp16 = transpose(perm = key_states_217_perm_0, x = key_states_215_cast_fp16)[name = string("transpose_281")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_22_cast_fp16 = slice_update(begin = concat_257, begin_mask = key_cache_internal_tensor_assign_22_begin_mask_0, end = concat_258, end_mask = key_cache_internal_tensor_assign_22_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_22_squeeze_mask_0, stride = key_cache_internal_tensor_assign_22_stride_0, update = key_states_217_cast_fp16, x = coreml_update_state_208)[name = string("key_cache_internal_tensor_assign_22_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_22_cast_fp16, input = key_cache)[name = string("coreml_update_state_210_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_210 = read_state(input = key_cache)[name = string("coreml_update_state_210")];
            tensor<int32, [4]> value_states_129_perm_0 = const()[name = string("value_states_129_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_22_stride_0 = const()[name = string("value_cache_internal_tensor_assign_22_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_22_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_22_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_22_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_22_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_22_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_22_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_129_cast_fp16 = transpose(perm = value_states_129_perm_0, x = var_7735_cast_fp16)[name = string("transpose_280")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_22_cast_fp16 = slice_update(begin = concat_257, begin_mask = value_cache_internal_tensor_assign_22_begin_mask_0, end = concat_258, end_mask = value_cache_internal_tensor_assign_22_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_22_squeeze_mask_0, stride = value_cache_internal_tensor_assign_22_stride_0, update = value_states_129_cast_fp16, x = coreml_update_state_209)[name = string("value_cache_internal_tensor_assign_22_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_22_cast_fp16, input = value_cache)[name = string("coreml_update_state_211_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_211 = read_state(input = value_cache)[name = string("coreml_update_state_211")];
            tensor<int32, [4]> var_7829_begin_0 = const()[name = string("op_7829_begin_0"), val = tensor<int32, [4]>([21, 0, 0, 0])];
            tensor<int32, [4]> var_7829_end_0 = const()[name = string("op_7829_end_0"), val = tensor<int32, [4]>([22, 2, 2048, 128])];
            tensor<bool, [4]> var_7829_end_mask_0 = const()[name = string("op_7829_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7829_cast_fp16 = slice_by_index(begin = var_7829_begin_0, end = var_7829_end_0, end_mask = var_7829_end_mask_0, x = coreml_update_state_210)[name = string("op_7829_cast_fp16")];
            tensor<int32, [2]> tile_42 = const()[name = string("tile_42"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7832_axis_0 = const()[name = string("op_7832_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7832_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7832_cast_fp16_1 = split(axis = var_7832_axis_0, split_sizes = tile_42, x = var_7829_cast_fp16)[name = string("op_7832_cast_fp16")];
            tensor<int32, [4]> var_7839_begin_0 = const()[name = string("op_7839_begin_0"), val = tensor<int32, [4]>([21, 0, 0, 0])];
            tensor<int32, [4]> var_7839_end_0 = const()[name = string("op_7839_end_0"), val = tensor<int32, [4]>([22, 2, 2048, 128])];
            tensor<bool, [4]> var_7839_end_mask_0 = const()[name = string("op_7839_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7839_cast_fp16 = slice_by_index(begin = var_7839_begin_0, end = var_7839_end_0, end_mask = var_7839_end_mask_0, x = coreml_update_state_211)[name = string("op_7839_cast_fp16")];
            tensor<int32, [2]> tile_43 = const()[name = string("tile_43"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7842_axis_0 = const()[name = string("op_7842_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7842_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7842_cast_fp16_1 = split(axis = var_7842_axis_0, split_sizes = tile_43, x = var_7839_cast_fp16)[name = string("op_7842_cast_fp16")];
            tensor<int32, [2]> var_7845_split_sizes_0 = const()[name = string("op_7845_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_7845_axis_0 = const()[name = string("op_7845_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_7845_0, tensor<fp16, [1, 8, 128, 16]> var_7845_1 = split(axis = var_7845_axis_0, split_sizes = var_7845_split_sizes_0, x = query_states_129_cast_fp16)[name = string("op_7845")];
            bool attn_weights_337_transpose_x_0 = const()[name = string("attn_weights_337_transpose_x_0"), val = bool(false)];
            bool attn_weights_337_transpose_y_0 = const()[name = string("attn_weights_337_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_337_cast_fp16 = matmul(transpose_x = attn_weights_337_transpose_x_0, transpose_y = attn_weights_337_transpose_y_0, x = var_7832_cast_fp16_0, y = var_7845_0)[name = string("attn_weights_337_cast_fp16")];
            fp16 var_7848_to_fp16 = const()[name = string("op_7848_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_339_cast_fp16 = mul(x = attn_weights_337_cast_fp16, y = var_7848_to_fp16)[name = string("attn_weights_339_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_341_cast_fp16 = add(x = attn_weights_339_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_341_cast_fp16")];
            int32 var_7852 = const()[name = string("op_7852"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_343_cast_fp16 = softmax(axis = var_7852, x = attn_weights_341_cast_fp16)[name = string("attn_weights_343_cast_fp16")];
            bool var_7858_transpose_x_1 = const()[name = string("op_7858_transpose_x_1"), val = bool(true)];
            bool var_7858_transpose_y_1 = const()[name = string("op_7858_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_7858_cast_fp16 = matmul(transpose_x = var_7858_transpose_x_1, transpose_y = var_7858_transpose_y_1, x = attn_weights_343_cast_fp16, y = var_7842_cast_fp16_0)[name = string("op_7858_cast_fp16")];
            bool attn_weights_345_transpose_x_0 = const()[name = string("attn_weights_345_transpose_x_0"), val = bool(false)];
            bool attn_weights_345_transpose_y_0 = const()[name = string("attn_weights_345_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_345_cast_fp16 = matmul(transpose_x = attn_weights_345_transpose_x_0, transpose_y = attn_weights_345_transpose_y_0, x = var_7832_cast_fp16_1, y = var_7845_1)[name = string("attn_weights_345_cast_fp16")];
            fp16 var_7860_to_fp16 = const()[name = string("op_7860_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_347_cast_fp16 = mul(x = attn_weights_345_cast_fp16, y = var_7860_to_fp16)[name = string("attn_weights_347_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_349_cast_fp16 = add(x = attn_weights_347_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_349_cast_fp16")];
            int32 var_7864 = const()[name = string("op_7864"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_351_cast_fp16 = softmax(axis = var_7864, x = attn_weights_349_cast_fp16)[name = string("attn_weights_351_cast_fp16")];
            bool attn_output_169_transpose_x_1 = const()[name = string("attn_output_169_transpose_x_1"), val = bool(true)];
            bool attn_output_169_transpose_y_1 = const()[name = string("attn_output_169_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_169_cast_fp16 = matmul(transpose_x = attn_output_169_transpose_x_1, transpose_y = attn_output_169_transpose_y_1, x = attn_weights_351_cast_fp16, y = var_7842_cast_fp16_1)[name = string("attn_output_169_cast_fp16")];
            int32 var_7872 = const()[name = string("op_7872"), val = int32(1)];
            bool attn_output_171_interleave_0 = const()[name = string("attn_output_171_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_171_cast_fp16 = concat(axis = var_7872, interleave = attn_output_171_interleave_0, values = (var_7858_cast_fp16, attn_output_169_cast_fp16))[name = string("attn_output_171_cast_fp16")];
            tensor<int32, [4]> var_7876_perm_0 = const()[name = string("op_7876_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_263x = const()[name = string("concat_263x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_7876_cast_fp16 = transpose(perm = var_7876_perm_0, x = attn_output_171_cast_fp16)[name = string("transpose_279")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_175_cast_fp16 = reshape(shape = concat_263x, x = var_7876_cast_fp16)[name = string("attn_output_175_cast_fp16")];
            tensor<int32, [2]> hidden_states_213_strides_0 = const()[name = string("hidden_states_213_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_213_pad_type_0 = const()[name = string("hidden_states_213_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_213_pad_0 = const()[name = string("hidden_states_213_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_213_dilations_0 = const()[name = string("hidden_states_213_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_213_groups_0 = const()[name = string("hidden_states_213_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_213_cast_fp16 = conv(dilations = hidden_states_213_dilations_0, groups = hidden_states_213_groups_0, pad = hidden_states_213_pad_0, pad_type = hidden_states_213_pad_type_0, strides = hidden_states_213_strides_0, weight = layers_21_self_attn_o_proj_weight_cast_fp16, x = attn_output_175_cast_fp16)[name = string("hidden_states_213_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_215_cast_fp16 = add(x = hidden_states_209_cast_fp16, y = hidden_states_213_cast_fp16)[name = string("hidden_states_215_cast_fp16")];
            fp16 const_220_promoted_to_fp16 = const()[name = string("const_220_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_7909_cast_fp16 = mul(x = hidden_states_215_cast_fp16, y = const_220_promoted_to_fp16)[name = string("op_7909_cast_fp16")];
            int32 var_7907 = const()[name = string("op_7907"), val = int32(1)];
            bool doubled_173_interleave_0 = const()[name = string("doubled_173_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_173_cast_fp16 = concat(axis = var_7907, interleave = doubled_173_interleave_0, values = (hidden_states_215_cast_fp16, var_7909_cast_fp16))[name = string("doubled_173_cast_fp16")];
            tensor<int32, [1]> out_87_axes_0 = const()[name = string("out_87_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_87_gamma_0_to_fp16 = const()[name = string("out_87_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1520397888)))];
            fp16 var_7919_to_fp16 = const()[name = string("op_7919_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_87_cast_fp16 = layer_norm(axes = out_87_axes_0, epsilon = var_7919_to_fp16, gamma = out_87_gamma_0_to_fp16, x = doubled_173_cast_fp16)[name = string("out_87_cast_fp16")];
            tensor<int32, [2]> var_7930_split_sizes_0 = const()[name = string("op_7930_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7930_axis_0 = const()[name = string("op_7930_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_7930_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_7930_cast_fp16_1 = split(axis = var_7930_axis_0, split_sizes = var_7930_split_sizes_0, x = out_87_cast_fp16)[name = string("op_7930_cast_fp16")];
            tensor<int32, [2]> input_43_strides_0 = const()[name = string("input_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_43_pad_type_0 = const()[name = string("input_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_43_pad_0 = const()[name = string("input_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_43_dilations_0 = const()[name = string("input_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_43_groups_0 = const()[name = string("input_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_43_cast_fp16 = conv(dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = layers_21_mlp_gate_proj_weight_cast_fp16, x = var_7930_cast_fp16_0)[name = string("input_43_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_7947_cast_fp16 = silu(x = input_43_cast_fp16)[name = string("op_7947_cast_fp16")];
            tensor<int32, [2]> var_7953_strides_0 = const()[name = string("op_7953_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_7953_pad_type_0 = const()[name = string("op_7953_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_7953_pad_0 = const()[name = string("op_7953_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_7953_dilations_0 = const()[name = string("op_7953_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7953_groups_0 = const()[name = string("op_7953_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_7953_cast_fp16 = conv(dilations = var_7953_dilations_0, groups = var_7953_groups_0, pad = var_7953_pad_0, pad_type = var_7953_pad_type_0, strides = var_7953_strides_0, weight = layers_21_mlp_up_proj_weight_cast_fp16, x = var_7930_cast_fp16_0)[name = string("op_7953_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_219_cast_fp16 = mul(x = var_7947_cast_fp16, y = var_7953_cast_fp16)[name = string("x_219_cast_fp16")];
            tensor<int32, [2]> hidden_states_217_strides_0 = const()[name = string("hidden_states_217_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_217_pad_type_0 = const()[name = string("hidden_states_217_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_217_pad_0 = const()[name = string("hidden_states_217_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_217_dilations_0 = const()[name = string("hidden_states_217_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_217_groups_0 = const()[name = string("hidden_states_217_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_217_cast_fp16 = conv(dilations = hidden_states_217_dilations_0, groups = hidden_states_217_groups_0, pad = hidden_states_217_pad_0, pad_type = hidden_states_217_pad_type_0, strides = hidden_states_217_strides_0, weight = layers_21_mlp_down_proj_weight_cast_fp16, x = x_219_cast_fp16)[name = string("hidden_states_217_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_219_cast_fp16 = add(x = hidden_states_215_cast_fp16, y = hidden_states_217_cast_fp16)[name = string("hidden_states_219_cast_fp16")];
            fp16 const_222_promoted_to_fp16 = const()[name = string("const_222_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_7971_cast_fp16 = mul(x = hidden_states_219_cast_fp16, y = const_222_promoted_to_fp16)[name = string("op_7971_cast_fp16")];
            int32 var_7969 = const()[name = string("op_7969"), val = int32(1)];
            bool doubled_177_interleave_0 = const()[name = string("doubled_177_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_177_cast_fp16 = concat(axis = var_7969, interleave = doubled_177_interleave_0, values = (hidden_states_219_cast_fp16, var_7971_cast_fp16))[name = string("doubled_177_cast_fp16")];
            tensor<int32, [1]> out_89_axes_0 = const()[name = string("out_89_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_89_gamma_0_to_fp16 = const()[name = string("out_89_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1520406144)))];
            fp16 var_7981_to_fp16 = const()[name = string("op_7981_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_89_cast_fp16 = layer_norm(axes = out_89_axes_0, epsilon = var_7981_to_fp16, gamma = out_89_gamma_0_to_fp16, x = doubled_177_cast_fp16)[name = string("out_89_cast_fp16")];
            tensor<int32, [2]> var_7992_split_sizes_0 = const()[name = string("op_7992_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7992_axis_0 = const()[name = string("op_7992_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_7992_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_7992_cast_fp16_1 = split(axis = var_7992_axis_0, split_sizes = var_7992_split_sizes_0, x = out_89_cast_fp16)[name = string("op_7992_cast_fp16")];
            tensor<int32, [2]> query_states_133_strides_0 = const()[name = string("query_states_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_133_pad_type_0 = const()[name = string("query_states_133_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_133_pad_0 = const()[name = string("query_states_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_133_dilations_0 = const()[name = string("query_states_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_133_groups_0 = const()[name = string("query_states_133_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_133_cast_fp16 = conv(dilations = query_states_133_dilations_0, groups = query_states_133_groups_0, pad = query_states_133_pad_0, pad_type = query_states_133_pad_type_0, strides = query_states_133_strides_0, weight = layers_22_self_attn_q_proj_weight_cast_fp16, x = var_7992_cast_fp16_0)[name = string("query_states_133_cast_fp16")];
            tensor<int32, [2]> key_states_221_strides_0 = const()[name = string("key_states_221_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_221_pad_type_0 = const()[name = string("key_states_221_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_221_pad_0 = const()[name = string("key_states_221_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_221_dilations_0 = const()[name = string("key_states_221_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_221_groups_0 = const()[name = string("key_states_221_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_221_cast_fp16 = conv(dilations = key_states_221_dilations_0, groups = key_states_221_groups_0, pad = key_states_221_pad_0, pad_type = key_states_221_pad_type_0, strides = key_states_221_strides_0, weight = layers_22_self_attn_k_proj_weight_cast_fp16, x = var_7992_cast_fp16_0)[name = string("key_states_221_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_22_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_22_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1520414400)))];
            tensor<int32, [2]> value_states_133_strides_0 = const()[name = string("value_states_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_133_pad_type_0 = const()[name = string("value_states_133_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_133_pad_0 = const()[name = string("value_states_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_133_dilations_0 = const()[name = string("value_states_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_133_groups_0 = const()[name = string("value_states_133_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_133_cast_fp16 = conv(dilations = value_states_133_dilations_0, groups = value_states_133_groups_0, pad = value_states_133_pad_0, pad_type = value_states_133_pad_type_0, strides = value_states_133_strides_0, weight = layers_22_self_attn_v_proj_weight_to_fp16, x = var_7992_cast_fp16_0)[name = string("value_states_133_cast_fp16")];
            tensor<int32, [4]> concat_264x = const()[name = string("concat_264x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_221_cast_fp16 = reshape(shape = concat_264x, x = query_states_133_cast_fp16)[name = string("x_221_cast_fp16")];
            tensor<int32, [4]> concat_265x = const()[name = string("concat_265x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_8049_cast_fp16 = reshape(shape = concat_265x, x = key_states_221_cast_fp16)[name = string("op_8049_cast_fp16")];
            tensor<int32, [4]> concat_266x = const()[name = string("concat_266x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_8056_cast_fp16 = reshape(shape = concat_266x, x = value_states_133_cast_fp16)[name = string("op_8056_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_8060_cast_fp16 = mul(x = x_221_cast_fp16, y = var_859_cast_fp16)[name = string("op_8060_cast_fp16")];
            tensor<int32, [2]> var_8061_split_sizes_0 = const()[name = string("op_8061_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8061_axis_0 = const()[name = string("op_8061_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_8061_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_8061_cast_fp16_1 = split(axis = var_8061_axis_0, split_sizes = var_8061_split_sizes_0, x = x_221_cast_fp16)[name = string("op_8061_cast_fp16")];
            fp16 const_224_promoted_to_fp16 = const()[name = string("const_224_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_8063_cast_fp16 = mul(x = var_8061_cast_fp16_1, y = const_224_promoted_to_fp16)[name = string("op_8063_cast_fp16")];
            int32 var_8065 = const()[name = string("op_8065"), val = int32(-2)];
            bool var_8066_interleave_0 = const()[name = string("op_8066_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_8066_cast_fp16 = concat(axis = var_8065, interleave = var_8066_interleave_0, values = (var_8063_cast_fp16, var_8061_cast_fp16_0))[name = string("op_8066_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_8067_cast_fp16 = mul(x = var_8066_cast_fp16, y = var_866_cast_fp16)[name = string("op_8067_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_135_cast_fp16 = add(x = var_8060_cast_fp16, y = var_8067_cast_fp16)[name = string("query_states_135_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_8073_cast_fp16 = mul(x = var_8049_cast_fp16, y = var_859_cast_fp16)[name = string("op_8073_cast_fp16")];
            tensor<int32, [2]> var_8074_split_sizes_0 = const()[name = string("op_8074_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8074_axis_0 = const()[name = string("op_8074_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_8074_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_8074_cast_fp16_1 = split(axis = var_8074_axis_0, split_sizes = var_8074_split_sizes_0, x = var_8049_cast_fp16)[name = string("op_8074_cast_fp16")];
            fp16 const_225_promoted_to_fp16 = const()[name = string("const_225_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_8076_cast_fp16 = mul(x = var_8074_cast_fp16_1, y = const_225_promoted_to_fp16)[name = string("op_8076_cast_fp16")];
            int32 var_8078 = const()[name = string("op_8078"), val = int32(-2)];
            bool var_8079_interleave_0 = const()[name = string("op_8079_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_8079_cast_fp16 = concat(axis = var_8078, interleave = var_8079_interleave_0, values = (var_8076_cast_fp16, var_8074_cast_fp16_0))[name = string("op_8079_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_8080_cast_fp16 = mul(x = var_8079_cast_fp16, y = var_866_cast_fp16)[name = string("op_8080_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_225_cast_fp16 = add(x = var_8073_cast_fp16, y = var_8080_cast_fp16)[name = string("key_states_225_cast_fp16")];
            tensor<int32, [1]> expand_dims_264 = const()[name = string("expand_dims_264"), val = tensor<int32, [1]>([22])];
            tensor<int32, [1]> expand_dims_265 = const()[name = string("expand_dims_265"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_267 = const()[name = string("expand_dims_267"), val = tensor<int32, [1]>([0])];
            int32 concat_269_axis_0 = const()[name = string("concat_269_axis_0"), val = int32(0)];
            bool concat_269_interleave_0 = const()[name = string("concat_269_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_269 = concat(axis = concat_269_axis_0, interleave = concat_269_interleave_0, values = (expand_dims_264, expand_dims_265, position_id, expand_dims_267))[name = string("concat_269")];
            tensor<int32, [1]> expand_dims_268 = const()[name = string("expand_dims_268"), val = tensor<int32, [1]>([23])];
            tensor<int32, [1]> concat_270_values1_0 = const()[name = string("concat_270_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_270_values3_0 = const()[name = string("concat_270_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_270_axis_0 = const()[name = string("concat_270_axis_0"), val = int32(0)];
            bool concat_270_interleave_0 = const()[name = string("concat_270_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_270 = concat(axis = concat_270_axis_0, interleave = concat_270_interleave_0, values = (expand_dims_268, concat_270_values1_0, cache_position_end, concat_270_values3_0))[name = string("concat_270")];
            tensor<int32, [4]> key_states_227_perm_0 = const()[name = string("key_states_227_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_23_stride_0 = const()[name = string("key_cache_internal_tensor_assign_23_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_23_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_23_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_23_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_23_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_23_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_23_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_227_cast_fp16 = transpose(perm = key_states_227_perm_0, x = key_states_225_cast_fp16)[name = string("transpose_278")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_23_cast_fp16 = slice_update(begin = concat_269, begin_mask = key_cache_internal_tensor_assign_23_begin_mask_0, end = concat_270, end_mask = key_cache_internal_tensor_assign_23_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_23_squeeze_mask_0, stride = key_cache_internal_tensor_assign_23_stride_0, update = key_states_227_cast_fp16, x = coreml_update_state_210)[name = string("key_cache_internal_tensor_assign_23_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_23_cast_fp16, input = key_cache)[name = string("coreml_update_state_212_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_212 = read_state(input = key_cache)[name = string("coreml_update_state_212")];
            tensor<int32, [4]> value_states_135_perm_0 = const()[name = string("value_states_135_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_23_stride_0 = const()[name = string("value_cache_internal_tensor_assign_23_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_23_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_23_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_23_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_23_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_23_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_23_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_135_cast_fp16 = transpose(perm = value_states_135_perm_0, x = var_8056_cast_fp16)[name = string("transpose_277")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_23_cast_fp16 = slice_update(begin = concat_269, begin_mask = value_cache_internal_tensor_assign_23_begin_mask_0, end = concat_270, end_mask = value_cache_internal_tensor_assign_23_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_23_squeeze_mask_0, stride = value_cache_internal_tensor_assign_23_stride_0, update = value_states_135_cast_fp16, x = coreml_update_state_211)[name = string("value_cache_internal_tensor_assign_23_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_23_cast_fp16, input = value_cache)[name = string("coreml_update_state_213_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_213 = read_state(input = value_cache)[name = string("coreml_update_state_213")];
            tensor<int32, [4]> var_8150_begin_0 = const()[name = string("op_8150_begin_0"), val = tensor<int32, [4]>([22, 0, 0, 0])];
            tensor<int32, [4]> var_8150_end_0 = const()[name = string("op_8150_end_0"), val = tensor<int32, [4]>([23, 2, 2048, 128])];
            tensor<bool, [4]> var_8150_end_mask_0 = const()[name = string("op_8150_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8150_cast_fp16 = slice_by_index(begin = var_8150_begin_0, end = var_8150_end_0, end_mask = var_8150_end_mask_0, x = coreml_update_state_212)[name = string("op_8150_cast_fp16")];
            tensor<int32, [2]> tile_44 = const()[name = string("tile_44"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8153_axis_0 = const()[name = string("op_8153_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8153_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8153_cast_fp16_1 = split(axis = var_8153_axis_0, split_sizes = tile_44, x = var_8150_cast_fp16)[name = string("op_8153_cast_fp16")];
            tensor<int32, [4]> var_8160_begin_0 = const()[name = string("op_8160_begin_0"), val = tensor<int32, [4]>([22, 0, 0, 0])];
            tensor<int32, [4]> var_8160_end_0 = const()[name = string("op_8160_end_0"), val = tensor<int32, [4]>([23, 2, 2048, 128])];
            tensor<bool, [4]> var_8160_end_mask_0 = const()[name = string("op_8160_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8160_cast_fp16 = slice_by_index(begin = var_8160_begin_0, end = var_8160_end_0, end_mask = var_8160_end_mask_0, x = coreml_update_state_213)[name = string("op_8160_cast_fp16")];
            tensor<int32, [2]> tile_45 = const()[name = string("tile_45"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8163_axis_0 = const()[name = string("op_8163_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8163_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8163_cast_fp16_1 = split(axis = var_8163_axis_0, split_sizes = tile_45, x = var_8160_cast_fp16)[name = string("op_8163_cast_fp16")];
            tensor<int32, [2]> var_8166_split_sizes_0 = const()[name = string("op_8166_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_8166_axis_0 = const()[name = string("op_8166_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_8166_0, tensor<fp16, [1, 8, 128, 16]> var_8166_1 = split(axis = var_8166_axis_0, split_sizes = var_8166_split_sizes_0, x = query_states_135_cast_fp16)[name = string("op_8166")];
            bool attn_weights_353_transpose_x_0 = const()[name = string("attn_weights_353_transpose_x_0"), val = bool(false)];
            bool attn_weights_353_transpose_y_0 = const()[name = string("attn_weights_353_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_353_cast_fp16 = matmul(transpose_x = attn_weights_353_transpose_x_0, transpose_y = attn_weights_353_transpose_y_0, x = var_8153_cast_fp16_0, y = var_8166_0)[name = string("attn_weights_353_cast_fp16")];
            fp16 var_8169_to_fp16 = const()[name = string("op_8169_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_355_cast_fp16 = mul(x = attn_weights_353_cast_fp16, y = var_8169_to_fp16)[name = string("attn_weights_355_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_357_cast_fp16 = add(x = attn_weights_355_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_357_cast_fp16")];
            int32 var_8173 = const()[name = string("op_8173"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_359_cast_fp16 = softmax(axis = var_8173, x = attn_weights_357_cast_fp16)[name = string("attn_weights_359_cast_fp16")];
            bool var_8179_transpose_x_1 = const()[name = string("op_8179_transpose_x_1"), val = bool(true)];
            bool var_8179_transpose_y_1 = const()[name = string("op_8179_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_8179_cast_fp16 = matmul(transpose_x = var_8179_transpose_x_1, transpose_y = var_8179_transpose_y_1, x = attn_weights_359_cast_fp16, y = var_8163_cast_fp16_0)[name = string("op_8179_cast_fp16")];
            bool attn_weights_361_transpose_x_0 = const()[name = string("attn_weights_361_transpose_x_0"), val = bool(false)];
            bool attn_weights_361_transpose_y_0 = const()[name = string("attn_weights_361_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_361_cast_fp16 = matmul(transpose_x = attn_weights_361_transpose_x_0, transpose_y = attn_weights_361_transpose_y_0, x = var_8153_cast_fp16_1, y = var_8166_1)[name = string("attn_weights_361_cast_fp16")];
            fp16 var_8181_to_fp16 = const()[name = string("op_8181_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_363_cast_fp16 = mul(x = attn_weights_361_cast_fp16, y = var_8181_to_fp16)[name = string("attn_weights_363_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_365_cast_fp16 = add(x = attn_weights_363_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_365_cast_fp16")];
            int32 var_8185 = const()[name = string("op_8185"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_367_cast_fp16 = softmax(axis = var_8185, x = attn_weights_365_cast_fp16)[name = string("attn_weights_367_cast_fp16")];
            bool attn_output_177_transpose_x_1 = const()[name = string("attn_output_177_transpose_x_1"), val = bool(true)];
            bool attn_output_177_transpose_y_1 = const()[name = string("attn_output_177_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_177_cast_fp16 = matmul(transpose_x = attn_output_177_transpose_x_1, transpose_y = attn_output_177_transpose_y_1, x = attn_weights_367_cast_fp16, y = var_8163_cast_fp16_1)[name = string("attn_output_177_cast_fp16")];
            int32 var_8193 = const()[name = string("op_8193"), val = int32(1)];
            bool attn_output_179_interleave_0 = const()[name = string("attn_output_179_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_179_cast_fp16 = concat(axis = var_8193, interleave = attn_output_179_interleave_0, values = (var_8179_cast_fp16, attn_output_177_cast_fp16))[name = string("attn_output_179_cast_fp16")];
            tensor<int32, [4]> var_8197_perm_0 = const()[name = string("op_8197_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_275x = const()[name = string("concat_275x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_8197_cast_fp16 = transpose(perm = var_8197_perm_0, x = attn_output_179_cast_fp16)[name = string("transpose_276")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_183_cast_fp16 = reshape(shape = concat_275x, x = var_8197_cast_fp16)[name = string("attn_output_183_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_22_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_22_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1521463040)))];
            tensor<int32, [2]> hidden_states_223_strides_0 = const()[name = string("hidden_states_223_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_223_pad_type_0 = const()[name = string("hidden_states_223_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_223_pad_0 = const()[name = string("hidden_states_223_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_223_dilations_0 = const()[name = string("hidden_states_223_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_223_groups_0 = const()[name = string("hidden_states_223_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_223_cast_fp16 = conv(dilations = hidden_states_223_dilations_0, groups = hidden_states_223_groups_0, pad = hidden_states_223_pad_0, pad_type = hidden_states_223_pad_type_0, strides = hidden_states_223_strides_0, weight = layers_22_self_attn_o_proj_weight_to_fp16, x = attn_output_183_cast_fp16)[name = string("hidden_states_223_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_225_cast_fp16 = add(x = hidden_states_219_cast_fp16, y = hidden_states_223_cast_fp16)[name = string("hidden_states_225_cast_fp16")];
            fp16 const_230_promoted_to_fp16 = const()[name = string("const_230_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_8230_cast_fp16 = mul(x = hidden_states_225_cast_fp16, y = const_230_promoted_to_fp16)[name = string("op_8230_cast_fp16")];
            int32 var_8228 = const()[name = string("op_8228"), val = int32(1)];
            bool doubled_181_interleave_0 = const()[name = string("doubled_181_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_181_cast_fp16 = concat(axis = var_8228, interleave = doubled_181_interleave_0, values = (hidden_states_225_cast_fp16, var_8230_cast_fp16))[name = string("doubled_181_cast_fp16")];
            tensor<int32, [1]> out_91_axes_0 = const()[name = string("out_91_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_91_gamma_0_to_fp16 = const()[name = string("out_91_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1529851712)))];
            fp16 var_8240_to_fp16 = const()[name = string("op_8240_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_91_cast_fp16 = layer_norm(axes = out_91_axes_0, epsilon = var_8240_to_fp16, gamma = out_91_gamma_0_to_fp16, x = doubled_181_cast_fp16)[name = string("out_91_cast_fp16")];
            tensor<int32, [2]> var_8251_split_sizes_0 = const()[name = string("op_8251_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8251_axis_0 = const()[name = string("op_8251_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_8251_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_8251_cast_fp16_1 = split(axis = var_8251_axis_0, split_sizes = var_8251_split_sizes_0, x = out_91_cast_fp16)[name = string("op_8251_cast_fp16")];
            tensor<int32, [2]> input_45_strides_0 = const()[name = string("input_45_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_45_pad_type_0 = const()[name = string("input_45_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_45_pad_0 = const()[name = string("input_45_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_45_dilations_0 = const()[name = string("input_45_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_45_groups_0 = const()[name = string("input_45_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_45_cast_fp16 = conv(dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = layers_22_mlp_gate_proj_weight_cast_fp16, x = var_8251_cast_fp16_0)[name = string("input_45_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_8268_cast_fp16 = silu(x = input_45_cast_fp16)[name = string("op_8268_cast_fp16")];
            tensor<int32, [2]> var_8274_strides_0 = const()[name = string("op_8274_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_8274_pad_type_0 = const()[name = string("op_8274_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_8274_pad_0 = const()[name = string("op_8274_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_8274_dilations_0 = const()[name = string("op_8274_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8274_groups_0 = const()[name = string("op_8274_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_8274_cast_fp16 = conv(dilations = var_8274_dilations_0, groups = var_8274_groups_0, pad = var_8274_pad_0, pad_type = var_8274_pad_type_0, strides = var_8274_strides_0, weight = layers_22_mlp_up_proj_weight_cast_fp16, x = var_8251_cast_fp16_0)[name = string("op_8274_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_229_cast_fp16 = mul(x = var_8268_cast_fp16, y = var_8274_cast_fp16)[name = string("x_229_cast_fp16")];
            tensor<int32, [2]> hidden_states_227_strides_0 = const()[name = string("hidden_states_227_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_227_pad_type_0 = const()[name = string("hidden_states_227_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_227_pad_0 = const()[name = string("hidden_states_227_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_227_dilations_0 = const()[name = string("hidden_states_227_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_227_groups_0 = const()[name = string("hidden_states_227_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_227_cast_fp16 = conv(dilations = hidden_states_227_dilations_0, groups = hidden_states_227_groups_0, pad = hidden_states_227_pad_0, pad_type = hidden_states_227_pad_type_0, strides = hidden_states_227_strides_0, weight = layers_22_mlp_down_proj_weight_cast_fp16, x = x_229_cast_fp16)[name = string("hidden_states_227_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_229_cast_fp16 = add(x = hidden_states_225_cast_fp16, y = hidden_states_227_cast_fp16)[name = string("hidden_states_229_cast_fp16")];
            fp16 const_232_promoted_to_fp16 = const()[name = string("const_232_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_8292_cast_fp16 = mul(x = hidden_states_229_cast_fp16, y = const_232_promoted_to_fp16)[name = string("op_8292_cast_fp16")];
            int32 var_8290 = const()[name = string("op_8290"), val = int32(1)];
            bool doubled_185_interleave_0 = const()[name = string("doubled_185_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_185_cast_fp16 = concat(axis = var_8290, interleave = doubled_185_interleave_0, values = (hidden_states_229_cast_fp16, var_8292_cast_fp16))[name = string("doubled_185_cast_fp16")];
            tensor<int32, [1]> out_93_axes_0 = const()[name = string("out_93_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_93_gamma_0_to_fp16 = const()[name = string("out_93_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1529859968)))];
            fp16 var_8302_to_fp16 = const()[name = string("op_8302_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_93_cast_fp16 = layer_norm(axes = out_93_axes_0, epsilon = var_8302_to_fp16, gamma = out_93_gamma_0_to_fp16, x = doubled_185_cast_fp16)[name = string("out_93_cast_fp16")];
            tensor<int32, [2]> var_8313_split_sizes_0 = const()[name = string("op_8313_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8313_axis_0 = const()[name = string("op_8313_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_8313_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_8313_cast_fp16_1 = split(axis = var_8313_axis_0, split_sizes = var_8313_split_sizes_0, x = out_93_cast_fp16)[name = string("op_8313_cast_fp16")];
            tensor<int32, [2]> query_states_139_strides_0 = const()[name = string("query_states_139_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_139_pad_type_0 = const()[name = string("query_states_139_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_139_pad_0 = const()[name = string("query_states_139_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_139_dilations_0 = const()[name = string("query_states_139_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_139_groups_0 = const()[name = string("query_states_139_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_139_cast_fp16 = conv(dilations = query_states_139_dilations_0, groups = query_states_139_groups_0, pad = query_states_139_pad_0, pad_type = query_states_139_pad_type_0, strides = query_states_139_strides_0, weight = layers_23_self_attn_q_proj_weight_cast_fp16, x = var_8313_cast_fp16_0)[name = string("query_states_139_cast_fp16")];
            tensor<int32, [2]> key_states_231_strides_0 = const()[name = string("key_states_231_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_231_pad_type_0 = const()[name = string("key_states_231_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_231_pad_0 = const()[name = string("key_states_231_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_231_dilations_0 = const()[name = string("key_states_231_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_231_groups_0 = const()[name = string("key_states_231_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_231_cast_fp16 = conv(dilations = key_states_231_dilations_0, groups = key_states_231_groups_0, pad = key_states_231_pad_0, pad_type = key_states_231_pad_type_0, strides = key_states_231_strides_0, weight = layers_23_self_attn_k_proj_weight_cast_fp16, x = var_8313_cast_fp16_0)[name = string("key_states_231_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_23_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_23_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1529868224)))];
            tensor<int32, [2]> value_states_139_strides_0 = const()[name = string("value_states_139_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_139_pad_type_0 = const()[name = string("value_states_139_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_139_pad_0 = const()[name = string("value_states_139_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_139_dilations_0 = const()[name = string("value_states_139_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_139_groups_0 = const()[name = string("value_states_139_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_139_cast_fp16 = conv(dilations = value_states_139_dilations_0, groups = value_states_139_groups_0, pad = value_states_139_pad_0, pad_type = value_states_139_pad_type_0, strides = value_states_139_strides_0, weight = layers_23_self_attn_v_proj_weight_to_fp16, x = var_8313_cast_fp16_0)[name = string("value_states_139_cast_fp16")];
            tensor<int32, [4]> concat_276x = const()[name = string("concat_276x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_231_cast_fp16 = reshape(shape = concat_276x, x = query_states_139_cast_fp16)[name = string("x_231_cast_fp16")];
            tensor<int32, [4]> concat_277x = const()[name = string("concat_277x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_8370_cast_fp16 = reshape(shape = concat_277x, x = key_states_231_cast_fp16)[name = string("op_8370_cast_fp16")];
            tensor<int32, [4]> concat_278x = const()[name = string("concat_278x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_8377_cast_fp16 = reshape(shape = concat_278x, x = value_states_139_cast_fp16)[name = string("op_8377_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_8381_cast_fp16 = mul(x = x_231_cast_fp16, y = var_859_cast_fp16)[name = string("op_8381_cast_fp16")];
            tensor<int32, [2]> var_8382_split_sizes_0 = const()[name = string("op_8382_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8382_axis_0 = const()[name = string("op_8382_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_8382_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_8382_cast_fp16_1 = split(axis = var_8382_axis_0, split_sizes = var_8382_split_sizes_0, x = x_231_cast_fp16)[name = string("op_8382_cast_fp16")];
            fp16 const_234_promoted_to_fp16 = const()[name = string("const_234_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_8384_cast_fp16 = mul(x = var_8382_cast_fp16_1, y = const_234_promoted_to_fp16)[name = string("op_8384_cast_fp16")];
            int32 var_8386 = const()[name = string("op_8386"), val = int32(-2)];
            bool var_8387_interleave_0 = const()[name = string("op_8387_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_8387_cast_fp16 = concat(axis = var_8386, interleave = var_8387_interleave_0, values = (var_8384_cast_fp16, var_8382_cast_fp16_0))[name = string("op_8387_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_8388_cast_fp16 = mul(x = var_8387_cast_fp16, y = var_866_cast_fp16)[name = string("op_8388_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_141_cast_fp16 = add(x = var_8381_cast_fp16, y = var_8388_cast_fp16)[name = string("query_states_141_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_8394_cast_fp16 = mul(x = var_8370_cast_fp16, y = var_859_cast_fp16)[name = string("op_8394_cast_fp16")];
            tensor<int32, [2]> var_8395_split_sizes_0 = const()[name = string("op_8395_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8395_axis_0 = const()[name = string("op_8395_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_8395_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_8395_cast_fp16_1 = split(axis = var_8395_axis_0, split_sizes = var_8395_split_sizes_0, x = var_8370_cast_fp16)[name = string("op_8395_cast_fp16")];
            fp16 const_235_promoted_to_fp16 = const()[name = string("const_235_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_8397_cast_fp16 = mul(x = var_8395_cast_fp16_1, y = const_235_promoted_to_fp16)[name = string("op_8397_cast_fp16")];
            int32 var_8399 = const()[name = string("op_8399"), val = int32(-2)];
            bool var_8400_interleave_0 = const()[name = string("op_8400_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_8400_cast_fp16 = concat(axis = var_8399, interleave = var_8400_interleave_0, values = (var_8397_cast_fp16, var_8395_cast_fp16_0))[name = string("op_8400_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_8401_cast_fp16 = mul(x = var_8400_cast_fp16, y = var_866_cast_fp16)[name = string("op_8401_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_235_cast_fp16 = add(x = var_8394_cast_fp16, y = var_8401_cast_fp16)[name = string("key_states_235_cast_fp16")];
            tensor<int32, [1]> expand_dims_276 = const()[name = string("expand_dims_276"), val = tensor<int32, [1]>([23])];
            tensor<int32, [1]> expand_dims_277 = const()[name = string("expand_dims_277"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_279 = const()[name = string("expand_dims_279"), val = tensor<int32, [1]>([0])];
            int32 concat_281_axis_0 = const()[name = string("concat_281_axis_0"), val = int32(0)];
            bool concat_281_interleave_0 = const()[name = string("concat_281_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_281 = concat(axis = concat_281_axis_0, interleave = concat_281_interleave_0, values = (expand_dims_276, expand_dims_277, position_id, expand_dims_279))[name = string("concat_281")];
            tensor<int32, [1]> expand_dims_280 = const()[name = string("expand_dims_280"), val = tensor<int32, [1]>([24])];
            tensor<int32, [1]> concat_282_values1_0 = const()[name = string("concat_282_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_282_values3_0 = const()[name = string("concat_282_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_282_axis_0 = const()[name = string("concat_282_axis_0"), val = int32(0)];
            bool concat_282_interleave_0 = const()[name = string("concat_282_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_282 = concat(axis = concat_282_axis_0, interleave = concat_282_interleave_0, values = (expand_dims_280, concat_282_values1_0, cache_position_end, concat_282_values3_0))[name = string("concat_282")];
            tensor<int32, [4]> key_states_237_perm_0 = const()[name = string("key_states_237_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_24_stride_0 = const()[name = string("key_cache_internal_tensor_assign_24_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_24_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_24_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_24_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_24_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_24_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_24_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_237_cast_fp16 = transpose(perm = key_states_237_perm_0, x = key_states_235_cast_fp16)[name = string("transpose_275")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_24_cast_fp16 = slice_update(begin = concat_281, begin_mask = key_cache_internal_tensor_assign_24_begin_mask_0, end = concat_282, end_mask = key_cache_internal_tensor_assign_24_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_24_squeeze_mask_0, stride = key_cache_internal_tensor_assign_24_stride_0, update = key_states_237_cast_fp16, x = coreml_update_state_212)[name = string("key_cache_internal_tensor_assign_24_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_24_cast_fp16, input = key_cache)[name = string("coreml_update_state_214_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_214 = read_state(input = key_cache)[name = string("coreml_update_state_214")];
            tensor<int32, [4]> value_states_141_perm_0 = const()[name = string("value_states_141_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_24_stride_0 = const()[name = string("value_cache_internal_tensor_assign_24_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_24_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_24_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_24_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_24_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_24_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_24_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_141_cast_fp16 = transpose(perm = value_states_141_perm_0, x = var_8377_cast_fp16)[name = string("transpose_274")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_24_cast_fp16 = slice_update(begin = concat_281, begin_mask = value_cache_internal_tensor_assign_24_begin_mask_0, end = concat_282, end_mask = value_cache_internal_tensor_assign_24_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_24_squeeze_mask_0, stride = value_cache_internal_tensor_assign_24_stride_0, update = value_states_141_cast_fp16, x = coreml_update_state_213)[name = string("value_cache_internal_tensor_assign_24_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_24_cast_fp16, input = value_cache)[name = string("coreml_update_state_215_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_215 = read_state(input = value_cache)[name = string("coreml_update_state_215")];
            tensor<int32, [4]> var_8471_begin_0 = const()[name = string("op_8471_begin_0"), val = tensor<int32, [4]>([23, 0, 0, 0])];
            tensor<int32, [4]> var_8471_end_0 = const()[name = string("op_8471_end_0"), val = tensor<int32, [4]>([24, 2, 2048, 128])];
            tensor<bool, [4]> var_8471_end_mask_0 = const()[name = string("op_8471_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8471_cast_fp16 = slice_by_index(begin = var_8471_begin_0, end = var_8471_end_0, end_mask = var_8471_end_mask_0, x = coreml_update_state_214)[name = string("op_8471_cast_fp16")];
            tensor<int32, [2]> tile_46 = const()[name = string("tile_46"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8474_axis_0 = const()[name = string("op_8474_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8474_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8474_cast_fp16_1 = split(axis = var_8474_axis_0, split_sizes = tile_46, x = var_8471_cast_fp16)[name = string("op_8474_cast_fp16")];
            tensor<int32, [4]> var_8481_begin_0 = const()[name = string("op_8481_begin_0"), val = tensor<int32, [4]>([23, 0, 0, 0])];
            tensor<int32, [4]> var_8481_end_0 = const()[name = string("op_8481_end_0"), val = tensor<int32, [4]>([24, 2, 2048, 128])];
            tensor<bool, [4]> var_8481_end_mask_0 = const()[name = string("op_8481_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8481_cast_fp16 = slice_by_index(begin = var_8481_begin_0, end = var_8481_end_0, end_mask = var_8481_end_mask_0, x = coreml_update_state_215)[name = string("op_8481_cast_fp16")];
            tensor<int32, [2]> tile_47 = const()[name = string("tile_47"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8484_axis_0 = const()[name = string("op_8484_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8484_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8484_cast_fp16_1 = split(axis = var_8484_axis_0, split_sizes = tile_47, x = var_8481_cast_fp16)[name = string("op_8484_cast_fp16")];
            tensor<int32, [2]> var_8487_split_sizes_0 = const()[name = string("op_8487_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_8487_axis_0 = const()[name = string("op_8487_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_8487_0, tensor<fp16, [1, 8, 128, 16]> var_8487_1 = split(axis = var_8487_axis_0, split_sizes = var_8487_split_sizes_0, x = query_states_141_cast_fp16)[name = string("op_8487")];
            bool attn_weights_369_transpose_x_0 = const()[name = string("attn_weights_369_transpose_x_0"), val = bool(false)];
            bool attn_weights_369_transpose_y_0 = const()[name = string("attn_weights_369_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_369_cast_fp16 = matmul(transpose_x = attn_weights_369_transpose_x_0, transpose_y = attn_weights_369_transpose_y_0, x = var_8474_cast_fp16_0, y = var_8487_0)[name = string("attn_weights_369_cast_fp16")];
            fp16 var_8490_to_fp16 = const()[name = string("op_8490_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_371_cast_fp16 = mul(x = attn_weights_369_cast_fp16, y = var_8490_to_fp16)[name = string("attn_weights_371_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_373_cast_fp16 = add(x = attn_weights_371_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_373_cast_fp16")];
            int32 var_8494 = const()[name = string("op_8494"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_375_cast_fp16 = softmax(axis = var_8494, x = attn_weights_373_cast_fp16)[name = string("attn_weights_375_cast_fp16")];
            bool var_8500_transpose_x_1 = const()[name = string("op_8500_transpose_x_1"), val = bool(true)];
            bool var_8500_transpose_y_1 = const()[name = string("op_8500_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_8500_cast_fp16 = matmul(transpose_x = var_8500_transpose_x_1, transpose_y = var_8500_transpose_y_1, x = attn_weights_375_cast_fp16, y = var_8484_cast_fp16_0)[name = string("op_8500_cast_fp16")];
            bool attn_weights_377_transpose_x_0 = const()[name = string("attn_weights_377_transpose_x_0"), val = bool(false)];
            bool attn_weights_377_transpose_y_0 = const()[name = string("attn_weights_377_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_377_cast_fp16 = matmul(transpose_x = attn_weights_377_transpose_x_0, transpose_y = attn_weights_377_transpose_y_0, x = var_8474_cast_fp16_1, y = var_8487_1)[name = string("attn_weights_377_cast_fp16")];
            fp16 var_8502_to_fp16 = const()[name = string("op_8502_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_379_cast_fp16 = mul(x = attn_weights_377_cast_fp16, y = var_8502_to_fp16)[name = string("attn_weights_379_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_381_cast_fp16 = add(x = attn_weights_379_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_381_cast_fp16")];
            int32 var_8506 = const()[name = string("op_8506"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_383_cast_fp16 = softmax(axis = var_8506, x = attn_weights_381_cast_fp16)[name = string("attn_weights_383_cast_fp16")];
            bool attn_output_185_transpose_x_1 = const()[name = string("attn_output_185_transpose_x_1"), val = bool(true)];
            bool attn_output_185_transpose_y_1 = const()[name = string("attn_output_185_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_185_cast_fp16 = matmul(transpose_x = attn_output_185_transpose_x_1, transpose_y = attn_output_185_transpose_y_1, x = attn_weights_383_cast_fp16, y = var_8484_cast_fp16_1)[name = string("attn_output_185_cast_fp16")];
            int32 var_8514 = const()[name = string("op_8514"), val = int32(1)];
            bool attn_output_187_interleave_0 = const()[name = string("attn_output_187_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_187_cast_fp16 = concat(axis = var_8514, interleave = attn_output_187_interleave_0, values = (var_8500_cast_fp16, attn_output_185_cast_fp16))[name = string("attn_output_187_cast_fp16")];
            tensor<int32, [4]> var_8518_perm_0 = const()[name = string("op_8518_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_287x = const()[name = string("concat_287x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_8518_cast_fp16 = transpose(perm = var_8518_perm_0, x = attn_output_187_cast_fp16)[name = string("transpose_273")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_191_cast_fp16 = reshape(shape = concat_287x, x = var_8518_cast_fp16)[name = string("attn_output_191_cast_fp16")];
            tensor<int32, [2]> hidden_states_233_strides_0 = const()[name = string("hidden_states_233_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_233_pad_type_0 = const()[name = string("hidden_states_233_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_233_pad_0 = const()[name = string("hidden_states_233_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_233_dilations_0 = const()[name = string("hidden_states_233_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_233_groups_0 = const()[name = string("hidden_states_233_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_233_cast_fp16 = conv(dilations = hidden_states_233_dilations_0, groups = hidden_states_233_groups_0, pad = hidden_states_233_pad_0, pad_type = hidden_states_233_pad_type_0, strides = hidden_states_233_strides_0, weight = layers_23_self_attn_o_proj_weight_cast_fp16, x = attn_output_191_cast_fp16)[name = string("hidden_states_233_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_235_cast_fp16 = add(x = hidden_states_229_cast_fp16, y = hidden_states_233_cast_fp16)[name = string("hidden_states_235_cast_fp16")];
            fp16 const_240_promoted_to_fp16 = const()[name = string("const_240_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_8551_cast_fp16 = mul(x = hidden_states_235_cast_fp16, y = const_240_promoted_to_fp16)[name = string("op_8551_cast_fp16")];
            int32 var_8549 = const()[name = string("op_8549"), val = int32(1)];
            bool doubled_189_interleave_0 = const()[name = string("doubled_189_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_189_cast_fp16 = concat(axis = var_8549, interleave = doubled_189_interleave_0, values = (hidden_states_235_cast_fp16, var_8551_cast_fp16))[name = string("doubled_189_cast_fp16")];
            tensor<int32, [1]> out_95_axes_0 = const()[name = string("out_95_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_95_gamma_0_to_fp16 = const()[name = string("out_95_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1530916864)))];
            fp16 var_8561_to_fp16 = const()[name = string("op_8561_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_95_cast_fp16 = layer_norm(axes = out_95_axes_0, epsilon = var_8561_to_fp16, gamma = out_95_gamma_0_to_fp16, x = doubled_189_cast_fp16)[name = string("out_95_cast_fp16")];
            tensor<int32, [2]> var_8572_split_sizes_0 = const()[name = string("op_8572_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8572_axis_0 = const()[name = string("op_8572_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_8572_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_8572_cast_fp16_1 = split(axis = var_8572_axis_0, split_sizes = var_8572_split_sizes_0, x = out_95_cast_fp16)[name = string("op_8572_cast_fp16")];
            tensor<int32, [2]> input_47_strides_0 = const()[name = string("input_47_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_47_pad_type_0 = const()[name = string("input_47_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_47_dilations_0 = const()[name = string("input_47_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_47_groups_0 = const()[name = string("input_47_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_47_cast_fp16 = conv(dilations = input_47_dilations_0, groups = input_47_groups_0, pad = input_47_pad_0, pad_type = input_47_pad_type_0, strides = input_47_strides_0, weight = layers_23_mlp_gate_proj_weight_cast_fp16, x = var_8572_cast_fp16_0)[name = string("input_47_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_8589_cast_fp16 = silu(x = input_47_cast_fp16)[name = string("op_8589_cast_fp16")];
            tensor<int32, [2]> var_8595_strides_0 = const()[name = string("op_8595_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_8595_pad_type_0 = const()[name = string("op_8595_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_8595_pad_0 = const()[name = string("op_8595_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_8595_dilations_0 = const()[name = string("op_8595_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8595_groups_0 = const()[name = string("op_8595_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_8595_cast_fp16 = conv(dilations = var_8595_dilations_0, groups = var_8595_groups_0, pad = var_8595_pad_0, pad_type = var_8595_pad_type_0, strides = var_8595_strides_0, weight = layers_23_mlp_up_proj_weight_cast_fp16, x = var_8572_cast_fp16_0)[name = string("op_8595_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_239_cast_fp16 = mul(x = var_8589_cast_fp16, y = var_8595_cast_fp16)[name = string("x_239_cast_fp16")];
            tensor<int32, [2]> hidden_states_237_strides_0 = const()[name = string("hidden_states_237_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_237_pad_type_0 = const()[name = string("hidden_states_237_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_237_pad_0 = const()[name = string("hidden_states_237_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_237_dilations_0 = const()[name = string("hidden_states_237_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_237_groups_0 = const()[name = string("hidden_states_237_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_237_cast_fp16 = conv(dilations = hidden_states_237_dilations_0, groups = hidden_states_237_groups_0, pad = hidden_states_237_pad_0, pad_type = hidden_states_237_pad_type_0, strides = hidden_states_237_strides_0, weight = layers_23_mlp_down_proj_weight_cast_fp16, x = x_239_cast_fp16)[name = string("hidden_states_237_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_239_cast_fp16 = add(x = hidden_states_235_cast_fp16, y = hidden_states_237_cast_fp16)[name = string("hidden_states_239_cast_fp16")];
            fp16 const_242_promoted_to_fp16 = const()[name = string("const_242_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_8613_cast_fp16 = mul(x = hidden_states_239_cast_fp16, y = const_242_promoted_to_fp16)[name = string("op_8613_cast_fp16")];
            int32 var_8611 = const()[name = string("op_8611"), val = int32(1)];
            bool doubled_193_interleave_0 = const()[name = string("doubled_193_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_193_cast_fp16 = concat(axis = var_8611, interleave = doubled_193_interleave_0, values = (hidden_states_239_cast_fp16, var_8613_cast_fp16))[name = string("doubled_193_cast_fp16")];
            tensor<int32, [1]> out_97_axes_0 = const()[name = string("out_97_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_97_gamma_0_to_fp16 = const()[name = string("out_97_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1530925120)))];
            fp16 var_8623_to_fp16 = const()[name = string("op_8623_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_97_cast_fp16 = layer_norm(axes = out_97_axes_0, epsilon = var_8623_to_fp16, gamma = out_97_gamma_0_to_fp16, x = doubled_193_cast_fp16)[name = string("out_97_cast_fp16")];
            tensor<int32, [2]> var_8634_split_sizes_0 = const()[name = string("op_8634_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8634_axis_0 = const()[name = string("op_8634_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_8634_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_8634_cast_fp16_1 = split(axis = var_8634_axis_0, split_sizes = var_8634_split_sizes_0, x = out_97_cast_fp16)[name = string("op_8634_cast_fp16")];
            tensor<int32, [2]> query_states_145_strides_0 = const()[name = string("query_states_145_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_145_pad_type_0 = const()[name = string("query_states_145_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_145_pad_0 = const()[name = string("query_states_145_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_145_dilations_0 = const()[name = string("query_states_145_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_145_groups_0 = const()[name = string("query_states_145_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_145_cast_fp16 = conv(dilations = query_states_145_dilations_0, groups = query_states_145_groups_0, pad = query_states_145_pad_0, pad_type = query_states_145_pad_type_0, strides = query_states_145_strides_0, weight = layers_24_self_attn_q_proj_weight_cast_fp16, x = var_8634_cast_fp16_0)[name = string("query_states_145_cast_fp16")];
            tensor<int32, [2]> key_states_241_strides_0 = const()[name = string("key_states_241_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_241_pad_type_0 = const()[name = string("key_states_241_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_241_pad_0 = const()[name = string("key_states_241_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_241_dilations_0 = const()[name = string("key_states_241_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_241_groups_0 = const()[name = string("key_states_241_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_241_cast_fp16 = conv(dilations = key_states_241_dilations_0, groups = key_states_241_groups_0, pad = key_states_241_pad_0, pad_type = key_states_241_pad_type_0, strides = key_states_241_strides_0, weight = layers_24_self_attn_k_proj_weight_cast_fp16, x = var_8634_cast_fp16_0)[name = string("key_states_241_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_24_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_24_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1530933376)))];
            tensor<int32, [2]> value_states_145_strides_0 = const()[name = string("value_states_145_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_145_pad_type_0 = const()[name = string("value_states_145_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_145_pad_0 = const()[name = string("value_states_145_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_145_dilations_0 = const()[name = string("value_states_145_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_145_groups_0 = const()[name = string("value_states_145_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_145_cast_fp16 = conv(dilations = value_states_145_dilations_0, groups = value_states_145_groups_0, pad = value_states_145_pad_0, pad_type = value_states_145_pad_type_0, strides = value_states_145_strides_0, weight = layers_24_self_attn_v_proj_weight_to_fp16, x = var_8634_cast_fp16_0)[name = string("value_states_145_cast_fp16")];
            tensor<int32, [4]> concat_288x = const()[name = string("concat_288x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_241_cast_fp16 = reshape(shape = concat_288x, x = query_states_145_cast_fp16)[name = string("x_241_cast_fp16")];
            tensor<int32, [4]> concat_289x = const()[name = string("concat_289x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_8691_cast_fp16 = reshape(shape = concat_289x, x = key_states_241_cast_fp16)[name = string("op_8691_cast_fp16")];
            tensor<int32, [4]> concat_290x = const()[name = string("concat_290x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_8698_cast_fp16 = reshape(shape = concat_290x, x = value_states_145_cast_fp16)[name = string("op_8698_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_8702_cast_fp16 = mul(x = x_241_cast_fp16, y = var_859_cast_fp16)[name = string("op_8702_cast_fp16")];
            tensor<int32, [2]> var_8703_split_sizes_0 = const()[name = string("op_8703_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8703_axis_0 = const()[name = string("op_8703_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_8703_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_8703_cast_fp16_1 = split(axis = var_8703_axis_0, split_sizes = var_8703_split_sizes_0, x = x_241_cast_fp16)[name = string("op_8703_cast_fp16")];
            fp16 const_244_promoted_to_fp16 = const()[name = string("const_244_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_8705_cast_fp16 = mul(x = var_8703_cast_fp16_1, y = const_244_promoted_to_fp16)[name = string("op_8705_cast_fp16")];
            int32 var_8707 = const()[name = string("op_8707"), val = int32(-2)];
            bool var_8708_interleave_0 = const()[name = string("op_8708_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_8708_cast_fp16 = concat(axis = var_8707, interleave = var_8708_interleave_0, values = (var_8705_cast_fp16, var_8703_cast_fp16_0))[name = string("op_8708_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_8709_cast_fp16 = mul(x = var_8708_cast_fp16, y = var_866_cast_fp16)[name = string("op_8709_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_147_cast_fp16 = add(x = var_8702_cast_fp16, y = var_8709_cast_fp16)[name = string("query_states_147_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_8715_cast_fp16 = mul(x = var_8691_cast_fp16, y = var_859_cast_fp16)[name = string("op_8715_cast_fp16")];
            tensor<int32, [2]> var_8716_split_sizes_0 = const()[name = string("op_8716_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8716_axis_0 = const()[name = string("op_8716_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_8716_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_8716_cast_fp16_1 = split(axis = var_8716_axis_0, split_sizes = var_8716_split_sizes_0, x = var_8691_cast_fp16)[name = string("op_8716_cast_fp16")];
            fp16 const_245_promoted_to_fp16 = const()[name = string("const_245_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_8718_cast_fp16 = mul(x = var_8716_cast_fp16_1, y = const_245_promoted_to_fp16)[name = string("op_8718_cast_fp16")];
            int32 var_8720 = const()[name = string("op_8720"), val = int32(-2)];
            bool var_8721_interleave_0 = const()[name = string("op_8721_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_8721_cast_fp16 = concat(axis = var_8720, interleave = var_8721_interleave_0, values = (var_8718_cast_fp16, var_8716_cast_fp16_0))[name = string("op_8721_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_8722_cast_fp16 = mul(x = var_8721_cast_fp16, y = var_866_cast_fp16)[name = string("op_8722_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_245_cast_fp16 = add(x = var_8715_cast_fp16, y = var_8722_cast_fp16)[name = string("key_states_245_cast_fp16")];
            tensor<int32, [1]> expand_dims_288 = const()[name = string("expand_dims_288"), val = tensor<int32, [1]>([24])];
            tensor<int32, [1]> expand_dims_289 = const()[name = string("expand_dims_289"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_291 = const()[name = string("expand_dims_291"), val = tensor<int32, [1]>([0])];
            int32 concat_293_axis_0 = const()[name = string("concat_293_axis_0"), val = int32(0)];
            bool concat_293_interleave_0 = const()[name = string("concat_293_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_293 = concat(axis = concat_293_axis_0, interleave = concat_293_interleave_0, values = (expand_dims_288, expand_dims_289, position_id, expand_dims_291))[name = string("concat_293")];
            tensor<int32, [1]> expand_dims_292 = const()[name = string("expand_dims_292"), val = tensor<int32, [1]>([25])];
            tensor<int32, [1]> concat_294_values1_0 = const()[name = string("concat_294_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_294_values3_0 = const()[name = string("concat_294_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_294_axis_0 = const()[name = string("concat_294_axis_0"), val = int32(0)];
            bool concat_294_interleave_0 = const()[name = string("concat_294_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_294 = concat(axis = concat_294_axis_0, interleave = concat_294_interleave_0, values = (expand_dims_292, concat_294_values1_0, cache_position_end, concat_294_values3_0))[name = string("concat_294")];
            tensor<int32, [4]> key_states_247_perm_0 = const()[name = string("key_states_247_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_25_stride_0 = const()[name = string("key_cache_internal_tensor_assign_25_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_25_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_25_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_25_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_25_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_25_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_25_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_247_cast_fp16 = transpose(perm = key_states_247_perm_0, x = key_states_245_cast_fp16)[name = string("transpose_272")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_25_cast_fp16 = slice_update(begin = concat_293, begin_mask = key_cache_internal_tensor_assign_25_begin_mask_0, end = concat_294, end_mask = key_cache_internal_tensor_assign_25_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_25_squeeze_mask_0, stride = key_cache_internal_tensor_assign_25_stride_0, update = key_states_247_cast_fp16, x = coreml_update_state_214)[name = string("key_cache_internal_tensor_assign_25_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_25_cast_fp16, input = key_cache)[name = string("coreml_update_state_216_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_216 = read_state(input = key_cache)[name = string("coreml_update_state_216")];
            tensor<int32, [4]> value_states_147_perm_0 = const()[name = string("value_states_147_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_25_stride_0 = const()[name = string("value_cache_internal_tensor_assign_25_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_25_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_25_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_25_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_25_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_25_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_25_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_147_cast_fp16 = transpose(perm = value_states_147_perm_0, x = var_8698_cast_fp16)[name = string("transpose_271")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_25_cast_fp16 = slice_update(begin = concat_293, begin_mask = value_cache_internal_tensor_assign_25_begin_mask_0, end = concat_294, end_mask = value_cache_internal_tensor_assign_25_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_25_squeeze_mask_0, stride = value_cache_internal_tensor_assign_25_stride_0, update = value_states_147_cast_fp16, x = coreml_update_state_215)[name = string("value_cache_internal_tensor_assign_25_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_25_cast_fp16, input = value_cache)[name = string("coreml_update_state_217_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_217 = read_state(input = value_cache)[name = string("coreml_update_state_217")];
            tensor<int32, [4]> var_8792_begin_0 = const()[name = string("op_8792_begin_0"), val = tensor<int32, [4]>([24, 0, 0, 0])];
            tensor<int32, [4]> var_8792_end_0 = const()[name = string("op_8792_end_0"), val = tensor<int32, [4]>([25, 2, 2048, 128])];
            tensor<bool, [4]> var_8792_end_mask_0 = const()[name = string("op_8792_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8792_cast_fp16 = slice_by_index(begin = var_8792_begin_0, end = var_8792_end_0, end_mask = var_8792_end_mask_0, x = coreml_update_state_216)[name = string("op_8792_cast_fp16")];
            tensor<int32, [2]> tile_48 = const()[name = string("tile_48"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8795_axis_0 = const()[name = string("op_8795_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8795_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8795_cast_fp16_1 = split(axis = var_8795_axis_0, split_sizes = tile_48, x = var_8792_cast_fp16)[name = string("op_8795_cast_fp16")];
            tensor<int32, [4]> var_8802_begin_0 = const()[name = string("op_8802_begin_0"), val = tensor<int32, [4]>([24, 0, 0, 0])];
            tensor<int32, [4]> var_8802_end_0 = const()[name = string("op_8802_end_0"), val = tensor<int32, [4]>([25, 2, 2048, 128])];
            tensor<bool, [4]> var_8802_end_mask_0 = const()[name = string("op_8802_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8802_cast_fp16 = slice_by_index(begin = var_8802_begin_0, end = var_8802_end_0, end_mask = var_8802_end_mask_0, x = coreml_update_state_217)[name = string("op_8802_cast_fp16")];
            tensor<int32, [2]> tile_49 = const()[name = string("tile_49"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8805_axis_0 = const()[name = string("op_8805_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8805_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8805_cast_fp16_1 = split(axis = var_8805_axis_0, split_sizes = tile_49, x = var_8802_cast_fp16)[name = string("op_8805_cast_fp16")];
            tensor<int32, [2]> var_8808_split_sizes_0 = const()[name = string("op_8808_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_8808_axis_0 = const()[name = string("op_8808_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_8808_0, tensor<fp16, [1, 8, 128, 16]> var_8808_1 = split(axis = var_8808_axis_0, split_sizes = var_8808_split_sizes_0, x = query_states_147_cast_fp16)[name = string("op_8808")];
            bool attn_weights_385_transpose_x_0 = const()[name = string("attn_weights_385_transpose_x_0"), val = bool(false)];
            bool attn_weights_385_transpose_y_0 = const()[name = string("attn_weights_385_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_385_cast_fp16 = matmul(transpose_x = attn_weights_385_transpose_x_0, transpose_y = attn_weights_385_transpose_y_0, x = var_8795_cast_fp16_0, y = var_8808_0)[name = string("attn_weights_385_cast_fp16")];
            fp16 var_8811_to_fp16 = const()[name = string("op_8811_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_387_cast_fp16 = mul(x = attn_weights_385_cast_fp16, y = var_8811_to_fp16)[name = string("attn_weights_387_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_389_cast_fp16 = add(x = attn_weights_387_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_389_cast_fp16")];
            int32 var_8815 = const()[name = string("op_8815"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_391_cast_fp16 = softmax(axis = var_8815, x = attn_weights_389_cast_fp16)[name = string("attn_weights_391_cast_fp16")];
            bool var_8821_transpose_x_1 = const()[name = string("op_8821_transpose_x_1"), val = bool(true)];
            bool var_8821_transpose_y_1 = const()[name = string("op_8821_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_8821_cast_fp16 = matmul(transpose_x = var_8821_transpose_x_1, transpose_y = var_8821_transpose_y_1, x = attn_weights_391_cast_fp16, y = var_8805_cast_fp16_0)[name = string("op_8821_cast_fp16")];
            bool attn_weights_393_transpose_x_0 = const()[name = string("attn_weights_393_transpose_x_0"), val = bool(false)];
            bool attn_weights_393_transpose_y_0 = const()[name = string("attn_weights_393_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_393_cast_fp16 = matmul(transpose_x = attn_weights_393_transpose_x_0, transpose_y = attn_weights_393_transpose_y_0, x = var_8795_cast_fp16_1, y = var_8808_1)[name = string("attn_weights_393_cast_fp16")];
            fp16 var_8823_to_fp16 = const()[name = string("op_8823_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_395_cast_fp16 = mul(x = attn_weights_393_cast_fp16, y = var_8823_to_fp16)[name = string("attn_weights_395_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_397_cast_fp16 = add(x = attn_weights_395_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_397_cast_fp16")];
            int32 var_8827 = const()[name = string("op_8827"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_399_cast_fp16 = softmax(axis = var_8827, x = attn_weights_397_cast_fp16)[name = string("attn_weights_399_cast_fp16")];
            bool attn_output_193_transpose_x_1 = const()[name = string("attn_output_193_transpose_x_1"), val = bool(true)];
            bool attn_output_193_transpose_y_1 = const()[name = string("attn_output_193_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_193_cast_fp16 = matmul(transpose_x = attn_output_193_transpose_x_1, transpose_y = attn_output_193_transpose_y_1, x = attn_weights_399_cast_fp16, y = var_8805_cast_fp16_1)[name = string("attn_output_193_cast_fp16")];
            int32 var_8835 = const()[name = string("op_8835"), val = int32(1)];
            bool attn_output_195_interleave_0 = const()[name = string("attn_output_195_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_195_cast_fp16 = concat(axis = var_8835, interleave = attn_output_195_interleave_0, values = (var_8821_cast_fp16, attn_output_193_cast_fp16))[name = string("attn_output_195_cast_fp16")];
            tensor<int32, [4]> var_8839_perm_0 = const()[name = string("op_8839_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_299x = const()[name = string("concat_299x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_8839_cast_fp16 = transpose(perm = var_8839_perm_0, x = attn_output_195_cast_fp16)[name = string("transpose_270")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_199_cast_fp16 = reshape(shape = concat_299x, x = var_8839_cast_fp16)[name = string("attn_output_199_cast_fp16")];
            tensor<int32, [2]> hidden_states_243_strides_0 = const()[name = string("hidden_states_243_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_243_pad_type_0 = const()[name = string("hidden_states_243_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_243_pad_0 = const()[name = string("hidden_states_243_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_243_dilations_0 = const()[name = string("hidden_states_243_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_243_groups_0 = const()[name = string("hidden_states_243_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_243_cast_fp16 = conv(dilations = hidden_states_243_dilations_0, groups = hidden_states_243_groups_0, pad = hidden_states_243_pad_0, pad_type = hidden_states_243_pad_type_0, strides = hidden_states_243_strides_0, weight = layers_24_self_attn_o_proj_weight_cast_fp16, x = attn_output_199_cast_fp16)[name = string("hidden_states_243_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_245_cast_fp16 = add(x = hidden_states_239_cast_fp16, y = hidden_states_243_cast_fp16)[name = string("hidden_states_245_cast_fp16")];
            fp16 const_250_promoted_to_fp16 = const()[name = string("const_250_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_8872_cast_fp16 = mul(x = hidden_states_245_cast_fp16, y = const_250_promoted_to_fp16)[name = string("op_8872_cast_fp16")];
            int32 var_8870 = const()[name = string("op_8870"), val = int32(1)];
            bool doubled_197_interleave_0 = const()[name = string("doubled_197_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_197_cast_fp16 = concat(axis = var_8870, interleave = doubled_197_interleave_0, values = (hidden_states_245_cast_fp16, var_8872_cast_fp16))[name = string("doubled_197_cast_fp16")];
            tensor<int32, [1]> out_99_axes_0 = const()[name = string("out_99_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_99_gamma_0_to_fp16 = const()[name = string("out_99_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1531982016)))];
            fp16 var_8882_to_fp16 = const()[name = string("op_8882_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_99_cast_fp16 = layer_norm(axes = out_99_axes_0, epsilon = var_8882_to_fp16, gamma = out_99_gamma_0_to_fp16, x = doubled_197_cast_fp16)[name = string("out_99_cast_fp16")];
            tensor<int32, [2]> var_8893_split_sizes_0 = const()[name = string("op_8893_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8893_axis_0 = const()[name = string("op_8893_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_8893_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_8893_cast_fp16_1 = split(axis = var_8893_axis_0, split_sizes = var_8893_split_sizes_0, x = out_99_cast_fp16)[name = string("op_8893_cast_fp16")];
            tensor<int32, [2]> input_49_strides_0 = const()[name = string("input_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_49_pad_type_0 = const()[name = string("input_49_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_49_pad_0 = const()[name = string("input_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_49_dilations_0 = const()[name = string("input_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_49_groups_0 = const()[name = string("input_49_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_49_cast_fp16 = conv(dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = layers_24_mlp_gate_proj_weight_cast_fp16, x = var_8893_cast_fp16_0)[name = string("input_49_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_8910_cast_fp16 = silu(x = input_49_cast_fp16)[name = string("op_8910_cast_fp16")];
            tensor<int32, [2]> var_8916_strides_0 = const()[name = string("op_8916_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_8916_pad_type_0 = const()[name = string("op_8916_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_8916_pad_0 = const()[name = string("op_8916_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_8916_dilations_0 = const()[name = string("op_8916_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8916_groups_0 = const()[name = string("op_8916_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_8916_cast_fp16 = conv(dilations = var_8916_dilations_0, groups = var_8916_groups_0, pad = var_8916_pad_0, pad_type = var_8916_pad_type_0, strides = var_8916_strides_0, weight = layers_24_mlp_up_proj_weight_cast_fp16, x = var_8893_cast_fp16_0)[name = string("op_8916_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_249_cast_fp16 = mul(x = var_8910_cast_fp16, y = var_8916_cast_fp16)[name = string("x_249_cast_fp16")];
            tensor<int32, [2]> hidden_states_247_strides_0 = const()[name = string("hidden_states_247_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_247_pad_type_0 = const()[name = string("hidden_states_247_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_247_pad_0 = const()[name = string("hidden_states_247_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_247_dilations_0 = const()[name = string("hidden_states_247_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_247_groups_0 = const()[name = string("hidden_states_247_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_247_cast_fp16 = conv(dilations = hidden_states_247_dilations_0, groups = hidden_states_247_groups_0, pad = hidden_states_247_pad_0, pad_type = hidden_states_247_pad_type_0, strides = hidden_states_247_strides_0, weight = layers_24_mlp_down_proj_weight_cast_fp16, x = x_249_cast_fp16)[name = string("hidden_states_247_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_249_cast_fp16 = add(x = hidden_states_245_cast_fp16, y = hidden_states_247_cast_fp16)[name = string("hidden_states_249_cast_fp16")];
            fp16 const_252_promoted_to_fp16 = const()[name = string("const_252_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_8934_cast_fp16 = mul(x = hidden_states_249_cast_fp16, y = const_252_promoted_to_fp16)[name = string("op_8934_cast_fp16")];
            int32 var_8932 = const()[name = string("op_8932"), val = int32(1)];
            bool doubled_201_interleave_0 = const()[name = string("doubled_201_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_201_cast_fp16 = concat(axis = var_8932, interleave = doubled_201_interleave_0, values = (hidden_states_249_cast_fp16, var_8934_cast_fp16))[name = string("doubled_201_cast_fp16")];
            tensor<int32, [1]> out_101_axes_0 = const()[name = string("out_101_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_101_gamma_0_to_fp16 = const()[name = string("out_101_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1531990272)))];
            fp16 var_8944_to_fp16 = const()[name = string("op_8944_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_101_cast_fp16 = layer_norm(axes = out_101_axes_0, epsilon = var_8944_to_fp16, gamma = out_101_gamma_0_to_fp16, x = doubled_201_cast_fp16)[name = string("out_101_cast_fp16")];
            tensor<int32, [2]> var_8955_split_sizes_0 = const()[name = string("op_8955_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8955_axis_0 = const()[name = string("op_8955_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_8955_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_8955_cast_fp16_1 = split(axis = var_8955_axis_0, split_sizes = var_8955_split_sizes_0, x = out_101_cast_fp16)[name = string("op_8955_cast_fp16")];
            tensor<int32, [2]> query_states_151_strides_0 = const()[name = string("query_states_151_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_151_pad_type_0 = const()[name = string("query_states_151_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_151_pad_0 = const()[name = string("query_states_151_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_151_dilations_0 = const()[name = string("query_states_151_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_151_groups_0 = const()[name = string("query_states_151_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_151_cast_fp16 = conv(dilations = query_states_151_dilations_0, groups = query_states_151_groups_0, pad = query_states_151_pad_0, pad_type = query_states_151_pad_type_0, strides = query_states_151_strides_0, weight = layers_25_self_attn_q_proj_weight_cast_fp16, x = var_8955_cast_fp16_0)[name = string("query_states_151_cast_fp16")];
            tensor<int32, [2]> key_states_251_strides_0 = const()[name = string("key_states_251_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_251_pad_type_0 = const()[name = string("key_states_251_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_251_pad_0 = const()[name = string("key_states_251_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_251_dilations_0 = const()[name = string("key_states_251_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_251_groups_0 = const()[name = string("key_states_251_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_251_cast_fp16 = conv(dilations = key_states_251_dilations_0, groups = key_states_251_groups_0, pad = key_states_251_pad_0, pad_type = key_states_251_pad_type_0, strides = key_states_251_strides_0, weight = layers_25_self_attn_k_proj_weight_cast_fp16, x = var_8955_cast_fp16_0)[name = string("key_states_251_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_25_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_25_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1531998528)))];
            tensor<int32, [2]> value_states_151_strides_0 = const()[name = string("value_states_151_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_151_pad_type_0 = const()[name = string("value_states_151_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_151_pad_0 = const()[name = string("value_states_151_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_151_dilations_0 = const()[name = string("value_states_151_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_151_groups_0 = const()[name = string("value_states_151_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_151_cast_fp16 = conv(dilations = value_states_151_dilations_0, groups = value_states_151_groups_0, pad = value_states_151_pad_0, pad_type = value_states_151_pad_type_0, strides = value_states_151_strides_0, weight = layers_25_self_attn_v_proj_weight_to_fp16, x = var_8955_cast_fp16_0)[name = string("value_states_151_cast_fp16")];
            tensor<int32, [4]> concat_300x = const()[name = string("concat_300x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_251_cast_fp16 = reshape(shape = concat_300x, x = query_states_151_cast_fp16)[name = string("x_251_cast_fp16")];
            tensor<int32, [4]> concat_301x = const()[name = string("concat_301x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_9012_cast_fp16 = reshape(shape = concat_301x, x = key_states_251_cast_fp16)[name = string("op_9012_cast_fp16")];
            tensor<int32, [4]> concat_302x = const()[name = string("concat_302x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_9019_cast_fp16 = reshape(shape = concat_302x, x = value_states_151_cast_fp16)[name = string("op_9019_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_9023_cast_fp16 = mul(x = x_251_cast_fp16, y = var_859_cast_fp16)[name = string("op_9023_cast_fp16")];
            tensor<int32, [2]> var_9024_split_sizes_0 = const()[name = string("op_9024_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9024_axis_0 = const()[name = string("op_9024_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_9024_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_9024_cast_fp16_1 = split(axis = var_9024_axis_0, split_sizes = var_9024_split_sizes_0, x = x_251_cast_fp16)[name = string("op_9024_cast_fp16")];
            fp16 const_254_promoted_to_fp16 = const()[name = string("const_254_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_9026_cast_fp16 = mul(x = var_9024_cast_fp16_1, y = const_254_promoted_to_fp16)[name = string("op_9026_cast_fp16")];
            int32 var_9028 = const()[name = string("op_9028"), val = int32(-2)];
            bool var_9029_interleave_0 = const()[name = string("op_9029_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_9029_cast_fp16 = concat(axis = var_9028, interleave = var_9029_interleave_0, values = (var_9026_cast_fp16, var_9024_cast_fp16_0))[name = string("op_9029_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_9030_cast_fp16 = mul(x = var_9029_cast_fp16, y = var_866_cast_fp16)[name = string("op_9030_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_153_cast_fp16 = add(x = var_9023_cast_fp16, y = var_9030_cast_fp16)[name = string("query_states_153_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_9036_cast_fp16 = mul(x = var_9012_cast_fp16, y = var_859_cast_fp16)[name = string("op_9036_cast_fp16")];
            tensor<int32, [2]> var_9037_split_sizes_0 = const()[name = string("op_9037_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9037_axis_0 = const()[name = string("op_9037_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_9037_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_9037_cast_fp16_1 = split(axis = var_9037_axis_0, split_sizes = var_9037_split_sizes_0, x = var_9012_cast_fp16)[name = string("op_9037_cast_fp16")];
            fp16 const_255_promoted_to_fp16 = const()[name = string("const_255_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_9039_cast_fp16 = mul(x = var_9037_cast_fp16_1, y = const_255_promoted_to_fp16)[name = string("op_9039_cast_fp16")];
            int32 var_9041 = const()[name = string("op_9041"), val = int32(-2)];
            bool var_9042_interleave_0 = const()[name = string("op_9042_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_9042_cast_fp16 = concat(axis = var_9041, interleave = var_9042_interleave_0, values = (var_9039_cast_fp16, var_9037_cast_fp16_0))[name = string("op_9042_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_9043_cast_fp16 = mul(x = var_9042_cast_fp16, y = var_866_cast_fp16)[name = string("op_9043_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_255_cast_fp16 = add(x = var_9036_cast_fp16, y = var_9043_cast_fp16)[name = string("key_states_255_cast_fp16")];
            tensor<int32, [1]> expand_dims_300 = const()[name = string("expand_dims_300"), val = tensor<int32, [1]>([25])];
            tensor<int32, [1]> expand_dims_301 = const()[name = string("expand_dims_301"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_303 = const()[name = string("expand_dims_303"), val = tensor<int32, [1]>([0])];
            int32 concat_305_axis_0 = const()[name = string("concat_305_axis_0"), val = int32(0)];
            bool concat_305_interleave_0 = const()[name = string("concat_305_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_305 = concat(axis = concat_305_axis_0, interleave = concat_305_interleave_0, values = (expand_dims_300, expand_dims_301, position_id, expand_dims_303))[name = string("concat_305")];
            tensor<int32, [1]> expand_dims_304 = const()[name = string("expand_dims_304"), val = tensor<int32, [1]>([26])];
            tensor<int32, [1]> concat_306_values1_0 = const()[name = string("concat_306_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_306_values3_0 = const()[name = string("concat_306_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_306_axis_0 = const()[name = string("concat_306_axis_0"), val = int32(0)];
            bool concat_306_interleave_0 = const()[name = string("concat_306_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_306 = concat(axis = concat_306_axis_0, interleave = concat_306_interleave_0, values = (expand_dims_304, concat_306_values1_0, cache_position_end, concat_306_values3_0))[name = string("concat_306")];
            tensor<int32, [4]> key_states_257_perm_0 = const()[name = string("key_states_257_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_26_stride_0 = const()[name = string("key_cache_internal_tensor_assign_26_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_26_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_26_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_26_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_26_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_26_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_26_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_257_cast_fp16 = transpose(perm = key_states_257_perm_0, x = key_states_255_cast_fp16)[name = string("transpose_269")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_26_cast_fp16 = slice_update(begin = concat_305, begin_mask = key_cache_internal_tensor_assign_26_begin_mask_0, end = concat_306, end_mask = key_cache_internal_tensor_assign_26_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_26_squeeze_mask_0, stride = key_cache_internal_tensor_assign_26_stride_0, update = key_states_257_cast_fp16, x = coreml_update_state_216)[name = string("key_cache_internal_tensor_assign_26_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_26_cast_fp16, input = key_cache)[name = string("coreml_update_state_218_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_218 = read_state(input = key_cache)[name = string("coreml_update_state_218")];
            tensor<int32, [4]> value_states_153_perm_0 = const()[name = string("value_states_153_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_26_stride_0 = const()[name = string("value_cache_internal_tensor_assign_26_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_26_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_26_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_26_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_26_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_26_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_26_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_153_cast_fp16 = transpose(perm = value_states_153_perm_0, x = var_9019_cast_fp16)[name = string("transpose_268")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_26_cast_fp16 = slice_update(begin = concat_305, begin_mask = value_cache_internal_tensor_assign_26_begin_mask_0, end = concat_306, end_mask = value_cache_internal_tensor_assign_26_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_26_squeeze_mask_0, stride = value_cache_internal_tensor_assign_26_stride_0, update = value_states_153_cast_fp16, x = coreml_update_state_217)[name = string("value_cache_internal_tensor_assign_26_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_26_cast_fp16, input = value_cache)[name = string("coreml_update_state_219_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_219 = read_state(input = value_cache)[name = string("coreml_update_state_219")];
            tensor<int32, [4]> var_9113_begin_0 = const()[name = string("op_9113_begin_0"), val = tensor<int32, [4]>([25, 0, 0, 0])];
            tensor<int32, [4]> var_9113_end_0 = const()[name = string("op_9113_end_0"), val = tensor<int32, [4]>([26, 2, 2048, 128])];
            tensor<bool, [4]> var_9113_end_mask_0 = const()[name = string("op_9113_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9113_cast_fp16 = slice_by_index(begin = var_9113_begin_0, end = var_9113_end_0, end_mask = var_9113_end_mask_0, x = coreml_update_state_218)[name = string("op_9113_cast_fp16")];
            tensor<int32, [2]> tile_50 = const()[name = string("tile_50"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9116_axis_0 = const()[name = string("op_9116_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9116_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9116_cast_fp16_1 = split(axis = var_9116_axis_0, split_sizes = tile_50, x = var_9113_cast_fp16)[name = string("op_9116_cast_fp16")];
            tensor<int32, [4]> var_9123_begin_0 = const()[name = string("op_9123_begin_0"), val = tensor<int32, [4]>([25, 0, 0, 0])];
            tensor<int32, [4]> var_9123_end_0 = const()[name = string("op_9123_end_0"), val = tensor<int32, [4]>([26, 2, 2048, 128])];
            tensor<bool, [4]> var_9123_end_mask_0 = const()[name = string("op_9123_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9123_cast_fp16 = slice_by_index(begin = var_9123_begin_0, end = var_9123_end_0, end_mask = var_9123_end_mask_0, x = coreml_update_state_219)[name = string("op_9123_cast_fp16")];
            tensor<int32, [2]> tile_51 = const()[name = string("tile_51"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9126_axis_0 = const()[name = string("op_9126_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9126_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9126_cast_fp16_1 = split(axis = var_9126_axis_0, split_sizes = tile_51, x = var_9123_cast_fp16)[name = string("op_9126_cast_fp16")];
            tensor<int32, [2]> var_9129_split_sizes_0 = const()[name = string("op_9129_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_9129_axis_0 = const()[name = string("op_9129_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_9129_0, tensor<fp16, [1, 8, 128, 16]> var_9129_1 = split(axis = var_9129_axis_0, split_sizes = var_9129_split_sizes_0, x = query_states_153_cast_fp16)[name = string("op_9129")];
            bool attn_weights_401_transpose_x_0 = const()[name = string("attn_weights_401_transpose_x_0"), val = bool(false)];
            bool attn_weights_401_transpose_y_0 = const()[name = string("attn_weights_401_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_401_cast_fp16 = matmul(transpose_x = attn_weights_401_transpose_x_0, transpose_y = attn_weights_401_transpose_y_0, x = var_9116_cast_fp16_0, y = var_9129_0)[name = string("attn_weights_401_cast_fp16")];
            fp16 var_9132_to_fp16 = const()[name = string("op_9132_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_403_cast_fp16 = mul(x = attn_weights_401_cast_fp16, y = var_9132_to_fp16)[name = string("attn_weights_403_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_405_cast_fp16 = add(x = attn_weights_403_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_405_cast_fp16")];
            int32 var_9136 = const()[name = string("op_9136"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_407_cast_fp16 = softmax(axis = var_9136, x = attn_weights_405_cast_fp16)[name = string("attn_weights_407_cast_fp16")];
            bool var_9142_transpose_x_1 = const()[name = string("op_9142_transpose_x_1"), val = bool(true)];
            bool var_9142_transpose_y_1 = const()[name = string("op_9142_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_9142_cast_fp16 = matmul(transpose_x = var_9142_transpose_x_1, transpose_y = var_9142_transpose_y_1, x = attn_weights_407_cast_fp16, y = var_9126_cast_fp16_0)[name = string("op_9142_cast_fp16")];
            bool attn_weights_409_transpose_x_0 = const()[name = string("attn_weights_409_transpose_x_0"), val = bool(false)];
            bool attn_weights_409_transpose_y_0 = const()[name = string("attn_weights_409_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_409_cast_fp16 = matmul(transpose_x = attn_weights_409_transpose_x_0, transpose_y = attn_weights_409_transpose_y_0, x = var_9116_cast_fp16_1, y = var_9129_1)[name = string("attn_weights_409_cast_fp16")];
            fp16 var_9144_to_fp16 = const()[name = string("op_9144_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_411_cast_fp16 = mul(x = attn_weights_409_cast_fp16, y = var_9144_to_fp16)[name = string("attn_weights_411_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_413_cast_fp16 = add(x = attn_weights_411_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_413_cast_fp16")];
            int32 var_9148 = const()[name = string("op_9148"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_415_cast_fp16 = softmax(axis = var_9148, x = attn_weights_413_cast_fp16)[name = string("attn_weights_415_cast_fp16")];
            bool attn_output_201_transpose_x_1 = const()[name = string("attn_output_201_transpose_x_1"), val = bool(true)];
            bool attn_output_201_transpose_y_1 = const()[name = string("attn_output_201_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_201_cast_fp16 = matmul(transpose_x = attn_output_201_transpose_x_1, transpose_y = attn_output_201_transpose_y_1, x = attn_weights_415_cast_fp16, y = var_9126_cast_fp16_1)[name = string("attn_output_201_cast_fp16")];
            int32 var_9156 = const()[name = string("op_9156"), val = int32(1)];
            bool attn_output_203_interleave_0 = const()[name = string("attn_output_203_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_203_cast_fp16 = concat(axis = var_9156, interleave = attn_output_203_interleave_0, values = (var_9142_cast_fp16, attn_output_201_cast_fp16))[name = string("attn_output_203_cast_fp16")];
            tensor<int32, [4]> var_9160_perm_0 = const()[name = string("op_9160_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_311x = const()[name = string("concat_311x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_9160_cast_fp16 = transpose(perm = var_9160_perm_0, x = attn_output_203_cast_fp16)[name = string("transpose_267")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_207_cast_fp16 = reshape(shape = concat_311x, x = var_9160_cast_fp16)[name = string("attn_output_207_cast_fp16")];
            tensor<int32, [2]> hidden_states_253_strides_0 = const()[name = string("hidden_states_253_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_253_pad_type_0 = const()[name = string("hidden_states_253_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_253_pad_0 = const()[name = string("hidden_states_253_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_253_dilations_0 = const()[name = string("hidden_states_253_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_253_groups_0 = const()[name = string("hidden_states_253_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_253_cast_fp16 = conv(dilations = hidden_states_253_dilations_0, groups = hidden_states_253_groups_0, pad = hidden_states_253_pad_0, pad_type = hidden_states_253_pad_type_0, strides = hidden_states_253_strides_0, weight = layers_25_self_attn_o_proj_weight_cast_fp16, x = attn_output_207_cast_fp16)[name = string("hidden_states_253_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_255_cast_fp16 = add(x = hidden_states_249_cast_fp16, y = hidden_states_253_cast_fp16)[name = string("hidden_states_255_cast_fp16")];
            fp16 const_260_promoted_to_fp16 = const()[name = string("const_260_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_9193_cast_fp16 = mul(x = hidden_states_255_cast_fp16, y = const_260_promoted_to_fp16)[name = string("op_9193_cast_fp16")];
            int32 var_9191 = const()[name = string("op_9191"), val = int32(1)];
            bool doubled_205_interleave_0 = const()[name = string("doubled_205_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_205_cast_fp16 = concat(axis = var_9191, interleave = doubled_205_interleave_0, values = (hidden_states_255_cast_fp16, var_9193_cast_fp16))[name = string("doubled_205_cast_fp16")];
            tensor<int32, [1]> out_103_axes_0 = const()[name = string("out_103_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_103_gamma_0_to_fp16 = const()[name = string("out_103_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1533047168)))];
            fp16 var_9203_to_fp16 = const()[name = string("op_9203_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_103_cast_fp16 = layer_norm(axes = out_103_axes_0, epsilon = var_9203_to_fp16, gamma = out_103_gamma_0_to_fp16, x = doubled_205_cast_fp16)[name = string("out_103_cast_fp16")];
            tensor<int32, [2]> var_9214_split_sizes_0 = const()[name = string("op_9214_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9214_axis_0 = const()[name = string("op_9214_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_9214_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_9214_cast_fp16_1 = split(axis = var_9214_axis_0, split_sizes = var_9214_split_sizes_0, x = out_103_cast_fp16)[name = string("op_9214_cast_fp16")];
            tensor<int32, [2]> input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_51_cast_fp16 = conv(dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = layers_25_mlp_gate_proj_weight_cast_fp16, x = var_9214_cast_fp16_0)[name = string("input_51_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_9231_cast_fp16 = silu(x = input_51_cast_fp16)[name = string("op_9231_cast_fp16")];
            tensor<int32, [2]> var_9237_strides_0 = const()[name = string("op_9237_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_9237_pad_type_0 = const()[name = string("op_9237_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_9237_pad_0 = const()[name = string("op_9237_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_9237_dilations_0 = const()[name = string("op_9237_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9237_groups_0 = const()[name = string("op_9237_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_9237_cast_fp16 = conv(dilations = var_9237_dilations_0, groups = var_9237_groups_0, pad = var_9237_pad_0, pad_type = var_9237_pad_type_0, strides = var_9237_strides_0, weight = layers_25_mlp_up_proj_weight_cast_fp16, x = var_9214_cast_fp16_0)[name = string("op_9237_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_259_cast_fp16 = mul(x = var_9231_cast_fp16, y = var_9237_cast_fp16)[name = string("x_259_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_25_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_25_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1533055424)))];
            tensor<int32, [2]> hidden_states_257_strides_0 = const()[name = string("hidden_states_257_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_257_pad_type_0 = const()[name = string("hidden_states_257_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_257_pad_0 = const()[name = string("hidden_states_257_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_257_dilations_0 = const()[name = string("hidden_states_257_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_257_groups_0 = const()[name = string("hidden_states_257_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_257_cast_fp16 = conv(dilations = hidden_states_257_dilations_0, groups = hidden_states_257_groups_0, pad = hidden_states_257_pad_0, pad_type = hidden_states_257_pad_type_0, strides = hidden_states_257_strides_0, weight = layers_25_mlp_down_proj_weight_to_fp16, x = x_259_cast_fp16)[name = string("hidden_states_257_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_259_cast_fp16 = add(x = hidden_states_255_cast_fp16, y = hidden_states_257_cast_fp16)[name = string("hidden_states_259_cast_fp16")];
            fp16 const_262_promoted_to_fp16 = const()[name = string("const_262_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_9255_cast_fp16 = mul(x = hidden_states_259_cast_fp16, y = const_262_promoted_to_fp16)[name = string("op_9255_cast_fp16")];
            int32 var_9253 = const()[name = string("op_9253"), val = int32(1)];
            bool doubled_209_interleave_0 = const()[name = string("doubled_209_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_209_cast_fp16 = concat(axis = var_9253, interleave = doubled_209_interleave_0, values = (hidden_states_259_cast_fp16, var_9255_cast_fp16))[name = string("doubled_209_cast_fp16")];
            tensor<int32, [1]> out_105_axes_0 = const()[name = string("out_105_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_105_gamma_0_to_fp16 = const()[name = string("out_105_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1558221312)))];
            fp16 var_9265_to_fp16 = const()[name = string("op_9265_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_105_cast_fp16 = layer_norm(axes = out_105_axes_0, epsilon = var_9265_to_fp16, gamma = out_105_gamma_0_to_fp16, x = doubled_209_cast_fp16)[name = string("out_105_cast_fp16")];
            tensor<int32, [2]> var_9276_split_sizes_0 = const()[name = string("op_9276_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9276_axis_0 = const()[name = string("op_9276_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_9276_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_9276_cast_fp16_1 = split(axis = var_9276_axis_0, split_sizes = var_9276_split_sizes_0, x = out_105_cast_fp16)[name = string("op_9276_cast_fp16")];
            tensor<int32, [2]> query_states_157_strides_0 = const()[name = string("query_states_157_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_157_pad_type_0 = const()[name = string("query_states_157_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_157_pad_0 = const()[name = string("query_states_157_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_157_dilations_0 = const()[name = string("query_states_157_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_157_groups_0 = const()[name = string("query_states_157_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_157_cast_fp16 = conv(dilations = query_states_157_dilations_0, groups = query_states_157_groups_0, pad = query_states_157_pad_0, pad_type = query_states_157_pad_type_0, strides = query_states_157_strides_0, weight = layers_26_self_attn_q_proj_weight_cast_fp16, x = var_9276_cast_fp16_0)[name = string("query_states_157_cast_fp16")];
            tensor<int32, [2]> key_states_261_strides_0 = const()[name = string("key_states_261_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_261_pad_type_0 = const()[name = string("key_states_261_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_261_pad_0 = const()[name = string("key_states_261_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_261_dilations_0 = const()[name = string("key_states_261_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_261_groups_0 = const()[name = string("key_states_261_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_261_cast_fp16 = conv(dilations = key_states_261_dilations_0, groups = key_states_261_groups_0, pad = key_states_261_pad_0, pad_type = key_states_261_pad_type_0, strides = key_states_261_strides_0, weight = layers_26_self_attn_k_proj_weight_cast_fp16, x = var_9276_cast_fp16_0)[name = string("key_states_261_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_26_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_26_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1558229568)))];
            tensor<int32, [2]> value_states_157_strides_0 = const()[name = string("value_states_157_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_157_pad_type_0 = const()[name = string("value_states_157_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_157_pad_0 = const()[name = string("value_states_157_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_157_dilations_0 = const()[name = string("value_states_157_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_157_groups_0 = const()[name = string("value_states_157_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_157_cast_fp16 = conv(dilations = value_states_157_dilations_0, groups = value_states_157_groups_0, pad = value_states_157_pad_0, pad_type = value_states_157_pad_type_0, strides = value_states_157_strides_0, weight = layers_26_self_attn_v_proj_weight_to_fp16, x = var_9276_cast_fp16_0)[name = string("value_states_157_cast_fp16")];
            tensor<int32, [4]> concat_312x = const()[name = string("concat_312x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_261_cast_fp16 = reshape(shape = concat_312x, x = query_states_157_cast_fp16)[name = string("x_261_cast_fp16")];
            tensor<int32, [4]> concat_313x = const()[name = string("concat_313x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_9333_cast_fp16 = reshape(shape = concat_313x, x = key_states_261_cast_fp16)[name = string("op_9333_cast_fp16")];
            tensor<int32, [4]> concat_314x = const()[name = string("concat_314x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_9340_cast_fp16 = reshape(shape = concat_314x, x = value_states_157_cast_fp16)[name = string("op_9340_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_9344_cast_fp16 = mul(x = x_261_cast_fp16, y = var_859_cast_fp16)[name = string("op_9344_cast_fp16")];
            tensor<int32, [2]> var_9345_split_sizes_0 = const()[name = string("op_9345_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9345_axis_0 = const()[name = string("op_9345_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_9345_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_9345_cast_fp16_1 = split(axis = var_9345_axis_0, split_sizes = var_9345_split_sizes_0, x = x_261_cast_fp16)[name = string("op_9345_cast_fp16")];
            fp16 const_264_promoted_to_fp16 = const()[name = string("const_264_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_9347_cast_fp16 = mul(x = var_9345_cast_fp16_1, y = const_264_promoted_to_fp16)[name = string("op_9347_cast_fp16")];
            int32 var_9349 = const()[name = string("op_9349"), val = int32(-2)];
            bool var_9350_interleave_0 = const()[name = string("op_9350_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_9350_cast_fp16 = concat(axis = var_9349, interleave = var_9350_interleave_0, values = (var_9347_cast_fp16, var_9345_cast_fp16_0))[name = string("op_9350_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_9351_cast_fp16 = mul(x = var_9350_cast_fp16, y = var_866_cast_fp16)[name = string("op_9351_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_159_cast_fp16 = add(x = var_9344_cast_fp16, y = var_9351_cast_fp16)[name = string("query_states_159_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_9357_cast_fp16 = mul(x = var_9333_cast_fp16, y = var_859_cast_fp16)[name = string("op_9357_cast_fp16")];
            tensor<int32, [2]> var_9358_split_sizes_0 = const()[name = string("op_9358_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9358_axis_0 = const()[name = string("op_9358_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_9358_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_9358_cast_fp16_1 = split(axis = var_9358_axis_0, split_sizes = var_9358_split_sizes_0, x = var_9333_cast_fp16)[name = string("op_9358_cast_fp16")];
            fp16 const_265_promoted_to_fp16 = const()[name = string("const_265_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_9360_cast_fp16 = mul(x = var_9358_cast_fp16_1, y = const_265_promoted_to_fp16)[name = string("op_9360_cast_fp16")];
            int32 var_9362 = const()[name = string("op_9362"), val = int32(-2)];
            bool var_9363_interleave_0 = const()[name = string("op_9363_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_9363_cast_fp16 = concat(axis = var_9362, interleave = var_9363_interleave_0, values = (var_9360_cast_fp16, var_9358_cast_fp16_0))[name = string("op_9363_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_9364_cast_fp16 = mul(x = var_9363_cast_fp16, y = var_866_cast_fp16)[name = string("op_9364_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_265_cast_fp16 = add(x = var_9357_cast_fp16, y = var_9364_cast_fp16)[name = string("key_states_265_cast_fp16")];
            tensor<int32, [1]> expand_dims_312 = const()[name = string("expand_dims_312"), val = tensor<int32, [1]>([26])];
            tensor<int32, [1]> expand_dims_313 = const()[name = string("expand_dims_313"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_315 = const()[name = string("expand_dims_315"), val = tensor<int32, [1]>([0])];
            int32 concat_317_axis_0 = const()[name = string("concat_317_axis_0"), val = int32(0)];
            bool concat_317_interleave_0 = const()[name = string("concat_317_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_317 = concat(axis = concat_317_axis_0, interleave = concat_317_interleave_0, values = (expand_dims_312, expand_dims_313, position_id, expand_dims_315))[name = string("concat_317")];
            tensor<int32, [1]> expand_dims_316 = const()[name = string("expand_dims_316"), val = tensor<int32, [1]>([27])];
            tensor<int32, [1]> concat_318_values1_0 = const()[name = string("concat_318_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_318_values3_0 = const()[name = string("concat_318_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_318_axis_0 = const()[name = string("concat_318_axis_0"), val = int32(0)];
            bool concat_318_interleave_0 = const()[name = string("concat_318_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_318 = concat(axis = concat_318_axis_0, interleave = concat_318_interleave_0, values = (expand_dims_316, concat_318_values1_0, cache_position_end, concat_318_values3_0))[name = string("concat_318")];
            tensor<int32, [4]> key_states_267_perm_0 = const()[name = string("key_states_267_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_27_stride_0 = const()[name = string("key_cache_internal_tensor_assign_27_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_27_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_27_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_27_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_27_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_27_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_27_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_267_cast_fp16 = transpose(perm = key_states_267_perm_0, x = key_states_265_cast_fp16)[name = string("transpose_266")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_27_cast_fp16 = slice_update(begin = concat_317, begin_mask = key_cache_internal_tensor_assign_27_begin_mask_0, end = concat_318, end_mask = key_cache_internal_tensor_assign_27_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_27_squeeze_mask_0, stride = key_cache_internal_tensor_assign_27_stride_0, update = key_states_267_cast_fp16, x = coreml_update_state_218)[name = string("key_cache_internal_tensor_assign_27_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_27_cast_fp16, input = key_cache)[name = string("coreml_update_state_220_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_220 = read_state(input = key_cache)[name = string("coreml_update_state_220")];
            tensor<int32, [4]> value_states_159_perm_0 = const()[name = string("value_states_159_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_27_stride_0 = const()[name = string("value_cache_internal_tensor_assign_27_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_27_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_27_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_27_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_27_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_27_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_27_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_159_cast_fp16 = transpose(perm = value_states_159_perm_0, x = var_9340_cast_fp16)[name = string("transpose_265")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_27_cast_fp16 = slice_update(begin = concat_317, begin_mask = value_cache_internal_tensor_assign_27_begin_mask_0, end = concat_318, end_mask = value_cache_internal_tensor_assign_27_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_27_squeeze_mask_0, stride = value_cache_internal_tensor_assign_27_stride_0, update = value_states_159_cast_fp16, x = coreml_update_state_219)[name = string("value_cache_internal_tensor_assign_27_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_27_cast_fp16, input = value_cache)[name = string("coreml_update_state_221_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_221 = read_state(input = value_cache)[name = string("coreml_update_state_221")];
            tensor<int32, [4]> var_9434_begin_0 = const()[name = string("op_9434_begin_0"), val = tensor<int32, [4]>([26, 0, 0, 0])];
            tensor<int32, [4]> var_9434_end_0 = const()[name = string("op_9434_end_0"), val = tensor<int32, [4]>([27, 2, 2048, 128])];
            tensor<bool, [4]> var_9434_end_mask_0 = const()[name = string("op_9434_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9434_cast_fp16 = slice_by_index(begin = var_9434_begin_0, end = var_9434_end_0, end_mask = var_9434_end_mask_0, x = coreml_update_state_220)[name = string("op_9434_cast_fp16")];
            tensor<int32, [2]> tile_52 = const()[name = string("tile_52"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9437_axis_0 = const()[name = string("op_9437_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9437_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9437_cast_fp16_1 = split(axis = var_9437_axis_0, split_sizes = tile_52, x = var_9434_cast_fp16)[name = string("op_9437_cast_fp16")];
            tensor<int32, [4]> var_9444_begin_0 = const()[name = string("op_9444_begin_0"), val = tensor<int32, [4]>([26, 0, 0, 0])];
            tensor<int32, [4]> var_9444_end_0 = const()[name = string("op_9444_end_0"), val = tensor<int32, [4]>([27, 2, 2048, 128])];
            tensor<bool, [4]> var_9444_end_mask_0 = const()[name = string("op_9444_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9444_cast_fp16 = slice_by_index(begin = var_9444_begin_0, end = var_9444_end_0, end_mask = var_9444_end_mask_0, x = coreml_update_state_221)[name = string("op_9444_cast_fp16")];
            tensor<int32, [2]> tile_53 = const()[name = string("tile_53"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9447_axis_0 = const()[name = string("op_9447_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9447_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9447_cast_fp16_1 = split(axis = var_9447_axis_0, split_sizes = tile_53, x = var_9444_cast_fp16)[name = string("op_9447_cast_fp16")];
            tensor<int32, [2]> var_9450_split_sizes_0 = const()[name = string("op_9450_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_9450_axis_0 = const()[name = string("op_9450_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_9450_0, tensor<fp16, [1, 8, 128, 16]> var_9450_1 = split(axis = var_9450_axis_0, split_sizes = var_9450_split_sizes_0, x = query_states_159_cast_fp16)[name = string("op_9450")];
            bool attn_weights_417_transpose_x_0 = const()[name = string("attn_weights_417_transpose_x_0"), val = bool(false)];
            bool attn_weights_417_transpose_y_0 = const()[name = string("attn_weights_417_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_417_cast_fp16 = matmul(transpose_x = attn_weights_417_transpose_x_0, transpose_y = attn_weights_417_transpose_y_0, x = var_9437_cast_fp16_0, y = var_9450_0)[name = string("attn_weights_417_cast_fp16")];
            fp16 var_9453_to_fp16 = const()[name = string("op_9453_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_419_cast_fp16 = mul(x = attn_weights_417_cast_fp16, y = var_9453_to_fp16)[name = string("attn_weights_419_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_421_cast_fp16 = add(x = attn_weights_419_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_421_cast_fp16")];
            int32 var_9457 = const()[name = string("op_9457"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_423_cast_fp16 = softmax(axis = var_9457, x = attn_weights_421_cast_fp16)[name = string("attn_weights_423_cast_fp16")];
            bool var_9463_transpose_x_1 = const()[name = string("op_9463_transpose_x_1"), val = bool(true)];
            bool var_9463_transpose_y_1 = const()[name = string("op_9463_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_9463_cast_fp16 = matmul(transpose_x = var_9463_transpose_x_1, transpose_y = var_9463_transpose_y_1, x = attn_weights_423_cast_fp16, y = var_9447_cast_fp16_0)[name = string("op_9463_cast_fp16")];
            bool attn_weights_425_transpose_x_0 = const()[name = string("attn_weights_425_transpose_x_0"), val = bool(false)];
            bool attn_weights_425_transpose_y_0 = const()[name = string("attn_weights_425_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_425_cast_fp16 = matmul(transpose_x = attn_weights_425_transpose_x_0, transpose_y = attn_weights_425_transpose_y_0, x = var_9437_cast_fp16_1, y = var_9450_1)[name = string("attn_weights_425_cast_fp16")];
            fp16 var_9465_to_fp16 = const()[name = string("op_9465_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_427_cast_fp16 = mul(x = attn_weights_425_cast_fp16, y = var_9465_to_fp16)[name = string("attn_weights_427_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_429_cast_fp16 = add(x = attn_weights_427_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_429_cast_fp16")];
            int32 var_9469 = const()[name = string("op_9469"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_431_cast_fp16 = softmax(axis = var_9469, x = attn_weights_429_cast_fp16)[name = string("attn_weights_431_cast_fp16")];
            bool attn_output_209_transpose_x_1 = const()[name = string("attn_output_209_transpose_x_1"), val = bool(true)];
            bool attn_output_209_transpose_y_1 = const()[name = string("attn_output_209_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_209_cast_fp16 = matmul(transpose_x = attn_output_209_transpose_x_1, transpose_y = attn_output_209_transpose_y_1, x = attn_weights_431_cast_fp16, y = var_9447_cast_fp16_1)[name = string("attn_output_209_cast_fp16")];
            int32 var_9477 = const()[name = string("op_9477"), val = int32(1)];
            bool attn_output_211_interleave_0 = const()[name = string("attn_output_211_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_211_cast_fp16 = concat(axis = var_9477, interleave = attn_output_211_interleave_0, values = (var_9463_cast_fp16, attn_output_209_cast_fp16))[name = string("attn_output_211_cast_fp16")];
            tensor<int32, [4]> var_9481_perm_0 = const()[name = string("op_9481_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_323x = const()[name = string("concat_323x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_9481_cast_fp16 = transpose(perm = var_9481_perm_0, x = attn_output_211_cast_fp16)[name = string("transpose_264")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_215_cast_fp16 = reshape(shape = concat_323x, x = var_9481_cast_fp16)[name = string("attn_output_215_cast_fp16")];
            tensor<int32, [2]> hidden_states_263_strides_0 = const()[name = string("hidden_states_263_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_263_pad_type_0 = const()[name = string("hidden_states_263_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_263_pad_0 = const()[name = string("hidden_states_263_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_263_dilations_0 = const()[name = string("hidden_states_263_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_263_groups_0 = const()[name = string("hidden_states_263_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_263_cast_fp16 = conv(dilations = hidden_states_263_dilations_0, groups = hidden_states_263_groups_0, pad = hidden_states_263_pad_0, pad_type = hidden_states_263_pad_type_0, strides = hidden_states_263_strides_0, weight = layers_26_self_attn_o_proj_weight_cast_fp16, x = attn_output_215_cast_fp16)[name = string("hidden_states_263_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_265_cast_fp16 = add(x = hidden_states_259_cast_fp16, y = hidden_states_263_cast_fp16)[name = string("hidden_states_265_cast_fp16")];
            fp16 const_270_promoted_to_fp16 = const()[name = string("const_270_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_9514_cast_fp16 = mul(x = hidden_states_265_cast_fp16, y = const_270_promoted_to_fp16)[name = string("op_9514_cast_fp16")];
            int32 var_9512 = const()[name = string("op_9512"), val = int32(1)];
            bool doubled_213_interleave_0 = const()[name = string("doubled_213_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_213_cast_fp16 = concat(axis = var_9512, interleave = doubled_213_interleave_0, values = (hidden_states_265_cast_fp16, var_9514_cast_fp16))[name = string("doubled_213_cast_fp16")];
            tensor<int32, [1]> out_107_axes_0 = const()[name = string("out_107_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_107_gamma_0_to_fp16 = const()[name = string("out_107_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1559278208)))];
            fp16 var_9524_to_fp16 = const()[name = string("op_9524_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_107_cast_fp16 = layer_norm(axes = out_107_axes_0, epsilon = var_9524_to_fp16, gamma = out_107_gamma_0_to_fp16, x = doubled_213_cast_fp16)[name = string("out_107_cast_fp16")];
            tensor<int32, [2]> var_9535_split_sizes_0 = const()[name = string("op_9535_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9535_axis_0 = const()[name = string("op_9535_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_9535_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_9535_cast_fp16_1 = split(axis = var_9535_axis_0, split_sizes = var_9535_split_sizes_0, x = out_107_cast_fp16)[name = string("op_9535_cast_fp16")];
            tensor<int32, [2]> input_53_strides_0 = const()[name = string("input_53_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_53_pad_type_0 = const()[name = string("input_53_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_53_dilations_0 = const()[name = string("input_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_53_groups_0 = const()[name = string("input_53_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_53_cast_fp16 = conv(dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = layers_26_mlp_gate_proj_weight_cast_fp16, x = var_9535_cast_fp16_0)[name = string("input_53_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_9552_cast_fp16 = silu(x = input_53_cast_fp16)[name = string("op_9552_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_26_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_26_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1559286464)))];
            tensor<int32, [2]> var_9558_strides_0 = const()[name = string("op_9558_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_9558_pad_type_0 = const()[name = string("op_9558_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_9558_pad_0 = const()[name = string("op_9558_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_9558_dilations_0 = const()[name = string("op_9558_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9558_groups_0 = const()[name = string("op_9558_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_9558_cast_fp16 = conv(dilations = var_9558_dilations_0, groups = var_9558_groups_0, pad = var_9558_pad_0, pad_type = var_9558_pad_type_0, strides = var_9558_strides_0, weight = layers_26_mlp_up_proj_weight_to_fp16, x = var_9535_cast_fp16_0)[name = string("op_9558_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_269_cast_fp16 = mul(x = var_9552_cast_fp16, y = var_9558_cast_fp16)[name = string("x_269_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_26_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_26_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1584452352)))];
            tensor<int32, [2]> hidden_states_267_strides_0 = const()[name = string("hidden_states_267_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_267_pad_type_0 = const()[name = string("hidden_states_267_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_267_pad_0 = const()[name = string("hidden_states_267_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_267_dilations_0 = const()[name = string("hidden_states_267_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_267_groups_0 = const()[name = string("hidden_states_267_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_267_cast_fp16 = conv(dilations = hidden_states_267_dilations_0, groups = hidden_states_267_groups_0, pad = hidden_states_267_pad_0, pad_type = hidden_states_267_pad_type_0, strides = hidden_states_267_strides_0, weight = layers_26_mlp_down_proj_weight_to_fp16, x = x_269_cast_fp16)[name = string("hidden_states_267_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_269_cast_fp16 = add(x = hidden_states_265_cast_fp16, y = hidden_states_267_cast_fp16)[name = string("hidden_states_269_cast_fp16")];
            fp16 const_272_promoted_to_fp16 = const()[name = string("const_272_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_9576_cast_fp16 = mul(x = hidden_states_269_cast_fp16, y = const_272_promoted_to_fp16)[name = string("op_9576_cast_fp16")];
            int32 var_9574 = const()[name = string("op_9574"), val = int32(1)];
            bool doubled_217_interleave_0 = const()[name = string("doubled_217_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_217_cast_fp16 = concat(axis = var_9574, interleave = doubled_217_interleave_0, values = (hidden_states_269_cast_fp16, var_9576_cast_fp16))[name = string("doubled_217_cast_fp16")];
            tensor<int32, [1]> out_109_axes_0 = const()[name = string("out_109_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_109_gamma_0_to_fp16 = const()[name = string("out_109_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1609618240)))];
            fp16 var_9586_to_fp16 = const()[name = string("op_9586_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_109_cast_fp16 = layer_norm(axes = out_109_axes_0, epsilon = var_9586_to_fp16, gamma = out_109_gamma_0_to_fp16, x = doubled_217_cast_fp16)[name = string("out_109_cast_fp16")];
            tensor<int32, [2]> var_9597_split_sizes_0 = const()[name = string("op_9597_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9597_axis_0 = const()[name = string("op_9597_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_9597_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_9597_cast_fp16_1 = split(axis = var_9597_axis_0, split_sizes = var_9597_split_sizes_0, x = out_109_cast_fp16)[name = string("op_9597_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_27_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1609626496)))];
            tensor<int32, [2]> query_states_163_strides_0 = const()[name = string("query_states_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_163_pad_type_0 = const()[name = string("query_states_163_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_163_pad_0 = const()[name = string("query_states_163_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_163_dilations_0 = const()[name = string("query_states_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_163_groups_0 = const()[name = string("query_states_163_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> query_states_163_cast_fp16 = conv(dilations = query_states_163_dilations_0, groups = query_states_163_groups_0, pad = query_states_163_pad_0, pad_type = query_states_163_pad_type_0, strides = query_states_163_strides_0, weight = layers_27_self_attn_q_proj_weight_to_fp16, x = var_9597_cast_fp16_0)[name = string("query_states_163_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_27_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1618015168)))];
            tensor<int32, [2]> key_states_271_strides_0 = const()[name = string("key_states_271_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_271_pad_type_0 = const()[name = string("key_states_271_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_271_pad_0 = const()[name = string("key_states_271_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_271_dilations_0 = const()[name = string("key_states_271_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_271_groups_0 = const()[name = string("key_states_271_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> key_states_271_cast_fp16 = conv(dilations = key_states_271_dilations_0, groups = key_states_271_groups_0, pad = key_states_271_pad_0, pad_type = key_states_271_pad_type_0, strides = key_states_271_strides_0, weight = layers_27_self_attn_k_proj_weight_to_fp16, x = var_9597_cast_fp16_0)[name = string("key_states_271_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_27_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1619063808)))];
            tensor<int32, [2]> value_states_163_strides_0 = const()[name = string("value_states_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_163_pad_type_0 = const()[name = string("value_states_163_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_163_pad_0 = const()[name = string("value_states_163_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_163_dilations_0 = const()[name = string("value_states_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_163_groups_0 = const()[name = string("value_states_163_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 16]> value_states_163_cast_fp16 = conv(dilations = value_states_163_dilations_0, groups = value_states_163_groups_0, pad = value_states_163_pad_0, pad_type = value_states_163_pad_type_0, strides = value_states_163_strides_0, weight = layers_27_self_attn_v_proj_weight_to_fp16, x = var_9597_cast_fp16_0)[name = string("value_states_163_cast_fp16")];
            tensor<int32, [4]> concat_324x = const()[name = string("concat_324x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 16]> x_271_cast_fp16 = reshape(shape = concat_324x, x = query_states_163_cast_fp16)[name = string("x_271_cast_fp16")];
            tensor<int32, [4]> concat_325x = const()[name = string("concat_325x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_9654_cast_fp16 = reshape(shape = concat_325x, x = key_states_271_cast_fp16)[name = string("op_9654_cast_fp16")];
            tensor<int32, [4]> concat_326x = const()[name = string("concat_326x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 16]> var_9661_cast_fp16 = reshape(shape = concat_326x, x = value_states_163_cast_fp16)[name = string("op_9661_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_9665_cast_fp16 = mul(x = x_271_cast_fp16, y = var_859_cast_fp16)[name = string("op_9665_cast_fp16")];
            tensor<int32, [2]> var_9666_split_sizes_0 = const()[name = string("op_9666_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9666_axis_0 = const()[name = string("op_9666_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 16]> var_9666_cast_fp16_0, tensor<fp16, [1, 16, 64, 16]> var_9666_cast_fp16_1 = split(axis = var_9666_axis_0, split_sizes = var_9666_split_sizes_0, x = x_271_cast_fp16)[name = string("op_9666_cast_fp16")];
            fp16 const_274_promoted_to_fp16 = const()[name = string("const_274_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 16]> var_9668_cast_fp16 = mul(x = var_9666_cast_fp16_1, y = const_274_promoted_to_fp16)[name = string("op_9668_cast_fp16")];
            int32 var_9670 = const()[name = string("op_9670"), val = int32(-2)];
            bool var_9671_interleave_0 = const()[name = string("op_9671_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 16]> var_9671_cast_fp16 = concat(axis = var_9670, interleave = var_9671_interleave_0, values = (var_9668_cast_fp16, var_9666_cast_fp16_0))[name = string("op_9671_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> var_9672_cast_fp16 = mul(x = var_9671_cast_fp16, y = var_866_cast_fp16)[name = string("op_9672_cast_fp16")];
            tensor<fp16, [1, 16, 128, 16]> query_states_165_cast_fp16 = add(x = var_9665_cast_fp16, y = var_9672_cast_fp16)[name = string("query_states_165_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_9678_cast_fp16 = mul(x = var_9654_cast_fp16, y = var_859_cast_fp16)[name = string("op_9678_cast_fp16")];
            tensor<int32, [2]> var_9679_split_sizes_0 = const()[name = string("op_9679_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9679_axis_0 = const()[name = string("op_9679_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 16]> var_9679_cast_fp16_0, tensor<fp16, [1, 2, 64, 16]> var_9679_cast_fp16_1 = split(axis = var_9679_axis_0, split_sizes = var_9679_split_sizes_0, x = var_9654_cast_fp16)[name = string("op_9679_cast_fp16")];
            fp16 const_275_promoted_to_fp16 = const()[name = string("const_275_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 16]> var_9681_cast_fp16 = mul(x = var_9679_cast_fp16_1, y = const_275_promoted_to_fp16)[name = string("op_9681_cast_fp16")];
            int32 var_9683 = const()[name = string("op_9683"), val = int32(-2)];
            bool var_9684_interleave_0 = const()[name = string("op_9684_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 16]> var_9684_cast_fp16 = concat(axis = var_9683, interleave = var_9684_interleave_0, values = (var_9681_cast_fp16, var_9679_cast_fp16_0))[name = string("op_9684_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> var_9685_cast_fp16 = mul(x = var_9684_cast_fp16, y = var_866_cast_fp16)[name = string("op_9685_cast_fp16")];
            tensor<fp16, [1, 2, 128, 16]> key_states_275_cast_fp16 = add(x = var_9678_cast_fp16, y = var_9685_cast_fp16)[name = string("key_states_275_cast_fp16")];
            tensor<int32, [1]> expand_dims_324 = const()[name = string("expand_dims_324"), val = tensor<int32, [1]>([27])];
            tensor<int32, [1]> expand_dims_325 = const()[name = string("expand_dims_325"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_327 = const()[name = string("expand_dims_327"), val = tensor<int32, [1]>([0])];
            int32 concat_329_axis_0 = const()[name = string("concat_329_axis_0"), val = int32(0)];
            bool concat_329_interleave_0 = const()[name = string("concat_329_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_329 = concat(axis = concat_329_axis_0, interleave = concat_329_interleave_0, values = (expand_dims_324, expand_dims_325, position_id, expand_dims_327))[name = string("concat_329")];
            tensor<int32, [1]> expand_dims_328 = const()[name = string("expand_dims_328"), val = tensor<int32, [1]>([28])];
            tensor<int32, [1]> concat_330_values1_0 = const()[name = string("concat_330_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_330_values3_0 = const()[name = string("concat_330_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_330_axis_0 = const()[name = string("concat_330_axis_0"), val = int32(0)];
            bool concat_330_interleave_0 = const()[name = string("concat_330_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_330 = concat(axis = concat_330_axis_0, interleave = concat_330_interleave_0, values = (expand_dims_328, concat_330_values1_0, cache_position_end, concat_330_values3_0))[name = string("concat_330")];
            tensor<int32, [4]> key_states_277_perm_0 = const()[name = string("key_states_277_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_28_stride_0 = const()[name = string("key_cache_internal_tensor_assign_28_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_28_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_28_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_28_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_28_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_28_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_28_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> key_states_277_cast_fp16 = transpose(perm = key_states_277_perm_0, x = key_states_275_cast_fp16)[name = string("transpose_263")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_28_cast_fp16 = slice_update(begin = concat_329, begin_mask = key_cache_internal_tensor_assign_28_begin_mask_0, end = concat_330, end_mask = key_cache_internal_tensor_assign_28_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_28_squeeze_mask_0, stride = key_cache_internal_tensor_assign_28_stride_0, update = key_states_277_cast_fp16, x = coreml_update_state_220)[name = string("key_cache_internal_tensor_assign_28_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_28_cast_fp16, input = key_cache)[name = string("coreml_update_state_222_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_222 = read_state(input = key_cache)[name = string("coreml_update_state_222")];
            tensor<int32, [4]> value_states_165_perm_0 = const()[name = string("value_states_165_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_28_stride_0 = const()[name = string("value_cache_internal_tensor_assign_28_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_28_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_28_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_28_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_28_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_28_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_28_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 16, 128]> value_states_165_cast_fp16 = transpose(perm = value_states_165_perm_0, x = var_9661_cast_fp16)[name = string("transpose_262")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_28_cast_fp16 = slice_update(begin = concat_329, begin_mask = value_cache_internal_tensor_assign_28_begin_mask_0, end = concat_330, end_mask = value_cache_internal_tensor_assign_28_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_28_squeeze_mask_0, stride = value_cache_internal_tensor_assign_28_stride_0, update = value_states_165_cast_fp16, x = coreml_update_state_221)[name = string("value_cache_internal_tensor_assign_28_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_28_cast_fp16, input = value_cache)[name = string("coreml_update_state_223_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_223 = read_state(input = value_cache)[name = string("coreml_update_state_223")];
            tensor<int32, [4]> var_9755_begin_0 = const()[name = string("op_9755_begin_0"), val = tensor<int32, [4]>([27, 0, 0, 0])];
            tensor<int32, [4]> var_9755_end_0 = const()[name = string("op_9755_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_9755_end_mask_0 = const()[name = string("op_9755_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9755_cast_fp16 = slice_by_index(begin = var_9755_begin_0, end = var_9755_end_0, end_mask = var_9755_end_mask_0, x = coreml_update_state_222)[name = string("op_9755_cast_fp16")];
            tensor<int32, [2]> tile_54 = const()[name = string("tile_54"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9758_axis_0 = const()[name = string("op_9758_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9758_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9758_cast_fp16_1 = split(axis = var_9758_axis_0, split_sizes = tile_54, x = var_9755_cast_fp16)[name = string("op_9758_cast_fp16")];
            tensor<int32, [4]> var_9765_begin_0 = const()[name = string("op_9765_begin_0"), val = tensor<int32, [4]>([27, 0, 0, 0])];
            tensor<int32, [4]> var_9765_end_0 = const()[name = string("op_9765_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_9765_end_mask_0 = const()[name = string("op_9765_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9765_cast_fp16 = slice_by_index(begin = var_9765_begin_0, end = var_9765_end_0, end_mask = var_9765_end_mask_0, x = coreml_update_state_223)[name = string("op_9765_cast_fp16")];
            tensor<int32, [2]> tile_55 = const()[name = string("tile_55"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9768_axis_0 = const()[name = string("op_9768_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9768_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9768_cast_fp16_1 = split(axis = var_9768_axis_0, split_sizes = tile_55, x = var_9765_cast_fp16)[name = string("op_9768_cast_fp16")];
            tensor<int32, [2]> var_9771_split_sizes_0 = const()[name = string("op_9771_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_9771_axis_0 = const()[name = string("op_9771_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 16]> var_9771_0, tensor<fp16, [1, 8, 128, 16]> var_9771_1 = split(axis = var_9771_axis_0, split_sizes = var_9771_split_sizes_0, x = query_states_165_cast_fp16)[name = string("op_9771")];
            bool attn_weights_433_transpose_x_0 = const()[name = string("attn_weights_433_transpose_x_0"), val = bool(false)];
            bool attn_weights_433_transpose_y_0 = const()[name = string("attn_weights_433_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_433_cast_fp16 = matmul(transpose_x = attn_weights_433_transpose_x_0, transpose_y = attn_weights_433_transpose_y_0, x = var_9758_cast_fp16_0, y = var_9771_0)[name = string("attn_weights_433_cast_fp16")];
            fp16 var_9774_to_fp16 = const()[name = string("op_9774_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_435_cast_fp16 = mul(x = attn_weights_433_cast_fp16, y = var_9774_to_fp16)[name = string("attn_weights_435_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_437_cast_fp16 = add(x = attn_weights_435_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_437_cast_fp16")];
            int32 var_9778 = const()[name = string("op_9778"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_439_cast_fp16 = softmax(axis = var_9778, x = attn_weights_437_cast_fp16)[name = string("attn_weights_439_cast_fp16")];
            bool var_9784_transpose_x_1 = const()[name = string("op_9784_transpose_x_1"), val = bool(true)];
            bool var_9784_transpose_y_1 = const()[name = string("op_9784_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> var_9784_cast_fp16 = matmul(transpose_x = var_9784_transpose_x_1, transpose_y = var_9784_transpose_y_1, x = attn_weights_439_cast_fp16, y = var_9768_cast_fp16_0)[name = string("op_9784_cast_fp16")];
            bool attn_weights_441_transpose_x_0 = const()[name = string("attn_weights_441_transpose_x_0"), val = bool(false)];
            bool attn_weights_441_transpose_y_0 = const()[name = string("attn_weights_441_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_441_cast_fp16 = matmul(transpose_x = attn_weights_441_transpose_x_0, transpose_y = attn_weights_441_transpose_y_0, x = var_9758_cast_fp16_1, y = var_9771_1)[name = string("attn_weights_441_cast_fp16")];
            fp16 var_9786_to_fp16 = const()[name = string("op_9786_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_443_cast_fp16 = mul(x = attn_weights_441_cast_fp16, y = var_9786_to_fp16)[name = string("attn_weights_443_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_445_cast_fp16 = add(x = attn_weights_443_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_445_cast_fp16")];
            int32 var_9790 = const()[name = string("op_9790"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 16]> attn_weights_cast_fp16 = softmax(axis = var_9790, x = attn_weights_445_cast_fp16)[name = string("attn_weights_cast_fp16")];
            bool attn_output_217_transpose_x_1 = const()[name = string("attn_output_217_transpose_x_1"), val = bool(true)];
            bool attn_output_217_transpose_y_1 = const()[name = string("attn_output_217_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 16, 128]> attn_output_217_cast_fp16 = matmul(transpose_x = attn_output_217_transpose_x_1, transpose_y = attn_output_217_transpose_y_1, x = attn_weights_cast_fp16, y = var_9768_cast_fp16_1)[name = string("attn_output_217_cast_fp16")];
            int32 var_9798 = const()[name = string("op_9798"), val = int32(1)];
            bool attn_output_219_interleave_0 = const()[name = string("attn_output_219_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 16, 128]> attn_output_219_cast_fp16 = concat(axis = var_9798, interleave = attn_output_219_interleave_0, values = (var_9784_cast_fp16, attn_output_217_cast_fp16))[name = string("attn_output_219_cast_fp16")];
            tensor<int32, [4]> var_9802_perm_0 = const()[name = string("op_9802_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_335x = const()[name = string("concat_335x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 16]> var_9802_cast_fp16 = transpose(perm = var_9802_perm_0, x = attn_output_219_cast_fp16)[name = string("transpose_261")];
            tensor<fp16, [1, 2048, 1, 16]> attn_output_cast_fp16 = reshape(shape = concat_335x, x = var_9802_cast_fp16)[name = string("attn_output_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_27_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1620112448)))];
            tensor<int32, [2]> hidden_states_273_strides_0 = const()[name = string("hidden_states_273_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_273_pad_type_0 = const()[name = string("hidden_states_273_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_273_pad_0 = const()[name = string("hidden_states_273_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_273_dilations_0 = const()[name = string("hidden_states_273_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_273_groups_0 = const()[name = string("hidden_states_273_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_273_cast_fp16 = conv(dilations = hidden_states_273_dilations_0, groups = hidden_states_273_groups_0, pad = hidden_states_273_pad_0, pad_type = hidden_states_273_pad_type_0, strides = hidden_states_273_strides_0, weight = layers_27_self_attn_o_proj_weight_to_fp16, x = attn_output_cast_fp16)[name = string("hidden_states_273_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_275_cast_fp16 = add(x = hidden_states_269_cast_fp16, y = hidden_states_273_cast_fp16)[name = string("hidden_states_275_cast_fp16")];
            fp16 const_280_promoted_to_fp16 = const()[name = string("const_280_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_9835_cast_fp16 = mul(x = hidden_states_275_cast_fp16, y = const_280_promoted_to_fp16)[name = string("op_9835_cast_fp16")];
            int32 var_9833 = const()[name = string("op_9833"), val = int32(1)];
            bool doubled_221_interleave_0 = const()[name = string("doubled_221_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_221_cast_fp16 = concat(axis = var_9833, interleave = doubled_221_interleave_0, values = (hidden_states_275_cast_fp16, var_9835_cast_fp16))[name = string("doubled_221_cast_fp16")];
            tensor<int32, [1]> out_111_axes_0 = const()[name = string("out_111_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_111_gamma_0_to_fp16 = const()[name = string("out_111_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1628501120)))];
            fp16 var_9845_to_fp16 = const()[name = string("op_9845_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_111_cast_fp16 = layer_norm(axes = out_111_axes_0, epsilon = var_9845_to_fp16, gamma = out_111_gamma_0_to_fp16, x = doubled_221_cast_fp16)[name = string("out_111_cast_fp16")];
            tensor<int32, [2]> var_9856_split_sizes_0 = const()[name = string("op_9856_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9856_axis_0 = const()[name = string("op_9856_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> var_9856_cast_fp16_0, tensor<fp16, [1, 2048, 1, 16]> var_9856_cast_fp16_1 = split(axis = var_9856_axis_0, split_sizes = var_9856_split_sizes_0, x = out_111_cast_fp16)[name = string("op_9856_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_27_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_27_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1628509376)))];
            tensor<int32, [2]> input_strides_0 = const()[name = string("input_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_pad_type_0 = const()[name = string("input_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_pad_0 = const()[name = string("input_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_dilations_0 = const()[name = string("input_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_groups_0 = const()[name = string("input_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> input_cast_fp16 = conv(dilations = input_dilations_0, groups = input_groups_0, pad = input_pad_0, pad_type = input_pad_type_0, strides = input_strides_0, weight = layers_27_mlp_gate_proj_weight_to_fp16, x = var_9856_cast_fp16_0)[name = string("input_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> var_9873_cast_fp16 = silu(x = input_cast_fp16)[name = string("op_9873_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_27_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_27_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1653675264)))];
            tensor<int32, [2]> var_9879_strides_0 = const()[name = string("op_9879_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_9879_pad_type_0 = const()[name = string("op_9879_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_9879_pad_0 = const()[name = string("op_9879_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_9879_dilations_0 = const()[name = string("op_9879_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9879_groups_0 = const()[name = string("op_9879_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 16]> var_9879_cast_fp16 = conv(dilations = var_9879_dilations_0, groups = var_9879_groups_0, pad = var_9879_pad_0, pad_type = var_9879_pad_type_0, strides = var_9879_strides_0, weight = layers_27_mlp_up_proj_weight_to_fp16, x = var_9856_cast_fp16_0)[name = string("op_9879_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 16]> x_cast_fp16 = mul(x = var_9873_cast_fp16, y = var_9879_cast_fp16)[name = string("x_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_27_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_27_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1678841152)))];
            tensor<int32, [2]> hidden_states_277_strides_0 = const()[name = string("hidden_states_277_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_277_pad_type_0 = const()[name = string("hidden_states_277_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_277_pad_0 = const()[name = string("hidden_states_277_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_277_dilations_0 = const()[name = string("hidden_states_277_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_277_groups_0 = const()[name = string("hidden_states_277_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_277_cast_fp16 = conv(dilations = hidden_states_277_dilations_0, groups = hidden_states_277_groups_0, pad = hidden_states_277_pad_0, pad_type = hidden_states_277_pad_type_0, strides = hidden_states_277_strides_0, weight = layers_27_mlp_down_proj_weight_to_fp16, x = x_cast_fp16)[name = string("hidden_states_277_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states_cast_fp16 = add(x = hidden_states_275_cast_fp16, y = hidden_states_277_cast_fp16)[name = string("hidden_states_cast_fp16")];
            fp16 const_282_promoted_to_fp16 = const()[name = string("const_282_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 16]> var_9897_cast_fp16 = mul(x = hidden_states_cast_fp16, y = const_282_promoted_to_fp16)[name = string("op_9897_cast_fp16")];
            int32 var_9895 = const()[name = string("op_9895"), val = int32(1)];
            bool doubled_225_interleave_0 = const()[name = string("doubled_225_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 16]> doubled_225_cast_fp16 = concat(axis = var_9895, interleave = doubled_225_interleave_0, values = (hidden_states_cast_fp16, var_9897_cast_fp16))[name = string("doubled_225_cast_fp16")];
            tensor<int32, [1]> out_axes_0 = const()[name = string("out_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_gamma_0_to_fp16 = const()[name = string("out_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1704007040)))];
            fp16 var_9907_to_fp16 = const()[name = string("op_9907_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 16]> out_cast_fp16 = layer_norm(axes = out_axes_0, epsilon = var_9907_to_fp16, gamma = out_gamma_0_to_fp16, x = doubled_225_cast_fp16)[name = string("out_cast_fp16")];
            tensor<int32, [2]> var_9918_split_sizes_0 = const()[name = string("op_9918_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9918_axis_0 = const()[name = string("op_9918_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 16]> hidden_states, tensor<fp16, [1, 2048, 1, 16]> var_9918_cast_fp16_1 = split(axis = var_9918_axis_0, split_sizes = var_9918_split_sizes_0, x = out_cast_fp16)[name = string("op_9918_cast_fp16")];
        } -> (hidden_states);
    func length_32<ios18>(tensor<fp32, [1, 2048, 1, 32]> inputs_embeds, state<tensor<fp16, [28, 2, 2048, 128]>> key_cache, tensor<int32, [1]> position_id, tensor<int32, [32]> position_index_seed, state<tensor<fp16, [28, 2, 2048, 128]>> value_cache) {
            tensor<fp16, [256, 2048, 1, 1]> layers_1_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524992))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524416))))[name = string("layers_1_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_1_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(525312))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13120640))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13108288))))[name = string("layers_1_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_2_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13126848))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13651776))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13651200))))[name = string("layers_2_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_2_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13652096))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26247424))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26235072))))[name = string("layers_2_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_3_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26253632))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26778560))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26777984))))[name = string("layers_3_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_3_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26778880))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30977408))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30973248))))[name = string("layers_3_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_3_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30979520))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43566656))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43562496))))[name = string("layers_3_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_4_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43568768))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44093696))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44093120))))[name = string("layers_4_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_4_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44094016))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48292544))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48288384))))[name = string("layers_4_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_4_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48294656))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60889984))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60877632))))[name = string("layers_4_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_4_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60896192))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73491520))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73479168))))[name = string("layers_4_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_4_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73497728))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86084864))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86080704))))[name = string("layers_4_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_5_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86086976))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86611904))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86611328))))[name = string("layers_5_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_5_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86612224))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90810752))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90806592))))[name = string("layers_5_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_5_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90812864))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103408192))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103395840))))[name = string("layers_5_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_5_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103414400))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116001536))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115997376))))[name = string("layers_5_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_6_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116003648))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116528576))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116528000))))[name = string("layers_6_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_6_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116528896))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120727424))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120723264))))[name = string("layers_6_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_6_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120729536))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133324864))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133312512))))[name = string("layers_6_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_6_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133331072))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145926400))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145914048))))[name = string("layers_6_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_6_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145932608))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158519744))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158515584))))[name = string("layers_6_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_7_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158521856))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159046784))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159046208))))[name = string("layers_7_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_7_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159047104))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163245632))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163241472))))[name = string("layers_7_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_7_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163247744))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175843072))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175830720))))[name = string("layers_7_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_8_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175849280))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176374208))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176373632))))[name = string("layers_8_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_8_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176374528))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180573056))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180568896))))[name = string("layers_8_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_8_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180575168))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193170496))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193158144))))[name = string("layers_8_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_8_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193176704))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205772032))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205759680))))[name = string("layers_8_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_8_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205778240))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218365376))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218361216))))[name = string("layers_8_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_9_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218367488))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218892416))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218891840))))[name = string("layers_9_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_9_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218892736))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223091264))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223087104))))[name = string("layers_9_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_9_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223093376))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235688704))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235676352))))[name = string("layers_9_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_9_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235694912))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248290240))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248277888))))[name = string("layers_9_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_9_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248296448))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260883584))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260879424))))[name = string("layers_9_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_10_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260885696))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261410624))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261410048))))[name = string("layers_10_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_10_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261410944))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265609472))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265605312))))[name = string("layers_10_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_10_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265611584))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278206912))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278194560))))[name = string("layers_10_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_10_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278213120))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290808448))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290796096))))[name = string("layers_10_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_10_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290814656))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303401792))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303397632))))[name = string("layers_10_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_11_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303403904))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307602432))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307598272))))[name = string("layers_11_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_11_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307604544))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308129472))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308128896))))[name = string("layers_11_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_11_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308129792))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308654720))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308654144))))[name = string("layers_11_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_11_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308655040))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312853568))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312849408))))[name = string("layers_11_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_11_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312855680))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325451008))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325438656))))[name = string("layers_11_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_11_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325457216))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338052544))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338040192))))[name = string("layers_11_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_11_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338058752))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350645888))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350641728))))[name = string("layers_11_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_12_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350648000))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354846528))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354842368))))[name = string("layers_12_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_12_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354848640))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355373568))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355372992))))[name = string("layers_12_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_12_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355373888))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355898816))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355898240))))[name = string("layers_12_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_12_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355899136))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360097664))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360093504))))[name = string("layers_12_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_12_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360099776))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372695104))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372682752))))[name = string("layers_12_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_12_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372701312))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385296640))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385284288))))[name = string("layers_12_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_12_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385302848))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397889984))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397885824))))[name = string("layers_12_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_13_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397892096))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402090624))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402086464))))[name = string("layers_13_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_13_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402092736))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402617664))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402617088))))[name = string("layers_13_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_13_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402617984))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403142912))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403142336))))[name = string("layers_13_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_13_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403143232))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407341760))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407337600))))[name = string("layers_13_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_13_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407343872))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419939200))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419926848))))[name = string("layers_13_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_13_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419945408))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432532544))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432528384))))[name = string("layers_13_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_14_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432534656))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436733184))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436729024))))[name = string("layers_14_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_14_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436735296))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437260224))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437259648))))[name = string("layers_14_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_14_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437260544))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441459072))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441454912))))[name = string("layers_14_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_14_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441461184))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454056512))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454044160))))[name = string("layers_14_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_14_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454062720))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(466658048))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(466645696))))[name = string("layers_14_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_14_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(466664256))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(479251392))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(479247232))))[name = string("layers_14_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_15_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(479253504))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483452032))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483447872))))[name = string("layers_15_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_15_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483454144))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483979072))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483978496))))[name = string("layers_15_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_15_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483979392))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484504320))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484503744))))[name = string("layers_15_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_15_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484504640))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488703168))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488699008))))[name = string("layers_15_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_15_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488705280))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(501300608))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(501288256))))[name = string("layers_15_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_15_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(501306816))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513902144))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513889792))))[name = string("layers_15_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_15_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513908352))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526495488))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526491328))))[name = string("layers_15_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_16_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526497600))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530696128))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530691968))))[name = string("layers_16_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_16_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530698240))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531223168))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531222592))))[name = string("layers_16_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_16_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531223488))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531748416))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531747840))))[name = string("layers_16_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_16_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531748736))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535947264))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535943104))))[name = string("layers_16_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_16_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535949376))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(548536512))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(548532352))))[name = string("layers_16_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_17_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(548538624))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552737152))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552732992))))[name = string("layers_17_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_17_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552739264))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553264192))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553263616))))[name = string("layers_17_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_17_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553264512))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553789440))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553788864))))[name = string("layers_17_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_17_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553789760))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557988288))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557984128))))[name = string("layers_17_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_17_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557990400))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570585728))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570573376))))[name = string("layers_17_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_17_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570591936))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583187264))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583174912))))[name = string("layers_17_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_17_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583193472))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(595780608))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(595776448))))[name = string("layers_17_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_18_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(595782720))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(599981248))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(599977088))))[name = string("layers_18_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_18_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(599983360))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(600508288))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(600507712))))[name = string("layers_18_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_18_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(600508608))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(601033536))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(601032960))))[name = string("layers_18_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_18_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(601033856))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(605232384))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(605228224))))[name = string("layers_18_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_18_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(605234496))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(617829824))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(617817472))))[name = string("layers_18_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_18_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(617836032))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(630431360))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(630419008))))[name = string("layers_18_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_18_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(630437568))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(643024704))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(643020544))))[name = string("layers_18_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_19_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(643026816))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647225344))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647221184))))[name = string("layers_19_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_19_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647227456))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647752384))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647751808))))[name = string("layers_19_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_19_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647752704))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(660348032))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(660335680))))[name = string("layers_19_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_19_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(660354240))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(672949568))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(672937216))))[name = string("layers_19_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_19_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(672955776))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(685542912))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(685538752))))[name = string("layers_19_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_20_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(685545024))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(689743552))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(689739392))))[name = string("layers_20_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_20_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(689745664))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(690270592))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(690270016))))[name = string("layers_20_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_20_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(690270912))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(694469440))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(694465280))))[name = string("layers_20_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_20_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(694471552))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(707066880))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(707054528))))[name = string("layers_20_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_20_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(707073088))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(719660224))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(719656064))))[name = string("layers_20_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_21_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(719662336))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(723860864))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(723856704))))[name = string("layers_21_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_21_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(723862976))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724387904))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724387328))))[name = string("layers_21_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_21_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724388224))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(728586752))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(728582592))))[name = string("layers_21_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_21_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(728588864))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(741184192))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(741171840))))[name = string("layers_21_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_21_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(741190400))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(753785728))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(753773376))))[name = string("layers_21_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_21_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(753791936))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(766379072))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(766374912))))[name = string("layers_21_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_22_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(766381184))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(770579712))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(770575552))))[name = string("layers_22_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_22_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(770581824))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(771106752))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(771106176))))[name = string("layers_22_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_22_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(771107072))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783702400))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783690048))))[name = string("layers_22_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_22_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783708608))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(796303936))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(796291584))))[name = string("layers_22_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_22_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(796310144))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808897280))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808893120))))[name = string("layers_22_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_23_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808899392))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813097920))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813093760))))[name = string("layers_23_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_23_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813100032))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813624960))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813624384))))[name = string("layers_23_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_23_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813625280))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(817823808))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(817819648))))[name = string("layers_23_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_23_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(817825920))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(830421248))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(830408896))))[name = string("layers_23_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_23_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(830427456))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(843022784))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(843010432))))[name = string("layers_23_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_23_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(843028992))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855616128))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855611968))))[name = string("layers_23_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_24_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855618240))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(859816768))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(859812608))))[name = string("layers_24_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_24_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(859818880))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(860343808))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(860343232))))[name = string("layers_24_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_24_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(860344128))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(864542656))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(864538496))))[name = string("layers_24_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_24_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(864544768))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(877140096))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(877127744))))[name = string("layers_24_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_24_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(877146304))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889741632))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889729280))))[name = string("layers_24_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_24_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889747840))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(902334976))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(902330816))))[name = string("layers_24_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_25_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(902337088))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(906535616))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(906531456))))[name = string("layers_25_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_25_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(906537728))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(907062656))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(907062080))))[name = string("layers_25_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_25_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(907062976))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(911261504))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(911257344))))[name = string("layers_25_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_25_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(911263616))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(923858944))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(923846592))))[name = string("layers_25_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_25_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(923865152))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936460480))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936448128))))[name = string("layers_25_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_26_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936466688))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(940665216))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(940661056))))[name = string("layers_26_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_26_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(940667328))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(941192256))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(941191680))))[name = string("layers_26_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_26_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(941192576))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(945391104))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(945386944))))[name = string("layers_26_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_26_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(945393216))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(957988544))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(957976192))))[name = string("layers_26_mlp_gate_proj_weight_cast_fp16")];
            int32 gather_0_cast_uint16_to_int32 = const()[name = string("gather_0_cast_uint16_to_int32"), val = int32(32)];
            tensor<int32, [1]> cache_position_end = add(x = position_id, y = gather_0_cast_uint16_to_int32)[name = string("cache_position_end")];
            fp16 fill_like_0_value_0_to_fp16 = const()[name = string("fill_like_0_value_0_to_fp16"), val = fp16(0x1p+0)];
            tensor<fp16, [32]> fill_like_0_cast_fp16 = fill_like(ref_tensor = position_index_seed, value = fill_like_0_value_0_to_fp16)[name = string("fill_like_0_cast_fp16")];
            int32 var_831 = const()[name = string("op_831"), val = int32(0)];
            bool var_833_exclusive_0 = const()[name = string("op_833_exclusive_0"), val = bool(false)];
            bool var_833_reverse_0 = const()[name = string("op_833_reverse_0"), val = bool(false)];
            tensor<fp16, [32]> var_833_cast_fp16 = cumsum(axis = var_831, exclusive = var_833_exclusive_0, reverse = var_833_reverse_0, x = fill_like_0_cast_fp16)[name = string("op_833_cast_fp16")];
            fp16 var_835_promoted_to_fp16 = const()[name = string("op_835_promoted_to_fp16"), val = fp16(0x1p+0)];
            tensor<fp16, [32]> position_offsets_cast_fp16 = sub(x = var_833_cast_fp16, y = var_835_promoted_to_fp16)[name = string("position_offsets_cast_fp16")];
            tensor<int32, [1]> var_838_axes_0 = const()[name = string("op_838_axes_0"), val = tensor<int32, [1]>([0])];
            tensor<fp16, [1, 32]> var_838_cast_fp16 = expand_dims(axes = var_838_axes_0, x = position_offsets_cast_fp16)[name = string("op_838_cast_fp16")];
            string position_id_promoted_to_fp16_dtype_0 = const()[name = string("position_id_promoted_to_fp16_dtype_0"), val = string("fp16")];
            tensor<fp16, [1]> position_id_to_fp16 = cast(dtype = position_id_promoted_to_fp16_dtype_0, x = position_id)[name = string("cast_19")];
            tensor<fp16, [1, 32]> position_ids_1_cast_fp16 = add(x = var_838_cast_fp16, y = position_id_to_fp16)[name = string("position_ids_1_cast_fp16")];
            string position_ids_dtype_0 = const()[name = string("position_ids_dtype_0"), val = string("int32")];
            int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)];
            tensor<int32, [1, 32]> position_ids_1_cast_fp16_to_int32 = cast(dtype = position_ids_dtype_0, x = position_ids_1_cast_fp16)[name = string("cast_18")];
            tensor<bool, [1, 32]> greater_equal_0 = greater_equal(x = position_ids_1_cast_fp16_to_int32, y = greater_equal_0_y_0)[name = string("greater_equal_0")];
            int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(32768)];
            tensor<int32, [1, 32]> add_0 = add(x = position_ids_1_cast_fp16_to_int32, y = slice_by_index_0)[name = string("add_0")];
            tensor<int32, [1, 32]> select_0 = select(a = position_ids_1_cast_fp16_to_int32, b = add_0, cond = greater_equal_0)[name = string("select_0")];
            tensor<fp16, [32768, 128]> rope_emb_cos_cached_to_fp16 = const()[name = string("rope_emb_cos_cached_to_fp16"), val = tensor<fp16, [32768, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(957994752)))];
            int32 cos_1_batch_dims_0 = const()[name = string("cos_1_batch_dims_0"), val = int32(0)];
            bool cos_1_validate_indices_0 = const()[name = string("cos_1_validate_indices_0"), val = bool(false)];
            int32 greater_equal_8_y_0 = const()[name = string("greater_equal_8_y_0"), val = int32(0)];
            tensor<bool, [1, 32]> greater_equal_8 = greater_equal(x = select_0, y = greater_equal_8_y_0)[name = string("greater_equal_8")];
            int32 slice_by_index_8 = const()[name = string("slice_by_index_8"), val = int32(32768)];
            tensor<int32, [1, 32]> add_8 = add(x = select_0, y = slice_by_index_8)[name = string("add_8")];
            tensor<int32, [1, 32]> select_8 = select(a = select_0, b = add_8, cond = greater_equal_8)[name = string("select_8")];
            int32 cos_1_cast_fp16_axis_4 = const()[name = string("cos_1_cast_fp16_axis_4"), val = int32(0)];
            tensor<fp16, [1, 32, 128]> cos_1_cast_fp16 = gather(axis = cos_1_cast_fp16_axis_4, batch_dims = cos_1_batch_dims_0, indices = select_8, validate_indices = cos_1_validate_indices_0, x = rope_emb_cos_cached_to_fp16)[name = string("cos_1_cast_fp16")];
            tensor<fp16, [32768, 128]> rope_emb_sin_cached_to_fp16 = const()[name = string("rope_emb_sin_cached_to_fp16"), val = tensor<fp16, [32768, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(966383424)))];
            int32 sin_1_batch_dims_0 = const()[name = string("sin_1_batch_dims_0"), val = int32(0)];
            bool sin_1_validate_indices_0 = const()[name = string("sin_1_validate_indices_0"), val = bool(false)];
            int32 sin_1_cast_fp16_axis_4 = const()[name = string("sin_1_cast_fp16_axis_4"), val = int32(0)];
            tensor<fp16, [1, 32, 128]> sin_1_cast_fp16 = gather(axis = sin_1_cast_fp16_axis_4, batch_dims = sin_1_batch_dims_0, indices = select_8, validate_indices = sin_1_validate_indices_0, x = rope_emb_sin_cached_to_fp16)[name = string("sin_1_cast_fp16")];
            tensor<int32, [3]> var_857_perm_0 = const()[name = string("op_857_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<int32, [1]> var_859_axes_0 = const()[name = string("op_859_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 128, 32]> var_857_cast_fp16 = transpose(perm = var_857_perm_0, x = cos_1_cast_fp16)[name = string("transpose_434")];
            tensor<fp16, [1, 1, 128, 32]> var_859_cast_fp16 = expand_dims(axes = var_859_axes_0, x = var_857_cast_fp16)[name = string("op_859_cast_fp16")];
            tensor<int32, [3]> var_864_perm_0 = const()[name = string("op_864_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<int32, [1]> var_866_axes_0 = const()[name = string("op_866_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 128, 32]> var_864_cast_fp16 = transpose(perm = var_864_perm_0, x = sin_1_cast_fp16)[name = string("transpose_433")];
            tensor<fp16, [1, 1, 128, 32]> var_866_cast_fp16 = expand_dims(axes = var_866_axes_0, x = var_864_cast_fp16)[name = string("op_866_cast_fp16")];
            tensor<int32, [1]> var_885_axes_0 = const()[name = string("op_885_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1, 32, 1]> var_885 = expand_dims(axes = var_885_axes_0, x = position_ids_1_cast_fp16_to_int32)[name = string("op_885")];
            tensor<int32, [1, 1, 2048]> var_878 = const()[name = string("op_878"), val = tensor<int32, [1, 1, 2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(974772096)))];
            tensor<bool, [1, 32, 2048]> var_886 = greater(x = var_878, y = var_885)[name = string("op_886")];
            string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")];
            tensor<int32, [1]> var_893_axes_0 = const()[name = string("op_893_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 32, 2048]> var_886_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = var_886)[name = string("cast_17")];
            tensor<fp16, [1, 1, 32, 2048]> var_893_cast_fp16 = expand_dims(axes = var_893_axes_0, x = var_886_to_fp16)[name = string("op_893_cast_fp16")];
            tensor<int32, [4]> mask_perm_0 = const()[name = string("mask_perm_0"), val = tensor<int32, [4]>([0, 1, -1, -2])];
            fp16 var_897_promoted_to_fp16 = const()[name = string("op_897_promoted_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1, 2048, 32]> mask_cast_fp16 = transpose(perm = mask_perm_0, x = var_893_cast_fp16)[name = string("transpose_432")];
            tensor<bool, [1, 1, 2048, 32]> var_898_cast_fp16 = equal(x = mask_cast_fp16, y = var_897_promoted_to_fp16)[name = string("op_898_cast_fp16")];
            fp16 var_899_to_fp16 = const()[name = string("op_899_to_fp16"), val = fp16(-inf)];
            tensor<fp16, [1, 1, 2048, 32]> attn_mask_1_cast_fp16 = select(a = mask_cast_fp16, b = var_899_to_fp16, cond = var_898_cast_fp16)[name = string("attn_mask_1_cast_fp16")];
            string inputs_embeds_to_fp16_dtype_0 = const()[name = string("inputs_embeds_to_fp16_dtype_0"), val = string("fp16")];
            fp16 const_2_promoted_to_fp16 = const()[name = string("const_2_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> inputs_embeds_to_fp16 = cast(dtype = inputs_embeds_to_fp16_dtype_0, x = inputs_embeds)[name = string("cast_16")];
            tensor<fp16, [1, 2048, 1, 32]> var_909_cast_fp16 = mul(x = inputs_embeds_to_fp16, y = const_2_promoted_to_fp16)[name = string("op_909_cast_fp16")];
            int32 var_907 = const()[name = string("op_907"), val = int32(1)];
            bool doubled_1_interleave_0 = const()[name = string("doubled_1_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_1_cast_fp16 = concat(axis = var_907, interleave = doubled_1_interleave_0, values = (inputs_embeds_to_fp16, var_909_cast_fp16))[name = string("doubled_1_cast_fp16")];
            tensor<int32, [1]> out_1_axes_0 = const()[name = string("out_1_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_1_gamma_0_to_fp16 = const()[name = string("out_1_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(974780352)))];
            fp16 var_919_to_fp16 = const()[name = string("op_919_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_1_cast_fp16 = layer_norm(axes = out_1_axes_0, epsilon = var_919_to_fp16, gamma = out_1_gamma_0_to_fp16, x = doubled_1_cast_fp16)[name = string("out_1_cast_fp16")];
            tensor<int32, [2]> var_930_split_sizes_0 = const()[name = string("op_930_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_930_axis_0 = const()[name = string("op_930_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_930_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_930_cast_fp16_1 = split(axis = var_930_axis_0, split_sizes = var_930_split_sizes_0, x = out_1_cast_fp16)[name = string("op_930_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_0_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(974788608)))];
            tensor<int32, [2]> query_states_1_strides_0 = const()[name = string("query_states_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_1_pad_type_0 = const()[name = string("query_states_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_1_pad_0 = const()[name = string("query_states_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_1_dilations_0 = const()[name = string("query_states_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_1_groups_0 = const()[name = string("query_states_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_1_cast_fp16 = conv(dilations = query_states_1_dilations_0, groups = query_states_1_groups_0, pad = query_states_1_pad_0, pad_type = query_states_1_pad_type_0, strides = query_states_1_strides_0, weight = layers_0_self_attn_q_proj_weight_to_fp16, x = var_930_cast_fp16_0)[name = string("query_states_1_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_0_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(983177280)))];
            tensor<int32, [2]> key_states_1_strides_0 = const()[name = string("key_states_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_1_pad_type_0 = const()[name = string("key_states_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_1_pad_0 = const()[name = string("key_states_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_1_dilations_0 = const()[name = string("key_states_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_1_groups_0 = const()[name = string("key_states_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_1_cast_fp16 = conv(dilations = key_states_1_dilations_0, groups = key_states_1_groups_0, pad = key_states_1_pad_0, pad_type = key_states_1_pad_type_0, strides = key_states_1_strides_0, weight = layers_0_self_attn_k_proj_weight_to_fp16, x = var_930_cast_fp16_0)[name = string("key_states_1_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_0_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(984225920)))];
            tensor<int32, [2]> value_states_1_strides_0 = const()[name = string("value_states_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_1_pad_type_0 = const()[name = string("value_states_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_1_pad_0 = const()[name = string("value_states_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_1_dilations_0 = const()[name = string("value_states_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_1_groups_0 = const()[name = string("value_states_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_1_cast_fp16 = conv(dilations = value_states_1_dilations_0, groups = value_states_1_groups_0, pad = value_states_1_pad_0, pad_type = value_states_1_pad_type_0, strides = value_states_1_strides_0, weight = layers_0_self_attn_v_proj_weight_to_fp16, x = var_930_cast_fp16_0)[name = string("value_states_1_cast_fp16")];
            tensor<int32, [4]> concat_0x = const()[name = string("concat_0x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_1_cast_fp16 = reshape(shape = concat_0x, x = query_states_1_cast_fp16)[name = string("x_1_cast_fp16")];
            tensor<int32, [4]> concat_1x = const()[name = string("concat_1x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_987_cast_fp16 = reshape(shape = concat_1x, x = key_states_1_cast_fp16)[name = string("op_987_cast_fp16")];
            tensor<int32, [4]> concat_2x = const()[name = string("concat_2x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_994_cast_fp16 = reshape(shape = concat_2x, x = value_states_1_cast_fp16)[name = string("op_994_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_998_cast_fp16 = mul(x = x_1_cast_fp16, y = var_859_cast_fp16)[name = string("op_998_cast_fp16")];
            tensor<int32, [2]> var_999_split_sizes_0 = const()[name = string("op_999_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_999_axis_0 = const()[name = string("op_999_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_999_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_999_cast_fp16_1 = split(axis = var_999_axis_0, split_sizes = var_999_split_sizes_0, x = x_1_cast_fp16)[name = string("op_999_cast_fp16")];
            fp16 const_4_promoted_to_fp16 = const()[name = string("const_4_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_1001_cast_fp16 = mul(x = var_999_cast_fp16_1, y = const_4_promoted_to_fp16)[name = string("op_1001_cast_fp16")];
            int32 var_1003 = const()[name = string("op_1003"), val = int32(-2)];
            bool var_1004_interleave_0 = const()[name = string("op_1004_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_1004_cast_fp16 = concat(axis = var_1003, interleave = var_1004_interleave_0, values = (var_1001_cast_fp16, var_999_cast_fp16_0))[name = string("op_1004_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_1005_cast_fp16 = mul(x = var_1004_cast_fp16, y = var_866_cast_fp16)[name = string("op_1005_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_3_cast_fp16 = add(x = var_998_cast_fp16, y = var_1005_cast_fp16)[name = string("query_states_3_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_1011_cast_fp16 = mul(x = var_987_cast_fp16, y = var_859_cast_fp16)[name = string("op_1011_cast_fp16")];
            tensor<int32, [2]> var_1012_split_sizes_0 = const()[name = string("op_1012_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1012_axis_0 = const()[name = string("op_1012_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_1012_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_1012_cast_fp16_1 = split(axis = var_1012_axis_0, split_sizes = var_1012_split_sizes_0, x = var_987_cast_fp16)[name = string("op_1012_cast_fp16")];
            fp16 const_5_promoted_to_fp16 = const()[name = string("const_5_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_1014_cast_fp16 = mul(x = var_1012_cast_fp16_1, y = const_5_promoted_to_fp16)[name = string("op_1014_cast_fp16")];
            int32 var_1016 = const()[name = string("op_1016"), val = int32(-2)];
            bool var_1017_interleave_0 = const()[name = string("op_1017_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_1017_cast_fp16 = concat(axis = var_1016, interleave = var_1017_interleave_0, values = (var_1014_cast_fp16, var_1012_cast_fp16_0))[name = string("op_1017_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_1018_cast_fp16 = mul(x = var_1017_cast_fp16, y = var_866_cast_fp16)[name = string("op_1018_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_5_cast_fp16 = add(x = var_1011_cast_fp16, y = var_1018_cast_fp16)[name = string("key_states_5_cast_fp16")];
            tensor<fp16, [28, 2, 2048, 128]> read_state_0 = read_state(input = key_cache)[name = string("read_state_0")];
            tensor<int32, [1]> expand_dims_0 = const()[name = string("expand_dims_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_1 = const()[name = string("expand_dims_1"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_3 = const()[name = string("expand_dims_3"), val = tensor<int32, [1]>([0])];
            int32 concat_5_axis_0 = const()[name = string("concat_5_axis_0"), val = int32(0)];
            bool concat_5_interleave_0 = const()[name = string("concat_5_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_5 = concat(axis = concat_5_axis_0, interleave = concat_5_interleave_0, values = (expand_dims_0, expand_dims_1, position_id, expand_dims_3))[name = string("concat_5")];
            tensor<int32, [1]> expand_dims_4 = const()[name = string("expand_dims_4"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> concat_6_values1_0 = const()[name = string("concat_6_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_6_values3_0 = const()[name = string("concat_6_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_6_axis_0 = const()[name = string("concat_6_axis_0"), val = int32(0)];
            bool concat_6_interleave_0 = const()[name = string("concat_6_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_6 = concat(axis = concat_6_axis_0, interleave = concat_6_interleave_0, values = (expand_dims_4, concat_6_values1_0, cache_position_end, concat_6_values3_0))[name = string("concat_6")];
            tensor<int32, [4]> key_states_7_perm_0 = const()[name = string("key_states_7_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_1_stride_0 = const()[name = string("key_cache_internal_tensor_assign_1_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_1_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_1_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_1_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_1_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_1_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_1_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_7_cast_fp16 = transpose(perm = key_states_7_perm_0, x = key_states_5_cast_fp16)[name = string("transpose_431")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_1_cast_fp16 = slice_update(begin = concat_5, begin_mask = key_cache_internal_tensor_assign_1_begin_mask_0, end = concat_6, end_mask = key_cache_internal_tensor_assign_1_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_1_squeeze_mask_0, stride = key_cache_internal_tensor_assign_1_stride_0, update = key_states_7_cast_fp16, x = read_state_0)[name = string("key_cache_internal_tensor_assign_1_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_1_cast_fp16, input = key_cache)[name = string("coreml_update_state_224_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_224 = read_state(input = key_cache)[name = string("coreml_update_state_224")];
            tensor<fp16, [28, 2, 2048, 128]> read_state_1 = read_state(input = value_cache)[name = string("read_state_1")];
            tensor<int32, [4]> value_states_3_perm_0 = const()[name = string("value_states_3_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_1_stride_0 = const()[name = string("value_cache_internal_tensor_assign_1_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_1_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_1_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_1_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_1_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_1_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_1_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_3_cast_fp16 = transpose(perm = value_states_3_perm_0, x = var_994_cast_fp16)[name = string("transpose_430")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_1_cast_fp16 = slice_update(begin = concat_5, begin_mask = value_cache_internal_tensor_assign_1_begin_mask_0, end = concat_6, end_mask = value_cache_internal_tensor_assign_1_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_1_squeeze_mask_0, stride = value_cache_internal_tensor_assign_1_stride_0, update = value_states_3_cast_fp16, x = read_state_1)[name = string("value_cache_internal_tensor_assign_1_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_1_cast_fp16, input = value_cache)[name = string("coreml_update_state_225_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_225 = read_state(input = value_cache)[name = string("coreml_update_state_225")];
            tensor<int32, [4]> var_1088_begin_0 = const()[name = string("op_1088_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> var_1088_end_0 = const()[name = string("op_1088_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_1088_end_mask_0 = const()[name = string("op_1088_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1088_cast_fp16 = slice_by_index(begin = var_1088_begin_0, end = var_1088_end_0, end_mask = var_1088_end_mask_0, x = coreml_update_state_224)[name = string("op_1088_cast_fp16")];
            tensor<int32, [2]> tile_0 = const()[name = string("tile_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1091_axis_0 = const()[name = string("op_1091_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1091_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1091_cast_fp16_1 = split(axis = var_1091_axis_0, split_sizes = tile_0, x = var_1088_cast_fp16)[name = string("op_1091_cast_fp16")];
            tensor<int32, [4]> var_1098_begin_0 = const()[name = string("op_1098_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> var_1098_end_0 = const()[name = string("op_1098_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_1098_end_mask_0 = const()[name = string("op_1098_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1098_cast_fp16 = slice_by_index(begin = var_1098_begin_0, end = var_1098_end_0, end_mask = var_1098_end_mask_0, x = coreml_update_state_225)[name = string("op_1098_cast_fp16")];
            tensor<int32, [2]> tile_1 = const()[name = string("tile_1"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1101_axis_0 = const()[name = string("op_1101_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1101_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1101_cast_fp16_1 = split(axis = var_1101_axis_0, split_sizes = tile_1, x = var_1098_cast_fp16)[name = string("op_1101_cast_fp16")];
            tensor<int32, [2]> var_1104_split_sizes_0 = const()[name = string("op_1104_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_1104_axis_0 = const()[name = string("op_1104_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_1104_0, tensor<fp16, [1, 8, 128, 32]> var_1104_1 = split(axis = var_1104_axis_0, split_sizes = var_1104_split_sizes_0, x = query_states_3_cast_fp16)[name = string("op_1104")];
            bool attn_weights_1_transpose_x_0 = const()[name = string("attn_weights_1_transpose_x_0"), val = bool(false)];
            bool attn_weights_1_transpose_y_0 = const()[name = string("attn_weights_1_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_1_cast_fp16 = matmul(transpose_x = attn_weights_1_transpose_x_0, transpose_y = attn_weights_1_transpose_y_0, x = var_1091_cast_fp16_0, y = var_1104_0)[name = string("attn_weights_1_cast_fp16")];
            fp16 var_1107_to_fp16 = const()[name = string("op_1107_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_3_cast_fp16 = mul(x = attn_weights_1_cast_fp16, y = var_1107_to_fp16)[name = string("attn_weights_3_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_5_cast_fp16 = add(x = attn_weights_3_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_5_cast_fp16")];
            int32 var_1111 = const()[name = string("op_1111"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_7_cast_fp16 = softmax(axis = var_1111, x = attn_weights_5_cast_fp16)[name = string("attn_weights_7_cast_fp16")];
            bool var_1117_transpose_x_1 = const()[name = string("op_1117_transpose_x_1"), val = bool(true)];
            bool var_1117_transpose_y_1 = const()[name = string("op_1117_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_1117_cast_fp16 = matmul(transpose_x = var_1117_transpose_x_1, transpose_y = var_1117_transpose_y_1, x = attn_weights_7_cast_fp16, y = var_1101_cast_fp16_0)[name = string("op_1117_cast_fp16")];
            bool attn_weights_9_transpose_x_0 = const()[name = string("attn_weights_9_transpose_x_0"), val = bool(false)];
            bool attn_weights_9_transpose_y_0 = const()[name = string("attn_weights_9_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_9_cast_fp16 = matmul(transpose_x = attn_weights_9_transpose_x_0, transpose_y = attn_weights_9_transpose_y_0, x = var_1091_cast_fp16_1, y = var_1104_1)[name = string("attn_weights_9_cast_fp16")];
            fp16 var_1119_to_fp16 = const()[name = string("op_1119_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_11_cast_fp16 = mul(x = attn_weights_9_cast_fp16, y = var_1119_to_fp16)[name = string("attn_weights_11_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_13_cast_fp16 = add(x = attn_weights_11_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_13_cast_fp16")];
            int32 var_1123 = const()[name = string("op_1123"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_15_cast_fp16 = softmax(axis = var_1123, x = attn_weights_13_cast_fp16)[name = string("attn_weights_15_cast_fp16")];
            bool attn_output_1_transpose_x_1 = const()[name = string("attn_output_1_transpose_x_1"), val = bool(true)];
            bool attn_output_1_transpose_y_1 = const()[name = string("attn_output_1_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_1_cast_fp16 = matmul(transpose_x = attn_output_1_transpose_x_1, transpose_y = attn_output_1_transpose_y_1, x = attn_weights_15_cast_fp16, y = var_1101_cast_fp16_1)[name = string("attn_output_1_cast_fp16")];
            int32 var_1131 = const()[name = string("op_1131"), val = int32(1)];
            bool attn_output_3_interleave_0 = const()[name = string("attn_output_3_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_3_cast_fp16 = concat(axis = var_1131, interleave = attn_output_3_interleave_0, values = (var_1117_cast_fp16, attn_output_1_cast_fp16))[name = string("attn_output_3_cast_fp16")];
            tensor<int32, [4]> var_1135_perm_0 = const()[name = string("op_1135_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_11x = const()[name = string("concat_11x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_1135_cast_fp16 = transpose(perm = var_1135_perm_0, x = attn_output_3_cast_fp16)[name = string("transpose_429")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_7_cast_fp16 = reshape(shape = concat_11x, x = var_1135_cast_fp16)[name = string("attn_output_7_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_0_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(985274560)))];
            tensor<int32, [2]> hidden_states_3_strides_0 = const()[name = string("hidden_states_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_3_pad_type_0 = const()[name = string("hidden_states_3_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_3_pad_0 = const()[name = string("hidden_states_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_3_dilations_0 = const()[name = string("hidden_states_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_3_groups_0 = const()[name = string("hidden_states_3_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_3_cast_fp16 = conv(dilations = hidden_states_3_dilations_0, groups = hidden_states_3_groups_0, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = hidden_states_3_strides_0, weight = layers_0_self_attn_o_proj_weight_to_fp16, x = attn_output_7_cast_fp16)[name = string("hidden_states_3_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_5_cast_fp16 = add(x = inputs_embeds_to_fp16, y = hidden_states_3_cast_fp16)[name = string("hidden_states_5_cast_fp16")];
            fp16 const_10_promoted_to_fp16 = const()[name = string("const_10_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_1168_cast_fp16 = mul(x = hidden_states_5_cast_fp16, y = const_10_promoted_to_fp16)[name = string("op_1168_cast_fp16")];
            int32 var_1166 = const()[name = string("op_1166"), val = int32(1)];
            bool doubled_5_interleave_0 = const()[name = string("doubled_5_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_5_cast_fp16 = concat(axis = var_1166, interleave = doubled_5_interleave_0, values = (hidden_states_5_cast_fp16, var_1168_cast_fp16))[name = string("doubled_5_cast_fp16")];
            tensor<int32, [1]> out_3_axes_0 = const()[name = string("out_3_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_3_gamma_0_to_fp16 = const()[name = string("out_3_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(993663232)))];
            fp16 var_1178_to_fp16 = const()[name = string("op_1178_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_3_cast_fp16 = layer_norm(axes = out_3_axes_0, epsilon = var_1178_to_fp16, gamma = out_3_gamma_0_to_fp16, x = doubled_5_cast_fp16)[name = string("out_3_cast_fp16")];
            tensor<int32, [2]> var_1189_split_sizes_0 = const()[name = string("op_1189_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1189_axis_0 = const()[name = string("op_1189_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_1189_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_1189_cast_fp16_1 = split(axis = var_1189_axis_0, split_sizes = var_1189_split_sizes_0, x = out_3_cast_fp16)[name = string("op_1189_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_0_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_0_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(993671488)))];
            tensor<int32, [2]> input_1_strides_0 = const()[name = string("input_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_1_pad_type_0 = const()[name = string("input_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_1_pad_0 = const()[name = string("input_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_1_dilations_0 = const()[name = string("input_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_1_groups_0 = const()[name = string("input_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_1_cast_fp16 = conv(dilations = input_1_dilations_0, groups = input_1_groups_0, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = input_1_strides_0, weight = layers_0_mlp_gate_proj_weight_to_fp16, x = var_1189_cast_fp16_0)[name = string("input_1_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_1206_cast_fp16 = silu(x = input_1_cast_fp16)[name = string("op_1206_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_0_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_0_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1018837376)))];
            tensor<int32, [2]> var_1212_strides_0 = const()[name = string("op_1212_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_1212_pad_type_0 = const()[name = string("op_1212_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_1212_pad_0 = const()[name = string("op_1212_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_1212_dilations_0 = const()[name = string("op_1212_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1212_groups_0 = const()[name = string("op_1212_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_1212_cast_fp16 = conv(dilations = var_1212_dilations_0, groups = var_1212_groups_0, pad = var_1212_pad_0, pad_type = var_1212_pad_type_0, strides = var_1212_strides_0, weight = layers_0_mlp_up_proj_weight_to_fp16, x = var_1189_cast_fp16_0)[name = string("op_1212_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_9_cast_fp16 = mul(x = var_1206_cast_fp16, y = var_1212_cast_fp16)[name = string("x_9_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_0_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_0_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1044003264)))];
            tensor<int32, [2]> hidden_states_7_strides_0 = const()[name = string("hidden_states_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_7_pad_type_0 = const()[name = string("hidden_states_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_7_pad_0 = const()[name = string("hidden_states_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_7_dilations_0 = const()[name = string("hidden_states_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_7_groups_0 = const()[name = string("hidden_states_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_7_cast_fp16 = conv(dilations = hidden_states_7_dilations_0, groups = hidden_states_7_groups_0, pad = hidden_states_7_pad_0, pad_type = hidden_states_7_pad_type_0, strides = hidden_states_7_strides_0, weight = layers_0_mlp_down_proj_weight_to_fp16, x = x_9_cast_fp16)[name = string("hidden_states_7_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_9_cast_fp16 = add(x = hidden_states_5_cast_fp16, y = hidden_states_7_cast_fp16)[name = string("hidden_states_9_cast_fp16")];
            fp16 const_12_promoted_to_fp16 = const()[name = string("const_12_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_1230_cast_fp16 = mul(x = hidden_states_9_cast_fp16, y = const_12_promoted_to_fp16)[name = string("op_1230_cast_fp16")];
            int32 var_1228 = const()[name = string("op_1228"), val = int32(1)];
            bool doubled_9_interleave_0 = const()[name = string("doubled_9_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_9_cast_fp16 = concat(axis = var_1228, interleave = doubled_9_interleave_0, values = (hidden_states_9_cast_fp16, var_1230_cast_fp16))[name = string("doubled_9_cast_fp16")];
            tensor<int32, [1]> out_5_axes_0 = const()[name = string("out_5_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_5_gamma_0_to_fp16 = const()[name = string("out_5_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1069169152)))];
            fp16 var_1240_to_fp16 = const()[name = string("op_1240_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_5_cast_fp16 = layer_norm(axes = out_5_axes_0, epsilon = var_1240_to_fp16, gamma = out_5_gamma_0_to_fp16, x = doubled_9_cast_fp16)[name = string("out_5_cast_fp16")];
            tensor<int32, [2]> var_1251_split_sizes_0 = const()[name = string("op_1251_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1251_axis_0 = const()[name = string("op_1251_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_1251_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_1251_cast_fp16_1 = split(axis = var_1251_axis_0, split_sizes = var_1251_split_sizes_0, x = out_5_cast_fp16)[name = string("op_1251_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_1_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_1_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1069177408)))];
            tensor<int32, [2]> query_states_7_strides_0 = const()[name = string("query_states_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_7_pad_type_0 = const()[name = string("query_states_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_7_pad_0 = const()[name = string("query_states_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_7_dilations_0 = const()[name = string("query_states_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_7_groups_0 = const()[name = string("query_states_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_7_cast_fp16 = conv(dilations = query_states_7_dilations_0, groups = query_states_7_groups_0, pad = query_states_7_pad_0, pad_type = query_states_7_pad_type_0, strides = query_states_7_strides_0, weight = layers_1_self_attn_q_proj_weight_to_fp16, x = var_1251_cast_fp16_0)[name = string("query_states_7_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_1_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_1_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1077566080)))];
            tensor<int32, [2]> key_states_11_strides_0 = const()[name = string("key_states_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_11_pad_type_0 = const()[name = string("key_states_11_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_11_pad_0 = const()[name = string("key_states_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_11_dilations_0 = const()[name = string("key_states_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_11_groups_0 = const()[name = string("key_states_11_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_11_cast_fp16 = conv(dilations = key_states_11_dilations_0, groups = key_states_11_groups_0, pad = key_states_11_pad_0, pad_type = key_states_11_pad_type_0, strides = key_states_11_strides_0, weight = layers_1_self_attn_k_proj_weight_to_fp16, x = var_1251_cast_fp16_0)[name = string("key_states_11_cast_fp16")];
            tensor<int32, [2]> value_states_7_strides_0 = const()[name = string("value_states_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_7_pad_type_0 = const()[name = string("value_states_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_7_pad_0 = const()[name = string("value_states_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_7_dilations_0 = const()[name = string("value_states_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_7_groups_0 = const()[name = string("value_states_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_7_cast_fp16 = conv(dilations = value_states_7_dilations_0, groups = value_states_7_groups_0, pad = value_states_7_pad_0, pad_type = value_states_7_pad_type_0, strides = value_states_7_strides_0, weight = layers_1_self_attn_v_proj_weight_cast_fp16, x = var_1251_cast_fp16_0)[name = string("value_states_7_cast_fp16")];
            tensor<int32, [4]> concat_12x = const()[name = string("concat_12x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_11_cast_fp16 = reshape(shape = concat_12x, x = query_states_7_cast_fp16)[name = string("x_11_cast_fp16")];
            tensor<int32, [4]> concat_13x = const()[name = string("concat_13x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_1308_cast_fp16 = reshape(shape = concat_13x, x = key_states_11_cast_fp16)[name = string("op_1308_cast_fp16")];
            tensor<int32, [4]> concat_14x = const()[name = string("concat_14x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_1315_cast_fp16 = reshape(shape = concat_14x, x = value_states_7_cast_fp16)[name = string("op_1315_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_1319_cast_fp16 = mul(x = x_11_cast_fp16, y = var_859_cast_fp16)[name = string("op_1319_cast_fp16")];
            tensor<int32, [2]> var_1320_split_sizes_0 = const()[name = string("op_1320_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1320_axis_0 = const()[name = string("op_1320_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_1320_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_1320_cast_fp16_1 = split(axis = var_1320_axis_0, split_sizes = var_1320_split_sizes_0, x = x_11_cast_fp16)[name = string("op_1320_cast_fp16")];
            fp16 const_14_promoted_to_fp16 = const()[name = string("const_14_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_1322_cast_fp16 = mul(x = var_1320_cast_fp16_1, y = const_14_promoted_to_fp16)[name = string("op_1322_cast_fp16")];
            int32 var_1324 = const()[name = string("op_1324"), val = int32(-2)];
            bool var_1325_interleave_0 = const()[name = string("op_1325_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_1325_cast_fp16 = concat(axis = var_1324, interleave = var_1325_interleave_0, values = (var_1322_cast_fp16, var_1320_cast_fp16_0))[name = string("op_1325_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_1326_cast_fp16 = mul(x = var_1325_cast_fp16, y = var_866_cast_fp16)[name = string("op_1326_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_9_cast_fp16 = add(x = var_1319_cast_fp16, y = var_1326_cast_fp16)[name = string("query_states_9_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_1332_cast_fp16 = mul(x = var_1308_cast_fp16, y = var_859_cast_fp16)[name = string("op_1332_cast_fp16")];
            tensor<int32, [2]> var_1333_split_sizes_0 = const()[name = string("op_1333_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1333_axis_0 = const()[name = string("op_1333_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_1333_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_1333_cast_fp16_1 = split(axis = var_1333_axis_0, split_sizes = var_1333_split_sizes_0, x = var_1308_cast_fp16)[name = string("op_1333_cast_fp16")];
            fp16 const_15_promoted_to_fp16 = const()[name = string("const_15_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_1335_cast_fp16 = mul(x = var_1333_cast_fp16_1, y = const_15_promoted_to_fp16)[name = string("op_1335_cast_fp16")];
            int32 var_1337 = const()[name = string("op_1337"), val = int32(-2)];
            bool var_1338_interleave_0 = const()[name = string("op_1338_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_1338_cast_fp16 = concat(axis = var_1337, interleave = var_1338_interleave_0, values = (var_1335_cast_fp16, var_1333_cast_fp16_0))[name = string("op_1338_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_1339_cast_fp16 = mul(x = var_1338_cast_fp16, y = var_866_cast_fp16)[name = string("op_1339_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_15_cast_fp16 = add(x = var_1332_cast_fp16, y = var_1339_cast_fp16)[name = string("key_states_15_cast_fp16")];
            tensor<int32, [1]> expand_dims_12 = const()[name = string("expand_dims_12"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> expand_dims_13 = const()[name = string("expand_dims_13"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_15 = const()[name = string("expand_dims_15"), val = tensor<int32, [1]>([0])];
            int32 concat_17_axis_0 = const()[name = string("concat_17_axis_0"), val = int32(0)];
            bool concat_17_interleave_0 = const()[name = string("concat_17_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_17 = concat(axis = concat_17_axis_0, interleave = concat_17_interleave_0, values = (expand_dims_12, expand_dims_13, position_id, expand_dims_15))[name = string("concat_17")];
            tensor<int32, [1]> expand_dims_16 = const()[name = string("expand_dims_16"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> concat_18_values1_0 = const()[name = string("concat_18_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_18_values3_0 = const()[name = string("concat_18_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_18_axis_0 = const()[name = string("concat_18_axis_0"), val = int32(0)];
            bool concat_18_interleave_0 = const()[name = string("concat_18_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_18 = concat(axis = concat_18_axis_0, interleave = concat_18_interleave_0, values = (expand_dims_16, concat_18_values1_0, cache_position_end, concat_18_values3_0))[name = string("concat_18")];
            tensor<int32, [4]> key_states_17_perm_0 = const()[name = string("key_states_17_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_2_stride_0 = const()[name = string("key_cache_internal_tensor_assign_2_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_2_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_2_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_2_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_2_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_2_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_2_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_17_cast_fp16 = transpose(perm = key_states_17_perm_0, x = key_states_15_cast_fp16)[name = string("transpose_428")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_2_cast_fp16 = slice_update(begin = concat_17, begin_mask = key_cache_internal_tensor_assign_2_begin_mask_0, end = concat_18, end_mask = key_cache_internal_tensor_assign_2_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_2_squeeze_mask_0, stride = key_cache_internal_tensor_assign_2_stride_0, update = key_states_17_cast_fp16, x = coreml_update_state_224)[name = string("key_cache_internal_tensor_assign_2_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_2_cast_fp16, input = key_cache)[name = string("coreml_update_state_226_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_226 = read_state(input = key_cache)[name = string("coreml_update_state_226")];
            tensor<int32, [4]> value_states_9_perm_0 = const()[name = string("value_states_9_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_2_stride_0 = const()[name = string("value_cache_internal_tensor_assign_2_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_2_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_2_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_2_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_2_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_2_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_2_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_9_cast_fp16 = transpose(perm = value_states_9_perm_0, x = var_1315_cast_fp16)[name = string("transpose_427")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_2_cast_fp16 = slice_update(begin = concat_17, begin_mask = value_cache_internal_tensor_assign_2_begin_mask_0, end = concat_18, end_mask = value_cache_internal_tensor_assign_2_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_2_squeeze_mask_0, stride = value_cache_internal_tensor_assign_2_stride_0, update = value_states_9_cast_fp16, x = coreml_update_state_225)[name = string("value_cache_internal_tensor_assign_2_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_2_cast_fp16, input = value_cache)[name = string("coreml_update_state_227_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_227 = read_state(input = value_cache)[name = string("coreml_update_state_227")];
            tensor<int32, [4]> var_1409_begin_0 = const()[name = string("op_1409_begin_0"), val = tensor<int32, [4]>([1, 0, 0, 0])];
            tensor<int32, [4]> var_1409_end_0 = const()[name = string("op_1409_end_0"), val = tensor<int32, [4]>([2, 2, 2048, 128])];
            tensor<bool, [4]> var_1409_end_mask_0 = const()[name = string("op_1409_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1409_cast_fp16 = slice_by_index(begin = var_1409_begin_0, end = var_1409_end_0, end_mask = var_1409_end_mask_0, x = coreml_update_state_226)[name = string("op_1409_cast_fp16")];
            tensor<int32, [2]> tile_2 = const()[name = string("tile_2"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1412_axis_0 = const()[name = string("op_1412_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1412_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1412_cast_fp16_1 = split(axis = var_1412_axis_0, split_sizes = tile_2, x = var_1409_cast_fp16)[name = string("op_1412_cast_fp16")];
            tensor<int32, [4]> var_1419_begin_0 = const()[name = string("op_1419_begin_0"), val = tensor<int32, [4]>([1, 0, 0, 0])];
            tensor<int32, [4]> var_1419_end_0 = const()[name = string("op_1419_end_0"), val = tensor<int32, [4]>([2, 2, 2048, 128])];
            tensor<bool, [4]> var_1419_end_mask_0 = const()[name = string("op_1419_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1419_cast_fp16 = slice_by_index(begin = var_1419_begin_0, end = var_1419_end_0, end_mask = var_1419_end_mask_0, x = coreml_update_state_227)[name = string("op_1419_cast_fp16")];
            tensor<int32, [2]> tile_3 = const()[name = string("tile_3"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1422_axis_0 = const()[name = string("op_1422_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1422_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1422_cast_fp16_1 = split(axis = var_1422_axis_0, split_sizes = tile_3, x = var_1419_cast_fp16)[name = string("op_1422_cast_fp16")];
            tensor<int32, [2]> var_1425_split_sizes_0 = const()[name = string("op_1425_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_1425_axis_0 = const()[name = string("op_1425_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_1425_0, tensor<fp16, [1, 8, 128, 32]> var_1425_1 = split(axis = var_1425_axis_0, split_sizes = var_1425_split_sizes_0, x = query_states_9_cast_fp16)[name = string("op_1425")];
            bool attn_weights_17_transpose_x_0 = const()[name = string("attn_weights_17_transpose_x_0"), val = bool(false)];
            bool attn_weights_17_transpose_y_0 = const()[name = string("attn_weights_17_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_17_cast_fp16 = matmul(transpose_x = attn_weights_17_transpose_x_0, transpose_y = attn_weights_17_transpose_y_0, x = var_1412_cast_fp16_0, y = var_1425_0)[name = string("attn_weights_17_cast_fp16")];
            fp16 var_1428_to_fp16 = const()[name = string("op_1428_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_19_cast_fp16 = mul(x = attn_weights_17_cast_fp16, y = var_1428_to_fp16)[name = string("attn_weights_19_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_21_cast_fp16 = add(x = attn_weights_19_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_21_cast_fp16")];
            int32 var_1432 = const()[name = string("op_1432"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_23_cast_fp16 = softmax(axis = var_1432, x = attn_weights_21_cast_fp16)[name = string("attn_weights_23_cast_fp16")];
            bool var_1438_transpose_x_1 = const()[name = string("op_1438_transpose_x_1"), val = bool(true)];
            bool var_1438_transpose_y_1 = const()[name = string("op_1438_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_1438_cast_fp16 = matmul(transpose_x = var_1438_transpose_x_1, transpose_y = var_1438_transpose_y_1, x = attn_weights_23_cast_fp16, y = var_1422_cast_fp16_0)[name = string("op_1438_cast_fp16")];
            bool attn_weights_25_transpose_x_0 = const()[name = string("attn_weights_25_transpose_x_0"), val = bool(false)];
            bool attn_weights_25_transpose_y_0 = const()[name = string("attn_weights_25_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_25_cast_fp16 = matmul(transpose_x = attn_weights_25_transpose_x_0, transpose_y = attn_weights_25_transpose_y_0, x = var_1412_cast_fp16_1, y = var_1425_1)[name = string("attn_weights_25_cast_fp16")];
            fp16 var_1440_to_fp16 = const()[name = string("op_1440_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_27_cast_fp16 = mul(x = attn_weights_25_cast_fp16, y = var_1440_to_fp16)[name = string("attn_weights_27_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_29_cast_fp16 = add(x = attn_weights_27_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_29_cast_fp16")];
            int32 var_1444 = const()[name = string("op_1444"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_31_cast_fp16 = softmax(axis = var_1444, x = attn_weights_29_cast_fp16)[name = string("attn_weights_31_cast_fp16")];
            bool attn_output_9_transpose_x_1 = const()[name = string("attn_output_9_transpose_x_1"), val = bool(true)];
            bool attn_output_9_transpose_y_1 = const()[name = string("attn_output_9_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_9_cast_fp16 = matmul(transpose_x = attn_output_9_transpose_x_1, transpose_y = attn_output_9_transpose_y_1, x = attn_weights_31_cast_fp16, y = var_1422_cast_fp16_1)[name = string("attn_output_9_cast_fp16")];
            int32 var_1452 = const()[name = string("op_1452"), val = int32(1)];
            bool attn_output_11_interleave_0 = const()[name = string("attn_output_11_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_11_cast_fp16 = concat(axis = var_1452, interleave = attn_output_11_interleave_0, values = (var_1438_cast_fp16, attn_output_9_cast_fp16))[name = string("attn_output_11_cast_fp16")];
            tensor<int32, [4]> var_1456_perm_0 = const()[name = string("op_1456_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_23x = const()[name = string("concat_23x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_1456_cast_fp16 = transpose(perm = var_1456_perm_0, x = attn_output_11_cast_fp16)[name = string("transpose_426")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_15_cast_fp16 = reshape(shape = concat_23x, x = var_1456_cast_fp16)[name = string("attn_output_15_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_1_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_1_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1078614720)))];
            tensor<int32, [2]> hidden_states_13_strides_0 = const()[name = string("hidden_states_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_13_pad_type_0 = const()[name = string("hidden_states_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_13_pad_0 = const()[name = string("hidden_states_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_13_dilations_0 = const()[name = string("hidden_states_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_13_groups_0 = const()[name = string("hidden_states_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_13_cast_fp16 = conv(dilations = hidden_states_13_dilations_0, groups = hidden_states_13_groups_0, pad = hidden_states_13_pad_0, pad_type = hidden_states_13_pad_type_0, strides = hidden_states_13_strides_0, weight = layers_1_self_attn_o_proj_weight_to_fp16, x = attn_output_15_cast_fp16)[name = string("hidden_states_13_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_15_cast_fp16 = add(x = hidden_states_9_cast_fp16, y = hidden_states_13_cast_fp16)[name = string("hidden_states_15_cast_fp16")];
            fp16 const_20_promoted_to_fp16 = const()[name = string("const_20_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_1489_cast_fp16 = mul(x = hidden_states_15_cast_fp16, y = const_20_promoted_to_fp16)[name = string("op_1489_cast_fp16")];
            int32 var_1487 = const()[name = string("op_1487"), val = int32(1)];
            bool doubled_13_interleave_0 = const()[name = string("doubled_13_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_13_cast_fp16 = concat(axis = var_1487, interleave = doubled_13_interleave_0, values = (hidden_states_15_cast_fp16, var_1489_cast_fp16))[name = string("doubled_13_cast_fp16")];
            tensor<int32, [1]> out_7_axes_0 = const()[name = string("out_7_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_7_gamma_0_to_fp16 = const()[name = string("out_7_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1087003392)))];
            fp16 var_1499_to_fp16 = const()[name = string("op_1499_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_7_cast_fp16 = layer_norm(axes = out_7_axes_0, epsilon = var_1499_to_fp16, gamma = out_7_gamma_0_to_fp16, x = doubled_13_cast_fp16)[name = string("out_7_cast_fp16")];
            tensor<int32, [2]> var_1510_split_sizes_0 = const()[name = string("op_1510_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1510_axis_0 = const()[name = string("op_1510_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_1510_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_1510_cast_fp16_1 = split(axis = var_1510_axis_0, split_sizes = var_1510_split_sizes_0, x = out_7_cast_fp16)[name = string("op_1510_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_1_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_1_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1087011648)))];
            tensor<int32, [2]> input_3_strides_0 = const()[name = string("input_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_3_dilations_0 = const()[name = string("input_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_3_groups_0 = const()[name = string("input_3_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_3_cast_fp16 = conv(dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = layers_1_mlp_gate_proj_weight_to_fp16, x = var_1510_cast_fp16_0)[name = string("input_3_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_1527_cast_fp16 = silu(x = input_3_cast_fp16)[name = string("op_1527_cast_fp16")];
            tensor<int32, [2]> var_1533_strides_0 = const()[name = string("op_1533_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_1533_pad_type_0 = const()[name = string("op_1533_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_1533_pad_0 = const()[name = string("op_1533_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_1533_dilations_0 = const()[name = string("op_1533_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1533_groups_0 = const()[name = string("op_1533_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_1533_cast_fp16 = conv(dilations = var_1533_dilations_0, groups = var_1533_groups_0, pad = var_1533_pad_0, pad_type = var_1533_pad_type_0, strides = var_1533_strides_0, weight = layers_1_mlp_up_proj_weight_cast_fp16, x = var_1510_cast_fp16_0)[name = string("op_1533_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_19_cast_fp16 = mul(x = var_1527_cast_fp16, y = var_1533_cast_fp16)[name = string("x_19_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_1_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_1_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1112177536)))];
            tensor<int32, [2]> hidden_states_17_strides_0 = const()[name = string("hidden_states_17_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_17_pad_type_0 = const()[name = string("hidden_states_17_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_17_pad_0 = const()[name = string("hidden_states_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_17_dilations_0 = const()[name = string("hidden_states_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_17_groups_0 = const()[name = string("hidden_states_17_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_17_cast_fp16 = conv(dilations = hidden_states_17_dilations_0, groups = hidden_states_17_groups_0, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = hidden_states_17_strides_0, weight = layers_1_mlp_down_proj_weight_to_fp16, x = x_19_cast_fp16)[name = string("hidden_states_17_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_19_cast_fp16 = add(x = hidden_states_15_cast_fp16, y = hidden_states_17_cast_fp16)[name = string("hidden_states_19_cast_fp16")];
            fp16 const_22_promoted_to_fp16 = const()[name = string("const_22_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_1551_cast_fp16 = mul(x = hidden_states_19_cast_fp16, y = const_22_promoted_to_fp16)[name = string("op_1551_cast_fp16")];
            int32 var_1549 = const()[name = string("op_1549"), val = int32(1)];
            bool doubled_17_interleave_0 = const()[name = string("doubled_17_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_17_cast_fp16 = concat(axis = var_1549, interleave = doubled_17_interleave_0, values = (hidden_states_19_cast_fp16, var_1551_cast_fp16))[name = string("doubled_17_cast_fp16")];
            tensor<int32, [1]> out_9_axes_0 = const()[name = string("out_9_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_9_gamma_0_to_fp16 = const()[name = string("out_9_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1137343424)))];
            fp16 var_1561_to_fp16 = const()[name = string("op_1561_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_9_cast_fp16 = layer_norm(axes = out_9_axes_0, epsilon = var_1561_to_fp16, gamma = out_9_gamma_0_to_fp16, x = doubled_17_cast_fp16)[name = string("out_9_cast_fp16")];
            tensor<int32, [2]> var_1572_split_sizes_0 = const()[name = string("op_1572_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1572_axis_0 = const()[name = string("op_1572_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_1572_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_1572_cast_fp16_1 = split(axis = var_1572_axis_0, split_sizes = var_1572_split_sizes_0, x = out_9_cast_fp16)[name = string("op_1572_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_2_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_2_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1137351680)))];
            tensor<int32, [2]> query_states_13_strides_0 = const()[name = string("query_states_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_13_pad_type_0 = const()[name = string("query_states_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_13_pad_0 = const()[name = string("query_states_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_13_dilations_0 = const()[name = string("query_states_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_13_groups_0 = const()[name = string("query_states_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_13_cast_fp16 = conv(dilations = query_states_13_dilations_0, groups = query_states_13_groups_0, pad = query_states_13_pad_0, pad_type = query_states_13_pad_type_0, strides = query_states_13_strides_0, weight = layers_2_self_attn_q_proj_weight_to_fp16, x = var_1572_cast_fp16_0)[name = string("query_states_13_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_2_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_2_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1145740352)))];
            tensor<int32, [2]> key_states_21_strides_0 = const()[name = string("key_states_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_21_pad_type_0 = const()[name = string("key_states_21_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_21_pad_0 = const()[name = string("key_states_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_21_dilations_0 = const()[name = string("key_states_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_21_groups_0 = const()[name = string("key_states_21_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_21_cast_fp16 = conv(dilations = key_states_21_dilations_0, groups = key_states_21_groups_0, pad = key_states_21_pad_0, pad_type = key_states_21_pad_type_0, strides = key_states_21_strides_0, weight = layers_2_self_attn_k_proj_weight_to_fp16, x = var_1572_cast_fp16_0)[name = string("key_states_21_cast_fp16")];
            tensor<int32, [2]> value_states_13_strides_0 = const()[name = string("value_states_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_13_pad_type_0 = const()[name = string("value_states_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_13_pad_0 = const()[name = string("value_states_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_13_dilations_0 = const()[name = string("value_states_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_13_groups_0 = const()[name = string("value_states_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_13_cast_fp16 = conv(dilations = value_states_13_dilations_0, groups = value_states_13_groups_0, pad = value_states_13_pad_0, pad_type = value_states_13_pad_type_0, strides = value_states_13_strides_0, weight = layers_2_self_attn_v_proj_weight_cast_fp16, x = var_1572_cast_fp16_0)[name = string("value_states_13_cast_fp16")];
            tensor<int32, [4]> concat_24x = const()[name = string("concat_24x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_21_cast_fp16 = reshape(shape = concat_24x, x = query_states_13_cast_fp16)[name = string("x_21_cast_fp16")];
            tensor<int32, [4]> concat_25x = const()[name = string("concat_25x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_1629_cast_fp16 = reshape(shape = concat_25x, x = key_states_21_cast_fp16)[name = string("op_1629_cast_fp16")];
            tensor<int32, [4]> concat_26x = const()[name = string("concat_26x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_1636_cast_fp16 = reshape(shape = concat_26x, x = value_states_13_cast_fp16)[name = string("op_1636_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_1640_cast_fp16 = mul(x = x_21_cast_fp16, y = var_859_cast_fp16)[name = string("op_1640_cast_fp16")];
            tensor<int32, [2]> var_1641_split_sizes_0 = const()[name = string("op_1641_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1641_axis_0 = const()[name = string("op_1641_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_1641_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_1641_cast_fp16_1 = split(axis = var_1641_axis_0, split_sizes = var_1641_split_sizes_0, x = x_21_cast_fp16)[name = string("op_1641_cast_fp16")];
            fp16 const_24_promoted_to_fp16 = const()[name = string("const_24_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_1643_cast_fp16 = mul(x = var_1641_cast_fp16_1, y = const_24_promoted_to_fp16)[name = string("op_1643_cast_fp16")];
            int32 var_1645 = const()[name = string("op_1645"), val = int32(-2)];
            bool var_1646_interleave_0 = const()[name = string("op_1646_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_1646_cast_fp16 = concat(axis = var_1645, interleave = var_1646_interleave_0, values = (var_1643_cast_fp16, var_1641_cast_fp16_0))[name = string("op_1646_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_1647_cast_fp16 = mul(x = var_1646_cast_fp16, y = var_866_cast_fp16)[name = string("op_1647_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_15_cast_fp16 = add(x = var_1640_cast_fp16, y = var_1647_cast_fp16)[name = string("query_states_15_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_1653_cast_fp16 = mul(x = var_1629_cast_fp16, y = var_859_cast_fp16)[name = string("op_1653_cast_fp16")];
            tensor<int32, [2]> var_1654_split_sizes_0 = const()[name = string("op_1654_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1654_axis_0 = const()[name = string("op_1654_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_1654_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_1654_cast_fp16_1 = split(axis = var_1654_axis_0, split_sizes = var_1654_split_sizes_0, x = var_1629_cast_fp16)[name = string("op_1654_cast_fp16")];
            fp16 const_25_promoted_to_fp16 = const()[name = string("const_25_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_1656_cast_fp16 = mul(x = var_1654_cast_fp16_1, y = const_25_promoted_to_fp16)[name = string("op_1656_cast_fp16")];
            int32 var_1658 = const()[name = string("op_1658"), val = int32(-2)];
            bool var_1659_interleave_0 = const()[name = string("op_1659_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_1659_cast_fp16 = concat(axis = var_1658, interleave = var_1659_interleave_0, values = (var_1656_cast_fp16, var_1654_cast_fp16_0))[name = string("op_1659_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_1660_cast_fp16 = mul(x = var_1659_cast_fp16, y = var_866_cast_fp16)[name = string("op_1660_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_25_cast_fp16 = add(x = var_1653_cast_fp16, y = var_1660_cast_fp16)[name = string("key_states_25_cast_fp16")];
            tensor<int32, [1]> expand_dims_24 = const()[name = string("expand_dims_24"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> expand_dims_25 = const()[name = string("expand_dims_25"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_27 = const()[name = string("expand_dims_27"), val = tensor<int32, [1]>([0])];
            int32 concat_29_axis_0 = const()[name = string("concat_29_axis_0"), val = int32(0)];
            bool concat_29_interleave_0 = const()[name = string("concat_29_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_29 = concat(axis = concat_29_axis_0, interleave = concat_29_interleave_0, values = (expand_dims_24, expand_dims_25, position_id, expand_dims_27))[name = string("concat_29")];
            tensor<int32, [1]> expand_dims_28 = const()[name = string("expand_dims_28"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> concat_30_values1_0 = const()[name = string("concat_30_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_30_values3_0 = const()[name = string("concat_30_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_30_axis_0 = const()[name = string("concat_30_axis_0"), val = int32(0)];
            bool concat_30_interleave_0 = const()[name = string("concat_30_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_30 = concat(axis = concat_30_axis_0, interleave = concat_30_interleave_0, values = (expand_dims_28, concat_30_values1_0, cache_position_end, concat_30_values3_0))[name = string("concat_30")];
            tensor<int32, [4]> key_states_27_perm_0 = const()[name = string("key_states_27_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_3_stride_0 = const()[name = string("key_cache_internal_tensor_assign_3_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_3_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_3_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_3_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_3_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_3_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_3_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_27_cast_fp16 = transpose(perm = key_states_27_perm_0, x = key_states_25_cast_fp16)[name = string("transpose_425")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_3_cast_fp16 = slice_update(begin = concat_29, begin_mask = key_cache_internal_tensor_assign_3_begin_mask_0, end = concat_30, end_mask = key_cache_internal_tensor_assign_3_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_3_squeeze_mask_0, stride = key_cache_internal_tensor_assign_3_stride_0, update = key_states_27_cast_fp16, x = coreml_update_state_226)[name = string("key_cache_internal_tensor_assign_3_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_3_cast_fp16, input = key_cache)[name = string("coreml_update_state_228_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_228 = read_state(input = key_cache)[name = string("coreml_update_state_228")];
            tensor<int32, [4]> value_states_15_perm_0 = const()[name = string("value_states_15_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_3_stride_0 = const()[name = string("value_cache_internal_tensor_assign_3_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_3_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_3_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_3_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_3_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_3_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_3_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_15_cast_fp16 = transpose(perm = value_states_15_perm_0, x = var_1636_cast_fp16)[name = string("transpose_424")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_3_cast_fp16 = slice_update(begin = concat_29, begin_mask = value_cache_internal_tensor_assign_3_begin_mask_0, end = concat_30, end_mask = value_cache_internal_tensor_assign_3_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_3_squeeze_mask_0, stride = value_cache_internal_tensor_assign_3_stride_0, update = value_states_15_cast_fp16, x = coreml_update_state_227)[name = string("value_cache_internal_tensor_assign_3_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_3_cast_fp16, input = value_cache)[name = string("coreml_update_state_229_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_229 = read_state(input = value_cache)[name = string("coreml_update_state_229")];
            tensor<int32, [4]> var_1730_begin_0 = const()[name = string("op_1730_begin_0"), val = tensor<int32, [4]>([2, 0, 0, 0])];
            tensor<int32, [4]> var_1730_end_0 = const()[name = string("op_1730_end_0"), val = tensor<int32, [4]>([3, 2, 2048, 128])];
            tensor<bool, [4]> var_1730_end_mask_0 = const()[name = string("op_1730_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1730_cast_fp16 = slice_by_index(begin = var_1730_begin_0, end = var_1730_end_0, end_mask = var_1730_end_mask_0, x = coreml_update_state_228)[name = string("op_1730_cast_fp16")];
            tensor<int32, [2]> tile_4 = const()[name = string("tile_4"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1733_axis_0 = const()[name = string("op_1733_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1733_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1733_cast_fp16_1 = split(axis = var_1733_axis_0, split_sizes = tile_4, x = var_1730_cast_fp16)[name = string("op_1733_cast_fp16")];
            tensor<int32, [4]> var_1740_begin_0 = const()[name = string("op_1740_begin_0"), val = tensor<int32, [4]>([2, 0, 0, 0])];
            tensor<int32, [4]> var_1740_end_0 = const()[name = string("op_1740_end_0"), val = tensor<int32, [4]>([3, 2, 2048, 128])];
            tensor<bool, [4]> var_1740_end_mask_0 = const()[name = string("op_1740_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1740_cast_fp16 = slice_by_index(begin = var_1740_begin_0, end = var_1740_end_0, end_mask = var_1740_end_mask_0, x = coreml_update_state_229)[name = string("op_1740_cast_fp16")];
            tensor<int32, [2]> tile_5 = const()[name = string("tile_5"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1743_axis_0 = const()[name = string("op_1743_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1743_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1743_cast_fp16_1 = split(axis = var_1743_axis_0, split_sizes = tile_5, x = var_1740_cast_fp16)[name = string("op_1743_cast_fp16")];
            tensor<int32, [2]> var_1746_split_sizes_0 = const()[name = string("op_1746_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_1746_axis_0 = const()[name = string("op_1746_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_1746_0, tensor<fp16, [1, 8, 128, 32]> var_1746_1 = split(axis = var_1746_axis_0, split_sizes = var_1746_split_sizes_0, x = query_states_15_cast_fp16)[name = string("op_1746")];
            bool attn_weights_33_transpose_x_0 = const()[name = string("attn_weights_33_transpose_x_0"), val = bool(false)];
            bool attn_weights_33_transpose_y_0 = const()[name = string("attn_weights_33_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_33_cast_fp16 = matmul(transpose_x = attn_weights_33_transpose_x_0, transpose_y = attn_weights_33_transpose_y_0, x = var_1733_cast_fp16_0, y = var_1746_0)[name = string("attn_weights_33_cast_fp16")];
            fp16 var_1749_to_fp16 = const()[name = string("op_1749_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_35_cast_fp16 = mul(x = attn_weights_33_cast_fp16, y = var_1749_to_fp16)[name = string("attn_weights_35_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_37_cast_fp16 = add(x = attn_weights_35_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_37_cast_fp16")];
            int32 var_1753 = const()[name = string("op_1753"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_39_cast_fp16 = softmax(axis = var_1753, x = attn_weights_37_cast_fp16)[name = string("attn_weights_39_cast_fp16")];
            bool var_1759_transpose_x_1 = const()[name = string("op_1759_transpose_x_1"), val = bool(true)];
            bool var_1759_transpose_y_1 = const()[name = string("op_1759_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_1759_cast_fp16 = matmul(transpose_x = var_1759_transpose_x_1, transpose_y = var_1759_transpose_y_1, x = attn_weights_39_cast_fp16, y = var_1743_cast_fp16_0)[name = string("op_1759_cast_fp16")];
            bool attn_weights_41_transpose_x_0 = const()[name = string("attn_weights_41_transpose_x_0"), val = bool(false)];
            bool attn_weights_41_transpose_y_0 = const()[name = string("attn_weights_41_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_41_cast_fp16 = matmul(transpose_x = attn_weights_41_transpose_x_0, transpose_y = attn_weights_41_transpose_y_0, x = var_1733_cast_fp16_1, y = var_1746_1)[name = string("attn_weights_41_cast_fp16")];
            fp16 var_1761_to_fp16 = const()[name = string("op_1761_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_43_cast_fp16 = mul(x = attn_weights_41_cast_fp16, y = var_1761_to_fp16)[name = string("attn_weights_43_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_45_cast_fp16 = add(x = attn_weights_43_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_45_cast_fp16")];
            int32 var_1765 = const()[name = string("op_1765"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_47_cast_fp16 = softmax(axis = var_1765, x = attn_weights_45_cast_fp16)[name = string("attn_weights_47_cast_fp16")];
            bool attn_output_17_transpose_x_1 = const()[name = string("attn_output_17_transpose_x_1"), val = bool(true)];
            bool attn_output_17_transpose_y_1 = const()[name = string("attn_output_17_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_17_cast_fp16 = matmul(transpose_x = attn_output_17_transpose_x_1, transpose_y = attn_output_17_transpose_y_1, x = attn_weights_47_cast_fp16, y = var_1743_cast_fp16_1)[name = string("attn_output_17_cast_fp16")];
            int32 var_1773 = const()[name = string("op_1773"), val = int32(1)];
            bool attn_output_19_interleave_0 = const()[name = string("attn_output_19_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_19_cast_fp16 = concat(axis = var_1773, interleave = attn_output_19_interleave_0, values = (var_1759_cast_fp16, attn_output_17_cast_fp16))[name = string("attn_output_19_cast_fp16")];
            tensor<int32, [4]> var_1777_perm_0 = const()[name = string("op_1777_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_35x = const()[name = string("concat_35x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_1777_cast_fp16 = transpose(perm = var_1777_perm_0, x = attn_output_19_cast_fp16)[name = string("transpose_423")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_23_cast_fp16 = reshape(shape = concat_35x, x = var_1777_cast_fp16)[name = string("attn_output_23_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_2_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_2_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1146788992)))];
            tensor<int32, [2]> hidden_states_23_strides_0 = const()[name = string("hidden_states_23_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_23_pad_type_0 = const()[name = string("hidden_states_23_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_23_pad_0 = const()[name = string("hidden_states_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_23_dilations_0 = const()[name = string("hidden_states_23_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_23_groups_0 = const()[name = string("hidden_states_23_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_23_cast_fp16 = conv(dilations = hidden_states_23_dilations_0, groups = hidden_states_23_groups_0, pad = hidden_states_23_pad_0, pad_type = hidden_states_23_pad_type_0, strides = hidden_states_23_strides_0, weight = layers_2_self_attn_o_proj_weight_to_fp16, x = attn_output_23_cast_fp16)[name = string("hidden_states_23_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_25_cast_fp16 = add(x = hidden_states_19_cast_fp16, y = hidden_states_23_cast_fp16)[name = string("hidden_states_25_cast_fp16")];
            fp16 const_30_promoted_to_fp16 = const()[name = string("const_30_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_1810_cast_fp16 = mul(x = hidden_states_25_cast_fp16, y = const_30_promoted_to_fp16)[name = string("op_1810_cast_fp16")];
            int32 var_1808 = const()[name = string("op_1808"), val = int32(1)];
            bool doubled_21_interleave_0 = const()[name = string("doubled_21_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_21_cast_fp16 = concat(axis = var_1808, interleave = doubled_21_interleave_0, values = (hidden_states_25_cast_fp16, var_1810_cast_fp16))[name = string("doubled_21_cast_fp16")];
            tensor<int32, [1]> out_11_axes_0 = const()[name = string("out_11_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_11_gamma_0_to_fp16 = const()[name = string("out_11_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1155177664)))];
            fp16 var_1820_to_fp16 = const()[name = string("op_1820_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_11_cast_fp16 = layer_norm(axes = out_11_axes_0, epsilon = var_1820_to_fp16, gamma = out_11_gamma_0_to_fp16, x = doubled_21_cast_fp16)[name = string("out_11_cast_fp16")];
            tensor<int32, [2]> var_1831_split_sizes_0 = const()[name = string("op_1831_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1831_axis_0 = const()[name = string("op_1831_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_1831_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_1831_cast_fp16_1 = split(axis = var_1831_axis_0, split_sizes = var_1831_split_sizes_0, x = out_11_cast_fp16)[name = string("op_1831_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_2_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_2_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1155185920)))];
            tensor<int32, [2]> input_5_strides_0 = const()[name = string("input_5_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_5_pad_type_0 = const()[name = string("input_5_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_5_dilations_0 = const()[name = string("input_5_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_5_groups_0 = const()[name = string("input_5_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_5_cast_fp16 = conv(dilations = input_5_dilations_0, groups = input_5_groups_0, pad = input_5_pad_0, pad_type = input_5_pad_type_0, strides = input_5_strides_0, weight = layers_2_mlp_gate_proj_weight_to_fp16, x = var_1831_cast_fp16_0)[name = string("input_5_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_1848_cast_fp16 = silu(x = input_5_cast_fp16)[name = string("op_1848_cast_fp16")];
            tensor<int32, [2]> var_1854_strides_0 = const()[name = string("op_1854_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_1854_pad_type_0 = const()[name = string("op_1854_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_1854_pad_0 = const()[name = string("op_1854_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_1854_dilations_0 = const()[name = string("op_1854_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1854_groups_0 = const()[name = string("op_1854_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_1854_cast_fp16 = conv(dilations = var_1854_dilations_0, groups = var_1854_groups_0, pad = var_1854_pad_0, pad_type = var_1854_pad_type_0, strides = var_1854_strides_0, weight = layers_2_mlp_up_proj_weight_cast_fp16, x = var_1831_cast_fp16_0)[name = string("op_1854_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_29_cast_fp16 = mul(x = var_1848_cast_fp16, y = var_1854_cast_fp16)[name = string("x_29_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_2_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_2_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1180351808)))];
            tensor<int32, [2]> hidden_states_27_strides_0 = const()[name = string("hidden_states_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_27_pad_type_0 = const()[name = string("hidden_states_27_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_27_pad_0 = const()[name = string("hidden_states_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_27_dilations_0 = const()[name = string("hidden_states_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_27_groups_0 = const()[name = string("hidden_states_27_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_27_cast_fp16 = conv(dilations = hidden_states_27_dilations_0, groups = hidden_states_27_groups_0, pad = hidden_states_27_pad_0, pad_type = hidden_states_27_pad_type_0, strides = hidden_states_27_strides_0, weight = layers_2_mlp_down_proj_weight_to_fp16, x = x_29_cast_fp16)[name = string("hidden_states_27_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_29_cast_fp16 = add(x = hidden_states_25_cast_fp16, y = hidden_states_27_cast_fp16)[name = string("hidden_states_29_cast_fp16")];
            fp16 const_32_promoted_to_fp16 = const()[name = string("const_32_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_1872_cast_fp16 = mul(x = hidden_states_29_cast_fp16, y = const_32_promoted_to_fp16)[name = string("op_1872_cast_fp16")];
            int32 var_1870 = const()[name = string("op_1870"), val = int32(1)];
            bool doubled_25_interleave_0 = const()[name = string("doubled_25_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_25_cast_fp16 = concat(axis = var_1870, interleave = doubled_25_interleave_0, values = (hidden_states_29_cast_fp16, var_1872_cast_fp16))[name = string("doubled_25_cast_fp16")];
            tensor<int32, [1]> out_13_axes_0 = const()[name = string("out_13_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_13_gamma_0_to_fp16 = const()[name = string("out_13_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1205517696)))];
            fp16 var_1882_to_fp16 = const()[name = string("op_1882_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_13_cast_fp16 = layer_norm(axes = out_13_axes_0, epsilon = var_1882_to_fp16, gamma = out_13_gamma_0_to_fp16, x = doubled_25_cast_fp16)[name = string("out_13_cast_fp16")];
            tensor<int32, [2]> var_1893_split_sizes_0 = const()[name = string("op_1893_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1893_axis_0 = const()[name = string("op_1893_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_1893_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_1893_cast_fp16_1 = split(axis = var_1893_axis_0, split_sizes = var_1893_split_sizes_0, x = out_13_cast_fp16)[name = string("op_1893_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_3_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_3_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1205525952)))];
            tensor<int32, [2]> query_states_19_strides_0 = const()[name = string("query_states_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_19_pad_type_0 = const()[name = string("query_states_19_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_19_pad_0 = const()[name = string("query_states_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_19_dilations_0 = const()[name = string("query_states_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_19_groups_0 = const()[name = string("query_states_19_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_19_cast_fp16 = conv(dilations = query_states_19_dilations_0, groups = query_states_19_groups_0, pad = query_states_19_pad_0, pad_type = query_states_19_pad_type_0, strides = query_states_19_strides_0, weight = layers_3_self_attn_q_proj_weight_to_fp16, x = var_1893_cast_fp16_0)[name = string("query_states_19_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_3_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_3_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1213914624)))];
            tensor<int32, [2]> key_states_31_strides_0 = const()[name = string("key_states_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_31_pad_type_0 = const()[name = string("key_states_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_31_pad_0 = const()[name = string("key_states_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_31_dilations_0 = const()[name = string("key_states_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_31_groups_0 = const()[name = string("key_states_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_31_cast_fp16 = conv(dilations = key_states_31_dilations_0, groups = key_states_31_groups_0, pad = key_states_31_pad_0, pad_type = key_states_31_pad_type_0, strides = key_states_31_strides_0, weight = layers_3_self_attn_k_proj_weight_to_fp16, x = var_1893_cast_fp16_0)[name = string("key_states_31_cast_fp16")];
            tensor<int32, [2]> value_states_19_strides_0 = const()[name = string("value_states_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_19_pad_type_0 = const()[name = string("value_states_19_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_19_pad_0 = const()[name = string("value_states_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_19_dilations_0 = const()[name = string("value_states_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_19_groups_0 = const()[name = string("value_states_19_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_19_cast_fp16 = conv(dilations = value_states_19_dilations_0, groups = value_states_19_groups_0, pad = value_states_19_pad_0, pad_type = value_states_19_pad_type_0, strides = value_states_19_strides_0, weight = layers_3_self_attn_v_proj_weight_cast_fp16, x = var_1893_cast_fp16_0)[name = string("value_states_19_cast_fp16")];
            tensor<int32, [4]> concat_36x = const()[name = string("concat_36x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_31_cast_fp16 = reshape(shape = concat_36x, x = query_states_19_cast_fp16)[name = string("x_31_cast_fp16")];
            tensor<int32, [4]> concat_37x = const()[name = string("concat_37x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_1950_cast_fp16 = reshape(shape = concat_37x, x = key_states_31_cast_fp16)[name = string("op_1950_cast_fp16")];
            tensor<int32, [4]> concat_38x = const()[name = string("concat_38x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_1957_cast_fp16 = reshape(shape = concat_38x, x = value_states_19_cast_fp16)[name = string("op_1957_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_1961_cast_fp16 = mul(x = x_31_cast_fp16, y = var_859_cast_fp16)[name = string("op_1961_cast_fp16")];
            tensor<int32, [2]> var_1962_split_sizes_0 = const()[name = string("op_1962_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1962_axis_0 = const()[name = string("op_1962_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_1962_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_1962_cast_fp16_1 = split(axis = var_1962_axis_0, split_sizes = var_1962_split_sizes_0, x = x_31_cast_fp16)[name = string("op_1962_cast_fp16")];
            fp16 const_34_promoted_to_fp16 = const()[name = string("const_34_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_1964_cast_fp16 = mul(x = var_1962_cast_fp16_1, y = const_34_promoted_to_fp16)[name = string("op_1964_cast_fp16")];
            int32 var_1966 = const()[name = string("op_1966"), val = int32(-2)];
            bool var_1967_interleave_0 = const()[name = string("op_1967_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_1967_cast_fp16 = concat(axis = var_1966, interleave = var_1967_interleave_0, values = (var_1964_cast_fp16, var_1962_cast_fp16_0))[name = string("op_1967_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_1968_cast_fp16 = mul(x = var_1967_cast_fp16, y = var_866_cast_fp16)[name = string("op_1968_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_21_cast_fp16 = add(x = var_1961_cast_fp16, y = var_1968_cast_fp16)[name = string("query_states_21_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_1974_cast_fp16 = mul(x = var_1950_cast_fp16, y = var_859_cast_fp16)[name = string("op_1974_cast_fp16")];
            tensor<int32, [2]> var_1975_split_sizes_0 = const()[name = string("op_1975_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1975_axis_0 = const()[name = string("op_1975_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_1975_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_1975_cast_fp16_1 = split(axis = var_1975_axis_0, split_sizes = var_1975_split_sizes_0, x = var_1950_cast_fp16)[name = string("op_1975_cast_fp16")];
            fp16 const_35_promoted_to_fp16 = const()[name = string("const_35_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_1977_cast_fp16 = mul(x = var_1975_cast_fp16_1, y = const_35_promoted_to_fp16)[name = string("op_1977_cast_fp16")];
            int32 var_1979 = const()[name = string("op_1979"), val = int32(-2)];
            bool var_1980_interleave_0 = const()[name = string("op_1980_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_1980_cast_fp16 = concat(axis = var_1979, interleave = var_1980_interleave_0, values = (var_1977_cast_fp16, var_1975_cast_fp16_0))[name = string("op_1980_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_1981_cast_fp16 = mul(x = var_1980_cast_fp16, y = var_866_cast_fp16)[name = string("op_1981_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_35_cast_fp16 = add(x = var_1974_cast_fp16, y = var_1981_cast_fp16)[name = string("key_states_35_cast_fp16")];
            tensor<int32, [1]> expand_dims_36 = const()[name = string("expand_dims_36"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> expand_dims_37 = const()[name = string("expand_dims_37"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_39 = const()[name = string("expand_dims_39"), val = tensor<int32, [1]>([0])];
            int32 concat_41_axis_0 = const()[name = string("concat_41_axis_0"), val = int32(0)];
            bool concat_41_interleave_0 = const()[name = string("concat_41_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_41 = concat(axis = concat_41_axis_0, interleave = concat_41_interleave_0, values = (expand_dims_36, expand_dims_37, position_id, expand_dims_39))[name = string("concat_41")];
            tensor<int32, [1]> expand_dims_40 = const()[name = string("expand_dims_40"), val = tensor<int32, [1]>([4])];
            tensor<int32, [1]> concat_42_values1_0 = const()[name = string("concat_42_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_42_values3_0 = const()[name = string("concat_42_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_42_axis_0 = const()[name = string("concat_42_axis_0"), val = int32(0)];
            bool concat_42_interleave_0 = const()[name = string("concat_42_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_42 = concat(axis = concat_42_axis_0, interleave = concat_42_interleave_0, values = (expand_dims_40, concat_42_values1_0, cache_position_end, concat_42_values3_0))[name = string("concat_42")];
            tensor<int32, [4]> key_states_37_perm_0 = const()[name = string("key_states_37_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_4_stride_0 = const()[name = string("key_cache_internal_tensor_assign_4_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_4_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_4_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_4_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_4_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_4_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_4_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_37_cast_fp16 = transpose(perm = key_states_37_perm_0, x = key_states_35_cast_fp16)[name = string("transpose_422")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_4_cast_fp16 = slice_update(begin = concat_41, begin_mask = key_cache_internal_tensor_assign_4_begin_mask_0, end = concat_42, end_mask = key_cache_internal_tensor_assign_4_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_4_squeeze_mask_0, stride = key_cache_internal_tensor_assign_4_stride_0, update = key_states_37_cast_fp16, x = coreml_update_state_228)[name = string("key_cache_internal_tensor_assign_4_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_4_cast_fp16, input = key_cache)[name = string("coreml_update_state_230_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_230 = read_state(input = key_cache)[name = string("coreml_update_state_230")];
            tensor<int32, [4]> value_states_21_perm_0 = const()[name = string("value_states_21_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_4_stride_0 = const()[name = string("value_cache_internal_tensor_assign_4_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_4_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_4_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_4_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_4_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_4_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_4_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_21_cast_fp16 = transpose(perm = value_states_21_perm_0, x = var_1957_cast_fp16)[name = string("transpose_421")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_4_cast_fp16 = slice_update(begin = concat_41, begin_mask = value_cache_internal_tensor_assign_4_begin_mask_0, end = concat_42, end_mask = value_cache_internal_tensor_assign_4_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_4_squeeze_mask_0, stride = value_cache_internal_tensor_assign_4_stride_0, update = value_states_21_cast_fp16, x = coreml_update_state_229)[name = string("value_cache_internal_tensor_assign_4_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_4_cast_fp16, input = value_cache)[name = string("coreml_update_state_231_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_231 = read_state(input = value_cache)[name = string("coreml_update_state_231")];
            tensor<int32, [4]> var_2051_begin_0 = const()[name = string("op_2051_begin_0"), val = tensor<int32, [4]>([3, 0, 0, 0])];
            tensor<int32, [4]> var_2051_end_0 = const()[name = string("op_2051_end_0"), val = tensor<int32, [4]>([4, 2, 2048, 128])];
            tensor<bool, [4]> var_2051_end_mask_0 = const()[name = string("op_2051_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2051_cast_fp16 = slice_by_index(begin = var_2051_begin_0, end = var_2051_end_0, end_mask = var_2051_end_mask_0, x = coreml_update_state_230)[name = string("op_2051_cast_fp16")];
            tensor<int32, [2]> tile_6 = const()[name = string("tile_6"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2054_axis_0 = const()[name = string("op_2054_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2054_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2054_cast_fp16_1 = split(axis = var_2054_axis_0, split_sizes = tile_6, x = var_2051_cast_fp16)[name = string("op_2054_cast_fp16")];
            tensor<int32, [4]> var_2061_begin_0 = const()[name = string("op_2061_begin_0"), val = tensor<int32, [4]>([3, 0, 0, 0])];
            tensor<int32, [4]> var_2061_end_0 = const()[name = string("op_2061_end_0"), val = tensor<int32, [4]>([4, 2, 2048, 128])];
            tensor<bool, [4]> var_2061_end_mask_0 = const()[name = string("op_2061_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2061_cast_fp16 = slice_by_index(begin = var_2061_begin_0, end = var_2061_end_0, end_mask = var_2061_end_mask_0, x = coreml_update_state_231)[name = string("op_2061_cast_fp16")];
            tensor<int32, [2]> tile_7 = const()[name = string("tile_7"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2064_axis_0 = const()[name = string("op_2064_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2064_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2064_cast_fp16_1 = split(axis = var_2064_axis_0, split_sizes = tile_7, x = var_2061_cast_fp16)[name = string("op_2064_cast_fp16")];
            tensor<int32, [2]> var_2067_split_sizes_0 = const()[name = string("op_2067_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_2067_axis_0 = const()[name = string("op_2067_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_2067_0, tensor<fp16, [1, 8, 128, 32]> var_2067_1 = split(axis = var_2067_axis_0, split_sizes = var_2067_split_sizes_0, x = query_states_21_cast_fp16)[name = string("op_2067")];
            bool attn_weights_49_transpose_x_0 = const()[name = string("attn_weights_49_transpose_x_0"), val = bool(false)];
            bool attn_weights_49_transpose_y_0 = const()[name = string("attn_weights_49_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_49_cast_fp16 = matmul(transpose_x = attn_weights_49_transpose_x_0, transpose_y = attn_weights_49_transpose_y_0, x = var_2054_cast_fp16_0, y = var_2067_0)[name = string("attn_weights_49_cast_fp16")];
            fp16 var_2070_to_fp16 = const()[name = string("op_2070_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_51_cast_fp16 = mul(x = attn_weights_49_cast_fp16, y = var_2070_to_fp16)[name = string("attn_weights_51_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_53_cast_fp16 = add(x = attn_weights_51_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_53_cast_fp16")];
            int32 var_2074 = const()[name = string("op_2074"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_55_cast_fp16 = softmax(axis = var_2074, x = attn_weights_53_cast_fp16)[name = string("attn_weights_55_cast_fp16")];
            bool var_2080_transpose_x_1 = const()[name = string("op_2080_transpose_x_1"), val = bool(true)];
            bool var_2080_transpose_y_1 = const()[name = string("op_2080_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_2080_cast_fp16 = matmul(transpose_x = var_2080_transpose_x_1, transpose_y = var_2080_transpose_y_1, x = attn_weights_55_cast_fp16, y = var_2064_cast_fp16_0)[name = string("op_2080_cast_fp16")];
            bool attn_weights_57_transpose_x_0 = const()[name = string("attn_weights_57_transpose_x_0"), val = bool(false)];
            bool attn_weights_57_transpose_y_0 = const()[name = string("attn_weights_57_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_57_cast_fp16 = matmul(transpose_x = attn_weights_57_transpose_x_0, transpose_y = attn_weights_57_transpose_y_0, x = var_2054_cast_fp16_1, y = var_2067_1)[name = string("attn_weights_57_cast_fp16")];
            fp16 var_2082_to_fp16 = const()[name = string("op_2082_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_59_cast_fp16 = mul(x = attn_weights_57_cast_fp16, y = var_2082_to_fp16)[name = string("attn_weights_59_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_61_cast_fp16 = add(x = attn_weights_59_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_61_cast_fp16")];
            int32 var_2086 = const()[name = string("op_2086"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_63_cast_fp16 = softmax(axis = var_2086, x = attn_weights_61_cast_fp16)[name = string("attn_weights_63_cast_fp16")];
            bool attn_output_25_transpose_x_1 = const()[name = string("attn_output_25_transpose_x_1"), val = bool(true)];
            bool attn_output_25_transpose_y_1 = const()[name = string("attn_output_25_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_25_cast_fp16 = matmul(transpose_x = attn_output_25_transpose_x_1, transpose_y = attn_output_25_transpose_y_1, x = attn_weights_63_cast_fp16, y = var_2064_cast_fp16_1)[name = string("attn_output_25_cast_fp16")];
            int32 var_2094 = const()[name = string("op_2094"), val = int32(1)];
            bool attn_output_27_interleave_0 = const()[name = string("attn_output_27_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_27_cast_fp16 = concat(axis = var_2094, interleave = attn_output_27_interleave_0, values = (var_2080_cast_fp16, attn_output_25_cast_fp16))[name = string("attn_output_27_cast_fp16")];
            tensor<int32, [4]> var_2098_perm_0 = const()[name = string("op_2098_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_47x = const()[name = string("concat_47x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_2098_cast_fp16 = transpose(perm = var_2098_perm_0, x = attn_output_27_cast_fp16)[name = string("transpose_420")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_31_cast_fp16 = reshape(shape = concat_47x, x = var_2098_cast_fp16)[name = string("attn_output_31_cast_fp16")];
            tensor<int32, [2]> hidden_states_33_strides_0 = const()[name = string("hidden_states_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_33_pad_type_0 = const()[name = string("hidden_states_33_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_33_pad_0 = const()[name = string("hidden_states_33_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_33_dilations_0 = const()[name = string("hidden_states_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_33_groups_0 = const()[name = string("hidden_states_33_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_33_cast_fp16 = conv(dilations = hidden_states_33_dilations_0, groups = hidden_states_33_groups_0, pad = hidden_states_33_pad_0, pad_type = hidden_states_33_pad_type_0, strides = hidden_states_33_strides_0, weight = layers_3_self_attn_o_proj_weight_cast_fp16, x = attn_output_31_cast_fp16)[name = string("hidden_states_33_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_35_cast_fp16 = add(x = hidden_states_29_cast_fp16, y = hidden_states_33_cast_fp16)[name = string("hidden_states_35_cast_fp16")];
            fp16 const_40_promoted_to_fp16 = const()[name = string("const_40_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_2131_cast_fp16 = mul(x = hidden_states_35_cast_fp16, y = const_40_promoted_to_fp16)[name = string("op_2131_cast_fp16")];
            int32 var_2129 = const()[name = string("op_2129"), val = int32(1)];
            bool doubled_29_interleave_0 = const()[name = string("doubled_29_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_29_cast_fp16 = concat(axis = var_2129, interleave = doubled_29_interleave_0, values = (hidden_states_35_cast_fp16, var_2131_cast_fp16))[name = string("doubled_29_cast_fp16")];
            tensor<int32, [1]> out_15_axes_0 = const()[name = string("out_15_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_15_gamma_0_to_fp16 = const()[name = string("out_15_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1214963264)))];
            fp16 var_2141_to_fp16 = const()[name = string("op_2141_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_15_cast_fp16 = layer_norm(axes = out_15_axes_0, epsilon = var_2141_to_fp16, gamma = out_15_gamma_0_to_fp16, x = doubled_29_cast_fp16)[name = string("out_15_cast_fp16")];
            tensor<int32, [2]> var_2152_split_sizes_0 = const()[name = string("op_2152_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2152_axis_0 = const()[name = string("op_2152_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_2152_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_2152_cast_fp16_1 = split(axis = var_2152_axis_0, split_sizes = var_2152_split_sizes_0, x = out_15_cast_fp16)[name = string("op_2152_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_3_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_3_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1214971520)))];
            tensor<int32, [2]> input_7_strides_0 = const()[name = string("input_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_7_pad_type_0 = const()[name = string("input_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_7_pad_0 = const()[name = string("input_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_7_dilations_0 = const()[name = string("input_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_7_groups_0 = const()[name = string("input_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_7_cast_fp16 = conv(dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = layers_3_mlp_gate_proj_weight_to_fp16, x = var_2152_cast_fp16_0)[name = string("input_7_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_2169_cast_fp16 = silu(x = input_7_cast_fp16)[name = string("op_2169_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_3_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_3_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1240137408)))];
            tensor<int32, [2]> var_2175_strides_0 = const()[name = string("op_2175_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_2175_pad_type_0 = const()[name = string("op_2175_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_2175_pad_0 = const()[name = string("op_2175_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_2175_dilations_0 = const()[name = string("op_2175_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2175_groups_0 = const()[name = string("op_2175_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_2175_cast_fp16 = conv(dilations = var_2175_dilations_0, groups = var_2175_groups_0, pad = var_2175_pad_0, pad_type = var_2175_pad_type_0, strides = var_2175_strides_0, weight = layers_3_mlp_up_proj_weight_to_fp16, x = var_2152_cast_fp16_0)[name = string("op_2175_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_39_cast_fp16 = mul(x = var_2169_cast_fp16, y = var_2175_cast_fp16)[name = string("x_39_cast_fp16")];
            tensor<int32, [2]> hidden_states_37_strides_0 = const()[name = string("hidden_states_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_37_pad_type_0 = const()[name = string("hidden_states_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_37_pad_0 = const()[name = string("hidden_states_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_37_dilations_0 = const()[name = string("hidden_states_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_37_groups_0 = const()[name = string("hidden_states_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_37_cast_fp16 = conv(dilations = hidden_states_37_dilations_0, groups = hidden_states_37_groups_0, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = hidden_states_37_strides_0, weight = layers_3_mlp_down_proj_weight_cast_fp16, x = x_39_cast_fp16)[name = string("hidden_states_37_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_39_cast_fp16 = add(x = hidden_states_35_cast_fp16, y = hidden_states_37_cast_fp16)[name = string("hidden_states_39_cast_fp16")];
            fp16 const_42_promoted_to_fp16 = const()[name = string("const_42_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_2193_cast_fp16 = mul(x = hidden_states_39_cast_fp16, y = const_42_promoted_to_fp16)[name = string("op_2193_cast_fp16")];
            int32 var_2191 = const()[name = string("op_2191"), val = int32(1)];
            bool doubled_33_interleave_0 = const()[name = string("doubled_33_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_33_cast_fp16 = concat(axis = var_2191, interleave = doubled_33_interleave_0, values = (hidden_states_39_cast_fp16, var_2193_cast_fp16))[name = string("doubled_33_cast_fp16")];
            tensor<int32, [1]> out_17_axes_0 = const()[name = string("out_17_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_17_gamma_0_to_fp16 = const()[name = string("out_17_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1265303296)))];
            fp16 var_2203_to_fp16 = const()[name = string("op_2203_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_17_cast_fp16 = layer_norm(axes = out_17_axes_0, epsilon = var_2203_to_fp16, gamma = out_17_gamma_0_to_fp16, x = doubled_33_cast_fp16)[name = string("out_17_cast_fp16")];
            tensor<int32, [2]> var_2214_split_sizes_0 = const()[name = string("op_2214_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2214_axis_0 = const()[name = string("op_2214_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_2214_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_2214_cast_fp16_1 = split(axis = var_2214_axis_0, split_sizes = var_2214_split_sizes_0, x = out_17_cast_fp16)[name = string("op_2214_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_4_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_4_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1265311552)))];
            tensor<int32, [2]> query_states_25_strides_0 = const()[name = string("query_states_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_25_pad_type_0 = const()[name = string("query_states_25_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_25_pad_0 = const()[name = string("query_states_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_25_dilations_0 = const()[name = string("query_states_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_25_groups_0 = const()[name = string("query_states_25_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_25_cast_fp16 = conv(dilations = query_states_25_dilations_0, groups = query_states_25_groups_0, pad = query_states_25_pad_0, pad_type = query_states_25_pad_type_0, strides = query_states_25_strides_0, weight = layers_4_self_attn_q_proj_weight_to_fp16, x = var_2214_cast_fp16_0)[name = string("query_states_25_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_4_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_4_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1273700224)))];
            tensor<int32, [2]> key_states_41_strides_0 = const()[name = string("key_states_41_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_41_pad_type_0 = const()[name = string("key_states_41_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_41_pad_0 = const()[name = string("key_states_41_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_41_dilations_0 = const()[name = string("key_states_41_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_41_groups_0 = const()[name = string("key_states_41_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_41_cast_fp16 = conv(dilations = key_states_41_dilations_0, groups = key_states_41_groups_0, pad = key_states_41_pad_0, pad_type = key_states_41_pad_type_0, strides = key_states_41_strides_0, weight = layers_4_self_attn_k_proj_weight_to_fp16, x = var_2214_cast_fp16_0)[name = string("key_states_41_cast_fp16")];
            tensor<int32, [2]> value_states_25_strides_0 = const()[name = string("value_states_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_25_pad_type_0 = const()[name = string("value_states_25_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_25_pad_0 = const()[name = string("value_states_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_25_dilations_0 = const()[name = string("value_states_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_25_groups_0 = const()[name = string("value_states_25_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_25_cast_fp16 = conv(dilations = value_states_25_dilations_0, groups = value_states_25_groups_0, pad = value_states_25_pad_0, pad_type = value_states_25_pad_type_0, strides = value_states_25_strides_0, weight = layers_4_self_attn_v_proj_weight_cast_fp16, x = var_2214_cast_fp16_0)[name = string("value_states_25_cast_fp16")];
            tensor<int32, [4]> concat_48x = const()[name = string("concat_48x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_41_cast_fp16 = reshape(shape = concat_48x, x = query_states_25_cast_fp16)[name = string("x_41_cast_fp16")];
            tensor<int32, [4]> concat_49x = const()[name = string("concat_49x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_2271_cast_fp16 = reshape(shape = concat_49x, x = key_states_41_cast_fp16)[name = string("op_2271_cast_fp16")];
            tensor<int32, [4]> concat_50x = const()[name = string("concat_50x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_2278_cast_fp16 = reshape(shape = concat_50x, x = value_states_25_cast_fp16)[name = string("op_2278_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_2282_cast_fp16 = mul(x = x_41_cast_fp16, y = var_859_cast_fp16)[name = string("op_2282_cast_fp16")];
            tensor<int32, [2]> var_2283_split_sizes_0 = const()[name = string("op_2283_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2283_axis_0 = const()[name = string("op_2283_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_2283_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_2283_cast_fp16_1 = split(axis = var_2283_axis_0, split_sizes = var_2283_split_sizes_0, x = x_41_cast_fp16)[name = string("op_2283_cast_fp16")];
            fp16 const_44_promoted_to_fp16 = const()[name = string("const_44_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_2285_cast_fp16 = mul(x = var_2283_cast_fp16_1, y = const_44_promoted_to_fp16)[name = string("op_2285_cast_fp16")];
            int32 var_2287 = const()[name = string("op_2287"), val = int32(-2)];
            bool var_2288_interleave_0 = const()[name = string("op_2288_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_2288_cast_fp16 = concat(axis = var_2287, interleave = var_2288_interleave_0, values = (var_2285_cast_fp16, var_2283_cast_fp16_0))[name = string("op_2288_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_2289_cast_fp16 = mul(x = var_2288_cast_fp16, y = var_866_cast_fp16)[name = string("op_2289_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_27_cast_fp16 = add(x = var_2282_cast_fp16, y = var_2289_cast_fp16)[name = string("query_states_27_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_2295_cast_fp16 = mul(x = var_2271_cast_fp16, y = var_859_cast_fp16)[name = string("op_2295_cast_fp16")];
            tensor<int32, [2]> var_2296_split_sizes_0 = const()[name = string("op_2296_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2296_axis_0 = const()[name = string("op_2296_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_2296_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_2296_cast_fp16_1 = split(axis = var_2296_axis_0, split_sizes = var_2296_split_sizes_0, x = var_2271_cast_fp16)[name = string("op_2296_cast_fp16")];
            fp16 const_45_promoted_to_fp16 = const()[name = string("const_45_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_2298_cast_fp16 = mul(x = var_2296_cast_fp16_1, y = const_45_promoted_to_fp16)[name = string("op_2298_cast_fp16")];
            int32 var_2300 = const()[name = string("op_2300"), val = int32(-2)];
            bool var_2301_interleave_0 = const()[name = string("op_2301_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_2301_cast_fp16 = concat(axis = var_2300, interleave = var_2301_interleave_0, values = (var_2298_cast_fp16, var_2296_cast_fp16_0))[name = string("op_2301_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_2302_cast_fp16 = mul(x = var_2301_cast_fp16, y = var_866_cast_fp16)[name = string("op_2302_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_45_cast_fp16 = add(x = var_2295_cast_fp16, y = var_2302_cast_fp16)[name = string("key_states_45_cast_fp16")];
            tensor<int32, [1]> expand_dims_48 = const()[name = string("expand_dims_48"), val = tensor<int32, [1]>([4])];
            tensor<int32, [1]> expand_dims_49 = const()[name = string("expand_dims_49"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_51 = const()[name = string("expand_dims_51"), val = tensor<int32, [1]>([0])];
            int32 concat_53_axis_0 = const()[name = string("concat_53_axis_0"), val = int32(0)];
            bool concat_53_interleave_0 = const()[name = string("concat_53_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_53 = concat(axis = concat_53_axis_0, interleave = concat_53_interleave_0, values = (expand_dims_48, expand_dims_49, position_id, expand_dims_51))[name = string("concat_53")];
            tensor<int32, [1]> expand_dims_52 = const()[name = string("expand_dims_52"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> concat_54_values1_0 = const()[name = string("concat_54_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_54_values3_0 = const()[name = string("concat_54_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_54_axis_0 = const()[name = string("concat_54_axis_0"), val = int32(0)];
            bool concat_54_interleave_0 = const()[name = string("concat_54_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_54 = concat(axis = concat_54_axis_0, interleave = concat_54_interleave_0, values = (expand_dims_52, concat_54_values1_0, cache_position_end, concat_54_values3_0))[name = string("concat_54")];
            tensor<int32, [4]> key_states_47_perm_0 = const()[name = string("key_states_47_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_5_stride_0 = const()[name = string("key_cache_internal_tensor_assign_5_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_5_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_5_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_5_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_5_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_5_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_5_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_47_cast_fp16 = transpose(perm = key_states_47_perm_0, x = key_states_45_cast_fp16)[name = string("transpose_419")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_5_cast_fp16 = slice_update(begin = concat_53, begin_mask = key_cache_internal_tensor_assign_5_begin_mask_0, end = concat_54, end_mask = key_cache_internal_tensor_assign_5_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_5_squeeze_mask_0, stride = key_cache_internal_tensor_assign_5_stride_0, update = key_states_47_cast_fp16, x = coreml_update_state_230)[name = string("key_cache_internal_tensor_assign_5_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_5_cast_fp16, input = key_cache)[name = string("coreml_update_state_232_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_232 = read_state(input = key_cache)[name = string("coreml_update_state_232")];
            tensor<int32, [4]> value_states_27_perm_0 = const()[name = string("value_states_27_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_5_stride_0 = const()[name = string("value_cache_internal_tensor_assign_5_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_5_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_5_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_5_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_5_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_5_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_5_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_27_cast_fp16 = transpose(perm = value_states_27_perm_0, x = var_2278_cast_fp16)[name = string("transpose_418")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_5_cast_fp16 = slice_update(begin = concat_53, begin_mask = value_cache_internal_tensor_assign_5_begin_mask_0, end = concat_54, end_mask = value_cache_internal_tensor_assign_5_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_5_squeeze_mask_0, stride = value_cache_internal_tensor_assign_5_stride_0, update = value_states_27_cast_fp16, x = coreml_update_state_231)[name = string("value_cache_internal_tensor_assign_5_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_5_cast_fp16, input = value_cache)[name = string("coreml_update_state_233_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_233 = read_state(input = value_cache)[name = string("coreml_update_state_233")];
            tensor<int32, [4]> var_2372_begin_0 = const()[name = string("op_2372_begin_0"), val = tensor<int32, [4]>([4, 0, 0, 0])];
            tensor<int32, [4]> var_2372_end_0 = const()[name = string("op_2372_end_0"), val = tensor<int32, [4]>([5, 2, 2048, 128])];
            tensor<bool, [4]> var_2372_end_mask_0 = const()[name = string("op_2372_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2372_cast_fp16 = slice_by_index(begin = var_2372_begin_0, end = var_2372_end_0, end_mask = var_2372_end_mask_0, x = coreml_update_state_232)[name = string("op_2372_cast_fp16")];
            tensor<int32, [2]> tile_8 = const()[name = string("tile_8"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2375_axis_0 = const()[name = string("op_2375_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2375_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2375_cast_fp16_1 = split(axis = var_2375_axis_0, split_sizes = tile_8, x = var_2372_cast_fp16)[name = string("op_2375_cast_fp16")];
            tensor<int32, [4]> var_2382_begin_0 = const()[name = string("op_2382_begin_0"), val = tensor<int32, [4]>([4, 0, 0, 0])];
            tensor<int32, [4]> var_2382_end_0 = const()[name = string("op_2382_end_0"), val = tensor<int32, [4]>([5, 2, 2048, 128])];
            tensor<bool, [4]> var_2382_end_mask_0 = const()[name = string("op_2382_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2382_cast_fp16 = slice_by_index(begin = var_2382_begin_0, end = var_2382_end_0, end_mask = var_2382_end_mask_0, x = coreml_update_state_233)[name = string("op_2382_cast_fp16")];
            tensor<int32, [2]> tile_9 = const()[name = string("tile_9"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2385_axis_0 = const()[name = string("op_2385_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2385_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2385_cast_fp16_1 = split(axis = var_2385_axis_0, split_sizes = tile_9, x = var_2382_cast_fp16)[name = string("op_2385_cast_fp16")];
            tensor<int32, [2]> var_2388_split_sizes_0 = const()[name = string("op_2388_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_2388_axis_0 = const()[name = string("op_2388_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_2388_0, tensor<fp16, [1, 8, 128, 32]> var_2388_1 = split(axis = var_2388_axis_0, split_sizes = var_2388_split_sizes_0, x = query_states_27_cast_fp16)[name = string("op_2388")];
            bool attn_weights_65_transpose_x_0 = const()[name = string("attn_weights_65_transpose_x_0"), val = bool(false)];
            bool attn_weights_65_transpose_y_0 = const()[name = string("attn_weights_65_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_65_cast_fp16 = matmul(transpose_x = attn_weights_65_transpose_x_0, transpose_y = attn_weights_65_transpose_y_0, x = var_2375_cast_fp16_0, y = var_2388_0)[name = string("attn_weights_65_cast_fp16")];
            fp16 var_2391_to_fp16 = const()[name = string("op_2391_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_67_cast_fp16 = mul(x = attn_weights_65_cast_fp16, y = var_2391_to_fp16)[name = string("attn_weights_67_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_69_cast_fp16 = add(x = attn_weights_67_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_69_cast_fp16")];
            int32 var_2395 = const()[name = string("op_2395"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_71_cast_fp16 = softmax(axis = var_2395, x = attn_weights_69_cast_fp16)[name = string("attn_weights_71_cast_fp16")];
            bool var_2401_transpose_x_1 = const()[name = string("op_2401_transpose_x_1"), val = bool(true)];
            bool var_2401_transpose_y_1 = const()[name = string("op_2401_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_2401_cast_fp16 = matmul(transpose_x = var_2401_transpose_x_1, transpose_y = var_2401_transpose_y_1, x = attn_weights_71_cast_fp16, y = var_2385_cast_fp16_0)[name = string("op_2401_cast_fp16")];
            bool attn_weights_73_transpose_x_0 = const()[name = string("attn_weights_73_transpose_x_0"), val = bool(false)];
            bool attn_weights_73_transpose_y_0 = const()[name = string("attn_weights_73_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_73_cast_fp16 = matmul(transpose_x = attn_weights_73_transpose_x_0, transpose_y = attn_weights_73_transpose_y_0, x = var_2375_cast_fp16_1, y = var_2388_1)[name = string("attn_weights_73_cast_fp16")];
            fp16 var_2403_to_fp16 = const()[name = string("op_2403_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_75_cast_fp16 = mul(x = attn_weights_73_cast_fp16, y = var_2403_to_fp16)[name = string("attn_weights_75_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_77_cast_fp16 = add(x = attn_weights_75_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_77_cast_fp16")];
            int32 var_2407 = const()[name = string("op_2407"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_79_cast_fp16 = softmax(axis = var_2407, x = attn_weights_77_cast_fp16)[name = string("attn_weights_79_cast_fp16")];
            bool attn_output_33_transpose_x_1 = const()[name = string("attn_output_33_transpose_x_1"), val = bool(true)];
            bool attn_output_33_transpose_y_1 = const()[name = string("attn_output_33_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_33_cast_fp16 = matmul(transpose_x = attn_output_33_transpose_x_1, transpose_y = attn_output_33_transpose_y_1, x = attn_weights_79_cast_fp16, y = var_2385_cast_fp16_1)[name = string("attn_output_33_cast_fp16")];
            int32 var_2415 = const()[name = string("op_2415"), val = int32(1)];
            bool attn_output_35_interleave_0 = const()[name = string("attn_output_35_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_35_cast_fp16 = concat(axis = var_2415, interleave = attn_output_35_interleave_0, values = (var_2401_cast_fp16, attn_output_33_cast_fp16))[name = string("attn_output_35_cast_fp16")];
            tensor<int32, [4]> var_2419_perm_0 = const()[name = string("op_2419_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_59x = const()[name = string("concat_59x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_2419_cast_fp16 = transpose(perm = var_2419_perm_0, x = attn_output_35_cast_fp16)[name = string("transpose_417")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_39_cast_fp16 = reshape(shape = concat_59x, x = var_2419_cast_fp16)[name = string("attn_output_39_cast_fp16")];
            tensor<int32, [2]> hidden_states_43_strides_0 = const()[name = string("hidden_states_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_43_pad_type_0 = const()[name = string("hidden_states_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_43_pad_0 = const()[name = string("hidden_states_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_43_dilations_0 = const()[name = string("hidden_states_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_43_groups_0 = const()[name = string("hidden_states_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_43_cast_fp16 = conv(dilations = hidden_states_43_dilations_0, groups = hidden_states_43_groups_0, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = hidden_states_43_strides_0, weight = layers_4_self_attn_o_proj_weight_cast_fp16, x = attn_output_39_cast_fp16)[name = string("hidden_states_43_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_45_cast_fp16 = add(x = hidden_states_39_cast_fp16, y = hidden_states_43_cast_fp16)[name = string("hidden_states_45_cast_fp16")];
            fp16 const_50_promoted_to_fp16 = const()[name = string("const_50_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_2452_cast_fp16 = mul(x = hidden_states_45_cast_fp16, y = const_50_promoted_to_fp16)[name = string("op_2452_cast_fp16")];
            int32 var_2450 = const()[name = string("op_2450"), val = int32(1)];
            bool doubled_37_interleave_0 = const()[name = string("doubled_37_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_37_cast_fp16 = concat(axis = var_2450, interleave = doubled_37_interleave_0, values = (hidden_states_45_cast_fp16, var_2452_cast_fp16))[name = string("doubled_37_cast_fp16")];
            tensor<int32, [1]> out_19_axes_0 = const()[name = string("out_19_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_19_gamma_0_to_fp16 = const()[name = string("out_19_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1274748864)))];
            fp16 var_2462_to_fp16 = const()[name = string("op_2462_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_19_cast_fp16 = layer_norm(axes = out_19_axes_0, epsilon = var_2462_to_fp16, gamma = out_19_gamma_0_to_fp16, x = doubled_37_cast_fp16)[name = string("out_19_cast_fp16")];
            tensor<int32, [2]> var_2473_split_sizes_0 = const()[name = string("op_2473_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2473_axis_0 = const()[name = string("op_2473_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_2473_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_2473_cast_fp16_1 = split(axis = var_2473_axis_0, split_sizes = var_2473_split_sizes_0, x = out_19_cast_fp16)[name = string("op_2473_cast_fp16")];
            tensor<int32, [2]> input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_9_cast_fp16 = conv(dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = layers_4_mlp_gate_proj_weight_cast_fp16, x = var_2473_cast_fp16_0)[name = string("input_9_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_2490_cast_fp16 = silu(x = input_9_cast_fp16)[name = string("op_2490_cast_fp16")];
            tensor<int32, [2]> var_2496_strides_0 = const()[name = string("op_2496_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_2496_pad_type_0 = const()[name = string("op_2496_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_2496_pad_0 = const()[name = string("op_2496_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_2496_dilations_0 = const()[name = string("op_2496_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2496_groups_0 = const()[name = string("op_2496_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_2496_cast_fp16 = conv(dilations = var_2496_dilations_0, groups = var_2496_groups_0, pad = var_2496_pad_0, pad_type = var_2496_pad_type_0, strides = var_2496_strides_0, weight = layers_4_mlp_up_proj_weight_cast_fp16, x = var_2473_cast_fp16_0)[name = string("op_2496_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_49_cast_fp16 = mul(x = var_2490_cast_fp16, y = var_2496_cast_fp16)[name = string("x_49_cast_fp16")];
            tensor<int32, [2]> hidden_states_47_strides_0 = const()[name = string("hidden_states_47_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_47_pad_type_0 = const()[name = string("hidden_states_47_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_47_pad_0 = const()[name = string("hidden_states_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_47_dilations_0 = const()[name = string("hidden_states_47_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_47_groups_0 = const()[name = string("hidden_states_47_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_47_cast_fp16 = conv(dilations = hidden_states_47_dilations_0, groups = hidden_states_47_groups_0, pad = hidden_states_47_pad_0, pad_type = hidden_states_47_pad_type_0, strides = hidden_states_47_strides_0, weight = layers_4_mlp_down_proj_weight_cast_fp16, x = x_49_cast_fp16)[name = string("hidden_states_47_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_49_cast_fp16 = add(x = hidden_states_45_cast_fp16, y = hidden_states_47_cast_fp16)[name = string("hidden_states_49_cast_fp16")];
            fp16 const_52_promoted_to_fp16 = const()[name = string("const_52_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_2514_cast_fp16 = mul(x = hidden_states_49_cast_fp16, y = const_52_promoted_to_fp16)[name = string("op_2514_cast_fp16")];
            int32 var_2512 = const()[name = string("op_2512"), val = int32(1)];
            bool doubled_41_interleave_0 = const()[name = string("doubled_41_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_41_cast_fp16 = concat(axis = var_2512, interleave = doubled_41_interleave_0, values = (hidden_states_49_cast_fp16, var_2514_cast_fp16))[name = string("doubled_41_cast_fp16")];
            tensor<int32, [1]> out_21_axes_0 = const()[name = string("out_21_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_21_gamma_0_to_fp16 = const()[name = string("out_21_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1274757120)))];
            fp16 var_2524_to_fp16 = const()[name = string("op_2524_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_21_cast_fp16 = layer_norm(axes = out_21_axes_0, epsilon = var_2524_to_fp16, gamma = out_21_gamma_0_to_fp16, x = doubled_41_cast_fp16)[name = string("out_21_cast_fp16")];
            tensor<int32, [2]> var_2535_split_sizes_0 = const()[name = string("op_2535_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2535_axis_0 = const()[name = string("op_2535_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_2535_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_2535_cast_fp16_1 = split(axis = var_2535_axis_0, split_sizes = var_2535_split_sizes_0, x = out_21_cast_fp16)[name = string("op_2535_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_5_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_5_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1274765376)))];
            tensor<int32, [2]> query_states_31_strides_0 = const()[name = string("query_states_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_31_pad_type_0 = const()[name = string("query_states_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_31_pad_0 = const()[name = string("query_states_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_31_dilations_0 = const()[name = string("query_states_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_31_groups_0 = const()[name = string("query_states_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_31_cast_fp16 = conv(dilations = query_states_31_dilations_0, groups = query_states_31_groups_0, pad = query_states_31_pad_0, pad_type = query_states_31_pad_type_0, strides = query_states_31_strides_0, weight = layers_5_self_attn_q_proj_weight_to_fp16, x = var_2535_cast_fp16_0)[name = string("query_states_31_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_5_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_5_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1283154048)))];
            tensor<int32, [2]> key_states_51_strides_0 = const()[name = string("key_states_51_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_51_pad_type_0 = const()[name = string("key_states_51_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_51_pad_0 = const()[name = string("key_states_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_51_dilations_0 = const()[name = string("key_states_51_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_51_groups_0 = const()[name = string("key_states_51_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_51_cast_fp16 = conv(dilations = key_states_51_dilations_0, groups = key_states_51_groups_0, pad = key_states_51_pad_0, pad_type = key_states_51_pad_type_0, strides = key_states_51_strides_0, weight = layers_5_self_attn_k_proj_weight_to_fp16, x = var_2535_cast_fp16_0)[name = string("key_states_51_cast_fp16")];
            tensor<int32, [2]> value_states_31_strides_0 = const()[name = string("value_states_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_31_pad_type_0 = const()[name = string("value_states_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_31_pad_0 = const()[name = string("value_states_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_31_dilations_0 = const()[name = string("value_states_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_31_groups_0 = const()[name = string("value_states_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_31_cast_fp16 = conv(dilations = value_states_31_dilations_0, groups = value_states_31_groups_0, pad = value_states_31_pad_0, pad_type = value_states_31_pad_type_0, strides = value_states_31_strides_0, weight = layers_5_self_attn_v_proj_weight_cast_fp16, x = var_2535_cast_fp16_0)[name = string("value_states_31_cast_fp16")];
            tensor<int32, [4]> concat_60x = const()[name = string("concat_60x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_51_cast_fp16 = reshape(shape = concat_60x, x = query_states_31_cast_fp16)[name = string("x_51_cast_fp16")];
            tensor<int32, [4]> concat_61x = const()[name = string("concat_61x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_2592_cast_fp16 = reshape(shape = concat_61x, x = key_states_51_cast_fp16)[name = string("op_2592_cast_fp16")];
            tensor<int32, [4]> concat_62x = const()[name = string("concat_62x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_2599_cast_fp16 = reshape(shape = concat_62x, x = value_states_31_cast_fp16)[name = string("op_2599_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_2603_cast_fp16 = mul(x = x_51_cast_fp16, y = var_859_cast_fp16)[name = string("op_2603_cast_fp16")];
            tensor<int32, [2]> var_2604_split_sizes_0 = const()[name = string("op_2604_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2604_axis_0 = const()[name = string("op_2604_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_2604_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_2604_cast_fp16_1 = split(axis = var_2604_axis_0, split_sizes = var_2604_split_sizes_0, x = x_51_cast_fp16)[name = string("op_2604_cast_fp16")];
            fp16 const_54_promoted_to_fp16 = const()[name = string("const_54_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_2606_cast_fp16 = mul(x = var_2604_cast_fp16_1, y = const_54_promoted_to_fp16)[name = string("op_2606_cast_fp16")];
            int32 var_2608 = const()[name = string("op_2608"), val = int32(-2)];
            bool var_2609_interleave_0 = const()[name = string("op_2609_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_2609_cast_fp16 = concat(axis = var_2608, interleave = var_2609_interleave_0, values = (var_2606_cast_fp16, var_2604_cast_fp16_0))[name = string("op_2609_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_2610_cast_fp16 = mul(x = var_2609_cast_fp16, y = var_866_cast_fp16)[name = string("op_2610_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_33_cast_fp16 = add(x = var_2603_cast_fp16, y = var_2610_cast_fp16)[name = string("query_states_33_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_2616_cast_fp16 = mul(x = var_2592_cast_fp16, y = var_859_cast_fp16)[name = string("op_2616_cast_fp16")];
            tensor<int32, [2]> var_2617_split_sizes_0 = const()[name = string("op_2617_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2617_axis_0 = const()[name = string("op_2617_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_2617_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_2617_cast_fp16_1 = split(axis = var_2617_axis_0, split_sizes = var_2617_split_sizes_0, x = var_2592_cast_fp16)[name = string("op_2617_cast_fp16")];
            fp16 const_55_promoted_to_fp16 = const()[name = string("const_55_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_2619_cast_fp16 = mul(x = var_2617_cast_fp16_1, y = const_55_promoted_to_fp16)[name = string("op_2619_cast_fp16")];
            int32 var_2621 = const()[name = string("op_2621"), val = int32(-2)];
            bool var_2622_interleave_0 = const()[name = string("op_2622_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_2622_cast_fp16 = concat(axis = var_2621, interleave = var_2622_interleave_0, values = (var_2619_cast_fp16, var_2617_cast_fp16_0))[name = string("op_2622_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_2623_cast_fp16 = mul(x = var_2622_cast_fp16, y = var_866_cast_fp16)[name = string("op_2623_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_55_cast_fp16 = add(x = var_2616_cast_fp16, y = var_2623_cast_fp16)[name = string("key_states_55_cast_fp16")];
            tensor<int32, [1]> expand_dims_60 = const()[name = string("expand_dims_60"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> expand_dims_61 = const()[name = string("expand_dims_61"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_63 = const()[name = string("expand_dims_63"), val = tensor<int32, [1]>([0])];
            int32 concat_65_axis_0 = const()[name = string("concat_65_axis_0"), val = int32(0)];
            bool concat_65_interleave_0 = const()[name = string("concat_65_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_65 = concat(axis = concat_65_axis_0, interleave = concat_65_interleave_0, values = (expand_dims_60, expand_dims_61, position_id, expand_dims_63))[name = string("concat_65")];
            tensor<int32, [1]> expand_dims_64 = const()[name = string("expand_dims_64"), val = tensor<int32, [1]>([6])];
            tensor<int32, [1]> concat_66_values1_0 = const()[name = string("concat_66_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_66_values3_0 = const()[name = string("concat_66_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_66_axis_0 = const()[name = string("concat_66_axis_0"), val = int32(0)];
            bool concat_66_interleave_0 = const()[name = string("concat_66_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_66 = concat(axis = concat_66_axis_0, interleave = concat_66_interleave_0, values = (expand_dims_64, concat_66_values1_0, cache_position_end, concat_66_values3_0))[name = string("concat_66")];
            tensor<int32, [4]> key_states_57_perm_0 = const()[name = string("key_states_57_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_6_stride_0 = const()[name = string("key_cache_internal_tensor_assign_6_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_6_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_6_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_6_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_6_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_6_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_6_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_57_cast_fp16 = transpose(perm = key_states_57_perm_0, x = key_states_55_cast_fp16)[name = string("transpose_416")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_6_cast_fp16 = slice_update(begin = concat_65, begin_mask = key_cache_internal_tensor_assign_6_begin_mask_0, end = concat_66, end_mask = key_cache_internal_tensor_assign_6_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_6_squeeze_mask_0, stride = key_cache_internal_tensor_assign_6_stride_0, update = key_states_57_cast_fp16, x = coreml_update_state_232)[name = string("key_cache_internal_tensor_assign_6_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_6_cast_fp16, input = key_cache)[name = string("coreml_update_state_234_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_234 = read_state(input = key_cache)[name = string("coreml_update_state_234")];
            tensor<int32, [4]> value_states_33_perm_0 = const()[name = string("value_states_33_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_6_stride_0 = const()[name = string("value_cache_internal_tensor_assign_6_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_6_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_6_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_6_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_6_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_6_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_6_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_33_cast_fp16 = transpose(perm = value_states_33_perm_0, x = var_2599_cast_fp16)[name = string("transpose_415")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_6_cast_fp16 = slice_update(begin = concat_65, begin_mask = value_cache_internal_tensor_assign_6_begin_mask_0, end = concat_66, end_mask = value_cache_internal_tensor_assign_6_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_6_squeeze_mask_0, stride = value_cache_internal_tensor_assign_6_stride_0, update = value_states_33_cast_fp16, x = coreml_update_state_233)[name = string("value_cache_internal_tensor_assign_6_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_6_cast_fp16, input = value_cache)[name = string("coreml_update_state_235_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_235 = read_state(input = value_cache)[name = string("coreml_update_state_235")];
            tensor<int32, [4]> var_2693_begin_0 = const()[name = string("op_2693_begin_0"), val = tensor<int32, [4]>([5, 0, 0, 0])];
            tensor<int32, [4]> var_2693_end_0 = const()[name = string("op_2693_end_0"), val = tensor<int32, [4]>([6, 2, 2048, 128])];
            tensor<bool, [4]> var_2693_end_mask_0 = const()[name = string("op_2693_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2693_cast_fp16 = slice_by_index(begin = var_2693_begin_0, end = var_2693_end_0, end_mask = var_2693_end_mask_0, x = coreml_update_state_234)[name = string("op_2693_cast_fp16")];
            tensor<int32, [2]> tile_10 = const()[name = string("tile_10"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2696_axis_0 = const()[name = string("op_2696_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2696_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2696_cast_fp16_1 = split(axis = var_2696_axis_0, split_sizes = tile_10, x = var_2693_cast_fp16)[name = string("op_2696_cast_fp16")];
            tensor<int32, [4]> var_2703_begin_0 = const()[name = string("op_2703_begin_0"), val = tensor<int32, [4]>([5, 0, 0, 0])];
            tensor<int32, [4]> var_2703_end_0 = const()[name = string("op_2703_end_0"), val = tensor<int32, [4]>([6, 2, 2048, 128])];
            tensor<bool, [4]> var_2703_end_mask_0 = const()[name = string("op_2703_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2703_cast_fp16 = slice_by_index(begin = var_2703_begin_0, end = var_2703_end_0, end_mask = var_2703_end_mask_0, x = coreml_update_state_235)[name = string("op_2703_cast_fp16")];
            tensor<int32, [2]> tile_11 = const()[name = string("tile_11"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2706_axis_0 = const()[name = string("op_2706_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2706_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2706_cast_fp16_1 = split(axis = var_2706_axis_0, split_sizes = tile_11, x = var_2703_cast_fp16)[name = string("op_2706_cast_fp16")];
            tensor<int32, [2]> var_2709_split_sizes_0 = const()[name = string("op_2709_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_2709_axis_0 = const()[name = string("op_2709_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_2709_0, tensor<fp16, [1, 8, 128, 32]> var_2709_1 = split(axis = var_2709_axis_0, split_sizes = var_2709_split_sizes_0, x = query_states_33_cast_fp16)[name = string("op_2709")];
            bool attn_weights_81_transpose_x_0 = const()[name = string("attn_weights_81_transpose_x_0"), val = bool(false)];
            bool attn_weights_81_transpose_y_0 = const()[name = string("attn_weights_81_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_81_cast_fp16 = matmul(transpose_x = attn_weights_81_transpose_x_0, transpose_y = attn_weights_81_transpose_y_0, x = var_2696_cast_fp16_0, y = var_2709_0)[name = string("attn_weights_81_cast_fp16")];
            fp16 var_2712_to_fp16 = const()[name = string("op_2712_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_83_cast_fp16 = mul(x = attn_weights_81_cast_fp16, y = var_2712_to_fp16)[name = string("attn_weights_83_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_85_cast_fp16 = add(x = attn_weights_83_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_85_cast_fp16")];
            int32 var_2716 = const()[name = string("op_2716"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_87_cast_fp16 = softmax(axis = var_2716, x = attn_weights_85_cast_fp16)[name = string("attn_weights_87_cast_fp16")];
            bool var_2722_transpose_x_1 = const()[name = string("op_2722_transpose_x_1"), val = bool(true)];
            bool var_2722_transpose_y_1 = const()[name = string("op_2722_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_2722_cast_fp16 = matmul(transpose_x = var_2722_transpose_x_1, transpose_y = var_2722_transpose_y_1, x = attn_weights_87_cast_fp16, y = var_2706_cast_fp16_0)[name = string("op_2722_cast_fp16")];
            bool attn_weights_89_transpose_x_0 = const()[name = string("attn_weights_89_transpose_x_0"), val = bool(false)];
            bool attn_weights_89_transpose_y_0 = const()[name = string("attn_weights_89_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_89_cast_fp16 = matmul(transpose_x = attn_weights_89_transpose_x_0, transpose_y = attn_weights_89_transpose_y_0, x = var_2696_cast_fp16_1, y = var_2709_1)[name = string("attn_weights_89_cast_fp16")];
            fp16 var_2724_to_fp16 = const()[name = string("op_2724_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_91_cast_fp16 = mul(x = attn_weights_89_cast_fp16, y = var_2724_to_fp16)[name = string("attn_weights_91_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_93_cast_fp16 = add(x = attn_weights_91_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_93_cast_fp16")];
            int32 var_2728 = const()[name = string("op_2728"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_95_cast_fp16 = softmax(axis = var_2728, x = attn_weights_93_cast_fp16)[name = string("attn_weights_95_cast_fp16")];
            bool attn_output_41_transpose_x_1 = const()[name = string("attn_output_41_transpose_x_1"), val = bool(true)];
            bool attn_output_41_transpose_y_1 = const()[name = string("attn_output_41_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_41_cast_fp16 = matmul(transpose_x = attn_output_41_transpose_x_1, transpose_y = attn_output_41_transpose_y_1, x = attn_weights_95_cast_fp16, y = var_2706_cast_fp16_1)[name = string("attn_output_41_cast_fp16")];
            int32 var_2736 = const()[name = string("op_2736"), val = int32(1)];
            bool attn_output_43_interleave_0 = const()[name = string("attn_output_43_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_43_cast_fp16 = concat(axis = var_2736, interleave = attn_output_43_interleave_0, values = (var_2722_cast_fp16, attn_output_41_cast_fp16))[name = string("attn_output_43_cast_fp16")];
            tensor<int32, [4]> var_2740_perm_0 = const()[name = string("op_2740_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_71x = const()[name = string("concat_71x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_2740_cast_fp16 = transpose(perm = var_2740_perm_0, x = attn_output_43_cast_fp16)[name = string("transpose_414")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_47_cast_fp16 = reshape(shape = concat_71x, x = var_2740_cast_fp16)[name = string("attn_output_47_cast_fp16")];
            tensor<int32, [2]> hidden_states_53_strides_0 = const()[name = string("hidden_states_53_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_53_pad_type_0 = const()[name = string("hidden_states_53_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_53_pad_0 = const()[name = string("hidden_states_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_53_dilations_0 = const()[name = string("hidden_states_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_53_groups_0 = const()[name = string("hidden_states_53_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_53_cast_fp16 = conv(dilations = hidden_states_53_dilations_0, groups = hidden_states_53_groups_0, pad = hidden_states_53_pad_0, pad_type = hidden_states_53_pad_type_0, strides = hidden_states_53_strides_0, weight = layers_5_self_attn_o_proj_weight_cast_fp16, x = attn_output_47_cast_fp16)[name = string("hidden_states_53_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_55_cast_fp16 = add(x = hidden_states_49_cast_fp16, y = hidden_states_53_cast_fp16)[name = string("hidden_states_55_cast_fp16")];
            fp16 const_60_promoted_to_fp16 = const()[name = string("const_60_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_2773_cast_fp16 = mul(x = hidden_states_55_cast_fp16, y = const_60_promoted_to_fp16)[name = string("op_2773_cast_fp16")];
            int32 var_2771 = const()[name = string("op_2771"), val = int32(1)];
            bool doubled_45_interleave_0 = const()[name = string("doubled_45_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_45_cast_fp16 = concat(axis = var_2771, interleave = doubled_45_interleave_0, values = (hidden_states_55_cast_fp16, var_2773_cast_fp16))[name = string("doubled_45_cast_fp16")];
            tensor<int32, [1]> out_23_axes_0 = const()[name = string("out_23_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_23_gamma_0_to_fp16 = const()[name = string("out_23_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1284202688)))];
            fp16 var_2783_to_fp16 = const()[name = string("op_2783_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_23_cast_fp16 = layer_norm(axes = out_23_axes_0, epsilon = var_2783_to_fp16, gamma = out_23_gamma_0_to_fp16, x = doubled_45_cast_fp16)[name = string("out_23_cast_fp16")];
            tensor<int32, [2]> var_2794_split_sizes_0 = const()[name = string("op_2794_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2794_axis_0 = const()[name = string("op_2794_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_2794_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_2794_cast_fp16_1 = split(axis = var_2794_axis_0, split_sizes = var_2794_split_sizes_0, x = out_23_cast_fp16)[name = string("op_2794_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_5_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_5_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1284210944)))];
            tensor<int32, [2]> input_11_strides_0 = const()[name = string("input_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_11_pad_type_0 = const()[name = string("input_11_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_11_dilations_0 = const()[name = string("input_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_11_groups_0 = const()[name = string("input_11_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_11_cast_fp16 = conv(dilations = input_11_dilations_0, groups = input_11_groups_0, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = input_11_strides_0, weight = layers_5_mlp_gate_proj_weight_to_fp16, x = var_2794_cast_fp16_0)[name = string("input_11_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_2811_cast_fp16 = silu(x = input_11_cast_fp16)[name = string("op_2811_cast_fp16")];
            tensor<int32, [2]> var_2817_strides_0 = const()[name = string("op_2817_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_2817_pad_type_0 = const()[name = string("op_2817_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_2817_pad_0 = const()[name = string("op_2817_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_2817_dilations_0 = const()[name = string("op_2817_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2817_groups_0 = const()[name = string("op_2817_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_2817_cast_fp16 = conv(dilations = var_2817_dilations_0, groups = var_2817_groups_0, pad = var_2817_pad_0, pad_type = var_2817_pad_type_0, strides = var_2817_strides_0, weight = layers_5_mlp_up_proj_weight_cast_fp16, x = var_2794_cast_fp16_0)[name = string("op_2817_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_59_cast_fp16 = mul(x = var_2811_cast_fp16, y = var_2817_cast_fp16)[name = string("x_59_cast_fp16")];
            tensor<int32, [2]> hidden_states_57_strides_0 = const()[name = string("hidden_states_57_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_57_pad_type_0 = const()[name = string("hidden_states_57_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_57_pad_0 = const()[name = string("hidden_states_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_57_dilations_0 = const()[name = string("hidden_states_57_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_57_groups_0 = const()[name = string("hidden_states_57_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_57_cast_fp16 = conv(dilations = hidden_states_57_dilations_0, groups = hidden_states_57_groups_0, pad = hidden_states_57_pad_0, pad_type = hidden_states_57_pad_type_0, strides = hidden_states_57_strides_0, weight = layers_5_mlp_down_proj_weight_cast_fp16, x = x_59_cast_fp16)[name = string("hidden_states_57_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_59_cast_fp16 = add(x = hidden_states_55_cast_fp16, y = hidden_states_57_cast_fp16)[name = string("hidden_states_59_cast_fp16")];
            fp16 const_62_promoted_to_fp16 = const()[name = string("const_62_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_2835_cast_fp16 = mul(x = hidden_states_59_cast_fp16, y = const_62_promoted_to_fp16)[name = string("op_2835_cast_fp16")];
            int32 var_2833 = const()[name = string("op_2833"), val = int32(1)];
            bool doubled_49_interleave_0 = const()[name = string("doubled_49_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_49_cast_fp16 = concat(axis = var_2833, interleave = doubled_49_interleave_0, values = (hidden_states_59_cast_fp16, var_2835_cast_fp16))[name = string("doubled_49_cast_fp16")];
            tensor<int32, [1]> out_25_axes_0 = const()[name = string("out_25_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_25_gamma_0_to_fp16 = const()[name = string("out_25_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1309376832)))];
            fp16 var_2845_to_fp16 = const()[name = string("op_2845_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_25_cast_fp16 = layer_norm(axes = out_25_axes_0, epsilon = var_2845_to_fp16, gamma = out_25_gamma_0_to_fp16, x = doubled_49_cast_fp16)[name = string("out_25_cast_fp16")];
            tensor<int32, [2]> var_2856_split_sizes_0 = const()[name = string("op_2856_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2856_axis_0 = const()[name = string("op_2856_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_2856_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_2856_cast_fp16_1 = split(axis = var_2856_axis_0, split_sizes = var_2856_split_sizes_0, x = out_25_cast_fp16)[name = string("op_2856_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_6_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_6_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1309385088)))];
            tensor<int32, [2]> query_states_37_strides_0 = const()[name = string("query_states_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_37_pad_type_0 = const()[name = string("query_states_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_37_pad_0 = const()[name = string("query_states_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_37_dilations_0 = const()[name = string("query_states_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_37_groups_0 = const()[name = string("query_states_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_37_cast_fp16 = conv(dilations = query_states_37_dilations_0, groups = query_states_37_groups_0, pad = query_states_37_pad_0, pad_type = query_states_37_pad_type_0, strides = query_states_37_strides_0, weight = layers_6_self_attn_q_proj_weight_to_fp16, x = var_2856_cast_fp16_0)[name = string("query_states_37_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_6_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_6_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1317773760)))];
            tensor<int32, [2]> key_states_61_strides_0 = const()[name = string("key_states_61_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_61_pad_type_0 = const()[name = string("key_states_61_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_61_pad_0 = const()[name = string("key_states_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_61_dilations_0 = const()[name = string("key_states_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_61_groups_0 = const()[name = string("key_states_61_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_61_cast_fp16 = conv(dilations = key_states_61_dilations_0, groups = key_states_61_groups_0, pad = key_states_61_pad_0, pad_type = key_states_61_pad_type_0, strides = key_states_61_strides_0, weight = layers_6_self_attn_k_proj_weight_to_fp16, x = var_2856_cast_fp16_0)[name = string("key_states_61_cast_fp16")];
            tensor<int32, [2]> value_states_37_strides_0 = const()[name = string("value_states_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_37_pad_type_0 = const()[name = string("value_states_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_37_pad_0 = const()[name = string("value_states_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_37_dilations_0 = const()[name = string("value_states_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_37_groups_0 = const()[name = string("value_states_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_37_cast_fp16 = conv(dilations = value_states_37_dilations_0, groups = value_states_37_groups_0, pad = value_states_37_pad_0, pad_type = value_states_37_pad_type_0, strides = value_states_37_strides_0, weight = layers_6_self_attn_v_proj_weight_cast_fp16, x = var_2856_cast_fp16_0)[name = string("value_states_37_cast_fp16")];
            tensor<int32, [4]> concat_72x = const()[name = string("concat_72x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_61_cast_fp16 = reshape(shape = concat_72x, x = query_states_37_cast_fp16)[name = string("x_61_cast_fp16")];
            tensor<int32, [4]> concat_73x = const()[name = string("concat_73x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_2913_cast_fp16 = reshape(shape = concat_73x, x = key_states_61_cast_fp16)[name = string("op_2913_cast_fp16")];
            tensor<int32, [4]> concat_74x = const()[name = string("concat_74x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_2920_cast_fp16 = reshape(shape = concat_74x, x = value_states_37_cast_fp16)[name = string("op_2920_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_2924_cast_fp16 = mul(x = x_61_cast_fp16, y = var_859_cast_fp16)[name = string("op_2924_cast_fp16")];
            tensor<int32, [2]> var_2925_split_sizes_0 = const()[name = string("op_2925_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2925_axis_0 = const()[name = string("op_2925_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_2925_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_2925_cast_fp16_1 = split(axis = var_2925_axis_0, split_sizes = var_2925_split_sizes_0, x = x_61_cast_fp16)[name = string("op_2925_cast_fp16")];
            fp16 const_64_promoted_to_fp16 = const()[name = string("const_64_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_2927_cast_fp16 = mul(x = var_2925_cast_fp16_1, y = const_64_promoted_to_fp16)[name = string("op_2927_cast_fp16")];
            int32 var_2929 = const()[name = string("op_2929"), val = int32(-2)];
            bool var_2930_interleave_0 = const()[name = string("op_2930_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_2930_cast_fp16 = concat(axis = var_2929, interleave = var_2930_interleave_0, values = (var_2927_cast_fp16, var_2925_cast_fp16_0))[name = string("op_2930_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_2931_cast_fp16 = mul(x = var_2930_cast_fp16, y = var_866_cast_fp16)[name = string("op_2931_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_39_cast_fp16 = add(x = var_2924_cast_fp16, y = var_2931_cast_fp16)[name = string("query_states_39_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_2937_cast_fp16 = mul(x = var_2913_cast_fp16, y = var_859_cast_fp16)[name = string("op_2937_cast_fp16")];
            tensor<int32, [2]> var_2938_split_sizes_0 = const()[name = string("op_2938_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2938_axis_0 = const()[name = string("op_2938_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_2938_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_2938_cast_fp16_1 = split(axis = var_2938_axis_0, split_sizes = var_2938_split_sizes_0, x = var_2913_cast_fp16)[name = string("op_2938_cast_fp16")];
            fp16 const_65_promoted_to_fp16 = const()[name = string("const_65_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_2940_cast_fp16 = mul(x = var_2938_cast_fp16_1, y = const_65_promoted_to_fp16)[name = string("op_2940_cast_fp16")];
            int32 var_2942 = const()[name = string("op_2942"), val = int32(-2)];
            bool var_2943_interleave_0 = const()[name = string("op_2943_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_2943_cast_fp16 = concat(axis = var_2942, interleave = var_2943_interleave_0, values = (var_2940_cast_fp16, var_2938_cast_fp16_0))[name = string("op_2943_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_2944_cast_fp16 = mul(x = var_2943_cast_fp16, y = var_866_cast_fp16)[name = string("op_2944_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_65_cast_fp16 = add(x = var_2937_cast_fp16, y = var_2944_cast_fp16)[name = string("key_states_65_cast_fp16")];
            tensor<int32, [1]> expand_dims_72 = const()[name = string("expand_dims_72"), val = tensor<int32, [1]>([6])];
            tensor<int32, [1]> expand_dims_73 = const()[name = string("expand_dims_73"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_75 = const()[name = string("expand_dims_75"), val = tensor<int32, [1]>([0])];
            int32 concat_77_axis_0 = const()[name = string("concat_77_axis_0"), val = int32(0)];
            bool concat_77_interleave_0 = const()[name = string("concat_77_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_77 = concat(axis = concat_77_axis_0, interleave = concat_77_interleave_0, values = (expand_dims_72, expand_dims_73, position_id, expand_dims_75))[name = string("concat_77")];
            tensor<int32, [1]> expand_dims_76 = const()[name = string("expand_dims_76"), val = tensor<int32, [1]>([7])];
            tensor<int32, [1]> concat_78_values1_0 = const()[name = string("concat_78_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_78_values3_0 = const()[name = string("concat_78_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_78_axis_0 = const()[name = string("concat_78_axis_0"), val = int32(0)];
            bool concat_78_interleave_0 = const()[name = string("concat_78_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_78 = concat(axis = concat_78_axis_0, interleave = concat_78_interleave_0, values = (expand_dims_76, concat_78_values1_0, cache_position_end, concat_78_values3_0))[name = string("concat_78")];
            tensor<int32, [4]> key_states_67_perm_0 = const()[name = string("key_states_67_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_7_stride_0 = const()[name = string("key_cache_internal_tensor_assign_7_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_7_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_7_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_7_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_7_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_7_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_7_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_67_cast_fp16 = transpose(perm = key_states_67_perm_0, x = key_states_65_cast_fp16)[name = string("transpose_413")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_7_cast_fp16 = slice_update(begin = concat_77, begin_mask = key_cache_internal_tensor_assign_7_begin_mask_0, end = concat_78, end_mask = key_cache_internal_tensor_assign_7_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_7_squeeze_mask_0, stride = key_cache_internal_tensor_assign_7_stride_0, update = key_states_67_cast_fp16, x = coreml_update_state_234)[name = string("key_cache_internal_tensor_assign_7_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_7_cast_fp16, input = key_cache)[name = string("coreml_update_state_236_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_236 = read_state(input = key_cache)[name = string("coreml_update_state_236")];
            tensor<int32, [4]> value_states_39_perm_0 = const()[name = string("value_states_39_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_7_stride_0 = const()[name = string("value_cache_internal_tensor_assign_7_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_7_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_7_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_7_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_7_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_7_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_7_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_39_cast_fp16 = transpose(perm = value_states_39_perm_0, x = var_2920_cast_fp16)[name = string("transpose_412")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_7_cast_fp16 = slice_update(begin = concat_77, begin_mask = value_cache_internal_tensor_assign_7_begin_mask_0, end = concat_78, end_mask = value_cache_internal_tensor_assign_7_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_7_squeeze_mask_0, stride = value_cache_internal_tensor_assign_7_stride_0, update = value_states_39_cast_fp16, x = coreml_update_state_235)[name = string("value_cache_internal_tensor_assign_7_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_7_cast_fp16, input = value_cache)[name = string("coreml_update_state_237_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_237 = read_state(input = value_cache)[name = string("coreml_update_state_237")];
            tensor<int32, [4]> var_3014_begin_0 = const()[name = string("op_3014_begin_0"), val = tensor<int32, [4]>([6, 0, 0, 0])];
            tensor<int32, [4]> var_3014_end_0 = const()[name = string("op_3014_end_0"), val = tensor<int32, [4]>([7, 2, 2048, 128])];
            tensor<bool, [4]> var_3014_end_mask_0 = const()[name = string("op_3014_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3014_cast_fp16 = slice_by_index(begin = var_3014_begin_0, end = var_3014_end_0, end_mask = var_3014_end_mask_0, x = coreml_update_state_236)[name = string("op_3014_cast_fp16")];
            tensor<int32, [2]> tile_12 = const()[name = string("tile_12"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3017_axis_0 = const()[name = string("op_3017_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3017_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3017_cast_fp16_1 = split(axis = var_3017_axis_0, split_sizes = tile_12, x = var_3014_cast_fp16)[name = string("op_3017_cast_fp16")];
            tensor<int32, [4]> var_3024_begin_0 = const()[name = string("op_3024_begin_0"), val = tensor<int32, [4]>([6, 0, 0, 0])];
            tensor<int32, [4]> var_3024_end_0 = const()[name = string("op_3024_end_0"), val = tensor<int32, [4]>([7, 2, 2048, 128])];
            tensor<bool, [4]> var_3024_end_mask_0 = const()[name = string("op_3024_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3024_cast_fp16 = slice_by_index(begin = var_3024_begin_0, end = var_3024_end_0, end_mask = var_3024_end_mask_0, x = coreml_update_state_237)[name = string("op_3024_cast_fp16")];
            tensor<int32, [2]> tile_13 = const()[name = string("tile_13"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3027_axis_0 = const()[name = string("op_3027_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3027_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3027_cast_fp16_1 = split(axis = var_3027_axis_0, split_sizes = tile_13, x = var_3024_cast_fp16)[name = string("op_3027_cast_fp16")];
            tensor<int32, [2]> var_3030_split_sizes_0 = const()[name = string("op_3030_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3030_axis_0 = const()[name = string("op_3030_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_3030_0, tensor<fp16, [1, 8, 128, 32]> var_3030_1 = split(axis = var_3030_axis_0, split_sizes = var_3030_split_sizes_0, x = query_states_39_cast_fp16)[name = string("op_3030")];
            bool attn_weights_97_transpose_x_0 = const()[name = string("attn_weights_97_transpose_x_0"), val = bool(false)];
            bool attn_weights_97_transpose_y_0 = const()[name = string("attn_weights_97_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_97_cast_fp16 = matmul(transpose_x = attn_weights_97_transpose_x_0, transpose_y = attn_weights_97_transpose_y_0, x = var_3017_cast_fp16_0, y = var_3030_0)[name = string("attn_weights_97_cast_fp16")];
            fp16 var_3033_to_fp16 = const()[name = string("op_3033_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_99_cast_fp16 = mul(x = attn_weights_97_cast_fp16, y = var_3033_to_fp16)[name = string("attn_weights_99_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_101_cast_fp16 = add(x = attn_weights_99_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_101_cast_fp16")];
            int32 var_3037 = const()[name = string("op_3037"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_103_cast_fp16 = softmax(axis = var_3037, x = attn_weights_101_cast_fp16)[name = string("attn_weights_103_cast_fp16")];
            bool var_3043_transpose_x_1 = const()[name = string("op_3043_transpose_x_1"), val = bool(true)];
            bool var_3043_transpose_y_1 = const()[name = string("op_3043_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_3043_cast_fp16 = matmul(transpose_x = var_3043_transpose_x_1, transpose_y = var_3043_transpose_y_1, x = attn_weights_103_cast_fp16, y = var_3027_cast_fp16_0)[name = string("op_3043_cast_fp16")];
            bool attn_weights_105_transpose_x_0 = const()[name = string("attn_weights_105_transpose_x_0"), val = bool(false)];
            bool attn_weights_105_transpose_y_0 = const()[name = string("attn_weights_105_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_105_cast_fp16 = matmul(transpose_x = attn_weights_105_transpose_x_0, transpose_y = attn_weights_105_transpose_y_0, x = var_3017_cast_fp16_1, y = var_3030_1)[name = string("attn_weights_105_cast_fp16")];
            fp16 var_3045_to_fp16 = const()[name = string("op_3045_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_107_cast_fp16 = mul(x = attn_weights_105_cast_fp16, y = var_3045_to_fp16)[name = string("attn_weights_107_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_109_cast_fp16 = add(x = attn_weights_107_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_109_cast_fp16")];
            int32 var_3049 = const()[name = string("op_3049"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_111_cast_fp16 = softmax(axis = var_3049, x = attn_weights_109_cast_fp16)[name = string("attn_weights_111_cast_fp16")];
            bool attn_output_49_transpose_x_1 = const()[name = string("attn_output_49_transpose_x_1"), val = bool(true)];
            bool attn_output_49_transpose_y_1 = const()[name = string("attn_output_49_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_49_cast_fp16 = matmul(transpose_x = attn_output_49_transpose_x_1, transpose_y = attn_output_49_transpose_y_1, x = attn_weights_111_cast_fp16, y = var_3027_cast_fp16_1)[name = string("attn_output_49_cast_fp16")];
            int32 var_3057 = const()[name = string("op_3057"), val = int32(1)];
            bool attn_output_51_interleave_0 = const()[name = string("attn_output_51_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_51_cast_fp16 = concat(axis = var_3057, interleave = attn_output_51_interleave_0, values = (var_3043_cast_fp16, attn_output_49_cast_fp16))[name = string("attn_output_51_cast_fp16")];
            tensor<int32, [4]> var_3061_perm_0 = const()[name = string("op_3061_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_83x = const()[name = string("concat_83x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_3061_cast_fp16 = transpose(perm = var_3061_perm_0, x = attn_output_51_cast_fp16)[name = string("transpose_411")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_55_cast_fp16 = reshape(shape = concat_83x, x = var_3061_cast_fp16)[name = string("attn_output_55_cast_fp16")];
            tensor<int32, [2]> hidden_states_63_strides_0 = const()[name = string("hidden_states_63_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_63_pad_type_0 = const()[name = string("hidden_states_63_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_63_pad_0 = const()[name = string("hidden_states_63_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_63_dilations_0 = const()[name = string("hidden_states_63_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_63_groups_0 = const()[name = string("hidden_states_63_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_63_cast_fp16 = conv(dilations = hidden_states_63_dilations_0, groups = hidden_states_63_groups_0, pad = hidden_states_63_pad_0, pad_type = hidden_states_63_pad_type_0, strides = hidden_states_63_strides_0, weight = layers_6_self_attn_o_proj_weight_cast_fp16, x = attn_output_55_cast_fp16)[name = string("hidden_states_63_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_65_cast_fp16 = add(x = hidden_states_59_cast_fp16, y = hidden_states_63_cast_fp16)[name = string("hidden_states_65_cast_fp16")];
            fp16 const_70_promoted_to_fp16 = const()[name = string("const_70_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_3094_cast_fp16 = mul(x = hidden_states_65_cast_fp16, y = const_70_promoted_to_fp16)[name = string("op_3094_cast_fp16")];
            int32 var_3092 = const()[name = string("op_3092"), val = int32(1)];
            bool doubled_53_interleave_0 = const()[name = string("doubled_53_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_53_cast_fp16 = concat(axis = var_3092, interleave = doubled_53_interleave_0, values = (hidden_states_65_cast_fp16, var_3094_cast_fp16))[name = string("doubled_53_cast_fp16")];
            tensor<int32, [1]> out_27_axes_0 = const()[name = string("out_27_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_27_gamma_0_to_fp16 = const()[name = string("out_27_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1318822400)))];
            fp16 var_3104_to_fp16 = const()[name = string("op_3104_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_27_cast_fp16 = layer_norm(axes = out_27_axes_0, epsilon = var_3104_to_fp16, gamma = out_27_gamma_0_to_fp16, x = doubled_53_cast_fp16)[name = string("out_27_cast_fp16")];
            tensor<int32, [2]> var_3115_split_sizes_0 = const()[name = string("op_3115_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3115_axis_0 = const()[name = string("op_3115_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_3115_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_3115_cast_fp16_1 = split(axis = var_3115_axis_0, split_sizes = var_3115_split_sizes_0, x = out_27_cast_fp16)[name = string("op_3115_cast_fp16")];
            tensor<int32, [2]> input_13_strides_0 = const()[name = string("input_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_13_pad_type_0 = const()[name = string("input_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_13_pad_0 = const()[name = string("input_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_13_dilations_0 = const()[name = string("input_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_13_groups_0 = const()[name = string("input_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_13_cast_fp16 = conv(dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = layers_6_mlp_gate_proj_weight_cast_fp16, x = var_3115_cast_fp16_0)[name = string("input_13_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_3132_cast_fp16 = silu(x = input_13_cast_fp16)[name = string("op_3132_cast_fp16")];
            tensor<int32, [2]> var_3138_strides_0 = const()[name = string("op_3138_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_3138_pad_type_0 = const()[name = string("op_3138_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_3138_pad_0 = const()[name = string("op_3138_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_3138_dilations_0 = const()[name = string("op_3138_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3138_groups_0 = const()[name = string("op_3138_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_3138_cast_fp16 = conv(dilations = var_3138_dilations_0, groups = var_3138_groups_0, pad = var_3138_pad_0, pad_type = var_3138_pad_type_0, strides = var_3138_strides_0, weight = layers_6_mlp_up_proj_weight_cast_fp16, x = var_3115_cast_fp16_0)[name = string("op_3138_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_69_cast_fp16 = mul(x = var_3132_cast_fp16, y = var_3138_cast_fp16)[name = string("x_69_cast_fp16")];
            tensor<int32, [2]> hidden_states_67_strides_0 = const()[name = string("hidden_states_67_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_67_pad_type_0 = const()[name = string("hidden_states_67_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_67_pad_0 = const()[name = string("hidden_states_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_67_dilations_0 = const()[name = string("hidden_states_67_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_67_groups_0 = const()[name = string("hidden_states_67_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_67_cast_fp16 = conv(dilations = hidden_states_67_dilations_0, groups = hidden_states_67_groups_0, pad = hidden_states_67_pad_0, pad_type = hidden_states_67_pad_type_0, strides = hidden_states_67_strides_0, weight = layers_6_mlp_down_proj_weight_cast_fp16, x = x_69_cast_fp16)[name = string("hidden_states_67_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_69_cast_fp16 = add(x = hidden_states_65_cast_fp16, y = hidden_states_67_cast_fp16)[name = string("hidden_states_69_cast_fp16")];
            fp16 const_72_promoted_to_fp16 = const()[name = string("const_72_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_3156_cast_fp16 = mul(x = hidden_states_69_cast_fp16, y = const_72_promoted_to_fp16)[name = string("op_3156_cast_fp16")];
            int32 var_3154 = const()[name = string("op_3154"), val = int32(1)];
            bool doubled_57_interleave_0 = const()[name = string("doubled_57_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_57_cast_fp16 = concat(axis = var_3154, interleave = doubled_57_interleave_0, values = (hidden_states_69_cast_fp16, var_3156_cast_fp16))[name = string("doubled_57_cast_fp16")];
            tensor<int32, [1]> out_29_axes_0 = const()[name = string("out_29_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_29_gamma_0_to_fp16 = const()[name = string("out_29_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1318830656)))];
            fp16 var_3166_to_fp16 = const()[name = string("op_3166_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_29_cast_fp16 = layer_norm(axes = out_29_axes_0, epsilon = var_3166_to_fp16, gamma = out_29_gamma_0_to_fp16, x = doubled_57_cast_fp16)[name = string("out_29_cast_fp16")];
            tensor<int32, [2]> var_3177_split_sizes_0 = const()[name = string("op_3177_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3177_axis_0 = const()[name = string("op_3177_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_3177_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_3177_cast_fp16_1 = split(axis = var_3177_axis_0, split_sizes = var_3177_split_sizes_0, x = out_29_cast_fp16)[name = string("op_3177_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_7_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_7_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1318838912)))];
            tensor<int32, [2]> query_states_43_strides_0 = const()[name = string("query_states_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_43_pad_type_0 = const()[name = string("query_states_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_43_pad_0 = const()[name = string("query_states_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_43_dilations_0 = const()[name = string("query_states_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_43_groups_0 = const()[name = string("query_states_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_43_cast_fp16 = conv(dilations = query_states_43_dilations_0, groups = query_states_43_groups_0, pad = query_states_43_pad_0, pad_type = query_states_43_pad_type_0, strides = query_states_43_strides_0, weight = layers_7_self_attn_q_proj_weight_to_fp16, x = var_3177_cast_fp16_0)[name = string("query_states_43_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_7_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_7_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1327227584)))];
            tensor<int32, [2]> key_states_71_strides_0 = const()[name = string("key_states_71_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_71_pad_type_0 = const()[name = string("key_states_71_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_71_pad_0 = const()[name = string("key_states_71_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_71_dilations_0 = const()[name = string("key_states_71_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_71_groups_0 = const()[name = string("key_states_71_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_71_cast_fp16 = conv(dilations = key_states_71_dilations_0, groups = key_states_71_groups_0, pad = key_states_71_pad_0, pad_type = key_states_71_pad_type_0, strides = key_states_71_strides_0, weight = layers_7_self_attn_k_proj_weight_to_fp16, x = var_3177_cast_fp16_0)[name = string("key_states_71_cast_fp16")];
            tensor<int32, [2]> value_states_43_strides_0 = const()[name = string("value_states_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_43_pad_type_0 = const()[name = string("value_states_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_43_pad_0 = const()[name = string("value_states_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_43_dilations_0 = const()[name = string("value_states_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_43_groups_0 = const()[name = string("value_states_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_43_cast_fp16 = conv(dilations = value_states_43_dilations_0, groups = value_states_43_groups_0, pad = value_states_43_pad_0, pad_type = value_states_43_pad_type_0, strides = value_states_43_strides_0, weight = layers_7_self_attn_v_proj_weight_cast_fp16, x = var_3177_cast_fp16_0)[name = string("value_states_43_cast_fp16")];
            tensor<int32, [4]> concat_84x = const()[name = string("concat_84x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_71_cast_fp16 = reshape(shape = concat_84x, x = query_states_43_cast_fp16)[name = string("x_71_cast_fp16")];
            tensor<int32, [4]> concat_85x = const()[name = string("concat_85x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_3234_cast_fp16 = reshape(shape = concat_85x, x = key_states_71_cast_fp16)[name = string("op_3234_cast_fp16")];
            tensor<int32, [4]> concat_86x = const()[name = string("concat_86x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_3241_cast_fp16 = reshape(shape = concat_86x, x = value_states_43_cast_fp16)[name = string("op_3241_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_3245_cast_fp16 = mul(x = x_71_cast_fp16, y = var_859_cast_fp16)[name = string("op_3245_cast_fp16")];
            tensor<int32, [2]> var_3246_split_sizes_0 = const()[name = string("op_3246_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3246_axis_0 = const()[name = string("op_3246_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_3246_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_3246_cast_fp16_1 = split(axis = var_3246_axis_0, split_sizes = var_3246_split_sizes_0, x = x_71_cast_fp16)[name = string("op_3246_cast_fp16")];
            fp16 const_74_promoted_to_fp16 = const()[name = string("const_74_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_3248_cast_fp16 = mul(x = var_3246_cast_fp16_1, y = const_74_promoted_to_fp16)[name = string("op_3248_cast_fp16")];
            int32 var_3250 = const()[name = string("op_3250"), val = int32(-2)];
            bool var_3251_interleave_0 = const()[name = string("op_3251_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_3251_cast_fp16 = concat(axis = var_3250, interleave = var_3251_interleave_0, values = (var_3248_cast_fp16, var_3246_cast_fp16_0))[name = string("op_3251_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_3252_cast_fp16 = mul(x = var_3251_cast_fp16, y = var_866_cast_fp16)[name = string("op_3252_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_45_cast_fp16 = add(x = var_3245_cast_fp16, y = var_3252_cast_fp16)[name = string("query_states_45_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_3258_cast_fp16 = mul(x = var_3234_cast_fp16, y = var_859_cast_fp16)[name = string("op_3258_cast_fp16")];
            tensor<int32, [2]> var_3259_split_sizes_0 = const()[name = string("op_3259_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3259_axis_0 = const()[name = string("op_3259_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_3259_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_3259_cast_fp16_1 = split(axis = var_3259_axis_0, split_sizes = var_3259_split_sizes_0, x = var_3234_cast_fp16)[name = string("op_3259_cast_fp16")];
            fp16 const_75_promoted_to_fp16 = const()[name = string("const_75_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_3261_cast_fp16 = mul(x = var_3259_cast_fp16_1, y = const_75_promoted_to_fp16)[name = string("op_3261_cast_fp16")];
            int32 var_3263 = const()[name = string("op_3263"), val = int32(-2)];
            bool var_3264_interleave_0 = const()[name = string("op_3264_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_3264_cast_fp16 = concat(axis = var_3263, interleave = var_3264_interleave_0, values = (var_3261_cast_fp16, var_3259_cast_fp16_0))[name = string("op_3264_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_3265_cast_fp16 = mul(x = var_3264_cast_fp16, y = var_866_cast_fp16)[name = string("op_3265_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_75_cast_fp16 = add(x = var_3258_cast_fp16, y = var_3265_cast_fp16)[name = string("key_states_75_cast_fp16")];
            tensor<int32, [1]> expand_dims_84 = const()[name = string("expand_dims_84"), val = tensor<int32, [1]>([7])];
            tensor<int32, [1]> expand_dims_85 = const()[name = string("expand_dims_85"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_87 = const()[name = string("expand_dims_87"), val = tensor<int32, [1]>([0])];
            int32 concat_89_axis_0 = const()[name = string("concat_89_axis_0"), val = int32(0)];
            bool concat_89_interleave_0 = const()[name = string("concat_89_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_89 = concat(axis = concat_89_axis_0, interleave = concat_89_interleave_0, values = (expand_dims_84, expand_dims_85, position_id, expand_dims_87))[name = string("concat_89")];
            tensor<int32, [1]> expand_dims_88 = const()[name = string("expand_dims_88"), val = tensor<int32, [1]>([8])];
            tensor<int32, [1]> concat_90_values1_0 = const()[name = string("concat_90_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_90_values3_0 = const()[name = string("concat_90_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_90_axis_0 = const()[name = string("concat_90_axis_0"), val = int32(0)];
            bool concat_90_interleave_0 = const()[name = string("concat_90_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_90 = concat(axis = concat_90_axis_0, interleave = concat_90_interleave_0, values = (expand_dims_88, concat_90_values1_0, cache_position_end, concat_90_values3_0))[name = string("concat_90")];
            tensor<int32, [4]> key_states_77_perm_0 = const()[name = string("key_states_77_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_8_stride_0 = const()[name = string("key_cache_internal_tensor_assign_8_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_8_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_8_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_8_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_8_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_8_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_8_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_77_cast_fp16 = transpose(perm = key_states_77_perm_0, x = key_states_75_cast_fp16)[name = string("transpose_410")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_8_cast_fp16 = slice_update(begin = concat_89, begin_mask = key_cache_internal_tensor_assign_8_begin_mask_0, end = concat_90, end_mask = key_cache_internal_tensor_assign_8_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_8_squeeze_mask_0, stride = key_cache_internal_tensor_assign_8_stride_0, update = key_states_77_cast_fp16, x = coreml_update_state_236)[name = string("key_cache_internal_tensor_assign_8_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_8_cast_fp16, input = key_cache)[name = string("coreml_update_state_238_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_238 = read_state(input = key_cache)[name = string("coreml_update_state_238")];
            tensor<int32, [4]> value_states_45_perm_0 = const()[name = string("value_states_45_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_8_stride_0 = const()[name = string("value_cache_internal_tensor_assign_8_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_8_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_8_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_8_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_8_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_8_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_8_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_45_cast_fp16 = transpose(perm = value_states_45_perm_0, x = var_3241_cast_fp16)[name = string("transpose_409")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_8_cast_fp16 = slice_update(begin = concat_89, begin_mask = value_cache_internal_tensor_assign_8_begin_mask_0, end = concat_90, end_mask = value_cache_internal_tensor_assign_8_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_8_squeeze_mask_0, stride = value_cache_internal_tensor_assign_8_stride_0, update = value_states_45_cast_fp16, x = coreml_update_state_237)[name = string("value_cache_internal_tensor_assign_8_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_8_cast_fp16, input = value_cache)[name = string("coreml_update_state_239_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_239 = read_state(input = value_cache)[name = string("coreml_update_state_239")];
            tensor<int32, [4]> var_3335_begin_0 = const()[name = string("op_3335_begin_0"), val = tensor<int32, [4]>([7, 0, 0, 0])];
            tensor<int32, [4]> var_3335_end_0 = const()[name = string("op_3335_end_0"), val = tensor<int32, [4]>([8, 2, 2048, 128])];
            tensor<bool, [4]> var_3335_end_mask_0 = const()[name = string("op_3335_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3335_cast_fp16 = slice_by_index(begin = var_3335_begin_0, end = var_3335_end_0, end_mask = var_3335_end_mask_0, x = coreml_update_state_238)[name = string("op_3335_cast_fp16")];
            tensor<int32, [2]> tile_14 = const()[name = string("tile_14"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3338_axis_0 = const()[name = string("op_3338_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3338_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3338_cast_fp16_1 = split(axis = var_3338_axis_0, split_sizes = tile_14, x = var_3335_cast_fp16)[name = string("op_3338_cast_fp16")];
            tensor<int32, [4]> var_3345_begin_0 = const()[name = string("op_3345_begin_0"), val = tensor<int32, [4]>([7, 0, 0, 0])];
            tensor<int32, [4]> var_3345_end_0 = const()[name = string("op_3345_end_0"), val = tensor<int32, [4]>([8, 2, 2048, 128])];
            tensor<bool, [4]> var_3345_end_mask_0 = const()[name = string("op_3345_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3345_cast_fp16 = slice_by_index(begin = var_3345_begin_0, end = var_3345_end_0, end_mask = var_3345_end_mask_0, x = coreml_update_state_239)[name = string("op_3345_cast_fp16")];
            tensor<int32, [2]> tile_15 = const()[name = string("tile_15"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3348_axis_0 = const()[name = string("op_3348_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3348_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3348_cast_fp16_1 = split(axis = var_3348_axis_0, split_sizes = tile_15, x = var_3345_cast_fp16)[name = string("op_3348_cast_fp16")];
            tensor<int32, [2]> var_3351_split_sizes_0 = const()[name = string("op_3351_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3351_axis_0 = const()[name = string("op_3351_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_3351_0, tensor<fp16, [1, 8, 128, 32]> var_3351_1 = split(axis = var_3351_axis_0, split_sizes = var_3351_split_sizes_0, x = query_states_45_cast_fp16)[name = string("op_3351")];
            bool attn_weights_113_transpose_x_0 = const()[name = string("attn_weights_113_transpose_x_0"), val = bool(false)];
            bool attn_weights_113_transpose_y_0 = const()[name = string("attn_weights_113_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_113_cast_fp16 = matmul(transpose_x = attn_weights_113_transpose_x_0, transpose_y = attn_weights_113_transpose_y_0, x = var_3338_cast_fp16_0, y = var_3351_0)[name = string("attn_weights_113_cast_fp16")];
            fp16 var_3354_to_fp16 = const()[name = string("op_3354_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_115_cast_fp16 = mul(x = attn_weights_113_cast_fp16, y = var_3354_to_fp16)[name = string("attn_weights_115_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_117_cast_fp16 = add(x = attn_weights_115_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_117_cast_fp16")];
            int32 var_3358 = const()[name = string("op_3358"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_119_cast_fp16 = softmax(axis = var_3358, x = attn_weights_117_cast_fp16)[name = string("attn_weights_119_cast_fp16")];
            bool var_3364_transpose_x_1 = const()[name = string("op_3364_transpose_x_1"), val = bool(true)];
            bool var_3364_transpose_y_1 = const()[name = string("op_3364_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_3364_cast_fp16 = matmul(transpose_x = var_3364_transpose_x_1, transpose_y = var_3364_transpose_y_1, x = attn_weights_119_cast_fp16, y = var_3348_cast_fp16_0)[name = string("op_3364_cast_fp16")];
            bool attn_weights_121_transpose_x_0 = const()[name = string("attn_weights_121_transpose_x_0"), val = bool(false)];
            bool attn_weights_121_transpose_y_0 = const()[name = string("attn_weights_121_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_121_cast_fp16 = matmul(transpose_x = attn_weights_121_transpose_x_0, transpose_y = attn_weights_121_transpose_y_0, x = var_3338_cast_fp16_1, y = var_3351_1)[name = string("attn_weights_121_cast_fp16")];
            fp16 var_3366_to_fp16 = const()[name = string("op_3366_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_123_cast_fp16 = mul(x = attn_weights_121_cast_fp16, y = var_3366_to_fp16)[name = string("attn_weights_123_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_125_cast_fp16 = add(x = attn_weights_123_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_125_cast_fp16")];
            int32 var_3370 = const()[name = string("op_3370"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_127_cast_fp16 = softmax(axis = var_3370, x = attn_weights_125_cast_fp16)[name = string("attn_weights_127_cast_fp16")];
            bool attn_output_57_transpose_x_1 = const()[name = string("attn_output_57_transpose_x_1"), val = bool(true)];
            bool attn_output_57_transpose_y_1 = const()[name = string("attn_output_57_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_57_cast_fp16 = matmul(transpose_x = attn_output_57_transpose_x_1, transpose_y = attn_output_57_transpose_y_1, x = attn_weights_127_cast_fp16, y = var_3348_cast_fp16_1)[name = string("attn_output_57_cast_fp16")];
            int32 var_3378 = const()[name = string("op_3378"), val = int32(1)];
            bool attn_output_59_interleave_0 = const()[name = string("attn_output_59_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_59_cast_fp16 = concat(axis = var_3378, interleave = attn_output_59_interleave_0, values = (var_3364_cast_fp16, attn_output_57_cast_fp16))[name = string("attn_output_59_cast_fp16")];
            tensor<int32, [4]> var_3382_perm_0 = const()[name = string("op_3382_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_95x = const()[name = string("concat_95x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_3382_cast_fp16 = transpose(perm = var_3382_perm_0, x = attn_output_59_cast_fp16)[name = string("transpose_408")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_63_cast_fp16 = reshape(shape = concat_95x, x = var_3382_cast_fp16)[name = string("attn_output_63_cast_fp16")];
            tensor<int32, [2]> hidden_states_73_strides_0 = const()[name = string("hidden_states_73_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_73_pad_type_0 = const()[name = string("hidden_states_73_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_73_pad_0 = const()[name = string("hidden_states_73_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_73_dilations_0 = const()[name = string("hidden_states_73_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_73_groups_0 = const()[name = string("hidden_states_73_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_73_cast_fp16 = conv(dilations = hidden_states_73_dilations_0, groups = hidden_states_73_groups_0, pad = hidden_states_73_pad_0, pad_type = hidden_states_73_pad_type_0, strides = hidden_states_73_strides_0, weight = layers_7_self_attn_o_proj_weight_cast_fp16, x = attn_output_63_cast_fp16)[name = string("hidden_states_73_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_75_cast_fp16 = add(x = hidden_states_69_cast_fp16, y = hidden_states_73_cast_fp16)[name = string("hidden_states_75_cast_fp16")];
            fp16 const_80_promoted_to_fp16 = const()[name = string("const_80_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_3415_cast_fp16 = mul(x = hidden_states_75_cast_fp16, y = const_80_promoted_to_fp16)[name = string("op_3415_cast_fp16")];
            int32 var_3413 = const()[name = string("op_3413"), val = int32(1)];
            bool doubled_61_interleave_0 = const()[name = string("doubled_61_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_61_cast_fp16 = concat(axis = var_3413, interleave = doubled_61_interleave_0, values = (hidden_states_75_cast_fp16, var_3415_cast_fp16))[name = string("doubled_61_cast_fp16")];
            tensor<int32, [1]> out_31_axes_0 = const()[name = string("out_31_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_31_gamma_0_to_fp16 = const()[name = string("out_31_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1328276224)))];
            fp16 var_3425_to_fp16 = const()[name = string("op_3425_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_31_cast_fp16 = layer_norm(axes = out_31_axes_0, epsilon = var_3425_to_fp16, gamma = out_31_gamma_0_to_fp16, x = doubled_61_cast_fp16)[name = string("out_31_cast_fp16")];
            tensor<int32, [2]> var_3436_split_sizes_0 = const()[name = string("op_3436_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3436_axis_0 = const()[name = string("op_3436_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_3436_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_3436_cast_fp16_1 = split(axis = var_3436_axis_0, split_sizes = var_3436_split_sizes_0, x = out_31_cast_fp16)[name = string("op_3436_cast_fp16")];
            tensor<int32, [2]> input_15_strides_0 = const()[name = string("input_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_15_pad_type_0 = const()[name = string("input_15_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_15_dilations_0 = const()[name = string("input_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_15_groups_0 = const()[name = string("input_15_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_15_cast_fp16 = conv(dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = layers_7_mlp_gate_proj_weight_cast_fp16, x = var_3436_cast_fp16_0)[name = string("input_15_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_3453_cast_fp16 = silu(x = input_15_cast_fp16)[name = string("op_3453_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_7_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_7_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1328284480)))];
            tensor<int32, [2]> var_3459_strides_0 = const()[name = string("op_3459_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_3459_pad_type_0 = const()[name = string("op_3459_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_3459_pad_0 = const()[name = string("op_3459_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_3459_dilations_0 = const()[name = string("op_3459_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3459_groups_0 = const()[name = string("op_3459_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_3459_cast_fp16 = conv(dilations = var_3459_dilations_0, groups = var_3459_groups_0, pad = var_3459_pad_0, pad_type = var_3459_pad_type_0, strides = var_3459_strides_0, weight = layers_7_mlp_up_proj_weight_to_fp16, x = var_3436_cast_fp16_0)[name = string("op_3459_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_79_cast_fp16 = mul(x = var_3453_cast_fp16, y = var_3459_cast_fp16)[name = string("x_79_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_7_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_7_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353450368)))];
            tensor<int32, [2]> hidden_states_77_strides_0 = const()[name = string("hidden_states_77_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_77_pad_type_0 = const()[name = string("hidden_states_77_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_77_pad_0 = const()[name = string("hidden_states_77_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_77_dilations_0 = const()[name = string("hidden_states_77_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_77_groups_0 = const()[name = string("hidden_states_77_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_77_cast_fp16 = conv(dilations = hidden_states_77_dilations_0, groups = hidden_states_77_groups_0, pad = hidden_states_77_pad_0, pad_type = hidden_states_77_pad_type_0, strides = hidden_states_77_strides_0, weight = layers_7_mlp_down_proj_weight_to_fp16, x = x_79_cast_fp16)[name = string("hidden_states_77_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_79_cast_fp16 = add(x = hidden_states_75_cast_fp16, y = hidden_states_77_cast_fp16)[name = string("hidden_states_79_cast_fp16")];
            fp16 const_82_promoted_to_fp16 = const()[name = string("const_82_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_3477_cast_fp16 = mul(x = hidden_states_79_cast_fp16, y = const_82_promoted_to_fp16)[name = string("op_3477_cast_fp16")];
            int32 var_3475 = const()[name = string("op_3475"), val = int32(1)];
            bool doubled_65_interleave_0 = const()[name = string("doubled_65_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_65_cast_fp16 = concat(axis = var_3475, interleave = doubled_65_interleave_0, values = (hidden_states_79_cast_fp16, var_3477_cast_fp16))[name = string("doubled_65_cast_fp16")];
            tensor<int32, [1]> out_33_axes_0 = const()[name = string("out_33_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_33_gamma_0_to_fp16 = const()[name = string("out_33_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1378616256)))];
            fp16 var_3487_to_fp16 = const()[name = string("op_3487_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_33_cast_fp16 = layer_norm(axes = out_33_axes_0, epsilon = var_3487_to_fp16, gamma = out_33_gamma_0_to_fp16, x = doubled_65_cast_fp16)[name = string("out_33_cast_fp16")];
            tensor<int32, [2]> var_3498_split_sizes_0 = const()[name = string("op_3498_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3498_axis_0 = const()[name = string("op_3498_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_3498_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_3498_cast_fp16_1 = split(axis = var_3498_axis_0, split_sizes = var_3498_split_sizes_0, x = out_33_cast_fp16)[name = string("op_3498_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_8_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_8_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1378624512)))];
            tensor<int32, [2]> query_states_49_strides_0 = const()[name = string("query_states_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_49_pad_type_0 = const()[name = string("query_states_49_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_49_pad_0 = const()[name = string("query_states_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_49_dilations_0 = const()[name = string("query_states_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_49_groups_0 = const()[name = string("query_states_49_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_49_cast_fp16 = conv(dilations = query_states_49_dilations_0, groups = query_states_49_groups_0, pad = query_states_49_pad_0, pad_type = query_states_49_pad_type_0, strides = query_states_49_strides_0, weight = layers_8_self_attn_q_proj_weight_to_fp16, x = var_3498_cast_fp16_0)[name = string("query_states_49_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_8_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_8_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1387013184)))];
            tensor<int32, [2]> key_states_81_strides_0 = const()[name = string("key_states_81_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_81_pad_type_0 = const()[name = string("key_states_81_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_81_pad_0 = const()[name = string("key_states_81_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_81_dilations_0 = const()[name = string("key_states_81_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_81_groups_0 = const()[name = string("key_states_81_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_81_cast_fp16 = conv(dilations = key_states_81_dilations_0, groups = key_states_81_groups_0, pad = key_states_81_pad_0, pad_type = key_states_81_pad_type_0, strides = key_states_81_strides_0, weight = layers_8_self_attn_k_proj_weight_to_fp16, x = var_3498_cast_fp16_0)[name = string("key_states_81_cast_fp16")];
            tensor<int32, [2]> value_states_49_strides_0 = const()[name = string("value_states_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_49_pad_type_0 = const()[name = string("value_states_49_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_49_pad_0 = const()[name = string("value_states_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_49_dilations_0 = const()[name = string("value_states_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_49_groups_0 = const()[name = string("value_states_49_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_49_cast_fp16 = conv(dilations = value_states_49_dilations_0, groups = value_states_49_groups_0, pad = value_states_49_pad_0, pad_type = value_states_49_pad_type_0, strides = value_states_49_strides_0, weight = layers_8_self_attn_v_proj_weight_cast_fp16, x = var_3498_cast_fp16_0)[name = string("value_states_49_cast_fp16")];
            tensor<int32, [4]> concat_96x = const()[name = string("concat_96x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_81_cast_fp16 = reshape(shape = concat_96x, x = query_states_49_cast_fp16)[name = string("x_81_cast_fp16")];
            tensor<int32, [4]> concat_97x = const()[name = string("concat_97x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_3555_cast_fp16 = reshape(shape = concat_97x, x = key_states_81_cast_fp16)[name = string("op_3555_cast_fp16")];
            tensor<int32, [4]> concat_98x = const()[name = string("concat_98x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_3562_cast_fp16 = reshape(shape = concat_98x, x = value_states_49_cast_fp16)[name = string("op_3562_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_3566_cast_fp16 = mul(x = x_81_cast_fp16, y = var_859_cast_fp16)[name = string("op_3566_cast_fp16")];
            tensor<int32, [2]> var_3567_split_sizes_0 = const()[name = string("op_3567_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3567_axis_0 = const()[name = string("op_3567_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_3567_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_3567_cast_fp16_1 = split(axis = var_3567_axis_0, split_sizes = var_3567_split_sizes_0, x = x_81_cast_fp16)[name = string("op_3567_cast_fp16")];
            fp16 const_84_promoted_to_fp16 = const()[name = string("const_84_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_3569_cast_fp16 = mul(x = var_3567_cast_fp16_1, y = const_84_promoted_to_fp16)[name = string("op_3569_cast_fp16")];
            int32 var_3571 = const()[name = string("op_3571"), val = int32(-2)];
            bool var_3572_interleave_0 = const()[name = string("op_3572_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_3572_cast_fp16 = concat(axis = var_3571, interleave = var_3572_interleave_0, values = (var_3569_cast_fp16, var_3567_cast_fp16_0))[name = string("op_3572_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_3573_cast_fp16 = mul(x = var_3572_cast_fp16, y = var_866_cast_fp16)[name = string("op_3573_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_51_cast_fp16 = add(x = var_3566_cast_fp16, y = var_3573_cast_fp16)[name = string("query_states_51_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_3579_cast_fp16 = mul(x = var_3555_cast_fp16, y = var_859_cast_fp16)[name = string("op_3579_cast_fp16")];
            tensor<int32, [2]> var_3580_split_sizes_0 = const()[name = string("op_3580_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3580_axis_0 = const()[name = string("op_3580_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_3580_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_3580_cast_fp16_1 = split(axis = var_3580_axis_0, split_sizes = var_3580_split_sizes_0, x = var_3555_cast_fp16)[name = string("op_3580_cast_fp16")];
            fp16 const_85_promoted_to_fp16 = const()[name = string("const_85_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_3582_cast_fp16 = mul(x = var_3580_cast_fp16_1, y = const_85_promoted_to_fp16)[name = string("op_3582_cast_fp16")];
            int32 var_3584 = const()[name = string("op_3584"), val = int32(-2)];
            bool var_3585_interleave_0 = const()[name = string("op_3585_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_3585_cast_fp16 = concat(axis = var_3584, interleave = var_3585_interleave_0, values = (var_3582_cast_fp16, var_3580_cast_fp16_0))[name = string("op_3585_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_3586_cast_fp16 = mul(x = var_3585_cast_fp16, y = var_866_cast_fp16)[name = string("op_3586_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_85_cast_fp16 = add(x = var_3579_cast_fp16, y = var_3586_cast_fp16)[name = string("key_states_85_cast_fp16")];
            tensor<int32, [1]> expand_dims_96 = const()[name = string("expand_dims_96"), val = tensor<int32, [1]>([8])];
            tensor<int32, [1]> expand_dims_97 = const()[name = string("expand_dims_97"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_99 = const()[name = string("expand_dims_99"), val = tensor<int32, [1]>([0])];
            int32 concat_101_axis_0 = const()[name = string("concat_101_axis_0"), val = int32(0)];
            bool concat_101_interleave_0 = const()[name = string("concat_101_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_101 = concat(axis = concat_101_axis_0, interleave = concat_101_interleave_0, values = (expand_dims_96, expand_dims_97, position_id, expand_dims_99))[name = string("concat_101")];
            tensor<int32, [1]> expand_dims_100 = const()[name = string("expand_dims_100"), val = tensor<int32, [1]>([9])];
            tensor<int32, [1]> concat_102_values1_0 = const()[name = string("concat_102_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_102_values3_0 = const()[name = string("concat_102_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_102_axis_0 = const()[name = string("concat_102_axis_0"), val = int32(0)];
            bool concat_102_interleave_0 = const()[name = string("concat_102_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_102 = concat(axis = concat_102_axis_0, interleave = concat_102_interleave_0, values = (expand_dims_100, concat_102_values1_0, cache_position_end, concat_102_values3_0))[name = string("concat_102")];
            tensor<int32, [4]> key_states_87_perm_0 = const()[name = string("key_states_87_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_9_stride_0 = const()[name = string("key_cache_internal_tensor_assign_9_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_9_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_9_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_9_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_9_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_9_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_9_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_87_cast_fp16 = transpose(perm = key_states_87_perm_0, x = key_states_85_cast_fp16)[name = string("transpose_407")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_9_cast_fp16 = slice_update(begin = concat_101, begin_mask = key_cache_internal_tensor_assign_9_begin_mask_0, end = concat_102, end_mask = key_cache_internal_tensor_assign_9_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_9_squeeze_mask_0, stride = key_cache_internal_tensor_assign_9_stride_0, update = key_states_87_cast_fp16, x = coreml_update_state_238)[name = string("key_cache_internal_tensor_assign_9_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_9_cast_fp16, input = key_cache)[name = string("coreml_update_state_240_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_240 = read_state(input = key_cache)[name = string("coreml_update_state_240")];
            tensor<int32, [4]> value_states_51_perm_0 = const()[name = string("value_states_51_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_9_stride_0 = const()[name = string("value_cache_internal_tensor_assign_9_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_9_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_9_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_9_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_9_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_9_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_9_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_51_cast_fp16 = transpose(perm = value_states_51_perm_0, x = var_3562_cast_fp16)[name = string("transpose_406")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_9_cast_fp16 = slice_update(begin = concat_101, begin_mask = value_cache_internal_tensor_assign_9_begin_mask_0, end = concat_102, end_mask = value_cache_internal_tensor_assign_9_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_9_squeeze_mask_0, stride = value_cache_internal_tensor_assign_9_stride_0, update = value_states_51_cast_fp16, x = coreml_update_state_239)[name = string("value_cache_internal_tensor_assign_9_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_9_cast_fp16, input = value_cache)[name = string("coreml_update_state_241_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_241 = read_state(input = value_cache)[name = string("coreml_update_state_241")];
            tensor<int32, [4]> var_3656_begin_0 = const()[name = string("op_3656_begin_0"), val = tensor<int32, [4]>([8, 0, 0, 0])];
            tensor<int32, [4]> var_3656_end_0 = const()[name = string("op_3656_end_0"), val = tensor<int32, [4]>([9, 2, 2048, 128])];
            tensor<bool, [4]> var_3656_end_mask_0 = const()[name = string("op_3656_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3656_cast_fp16 = slice_by_index(begin = var_3656_begin_0, end = var_3656_end_0, end_mask = var_3656_end_mask_0, x = coreml_update_state_240)[name = string("op_3656_cast_fp16")];
            tensor<int32, [2]> tile_16 = const()[name = string("tile_16"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3659_axis_0 = const()[name = string("op_3659_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3659_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3659_cast_fp16_1 = split(axis = var_3659_axis_0, split_sizes = tile_16, x = var_3656_cast_fp16)[name = string("op_3659_cast_fp16")];
            tensor<int32, [4]> var_3666_begin_0 = const()[name = string("op_3666_begin_0"), val = tensor<int32, [4]>([8, 0, 0, 0])];
            tensor<int32, [4]> var_3666_end_0 = const()[name = string("op_3666_end_0"), val = tensor<int32, [4]>([9, 2, 2048, 128])];
            tensor<bool, [4]> var_3666_end_mask_0 = const()[name = string("op_3666_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3666_cast_fp16 = slice_by_index(begin = var_3666_begin_0, end = var_3666_end_0, end_mask = var_3666_end_mask_0, x = coreml_update_state_241)[name = string("op_3666_cast_fp16")];
            tensor<int32, [2]> tile_17 = const()[name = string("tile_17"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3669_axis_0 = const()[name = string("op_3669_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3669_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3669_cast_fp16_1 = split(axis = var_3669_axis_0, split_sizes = tile_17, x = var_3666_cast_fp16)[name = string("op_3669_cast_fp16")];
            tensor<int32, [2]> var_3672_split_sizes_0 = const()[name = string("op_3672_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3672_axis_0 = const()[name = string("op_3672_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_3672_0, tensor<fp16, [1, 8, 128, 32]> var_3672_1 = split(axis = var_3672_axis_0, split_sizes = var_3672_split_sizes_0, x = query_states_51_cast_fp16)[name = string("op_3672")];
            bool attn_weights_129_transpose_x_0 = const()[name = string("attn_weights_129_transpose_x_0"), val = bool(false)];
            bool attn_weights_129_transpose_y_0 = const()[name = string("attn_weights_129_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_129_cast_fp16 = matmul(transpose_x = attn_weights_129_transpose_x_0, transpose_y = attn_weights_129_transpose_y_0, x = var_3659_cast_fp16_0, y = var_3672_0)[name = string("attn_weights_129_cast_fp16")];
            fp16 var_3675_to_fp16 = const()[name = string("op_3675_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_131_cast_fp16 = mul(x = attn_weights_129_cast_fp16, y = var_3675_to_fp16)[name = string("attn_weights_131_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_133_cast_fp16 = add(x = attn_weights_131_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_133_cast_fp16")];
            int32 var_3679 = const()[name = string("op_3679"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_135_cast_fp16 = softmax(axis = var_3679, x = attn_weights_133_cast_fp16)[name = string("attn_weights_135_cast_fp16")];
            bool var_3685_transpose_x_1 = const()[name = string("op_3685_transpose_x_1"), val = bool(true)];
            bool var_3685_transpose_y_1 = const()[name = string("op_3685_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_3685_cast_fp16 = matmul(transpose_x = var_3685_transpose_x_1, transpose_y = var_3685_transpose_y_1, x = attn_weights_135_cast_fp16, y = var_3669_cast_fp16_0)[name = string("op_3685_cast_fp16")];
            bool attn_weights_137_transpose_x_0 = const()[name = string("attn_weights_137_transpose_x_0"), val = bool(false)];
            bool attn_weights_137_transpose_y_0 = const()[name = string("attn_weights_137_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_137_cast_fp16 = matmul(transpose_x = attn_weights_137_transpose_x_0, transpose_y = attn_weights_137_transpose_y_0, x = var_3659_cast_fp16_1, y = var_3672_1)[name = string("attn_weights_137_cast_fp16")];
            fp16 var_3687_to_fp16 = const()[name = string("op_3687_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_139_cast_fp16 = mul(x = attn_weights_137_cast_fp16, y = var_3687_to_fp16)[name = string("attn_weights_139_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_141_cast_fp16 = add(x = attn_weights_139_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_141_cast_fp16")];
            int32 var_3691 = const()[name = string("op_3691"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_143_cast_fp16 = softmax(axis = var_3691, x = attn_weights_141_cast_fp16)[name = string("attn_weights_143_cast_fp16")];
            bool attn_output_65_transpose_x_1 = const()[name = string("attn_output_65_transpose_x_1"), val = bool(true)];
            bool attn_output_65_transpose_y_1 = const()[name = string("attn_output_65_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_65_cast_fp16 = matmul(transpose_x = attn_output_65_transpose_x_1, transpose_y = attn_output_65_transpose_y_1, x = attn_weights_143_cast_fp16, y = var_3669_cast_fp16_1)[name = string("attn_output_65_cast_fp16")];
            int32 var_3699 = const()[name = string("op_3699"), val = int32(1)];
            bool attn_output_67_interleave_0 = const()[name = string("attn_output_67_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_67_cast_fp16 = concat(axis = var_3699, interleave = attn_output_67_interleave_0, values = (var_3685_cast_fp16, attn_output_65_cast_fp16))[name = string("attn_output_67_cast_fp16")];
            tensor<int32, [4]> var_3703_perm_0 = const()[name = string("op_3703_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_107x = const()[name = string("concat_107x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_3703_cast_fp16 = transpose(perm = var_3703_perm_0, x = attn_output_67_cast_fp16)[name = string("transpose_405")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_71_cast_fp16 = reshape(shape = concat_107x, x = var_3703_cast_fp16)[name = string("attn_output_71_cast_fp16")];
            tensor<int32, [2]> hidden_states_83_strides_0 = const()[name = string("hidden_states_83_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_83_pad_type_0 = const()[name = string("hidden_states_83_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_83_pad_0 = const()[name = string("hidden_states_83_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_83_dilations_0 = const()[name = string("hidden_states_83_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_83_groups_0 = const()[name = string("hidden_states_83_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_83_cast_fp16 = conv(dilations = hidden_states_83_dilations_0, groups = hidden_states_83_groups_0, pad = hidden_states_83_pad_0, pad_type = hidden_states_83_pad_type_0, strides = hidden_states_83_strides_0, weight = layers_8_self_attn_o_proj_weight_cast_fp16, x = attn_output_71_cast_fp16)[name = string("hidden_states_83_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_85_cast_fp16 = add(x = hidden_states_79_cast_fp16, y = hidden_states_83_cast_fp16)[name = string("hidden_states_85_cast_fp16")];
            fp16 const_90_promoted_to_fp16 = const()[name = string("const_90_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_3736_cast_fp16 = mul(x = hidden_states_85_cast_fp16, y = const_90_promoted_to_fp16)[name = string("op_3736_cast_fp16")];
            int32 var_3734 = const()[name = string("op_3734"), val = int32(1)];
            bool doubled_69_interleave_0 = const()[name = string("doubled_69_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_69_cast_fp16 = concat(axis = var_3734, interleave = doubled_69_interleave_0, values = (hidden_states_85_cast_fp16, var_3736_cast_fp16))[name = string("doubled_69_cast_fp16")];
            tensor<int32, [1]> out_35_axes_0 = const()[name = string("out_35_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_35_gamma_0_to_fp16 = const()[name = string("out_35_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1388061824)))];
            fp16 var_3746_to_fp16 = const()[name = string("op_3746_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_35_cast_fp16 = layer_norm(axes = out_35_axes_0, epsilon = var_3746_to_fp16, gamma = out_35_gamma_0_to_fp16, x = doubled_69_cast_fp16)[name = string("out_35_cast_fp16")];
            tensor<int32, [2]> var_3757_split_sizes_0 = const()[name = string("op_3757_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3757_axis_0 = const()[name = string("op_3757_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_3757_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_3757_cast_fp16_1 = split(axis = var_3757_axis_0, split_sizes = var_3757_split_sizes_0, x = out_35_cast_fp16)[name = string("op_3757_cast_fp16")];
            tensor<int32, [2]> input_17_strides_0 = const()[name = string("input_17_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_17_pad_type_0 = const()[name = string("input_17_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_17_pad_0 = const()[name = string("input_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_17_dilations_0 = const()[name = string("input_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_17_groups_0 = const()[name = string("input_17_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_17_cast_fp16 = conv(dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = layers_8_mlp_gate_proj_weight_cast_fp16, x = var_3757_cast_fp16_0)[name = string("input_17_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_3774_cast_fp16 = silu(x = input_17_cast_fp16)[name = string("op_3774_cast_fp16")];
            tensor<int32, [2]> var_3780_strides_0 = const()[name = string("op_3780_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_3780_pad_type_0 = const()[name = string("op_3780_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_3780_pad_0 = const()[name = string("op_3780_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_3780_dilations_0 = const()[name = string("op_3780_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3780_groups_0 = const()[name = string("op_3780_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_3780_cast_fp16 = conv(dilations = var_3780_dilations_0, groups = var_3780_groups_0, pad = var_3780_pad_0, pad_type = var_3780_pad_type_0, strides = var_3780_strides_0, weight = layers_8_mlp_up_proj_weight_cast_fp16, x = var_3757_cast_fp16_0)[name = string("op_3780_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_89_cast_fp16 = mul(x = var_3774_cast_fp16, y = var_3780_cast_fp16)[name = string("x_89_cast_fp16")];
            tensor<int32, [2]> hidden_states_87_strides_0 = const()[name = string("hidden_states_87_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_87_pad_type_0 = const()[name = string("hidden_states_87_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_87_pad_0 = const()[name = string("hidden_states_87_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_87_dilations_0 = const()[name = string("hidden_states_87_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_87_groups_0 = const()[name = string("hidden_states_87_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_87_cast_fp16 = conv(dilations = hidden_states_87_dilations_0, groups = hidden_states_87_groups_0, pad = hidden_states_87_pad_0, pad_type = hidden_states_87_pad_type_0, strides = hidden_states_87_strides_0, weight = layers_8_mlp_down_proj_weight_cast_fp16, x = x_89_cast_fp16)[name = string("hidden_states_87_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_89_cast_fp16 = add(x = hidden_states_85_cast_fp16, y = hidden_states_87_cast_fp16)[name = string("hidden_states_89_cast_fp16")];
            fp16 const_92_promoted_to_fp16 = const()[name = string("const_92_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_3798_cast_fp16 = mul(x = hidden_states_89_cast_fp16, y = const_92_promoted_to_fp16)[name = string("op_3798_cast_fp16")];
            int32 var_3796 = const()[name = string("op_3796"), val = int32(1)];
            bool doubled_73_interleave_0 = const()[name = string("doubled_73_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_73_cast_fp16 = concat(axis = var_3796, interleave = doubled_73_interleave_0, values = (hidden_states_89_cast_fp16, var_3798_cast_fp16))[name = string("doubled_73_cast_fp16")];
            tensor<int32, [1]> out_37_axes_0 = const()[name = string("out_37_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_37_gamma_0_to_fp16 = const()[name = string("out_37_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1388070080)))];
            fp16 var_3808_to_fp16 = const()[name = string("op_3808_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_37_cast_fp16 = layer_norm(axes = out_37_axes_0, epsilon = var_3808_to_fp16, gamma = out_37_gamma_0_to_fp16, x = doubled_73_cast_fp16)[name = string("out_37_cast_fp16")];
            tensor<int32, [2]> var_3819_split_sizes_0 = const()[name = string("op_3819_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3819_axis_0 = const()[name = string("op_3819_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_3819_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_3819_cast_fp16_1 = split(axis = var_3819_axis_0, split_sizes = var_3819_split_sizes_0, x = out_37_cast_fp16)[name = string("op_3819_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_9_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_9_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1388078336)))];
            tensor<int32, [2]> query_states_55_strides_0 = const()[name = string("query_states_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_55_pad_type_0 = const()[name = string("query_states_55_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_55_pad_0 = const()[name = string("query_states_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_55_dilations_0 = const()[name = string("query_states_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_55_groups_0 = const()[name = string("query_states_55_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_55_cast_fp16 = conv(dilations = query_states_55_dilations_0, groups = query_states_55_groups_0, pad = query_states_55_pad_0, pad_type = query_states_55_pad_type_0, strides = query_states_55_strides_0, weight = layers_9_self_attn_q_proj_weight_to_fp16, x = var_3819_cast_fp16_0)[name = string("query_states_55_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_9_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_9_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1396467008)))];
            tensor<int32, [2]> key_states_91_strides_0 = const()[name = string("key_states_91_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_91_pad_type_0 = const()[name = string("key_states_91_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_91_pad_0 = const()[name = string("key_states_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_91_dilations_0 = const()[name = string("key_states_91_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_91_groups_0 = const()[name = string("key_states_91_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_91_cast_fp16 = conv(dilations = key_states_91_dilations_0, groups = key_states_91_groups_0, pad = key_states_91_pad_0, pad_type = key_states_91_pad_type_0, strides = key_states_91_strides_0, weight = layers_9_self_attn_k_proj_weight_to_fp16, x = var_3819_cast_fp16_0)[name = string("key_states_91_cast_fp16")];
            tensor<int32, [2]> value_states_55_strides_0 = const()[name = string("value_states_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_55_pad_type_0 = const()[name = string("value_states_55_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_55_pad_0 = const()[name = string("value_states_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_55_dilations_0 = const()[name = string("value_states_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_55_groups_0 = const()[name = string("value_states_55_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_55_cast_fp16 = conv(dilations = value_states_55_dilations_0, groups = value_states_55_groups_0, pad = value_states_55_pad_0, pad_type = value_states_55_pad_type_0, strides = value_states_55_strides_0, weight = layers_9_self_attn_v_proj_weight_cast_fp16, x = var_3819_cast_fp16_0)[name = string("value_states_55_cast_fp16")];
            tensor<int32, [4]> concat_108x = const()[name = string("concat_108x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_91_cast_fp16 = reshape(shape = concat_108x, x = query_states_55_cast_fp16)[name = string("x_91_cast_fp16")];
            tensor<int32, [4]> concat_109x = const()[name = string("concat_109x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_3876_cast_fp16 = reshape(shape = concat_109x, x = key_states_91_cast_fp16)[name = string("op_3876_cast_fp16")];
            tensor<int32, [4]> concat_110x = const()[name = string("concat_110x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_3883_cast_fp16 = reshape(shape = concat_110x, x = value_states_55_cast_fp16)[name = string("op_3883_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_3887_cast_fp16 = mul(x = x_91_cast_fp16, y = var_859_cast_fp16)[name = string("op_3887_cast_fp16")];
            tensor<int32, [2]> var_3888_split_sizes_0 = const()[name = string("op_3888_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3888_axis_0 = const()[name = string("op_3888_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_3888_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_3888_cast_fp16_1 = split(axis = var_3888_axis_0, split_sizes = var_3888_split_sizes_0, x = x_91_cast_fp16)[name = string("op_3888_cast_fp16")];
            fp16 const_94_promoted_to_fp16 = const()[name = string("const_94_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_3890_cast_fp16 = mul(x = var_3888_cast_fp16_1, y = const_94_promoted_to_fp16)[name = string("op_3890_cast_fp16")];
            int32 var_3892 = const()[name = string("op_3892"), val = int32(-2)];
            bool var_3893_interleave_0 = const()[name = string("op_3893_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_3893_cast_fp16 = concat(axis = var_3892, interleave = var_3893_interleave_0, values = (var_3890_cast_fp16, var_3888_cast_fp16_0))[name = string("op_3893_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_3894_cast_fp16 = mul(x = var_3893_cast_fp16, y = var_866_cast_fp16)[name = string("op_3894_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_57_cast_fp16 = add(x = var_3887_cast_fp16, y = var_3894_cast_fp16)[name = string("query_states_57_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_3900_cast_fp16 = mul(x = var_3876_cast_fp16, y = var_859_cast_fp16)[name = string("op_3900_cast_fp16")];
            tensor<int32, [2]> var_3901_split_sizes_0 = const()[name = string("op_3901_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3901_axis_0 = const()[name = string("op_3901_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_3901_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_3901_cast_fp16_1 = split(axis = var_3901_axis_0, split_sizes = var_3901_split_sizes_0, x = var_3876_cast_fp16)[name = string("op_3901_cast_fp16")];
            fp16 const_95_promoted_to_fp16 = const()[name = string("const_95_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_3903_cast_fp16 = mul(x = var_3901_cast_fp16_1, y = const_95_promoted_to_fp16)[name = string("op_3903_cast_fp16")];
            int32 var_3905 = const()[name = string("op_3905"), val = int32(-2)];
            bool var_3906_interleave_0 = const()[name = string("op_3906_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_3906_cast_fp16 = concat(axis = var_3905, interleave = var_3906_interleave_0, values = (var_3903_cast_fp16, var_3901_cast_fp16_0))[name = string("op_3906_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_3907_cast_fp16 = mul(x = var_3906_cast_fp16, y = var_866_cast_fp16)[name = string("op_3907_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_95_cast_fp16 = add(x = var_3900_cast_fp16, y = var_3907_cast_fp16)[name = string("key_states_95_cast_fp16")];
            tensor<int32, [1]> expand_dims_108 = const()[name = string("expand_dims_108"), val = tensor<int32, [1]>([9])];
            tensor<int32, [1]> expand_dims_109 = const()[name = string("expand_dims_109"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_111 = const()[name = string("expand_dims_111"), val = tensor<int32, [1]>([0])];
            int32 concat_113_axis_0 = const()[name = string("concat_113_axis_0"), val = int32(0)];
            bool concat_113_interleave_0 = const()[name = string("concat_113_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_113 = concat(axis = concat_113_axis_0, interleave = concat_113_interleave_0, values = (expand_dims_108, expand_dims_109, position_id, expand_dims_111))[name = string("concat_113")];
            tensor<int32, [1]> expand_dims_112 = const()[name = string("expand_dims_112"), val = tensor<int32, [1]>([10])];
            tensor<int32, [1]> concat_114_values1_0 = const()[name = string("concat_114_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_114_values3_0 = const()[name = string("concat_114_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_114_axis_0 = const()[name = string("concat_114_axis_0"), val = int32(0)];
            bool concat_114_interleave_0 = const()[name = string("concat_114_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_114 = concat(axis = concat_114_axis_0, interleave = concat_114_interleave_0, values = (expand_dims_112, concat_114_values1_0, cache_position_end, concat_114_values3_0))[name = string("concat_114")];
            tensor<int32, [4]> key_states_97_perm_0 = const()[name = string("key_states_97_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_10_stride_0 = const()[name = string("key_cache_internal_tensor_assign_10_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_10_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_10_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_10_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_10_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_10_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_10_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_97_cast_fp16 = transpose(perm = key_states_97_perm_0, x = key_states_95_cast_fp16)[name = string("transpose_404")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_10_cast_fp16 = slice_update(begin = concat_113, begin_mask = key_cache_internal_tensor_assign_10_begin_mask_0, end = concat_114, end_mask = key_cache_internal_tensor_assign_10_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_10_squeeze_mask_0, stride = key_cache_internal_tensor_assign_10_stride_0, update = key_states_97_cast_fp16, x = coreml_update_state_240)[name = string("key_cache_internal_tensor_assign_10_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_10_cast_fp16, input = key_cache)[name = string("coreml_update_state_242_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_242 = read_state(input = key_cache)[name = string("coreml_update_state_242")];
            tensor<int32, [4]> value_states_57_perm_0 = const()[name = string("value_states_57_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_10_stride_0 = const()[name = string("value_cache_internal_tensor_assign_10_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_10_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_10_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_10_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_10_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_10_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_10_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_57_cast_fp16 = transpose(perm = value_states_57_perm_0, x = var_3883_cast_fp16)[name = string("transpose_403")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_10_cast_fp16 = slice_update(begin = concat_113, begin_mask = value_cache_internal_tensor_assign_10_begin_mask_0, end = concat_114, end_mask = value_cache_internal_tensor_assign_10_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_10_squeeze_mask_0, stride = value_cache_internal_tensor_assign_10_stride_0, update = value_states_57_cast_fp16, x = coreml_update_state_241)[name = string("value_cache_internal_tensor_assign_10_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_10_cast_fp16, input = value_cache)[name = string("coreml_update_state_243_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_243 = read_state(input = value_cache)[name = string("coreml_update_state_243")];
            tensor<int32, [4]> var_3977_begin_0 = const()[name = string("op_3977_begin_0"), val = tensor<int32, [4]>([9, 0, 0, 0])];
            tensor<int32, [4]> var_3977_end_0 = const()[name = string("op_3977_end_0"), val = tensor<int32, [4]>([10, 2, 2048, 128])];
            tensor<bool, [4]> var_3977_end_mask_0 = const()[name = string("op_3977_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3977_cast_fp16 = slice_by_index(begin = var_3977_begin_0, end = var_3977_end_0, end_mask = var_3977_end_mask_0, x = coreml_update_state_242)[name = string("op_3977_cast_fp16")];
            tensor<int32, [2]> tile_18 = const()[name = string("tile_18"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3980_axis_0 = const()[name = string("op_3980_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3980_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3980_cast_fp16_1 = split(axis = var_3980_axis_0, split_sizes = tile_18, x = var_3977_cast_fp16)[name = string("op_3980_cast_fp16")];
            tensor<int32, [4]> var_3987_begin_0 = const()[name = string("op_3987_begin_0"), val = tensor<int32, [4]>([9, 0, 0, 0])];
            tensor<int32, [4]> var_3987_end_0 = const()[name = string("op_3987_end_0"), val = tensor<int32, [4]>([10, 2, 2048, 128])];
            tensor<bool, [4]> var_3987_end_mask_0 = const()[name = string("op_3987_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3987_cast_fp16 = slice_by_index(begin = var_3987_begin_0, end = var_3987_end_0, end_mask = var_3987_end_mask_0, x = coreml_update_state_243)[name = string("op_3987_cast_fp16")];
            tensor<int32, [2]> tile_19 = const()[name = string("tile_19"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3990_axis_0 = const()[name = string("op_3990_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3990_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3990_cast_fp16_1 = split(axis = var_3990_axis_0, split_sizes = tile_19, x = var_3987_cast_fp16)[name = string("op_3990_cast_fp16")];
            tensor<int32, [2]> var_3993_split_sizes_0 = const()[name = string("op_3993_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3993_axis_0 = const()[name = string("op_3993_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_3993_0, tensor<fp16, [1, 8, 128, 32]> var_3993_1 = split(axis = var_3993_axis_0, split_sizes = var_3993_split_sizes_0, x = query_states_57_cast_fp16)[name = string("op_3993")];
            bool attn_weights_145_transpose_x_0 = const()[name = string("attn_weights_145_transpose_x_0"), val = bool(false)];
            bool attn_weights_145_transpose_y_0 = const()[name = string("attn_weights_145_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_145_cast_fp16 = matmul(transpose_x = attn_weights_145_transpose_x_0, transpose_y = attn_weights_145_transpose_y_0, x = var_3980_cast_fp16_0, y = var_3993_0)[name = string("attn_weights_145_cast_fp16")];
            fp16 var_3996_to_fp16 = const()[name = string("op_3996_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_147_cast_fp16 = mul(x = attn_weights_145_cast_fp16, y = var_3996_to_fp16)[name = string("attn_weights_147_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_149_cast_fp16 = add(x = attn_weights_147_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_149_cast_fp16")];
            int32 var_4000 = const()[name = string("op_4000"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_151_cast_fp16 = softmax(axis = var_4000, x = attn_weights_149_cast_fp16)[name = string("attn_weights_151_cast_fp16")];
            bool var_4006_transpose_x_1 = const()[name = string("op_4006_transpose_x_1"), val = bool(true)];
            bool var_4006_transpose_y_1 = const()[name = string("op_4006_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_4006_cast_fp16 = matmul(transpose_x = var_4006_transpose_x_1, transpose_y = var_4006_transpose_y_1, x = attn_weights_151_cast_fp16, y = var_3990_cast_fp16_0)[name = string("op_4006_cast_fp16")];
            bool attn_weights_153_transpose_x_0 = const()[name = string("attn_weights_153_transpose_x_0"), val = bool(false)];
            bool attn_weights_153_transpose_y_0 = const()[name = string("attn_weights_153_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_153_cast_fp16 = matmul(transpose_x = attn_weights_153_transpose_x_0, transpose_y = attn_weights_153_transpose_y_0, x = var_3980_cast_fp16_1, y = var_3993_1)[name = string("attn_weights_153_cast_fp16")];
            fp16 var_4008_to_fp16 = const()[name = string("op_4008_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_155_cast_fp16 = mul(x = attn_weights_153_cast_fp16, y = var_4008_to_fp16)[name = string("attn_weights_155_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_157_cast_fp16 = add(x = attn_weights_155_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_157_cast_fp16")];
            int32 var_4012 = const()[name = string("op_4012"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_159_cast_fp16 = softmax(axis = var_4012, x = attn_weights_157_cast_fp16)[name = string("attn_weights_159_cast_fp16")];
            bool attn_output_73_transpose_x_1 = const()[name = string("attn_output_73_transpose_x_1"), val = bool(true)];
            bool attn_output_73_transpose_y_1 = const()[name = string("attn_output_73_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_73_cast_fp16 = matmul(transpose_x = attn_output_73_transpose_x_1, transpose_y = attn_output_73_transpose_y_1, x = attn_weights_159_cast_fp16, y = var_3990_cast_fp16_1)[name = string("attn_output_73_cast_fp16")];
            int32 var_4020 = const()[name = string("op_4020"), val = int32(1)];
            bool attn_output_75_interleave_0 = const()[name = string("attn_output_75_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_75_cast_fp16 = concat(axis = var_4020, interleave = attn_output_75_interleave_0, values = (var_4006_cast_fp16, attn_output_73_cast_fp16))[name = string("attn_output_75_cast_fp16")];
            tensor<int32, [4]> var_4024_perm_0 = const()[name = string("op_4024_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_119x = const()[name = string("concat_119x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_4024_cast_fp16 = transpose(perm = var_4024_perm_0, x = attn_output_75_cast_fp16)[name = string("transpose_402")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_79_cast_fp16 = reshape(shape = concat_119x, x = var_4024_cast_fp16)[name = string("attn_output_79_cast_fp16")];
            tensor<int32, [2]> hidden_states_93_strides_0 = const()[name = string("hidden_states_93_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_93_pad_type_0 = const()[name = string("hidden_states_93_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_93_pad_0 = const()[name = string("hidden_states_93_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_93_dilations_0 = const()[name = string("hidden_states_93_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_93_groups_0 = const()[name = string("hidden_states_93_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_93_cast_fp16 = conv(dilations = hidden_states_93_dilations_0, groups = hidden_states_93_groups_0, pad = hidden_states_93_pad_0, pad_type = hidden_states_93_pad_type_0, strides = hidden_states_93_strides_0, weight = layers_9_self_attn_o_proj_weight_cast_fp16, x = attn_output_79_cast_fp16)[name = string("hidden_states_93_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_95_cast_fp16 = add(x = hidden_states_89_cast_fp16, y = hidden_states_93_cast_fp16)[name = string("hidden_states_95_cast_fp16")];
            fp16 const_100_promoted_to_fp16 = const()[name = string("const_100_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_4057_cast_fp16 = mul(x = hidden_states_95_cast_fp16, y = const_100_promoted_to_fp16)[name = string("op_4057_cast_fp16")];
            int32 var_4055 = const()[name = string("op_4055"), val = int32(1)];
            bool doubled_77_interleave_0 = const()[name = string("doubled_77_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_77_cast_fp16 = concat(axis = var_4055, interleave = doubled_77_interleave_0, values = (hidden_states_95_cast_fp16, var_4057_cast_fp16))[name = string("doubled_77_cast_fp16")];
            tensor<int32, [1]> out_39_axes_0 = const()[name = string("out_39_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_39_gamma_0_to_fp16 = const()[name = string("out_39_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1397515648)))];
            fp16 var_4067_to_fp16 = const()[name = string("op_4067_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_39_cast_fp16 = layer_norm(axes = out_39_axes_0, epsilon = var_4067_to_fp16, gamma = out_39_gamma_0_to_fp16, x = doubled_77_cast_fp16)[name = string("out_39_cast_fp16")];
            tensor<int32, [2]> var_4078_split_sizes_0 = const()[name = string("op_4078_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4078_axis_0 = const()[name = string("op_4078_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_4078_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_4078_cast_fp16_1 = split(axis = var_4078_axis_0, split_sizes = var_4078_split_sizes_0, x = out_39_cast_fp16)[name = string("op_4078_cast_fp16")];
            tensor<int32, [2]> input_19_strides_0 = const()[name = string("input_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_19_pad_type_0 = const()[name = string("input_19_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_19_pad_0 = const()[name = string("input_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_19_dilations_0 = const()[name = string("input_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_19_groups_0 = const()[name = string("input_19_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_19_cast_fp16 = conv(dilations = input_19_dilations_0, groups = input_19_groups_0, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = input_19_strides_0, weight = layers_9_mlp_gate_proj_weight_cast_fp16, x = var_4078_cast_fp16_0)[name = string("input_19_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_4095_cast_fp16 = silu(x = input_19_cast_fp16)[name = string("op_4095_cast_fp16")];
            tensor<int32, [2]> var_4101_strides_0 = const()[name = string("op_4101_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_4101_pad_type_0 = const()[name = string("op_4101_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_4101_pad_0 = const()[name = string("op_4101_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_4101_dilations_0 = const()[name = string("op_4101_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4101_groups_0 = const()[name = string("op_4101_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_4101_cast_fp16 = conv(dilations = var_4101_dilations_0, groups = var_4101_groups_0, pad = var_4101_pad_0, pad_type = var_4101_pad_type_0, strides = var_4101_strides_0, weight = layers_9_mlp_up_proj_weight_cast_fp16, x = var_4078_cast_fp16_0)[name = string("op_4101_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_99_cast_fp16 = mul(x = var_4095_cast_fp16, y = var_4101_cast_fp16)[name = string("x_99_cast_fp16")];
            tensor<int32, [2]> hidden_states_97_strides_0 = const()[name = string("hidden_states_97_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_97_pad_type_0 = const()[name = string("hidden_states_97_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_97_pad_0 = const()[name = string("hidden_states_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_97_dilations_0 = const()[name = string("hidden_states_97_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_97_groups_0 = const()[name = string("hidden_states_97_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_97_cast_fp16 = conv(dilations = hidden_states_97_dilations_0, groups = hidden_states_97_groups_0, pad = hidden_states_97_pad_0, pad_type = hidden_states_97_pad_type_0, strides = hidden_states_97_strides_0, weight = layers_9_mlp_down_proj_weight_cast_fp16, x = x_99_cast_fp16)[name = string("hidden_states_97_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_99_cast_fp16 = add(x = hidden_states_95_cast_fp16, y = hidden_states_97_cast_fp16)[name = string("hidden_states_99_cast_fp16")];
            fp16 const_102_promoted_to_fp16 = const()[name = string("const_102_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_4119_cast_fp16 = mul(x = hidden_states_99_cast_fp16, y = const_102_promoted_to_fp16)[name = string("op_4119_cast_fp16")];
            int32 var_4117 = const()[name = string("op_4117"), val = int32(1)];
            bool doubled_81_interleave_0 = const()[name = string("doubled_81_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_81_cast_fp16 = concat(axis = var_4117, interleave = doubled_81_interleave_0, values = (hidden_states_99_cast_fp16, var_4119_cast_fp16))[name = string("doubled_81_cast_fp16")];
            tensor<int32, [1]> out_41_axes_0 = const()[name = string("out_41_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_41_gamma_0_to_fp16 = const()[name = string("out_41_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1397523904)))];
            fp16 var_4129_to_fp16 = const()[name = string("op_4129_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_41_cast_fp16 = layer_norm(axes = out_41_axes_0, epsilon = var_4129_to_fp16, gamma = out_41_gamma_0_to_fp16, x = doubled_81_cast_fp16)[name = string("out_41_cast_fp16")];
            tensor<int32, [2]> var_4140_split_sizes_0 = const()[name = string("op_4140_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4140_axis_0 = const()[name = string("op_4140_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_4140_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_4140_cast_fp16_1 = split(axis = var_4140_axis_0, split_sizes = var_4140_split_sizes_0, x = out_41_cast_fp16)[name = string("op_4140_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_10_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_10_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1397532160)))];
            tensor<int32, [2]> query_states_61_strides_0 = const()[name = string("query_states_61_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_61_pad_type_0 = const()[name = string("query_states_61_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_61_pad_0 = const()[name = string("query_states_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_61_dilations_0 = const()[name = string("query_states_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_61_groups_0 = const()[name = string("query_states_61_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_61_cast_fp16 = conv(dilations = query_states_61_dilations_0, groups = query_states_61_groups_0, pad = query_states_61_pad_0, pad_type = query_states_61_pad_type_0, strides = query_states_61_strides_0, weight = layers_10_self_attn_q_proj_weight_to_fp16, x = var_4140_cast_fp16_0)[name = string("query_states_61_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_10_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_10_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1405920832)))];
            tensor<int32, [2]> key_states_101_strides_0 = const()[name = string("key_states_101_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_101_pad_type_0 = const()[name = string("key_states_101_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_101_pad_0 = const()[name = string("key_states_101_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_101_dilations_0 = const()[name = string("key_states_101_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_101_groups_0 = const()[name = string("key_states_101_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_101_cast_fp16 = conv(dilations = key_states_101_dilations_0, groups = key_states_101_groups_0, pad = key_states_101_pad_0, pad_type = key_states_101_pad_type_0, strides = key_states_101_strides_0, weight = layers_10_self_attn_k_proj_weight_to_fp16, x = var_4140_cast_fp16_0)[name = string("key_states_101_cast_fp16")];
            tensor<int32, [2]> value_states_61_strides_0 = const()[name = string("value_states_61_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_61_pad_type_0 = const()[name = string("value_states_61_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_61_pad_0 = const()[name = string("value_states_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_61_dilations_0 = const()[name = string("value_states_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_61_groups_0 = const()[name = string("value_states_61_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_61_cast_fp16 = conv(dilations = value_states_61_dilations_0, groups = value_states_61_groups_0, pad = value_states_61_pad_0, pad_type = value_states_61_pad_type_0, strides = value_states_61_strides_0, weight = layers_10_self_attn_v_proj_weight_cast_fp16, x = var_4140_cast_fp16_0)[name = string("value_states_61_cast_fp16")];
            tensor<int32, [4]> concat_120x = const()[name = string("concat_120x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_101_cast_fp16 = reshape(shape = concat_120x, x = query_states_61_cast_fp16)[name = string("x_101_cast_fp16")];
            tensor<int32, [4]> concat_121x = const()[name = string("concat_121x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_4197_cast_fp16 = reshape(shape = concat_121x, x = key_states_101_cast_fp16)[name = string("op_4197_cast_fp16")];
            tensor<int32, [4]> concat_122x = const()[name = string("concat_122x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_4204_cast_fp16 = reshape(shape = concat_122x, x = value_states_61_cast_fp16)[name = string("op_4204_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_4208_cast_fp16 = mul(x = x_101_cast_fp16, y = var_859_cast_fp16)[name = string("op_4208_cast_fp16")];
            tensor<int32, [2]> var_4209_split_sizes_0 = const()[name = string("op_4209_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4209_axis_0 = const()[name = string("op_4209_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_4209_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_4209_cast_fp16_1 = split(axis = var_4209_axis_0, split_sizes = var_4209_split_sizes_0, x = x_101_cast_fp16)[name = string("op_4209_cast_fp16")];
            fp16 const_104_promoted_to_fp16 = const()[name = string("const_104_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_4211_cast_fp16 = mul(x = var_4209_cast_fp16_1, y = const_104_promoted_to_fp16)[name = string("op_4211_cast_fp16")];
            int32 var_4213 = const()[name = string("op_4213"), val = int32(-2)];
            bool var_4214_interleave_0 = const()[name = string("op_4214_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_4214_cast_fp16 = concat(axis = var_4213, interleave = var_4214_interleave_0, values = (var_4211_cast_fp16, var_4209_cast_fp16_0))[name = string("op_4214_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_4215_cast_fp16 = mul(x = var_4214_cast_fp16, y = var_866_cast_fp16)[name = string("op_4215_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_63_cast_fp16 = add(x = var_4208_cast_fp16, y = var_4215_cast_fp16)[name = string("query_states_63_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_4221_cast_fp16 = mul(x = var_4197_cast_fp16, y = var_859_cast_fp16)[name = string("op_4221_cast_fp16")];
            tensor<int32, [2]> var_4222_split_sizes_0 = const()[name = string("op_4222_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4222_axis_0 = const()[name = string("op_4222_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_4222_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_4222_cast_fp16_1 = split(axis = var_4222_axis_0, split_sizes = var_4222_split_sizes_0, x = var_4197_cast_fp16)[name = string("op_4222_cast_fp16")];
            fp16 const_105_promoted_to_fp16 = const()[name = string("const_105_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_4224_cast_fp16 = mul(x = var_4222_cast_fp16_1, y = const_105_promoted_to_fp16)[name = string("op_4224_cast_fp16")];
            int32 var_4226 = const()[name = string("op_4226"), val = int32(-2)];
            bool var_4227_interleave_0 = const()[name = string("op_4227_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_4227_cast_fp16 = concat(axis = var_4226, interleave = var_4227_interleave_0, values = (var_4224_cast_fp16, var_4222_cast_fp16_0))[name = string("op_4227_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_4228_cast_fp16 = mul(x = var_4227_cast_fp16, y = var_866_cast_fp16)[name = string("op_4228_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_105_cast_fp16 = add(x = var_4221_cast_fp16, y = var_4228_cast_fp16)[name = string("key_states_105_cast_fp16")];
            tensor<int32, [1]> expand_dims_120 = const()[name = string("expand_dims_120"), val = tensor<int32, [1]>([10])];
            tensor<int32, [1]> expand_dims_121 = const()[name = string("expand_dims_121"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_123 = const()[name = string("expand_dims_123"), val = tensor<int32, [1]>([0])];
            int32 concat_125_axis_0 = const()[name = string("concat_125_axis_0"), val = int32(0)];
            bool concat_125_interleave_0 = const()[name = string("concat_125_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_125 = concat(axis = concat_125_axis_0, interleave = concat_125_interleave_0, values = (expand_dims_120, expand_dims_121, position_id, expand_dims_123))[name = string("concat_125")];
            tensor<int32, [1]> expand_dims_124 = const()[name = string("expand_dims_124"), val = tensor<int32, [1]>([11])];
            tensor<int32, [1]> concat_126_values1_0 = const()[name = string("concat_126_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_126_values3_0 = const()[name = string("concat_126_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_126_axis_0 = const()[name = string("concat_126_axis_0"), val = int32(0)];
            bool concat_126_interleave_0 = const()[name = string("concat_126_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_126 = concat(axis = concat_126_axis_0, interleave = concat_126_interleave_0, values = (expand_dims_124, concat_126_values1_0, cache_position_end, concat_126_values3_0))[name = string("concat_126")];
            tensor<int32, [4]> key_states_107_perm_0 = const()[name = string("key_states_107_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_11_stride_0 = const()[name = string("key_cache_internal_tensor_assign_11_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_11_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_11_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_11_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_11_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_11_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_11_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_107_cast_fp16 = transpose(perm = key_states_107_perm_0, x = key_states_105_cast_fp16)[name = string("transpose_401")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_11_cast_fp16 = slice_update(begin = concat_125, begin_mask = key_cache_internal_tensor_assign_11_begin_mask_0, end = concat_126, end_mask = key_cache_internal_tensor_assign_11_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_11_squeeze_mask_0, stride = key_cache_internal_tensor_assign_11_stride_0, update = key_states_107_cast_fp16, x = coreml_update_state_242)[name = string("key_cache_internal_tensor_assign_11_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_11_cast_fp16, input = key_cache)[name = string("coreml_update_state_244_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_244 = read_state(input = key_cache)[name = string("coreml_update_state_244")];
            tensor<int32, [4]> value_states_63_perm_0 = const()[name = string("value_states_63_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_11_stride_0 = const()[name = string("value_cache_internal_tensor_assign_11_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_11_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_11_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_11_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_11_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_11_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_11_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_63_cast_fp16 = transpose(perm = value_states_63_perm_0, x = var_4204_cast_fp16)[name = string("transpose_400")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_11_cast_fp16 = slice_update(begin = concat_125, begin_mask = value_cache_internal_tensor_assign_11_begin_mask_0, end = concat_126, end_mask = value_cache_internal_tensor_assign_11_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_11_squeeze_mask_0, stride = value_cache_internal_tensor_assign_11_stride_0, update = value_states_63_cast_fp16, x = coreml_update_state_243)[name = string("value_cache_internal_tensor_assign_11_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_11_cast_fp16, input = value_cache)[name = string("coreml_update_state_245_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_245 = read_state(input = value_cache)[name = string("coreml_update_state_245")];
            tensor<int32, [4]> var_4298_begin_0 = const()[name = string("op_4298_begin_0"), val = tensor<int32, [4]>([10, 0, 0, 0])];
            tensor<int32, [4]> var_4298_end_0 = const()[name = string("op_4298_end_0"), val = tensor<int32, [4]>([11, 2, 2048, 128])];
            tensor<bool, [4]> var_4298_end_mask_0 = const()[name = string("op_4298_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4298_cast_fp16 = slice_by_index(begin = var_4298_begin_0, end = var_4298_end_0, end_mask = var_4298_end_mask_0, x = coreml_update_state_244)[name = string("op_4298_cast_fp16")];
            tensor<int32, [2]> tile_20 = const()[name = string("tile_20"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4301_axis_0 = const()[name = string("op_4301_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4301_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4301_cast_fp16_1 = split(axis = var_4301_axis_0, split_sizes = tile_20, x = var_4298_cast_fp16)[name = string("op_4301_cast_fp16")];
            tensor<int32, [4]> var_4308_begin_0 = const()[name = string("op_4308_begin_0"), val = tensor<int32, [4]>([10, 0, 0, 0])];
            tensor<int32, [4]> var_4308_end_0 = const()[name = string("op_4308_end_0"), val = tensor<int32, [4]>([11, 2, 2048, 128])];
            tensor<bool, [4]> var_4308_end_mask_0 = const()[name = string("op_4308_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4308_cast_fp16 = slice_by_index(begin = var_4308_begin_0, end = var_4308_end_0, end_mask = var_4308_end_mask_0, x = coreml_update_state_245)[name = string("op_4308_cast_fp16")];
            tensor<int32, [2]> tile_21 = const()[name = string("tile_21"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4311_axis_0 = const()[name = string("op_4311_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4311_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4311_cast_fp16_1 = split(axis = var_4311_axis_0, split_sizes = tile_21, x = var_4308_cast_fp16)[name = string("op_4311_cast_fp16")];
            tensor<int32, [2]> var_4314_split_sizes_0 = const()[name = string("op_4314_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_4314_axis_0 = const()[name = string("op_4314_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_4314_0, tensor<fp16, [1, 8, 128, 32]> var_4314_1 = split(axis = var_4314_axis_0, split_sizes = var_4314_split_sizes_0, x = query_states_63_cast_fp16)[name = string("op_4314")];
            bool attn_weights_161_transpose_x_0 = const()[name = string("attn_weights_161_transpose_x_0"), val = bool(false)];
            bool attn_weights_161_transpose_y_0 = const()[name = string("attn_weights_161_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_161_cast_fp16 = matmul(transpose_x = attn_weights_161_transpose_x_0, transpose_y = attn_weights_161_transpose_y_0, x = var_4301_cast_fp16_0, y = var_4314_0)[name = string("attn_weights_161_cast_fp16")];
            fp16 var_4317_to_fp16 = const()[name = string("op_4317_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_163_cast_fp16 = mul(x = attn_weights_161_cast_fp16, y = var_4317_to_fp16)[name = string("attn_weights_163_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_165_cast_fp16 = add(x = attn_weights_163_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_165_cast_fp16")];
            int32 var_4321 = const()[name = string("op_4321"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_167_cast_fp16 = softmax(axis = var_4321, x = attn_weights_165_cast_fp16)[name = string("attn_weights_167_cast_fp16")];
            bool var_4327_transpose_x_1 = const()[name = string("op_4327_transpose_x_1"), val = bool(true)];
            bool var_4327_transpose_y_1 = const()[name = string("op_4327_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_4327_cast_fp16 = matmul(transpose_x = var_4327_transpose_x_1, transpose_y = var_4327_transpose_y_1, x = attn_weights_167_cast_fp16, y = var_4311_cast_fp16_0)[name = string("op_4327_cast_fp16")];
            bool attn_weights_169_transpose_x_0 = const()[name = string("attn_weights_169_transpose_x_0"), val = bool(false)];
            bool attn_weights_169_transpose_y_0 = const()[name = string("attn_weights_169_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_169_cast_fp16 = matmul(transpose_x = attn_weights_169_transpose_x_0, transpose_y = attn_weights_169_transpose_y_0, x = var_4301_cast_fp16_1, y = var_4314_1)[name = string("attn_weights_169_cast_fp16")];
            fp16 var_4329_to_fp16 = const()[name = string("op_4329_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_171_cast_fp16 = mul(x = attn_weights_169_cast_fp16, y = var_4329_to_fp16)[name = string("attn_weights_171_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_173_cast_fp16 = add(x = attn_weights_171_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_173_cast_fp16")];
            int32 var_4333 = const()[name = string("op_4333"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_175_cast_fp16 = softmax(axis = var_4333, x = attn_weights_173_cast_fp16)[name = string("attn_weights_175_cast_fp16")];
            bool attn_output_81_transpose_x_1 = const()[name = string("attn_output_81_transpose_x_1"), val = bool(true)];
            bool attn_output_81_transpose_y_1 = const()[name = string("attn_output_81_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_81_cast_fp16 = matmul(transpose_x = attn_output_81_transpose_x_1, transpose_y = attn_output_81_transpose_y_1, x = attn_weights_175_cast_fp16, y = var_4311_cast_fp16_1)[name = string("attn_output_81_cast_fp16")];
            int32 var_4341 = const()[name = string("op_4341"), val = int32(1)];
            bool attn_output_83_interleave_0 = const()[name = string("attn_output_83_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_83_cast_fp16 = concat(axis = var_4341, interleave = attn_output_83_interleave_0, values = (var_4327_cast_fp16, attn_output_81_cast_fp16))[name = string("attn_output_83_cast_fp16")];
            tensor<int32, [4]> var_4345_perm_0 = const()[name = string("op_4345_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_131x = const()[name = string("concat_131x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_4345_cast_fp16 = transpose(perm = var_4345_perm_0, x = attn_output_83_cast_fp16)[name = string("transpose_399")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_87_cast_fp16 = reshape(shape = concat_131x, x = var_4345_cast_fp16)[name = string("attn_output_87_cast_fp16")];
            tensor<int32, [2]> hidden_states_103_strides_0 = const()[name = string("hidden_states_103_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_103_pad_type_0 = const()[name = string("hidden_states_103_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_103_pad_0 = const()[name = string("hidden_states_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_103_dilations_0 = const()[name = string("hidden_states_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_103_groups_0 = const()[name = string("hidden_states_103_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_103_cast_fp16 = conv(dilations = hidden_states_103_dilations_0, groups = hidden_states_103_groups_0, pad = hidden_states_103_pad_0, pad_type = hidden_states_103_pad_type_0, strides = hidden_states_103_strides_0, weight = layers_10_self_attn_o_proj_weight_cast_fp16, x = attn_output_87_cast_fp16)[name = string("hidden_states_103_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_105_cast_fp16 = add(x = hidden_states_99_cast_fp16, y = hidden_states_103_cast_fp16)[name = string("hidden_states_105_cast_fp16")];
            fp16 const_110_promoted_to_fp16 = const()[name = string("const_110_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_4378_cast_fp16 = mul(x = hidden_states_105_cast_fp16, y = const_110_promoted_to_fp16)[name = string("op_4378_cast_fp16")];
            int32 var_4376 = const()[name = string("op_4376"), val = int32(1)];
            bool doubled_85_interleave_0 = const()[name = string("doubled_85_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_85_cast_fp16 = concat(axis = var_4376, interleave = doubled_85_interleave_0, values = (hidden_states_105_cast_fp16, var_4378_cast_fp16))[name = string("doubled_85_cast_fp16")];
            tensor<int32, [1]> out_43_axes_0 = const()[name = string("out_43_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_43_gamma_0_to_fp16 = const()[name = string("out_43_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406969472)))];
            fp16 var_4388_to_fp16 = const()[name = string("op_4388_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_43_cast_fp16 = layer_norm(axes = out_43_axes_0, epsilon = var_4388_to_fp16, gamma = out_43_gamma_0_to_fp16, x = doubled_85_cast_fp16)[name = string("out_43_cast_fp16")];
            tensor<int32, [2]> var_4399_split_sizes_0 = const()[name = string("op_4399_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4399_axis_0 = const()[name = string("op_4399_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_4399_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_4399_cast_fp16_1 = split(axis = var_4399_axis_0, split_sizes = var_4399_split_sizes_0, x = out_43_cast_fp16)[name = string("op_4399_cast_fp16")];
            tensor<int32, [2]> input_21_strides_0 = const()[name = string("input_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_21_pad_type_0 = const()[name = string("input_21_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_21_pad_0 = const()[name = string("input_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_21_dilations_0 = const()[name = string("input_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_21_groups_0 = const()[name = string("input_21_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_21_cast_fp16 = conv(dilations = input_21_dilations_0, groups = input_21_groups_0, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = input_21_strides_0, weight = layers_10_mlp_gate_proj_weight_cast_fp16, x = var_4399_cast_fp16_0)[name = string("input_21_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_4416_cast_fp16 = silu(x = input_21_cast_fp16)[name = string("op_4416_cast_fp16")];
            tensor<int32, [2]> var_4422_strides_0 = const()[name = string("op_4422_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_4422_pad_type_0 = const()[name = string("op_4422_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_4422_pad_0 = const()[name = string("op_4422_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_4422_dilations_0 = const()[name = string("op_4422_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4422_groups_0 = const()[name = string("op_4422_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_4422_cast_fp16 = conv(dilations = var_4422_dilations_0, groups = var_4422_groups_0, pad = var_4422_pad_0, pad_type = var_4422_pad_type_0, strides = var_4422_strides_0, weight = layers_10_mlp_up_proj_weight_cast_fp16, x = var_4399_cast_fp16_0)[name = string("op_4422_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_109_cast_fp16 = mul(x = var_4416_cast_fp16, y = var_4422_cast_fp16)[name = string("x_109_cast_fp16")];
            tensor<int32, [2]> hidden_states_107_strides_0 = const()[name = string("hidden_states_107_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_107_pad_type_0 = const()[name = string("hidden_states_107_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_107_pad_0 = const()[name = string("hidden_states_107_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_107_dilations_0 = const()[name = string("hidden_states_107_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_107_groups_0 = const()[name = string("hidden_states_107_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_107_cast_fp16 = conv(dilations = hidden_states_107_dilations_0, groups = hidden_states_107_groups_0, pad = hidden_states_107_pad_0, pad_type = hidden_states_107_pad_type_0, strides = hidden_states_107_strides_0, weight = layers_10_mlp_down_proj_weight_cast_fp16, x = x_109_cast_fp16)[name = string("hidden_states_107_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_109_cast_fp16 = add(x = hidden_states_105_cast_fp16, y = hidden_states_107_cast_fp16)[name = string("hidden_states_109_cast_fp16")];
            fp16 const_112_promoted_to_fp16 = const()[name = string("const_112_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_4440_cast_fp16 = mul(x = hidden_states_109_cast_fp16, y = const_112_promoted_to_fp16)[name = string("op_4440_cast_fp16")];
            int32 var_4438 = const()[name = string("op_4438"), val = int32(1)];
            bool doubled_89_interleave_0 = const()[name = string("doubled_89_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_89_cast_fp16 = concat(axis = var_4438, interleave = doubled_89_interleave_0, values = (hidden_states_109_cast_fp16, var_4440_cast_fp16))[name = string("doubled_89_cast_fp16")];
            tensor<int32, [1]> out_45_axes_0 = const()[name = string("out_45_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_45_gamma_0_to_fp16 = const()[name = string("out_45_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406977728)))];
            fp16 var_4450_to_fp16 = const()[name = string("op_4450_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_45_cast_fp16 = layer_norm(axes = out_45_axes_0, epsilon = var_4450_to_fp16, gamma = out_45_gamma_0_to_fp16, x = doubled_89_cast_fp16)[name = string("out_45_cast_fp16")];
            tensor<int32, [2]> var_4461_split_sizes_0 = const()[name = string("op_4461_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4461_axis_0 = const()[name = string("op_4461_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_4461_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_4461_cast_fp16_1 = split(axis = var_4461_axis_0, split_sizes = var_4461_split_sizes_0, x = out_45_cast_fp16)[name = string("op_4461_cast_fp16")];
            tensor<int32, [2]> query_states_67_strides_0 = const()[name = string("query_states_67_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_67_pad_type_0 = const()[name = string("query_states_67_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_67_pad_0 = const()[name = string("query_states_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_67_dilations_0 = const()[name = string("query_states_67_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_67_groups_0 = const()[name = string("query_states_67_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_67_cast_fp16 = conv(dilations = query_states_67_dilations_0, groups = query_states_67_groups_0, pad = query_states_67_pad_0, pad_type = query_states_67_pad_type_0, strides = query_states_67_strides_0, weight = layers_11_self_attn_q_proj_weight_cast_fp16, x = var_4461_cast_fp16_0)[name = string("query_states_67_cast_fp16")];
            tensor<int32, [2]> key_states_111_strides_0 = const()[name = string("key_states_111_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_111_pad_type_0 = const()[name = string("key_states_111_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_111_pad_0 = const()[name = string("key_states_111_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_111_dilations_0 = const()[name = string("key_states_111_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_111_groups_0 = const()[name = string("key_states_111_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_111_cast_fp16 = conv(dilations = key_states_111_dilations_0, groups = key_states_111_groups_0, pad = key_states_111_pad_0, pad_type = key_states_111_pad_type_0, strides = key_states_111_strides_0, weight = layers_11_self_attn_k_proj_weight_cast_fp16, x = var_4461_cast_fp16_0)[name = string("key_states_111_cast_fp16")];
            tensor<int32, [2]> value_states_67_strides_0 = const()[name = string("value_states_67_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_67_pad_type_0 = const()[name = string("value_states_67_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_67_pad_0 = const()[name = string("value_states_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_67_dilations_0 = const()[name = string("value_states_67_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_67_groups_0 = const()[name = string("value_states_67_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_67_cast_fp16 = conv(dilations = value_states_67_dilations_0, groups = value_states_67_groups_0, pad = value_states_67_pad_0, pad_type = value_states_67_pad_type_0, strides = value_states_67_strides_0, weight = layers_11_self_attn_v_proj_weight_cast_fp16, x = var_4461_cast_fp16_0)[name = string("value_states_67_cast_fp16")];
            tensor<int32, [4]> concat_132x = const()[name = string("concat_132x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_111_cast_fp16 = reshape(shape = concat_132x, x = query_states_67_cast_fp16)[name = string("x_111_cast_fp16")];
            tensor<int32, [4]> concat_133x = const()[name = string("concat_133x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_4518_cast_fp16 = reshape(shape = concat_133x, x = key_states_111_cast_fp16)[name = string("op_4518_cast_fp16")];
            tensor<int32, [4]> concat_134x = const()[name = string("concat_134x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_4525_cast_fp16 = reshape(shape = concat_134x, x = value_states_67_cast_fp16)[name = string("op_4525_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_4529_cast_fp16 = mul(x = x_111_cast_fp16, y = var_859_cast_fp16)[name = string("op_4529_cast_fp16")];
            tensor<int32, [2]> var_4530_split_sizes_0 = const()[name = string("op_4530_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4530_axis_0 = const()[name = string("op_4530_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_4530_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_4530_cast_fp16_1 = split(axis = var_4530_axis_0, split_sizes = var_4530_split_sizes_0, x = x_111_cast_fp16)[name = string("op_4530_cast_fp16")];
            fp16 const_114_promoted_to_fp16 = const()[name = string("const_114_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_4532_cast_fp16 = mul(x = var_4530_cast_fp16_1, y = const_114_promoted_to_fp16)[name = string("op_4532_cast_fp16")];
            int32 var_4534 = const()[name = string("op_4534"), val = int32(-2)];
            bool var_4535_interleave_0 = const()[name = string("op_4535_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_4535_cast_fp16 = concat(axis = var_4534, interleave = var_4535_interleave_0, values = (var_4532_cast_fp16, var_4530_cast_fp16_0))[name = string("op_4535_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_4536_cast_fp16 = mul(x = var_4535_cast_fp16, y = var_866_cast_fp16)[name = string("op_4536_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_69_cast_fp16 = add(x = var_4529_cast_fp16, y = var_4536_cast_fp16)[name = string("query_states_69_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_4542_cast_fp16 = mul(x = var_4518_cast_fp16, y = var_859_cast_fp16)[name = string("op_4542_cast_fp16")];
            tensor<int32, [2]> var_4543_split_sizes_0 = const()[name = string("op_4543_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4543_axis_0 = const()[name = string("op_4543_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_4543_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_4543_cast_fp16_1 = split(axis = var_4543_axis_0, split_sizes = var_4543_split_sizes_0, x = var_4518_cast_fp16)[name = string("op_4543_cast_fp16")];
            fp16 const_115_promoted_to_fp16 = const()[name = string("const_115_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_4545_cast_fp16 = mul(x = var_4543_cast_fp16_1, y = const_115_promoted_to_fp16)[name = string("op_4545_cast_fp16")];
            int32 var_4547 = const()[name = string("op_4547"), val = int32(-2)];
            bool var_4548_interleave_0 = const()[name = string("op_4548_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_4548_cast_fp16 = concat(axis = var_4547, interleave = var_4548_interleave_0, values = (var_4545_cast_fp16, var_4543_cast_fp16_0))[name = string("op_4548_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_4549_cast_fp16 = mul(x = var_4548_cast_fp16, y = var_866_cast_fp16)[name = string("op_4549_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_115_cast_fp16 = add(x = var_4542_cast_fp16, y = var_4549_cast_fp16)[name = string("key_states_115_cast_fp16")];
            tensor<int32, [1]> expand_dims_132 = const()[name = string("expand_dims_132"), val = tensor<int32, [1]>([11])];
            tensor<int32, [1]> expand_dims_133 = const()[name = string("expand_dims_133"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_135 = const()[name = string("expand_dims_135"), val = tensor<int32, [1]>([0])];
            int32 concat_137_axis_0 = const()[name = string("concat_137_axis_0"), val = int32(0)];
            bool concat_137_interleave_0 = const()[name = string("concat_137_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_137 = concat(axis = concat_137_axis_0, interleave = concat_137_interleave_0, values = (expand_dims_132, expand_dims_133, position_id, expand_dims_135))[name = string("concat_137")];
            tensor<int32, [1]> expand_dims_136 = const()[name = string("expand_dims_136"), val = tensor<int32, [1]>([12])];
            tensor<int32, [1]> concat_138_values1_0 = const()[name = string("concat_138_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_138_values3_0 = const()[name = string("concat_138_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_138_axis_0 = const()[name = string("concat_138_axis_0"), val = int32(0)];
            bool concat_138_interleave_0 = const()[name = string("concat_138_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_138 = concat(axis = concat_138_axis_0, interleave = concat_138_interleave_0, values = (expand_dims_136, concat_138_values1_0, cache_position_end, concat_138_values3_0))[name = string("concat_138")];
            tensor<int32, [4]> key_states_117_perm_0 = const()[name = string("key_states_117_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_12_stride_0 = const()[name = string("key_cache_internal_tensor_assign_12_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_12_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_12_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_12_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_12_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_12_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_12_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_117_cast_fp16 = transpose(perm = key_states_117_perm_0, x = key_states_115_cast_fp16)[name = string("transpose_398")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_12_cast_fp16 = slice_update(begin = concat_137, begin_mask = key_cache_internal_tensor_assign_12_begin_mask_0, end = concat_138, end_mask = key_cache_internal_tensor_assign_12_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_12_squeeze_mask_0, stride = key_cache_internal_tensor_assign_12_stride_0, update = key_states_117_cast_fp16, x = coreml_update_state_244)[name = string("key_cache_internal_tensor_assign_12_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_12_cast_fp16, input = key_cache)[name = string("coreml_update_state_246_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_246 = read_state(input = key_cache)[name = string("coreml_update_state_246")];
            tensor<int32, [4]> value_states_69_perm_0 = const()[name = string("value_states_69_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_12_stride_0 = const()[name = string("value_cache_internal_tensor_assign_12_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_12_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_12_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_12_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_12_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_12_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_12_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_69_cast_fp16 = transpose(perm = value_states_69_perm_0, x = var_4525_cast_fp16)[name = string("transpose_397")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_12_cast_fp16 = slice_update(begin = concat_137, begin_mask = value_cache_internal_tensor_assign_12_begin_mask_0, end = concat_138, end_mask = value_cache_internal_tensor_assign_12_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_12_squeeze_mask_0, stride = value_cache_internal_tensor_assign_12_stride_0, update = value_states_69_cast_fp16, x = coreml_update_state_245)[name = string("value_cache_internal_tensor_assign_12_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_12_cast_fp16, input = value_cache)[name = string("coreml_update_state_247_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_247 = read_state(input = value_cache)[name = string("coreml_update_state_247")];
            tensor<int32, [4]> var_4619_begin_0 = const()[name = string("op_4619_begin_0"), val = tensor<int32, [4]>([11, 0, 0, 0])];
            tensor<int32, [4]> var_4619_end_0 = const()[name = string("op_4619_end_0"), val = tensor<int32, [4]>([12, 2, 2048, 128])];
            tensor<bool, [4]> var_4619_end_mask_0 = const()[name = string("op_4619_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4619_cast_fp16 = slice_by_index(begin = var_4619_begin_0, end = var_4619_end_0, end_mask = var_4619_end_mask_0, x = coreml_update_state_246)[name = string("op_4619_cast_fp16")];
            tensor<int32, [2]> tile_22 = const()[name = string("tile_22"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4622_axis_0 = const()[name = string("op_4622_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4622_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4622_cast_fp16_1 = split(axis = var_4622_axis_0, split_sizes = tile_22, x = var_4619_cast_fp16)[name = string("op_4622_cast_fp16")];
            tensor<int32, [4]> var_4629_begin_0 = const()[name = string("op_4629_begin_0"), val = tensor<int32, [4]>([11, 0, 0, 0])];
            tensor<int32, [4]> var_4629_end_0 = const()[name = string("op_4629_end_0"), val = tensor<int32, [4]>([12, 2, 2048, 128])];
            tensor<bool, [4]> var_4629_end_mask_0 = const()[name = string("op_4629_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4629_cast_fp16 = slice_by_index(begin = var_4629_begin_0, end = var_4629_end_0, end_mask = var_4629_end_mask_0, x = coreml_update_state_247)[name = string("op_4629_cast_fp16")];
            tensor<int32, [2]> tile_23 = const()[name = string("tile_23"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4632_axis_0 = const()[name = string("op_4632_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4632_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4632_cast_fp16_1 = split(axis = var_4632_axis_0, split_sizes = tile_23, x = var_4629_cast_fp16)[name = string("op_4632_cast_fp16")];
            tensor<int32, [2]> var_4635_split_sizes_0 = const()[name = string("op_4635_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_4635_axis_0 = const()[name = string("op_4635_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_4635_0, tensor<fp16, [1, 8, 128, 32]> var_4635_1 = split(axis = var_4635_axis_0, split_sizes = var_4635_split_sizes_0, x = query_states_69_cast_fp16)[name = string("op_4635")];
            bool attn_weights_177_transpose_x_0 = const()[name = string("attn_weights_177_transpose_x_0"), val = bool(false)];
            bool attn_weights_177_transpose_y_0 = const()[name = string("attn_weights_177_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_177_cast_fp16 = matmul(transpose_x = attn_weights_177_transpose_x_0, transpose_y = attn_weights_177_transpose_y_0, x = var_4622_cast_fp16_0, y = var_4635_0)[name = string("attn_weights_177_cast_fp16")];
            fp16 var_4638_to_fp16 = const()[name = string("op_4638_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_179_cast_fp16 = mul(x = attn_weights_177_cast_fp16, y = var_4638_to_fp16)[name = string("attn_weights_179_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_181_cast_fp16 = add(x = attn_weights_179_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_181_cast_fp16")];
            int32 var_4642 = const()[name = string("op_4642"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_183_cast_fp16 = softmax(axis = var_4642, x = attn_weights_181_cast_fp16)[name = string("attn_weights_183_cast_fp16")];
            bool var_4648_transpose_x_1 = const()[name = string("op_4648_transpose_x_1"), val = bool(true)];
            bool var_4648_transpose_y_1 = const()[name = string("op_4648_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_4648_cast_fp16 = matmul(transpose_x = var_4648_transpose_x_1, transpose_y = var_4648_transpose_y_1, x = attn_weights_183_cast_fp16, y = var_4632_cast_fp16_0)[name = string("op_4648_cast_fp16")];
            bool attn_weights_185_transpose_x_0 = const()[name = string("attn_weights_185_transpose_x_0"), val = bool(false)];
            bool attn_weights_185_transpose_y_0 = const()[name = string("attn_weights_185_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_185_cast_fp16 = matmul(transpose_x = attn_weights_185_transpose_x_0, transpose_y = attn_weights_185_transpose_y_0, x = var_4622_cast_fp16_1, y = var_4635_1)[name = string("attn_weights_185_cast_fp16")];
            fp16 var_4650_to_fp16 = const()[name = string("op_4650_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_187_cast_fp16 = mul(x = attn_weights_185_cast_fp16, y = var_4650_to_fp16)[name = string("attn_weights_187_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_189_cast_fp16 = add(x = attn_weights_187_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_189_cast_fp16")];
            int32 var_4654 = const()[name = string("op_4654"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_191_cast_fp16 = softmax(axis = var_4654, x = attn_weights_189_cast_fp16)[name = string("attn_weights_191_cast_fp16")];
            bool attn_output_89_transpose_x_1 = const()[name = string("attn_output_89_transpose_x_1"), val = bool(true)];
            bool attn_output_89_transpose_y_1 = const()[name = string("attn_output_89_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_89_cast_fp16 = matmul(transpose_x = attn_output_89_transpose_x_1, transpose_y = attn_output_89_transpose_y_1, x = attn_weights_191_cast_fp16, y = var_4632_cast_fp16_1)[name = string("attn_output_89_cast_fp16")];
            int32 var_4662 = const()[name = string("op_4662"), val = int32(1)];
            bool attn_output_91_interleave_0 = const()[name = string("attn_output_91_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_91_cast_fp16 = concat(axis = var_4662, interleave = attn_output_91_interleave_0, values = (var_4648_cast_fp16, attn_output_89_cast_fp16))[name = string("attn_output_91_cast_fp16")];
            tensor<int32, [4]> var_4666_perm_0 = const()[name = string("op_4666_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_143x = const()[name = string("concat_143x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_4666_cast_fp16 = transpose(perm = var_4666_perm_0, x = attn_output_91_cast_fp16)[name = string("transpose_396")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_95_cast_fp16 = reshape(shape = concat_143x, x = var_4666_cast_fp16)[name = string("attn_output_95_cast_fp16")];
            tensor<int32, [2]> hidden_states_113_strides_0 = const()[name = string("hidden_states_113_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_113_pad_type_0 = const()[name = string("hidden_states_113_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_113_pad_0 = const()[name = string("hidden_states_113_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_113_dilations_0 = const()[name = string("hidden_states_113_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_113_groups_0 = const()[name = string("hidden_states_113_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_113_cast_fp16 = conv(dilations = hidden_states_113_dilations_0, groups = hidden_states_113_groups_0, pad = hidden_states_113_pad_0, pad_type = hidden_states_113_pad_type_0, strides = hidden_states_113_strides_0, weight = layers_11_self_attn_o_proj_weight_cast_fp16, x = attn_output_95_cast_fp16)[name = string("hidden_states_113_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_115_cast_fp16 = add(x = hidden_states_109_cast_fp16, y = hidden_states_113_cast_fp16)[name = string("hidden_states_115_cast_fp16")];
            fp16 const_120_promoted_to_fp16 = const()[name = string("const_120_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_4699_cast_fp16 = mul(x = hidden_states_115_cast_fp16, y = const_120_promoted_to_fp16)[name = string("op_4699_cast_fp16")];
            int32 var_4697 = const()[name = string("op_4697"), val = int32(1)];
            bool doubled_93_interleave_0 = const()[name = string("doubled_93_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_93_cast_fp16 = concat(axis = var_4697, interleave = doubled_93_interleave_0, values = (hidden_states_115_cast_fp16, var_4699_cast_fp16))[name = string("doubled_93_cast_fp16")];
            tensor<int32, [1]> out_47_axes_0 = const()[name = string("out_47_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_47_gamma_0_to_fp16 = const()[name = string("out_47_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406985984)))];
            fp16 var_4709_to_fp16 = const()[name = string("op_4709_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_47_cast_fp16 = layer_norm(axes = out_47_axes_0, epsilon = var_4709_to_fp16, gamma = out_47_gamma_0_to_fp16, x = doubled_93_cast_fp16)[name = string("out_47_cast_fp16")];
            tensor<int32, [2]> var_4720_split_sizes_0 = const()[name = string("op_4720_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4720_axis_0 = const()[name = string("op_4720_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_4720_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_4720_cast_fp16_1 = split(axis = var_4720_axis_0, split_sizes = var_4720_split_sizes_0, x = out_47_cast_fp16)[name = string("op_4720_cast_fp16")];
            tensor<int32, [2]> input_23_strides_0 = const()[name = string("input_23_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_23_pad_type_0 = const()[name = string("input_23_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_23_pad_0 = const()[name = string("input_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_23_dilations_0 = const()[name = string("input_23_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_23_groups_0 = const()[name = string("input_23_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_23_cast_fp16 = conv(dilations = input_23_dilations_0, groups = input_23_groups_0, pad = input_23_pad_0, pad_type = input_23_pad_type_0, strides = input_23_strides_0, weight = layers_11_mlp_gate_proj_weight_cast_fp16, x = var_4720_cast_fp16_0)[name = string("input_23_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_4737_cast_fp16 = silu(x = input_23_cast_fp16)[name = string("op_4737_cast_fp16")];
            tensor<int32, [2]> var_4743_strides_0 = const()[name = string("op_4743_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_4743_pad_type_0 = const()[name = string("op_4743_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_4743_pad_0 = const()[name = string("op_4743_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_4743_dilations_0 = const()[name = string("op_4743_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4743_groups_0 = const()[name = string("op_4743_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_4743_cast_fp16 = conv(dilations = var_4743_dilations_0, groups = var_4743_groups_0, pad = var_4743_pad_0, pad_type = var_4743_pad_type_0, strides = var_4743_strides_0, weight = layers_11_mlp_up_proj_weight_cast_fp16, x = var_4720_cast_fp16_0)[name = string("op_4743_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_119_cast_fp16 = mul(x = var_4737_cast_fp16, y = var_4743_cast_fp16)[name = string("x_119_cast_fp16")];
            tensor<int32, [2]> hidden_states_117_strides_0 = const()[name = string("hidden_states_117_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_117_pad_type_0 = const()[name = string("hidden_states_117_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_117_pad_0 = const()[name = string("hidden_states_117_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_117_dilations_0 = const()[name = string("hidden_states_117_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_117_groups_0 = const()[name = string("hidden_states_117_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_117_cast_fp16 = conv(dilations = hidden_states_117_dilations_0, groups = hidden_states_117_groups_0, pad = hidden_states_117_pad_0, pad_type = hidden_states_117_pad_type_0, strides = hidden_states_117_strides_0, weight = layers_11_mlp_down_proj_weight_cast_fp16, x = x_119_cast_fp16)[name = string("hidden_states_117_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_119_cast_fp16 = add(x = hidden_states_115_cast_fp16, y = hidden_states_117_cast_fp16)[name = string("hidden_states_119_cast_fp16")];
            fp16 const_122_promoted_to_fp16 = const()[name = string("const_122_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_4761_cast_fp16 = mul(x = hidden_states_119_cast_fp16, y = const_122_promoted_to_fp16)[name = string("op_4761_cast_fp16")];
            int32 var_4759 = const()[name = string("op_4759"), val = int32(1)];
            bool doubled_97_interleave_0 = const()[name = string("doubled_97_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_97_cast_fp16 = concat(axis = var_4759, interleave = doubled_97_interleave_0, values = (hidden_states_119_cast_fp16, var_4761_cast_fp16))[name = string("doubled_97_cast_fp16")];
            tensor<int32, [1]> out_49_axes_0 = const()[name = string("out_49_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_49_gamma_0_to_fp16 = const()[name = string("out_49_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406994240)))];
            fp16 var_4771_to_fp16 = const()[name = string("op_4771_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_49_cast_fp16 = layer_norm(axes = out_49_axes_0, epsilon = var_4771_to_fp16, gamma = out_49_gamma_0_to_fp16, x = doubled_97_cast_fp16)[name = string("out_49_cast_fp16")];
            tensor<int32, [2]> var_4782_split_sizes_0 = const()[name = string("op_4782_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4782_axis_0 = const()[name = string("op_4782_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_4782_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_4782_cast_fp16_1 = split(axis = var_4782_axis_0, split_sizes = var_4782_split_sizes_0, x = out_49_cast_fp16)[name = string("op_4782_cast_fp16")];
            tensor<int32, [2]> query_states_73_strides_0 = const()[name = string("query_states_73_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_73_pad_type_0 = const()[name = string("query_states_73_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_73_pad_0 = const()[name = string("query_states_73_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_73_dilations_0 = const()[name = string("query_states_73_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_73_groups_0 = const()[name = string("query_states_73_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_73_cast_fp16 = conv(dilations = query_states_73_dilations_0, groups = query_states_73_groups_0, pad = query_states_73_pad_0, pad_type = query_states_73_pad_type_0, strides = query_states_73_strides_0, weight = layers_12_self_attn_q_proj_weight_cast_fp16, x = var_4782_cast_fp16_0)[name = string("query_states_73_cast_fp16")];
            tensor<int32, [2]> key_states_121_strides_0 = const()[name = string("key_states_121_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_121_pad_type_0 = const()[name = string("key_states_121_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_121_pad_0 = const()[name = string("key_states_121_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_121_dilations_0 = const()[name = string("key_states_121_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_121_groups_0 = const()[name = string("key_states_121_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_121_cast_fp16 = conv(dilations = key_states_121_dilations_0, groups = key_states_121_groups_0, pad = key_states_121_pad_0, pad_type = key_states_121_pad_type_0, strides = key_states_121_strides_0, weight = layers_12_self_attn_k_proj_weight_cast_fp16, x = var_4782_cast_fp16_0)[name = string("key_states_121_cast_fp16")];
            tensor<int32, [2]> value_states_73_strides_0 = const()[name = string("value_states_73_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_73_pad_type_0 = const()[name = string("value_states_73_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_73_pad_0 = const()[name = string("value_states_73_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_73_dilations_0 = const()[name = string("value_states_73_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_73_groups_0 = const()[name = string("value_states_73_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_73_cast_fp16 = conv(dilations = value_states_73_dilations_0, groups = value_states_73_groups_0, pad = value_states_73_pad_0, pad_type = value_states_73_pad_type_0, strides = value_states_73_strides_0, weight = layers_12_self_attn_v_proj_weight_cast_fp16, x = var_4782_cast_fp16_0)[name = string("value_states_73_cast_fp16")];
            tensor<int32, [4]> concat_144x = const()[name = string("concat_144x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_121_cast_fp16 = reshape(shape = concat_144x, x = query_states_73_cast_fp16)[name = string("x_121_cast_fp16")];
            tensor<int32, [4]> concat_145x = const()[name = string("concat_145x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_4839_cast_fp16 = reshape(shape = concat_145x, x = key_states_121_cast_fp16)[name = string("op_4839_cast_fp16")];
            tensor<int32, [4]> concat_146x = const()[name = string("concat_146x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_4846_cast_fp16 = reshape(shape = concat_146x, x = value_states_73_cast_fp16)[name = string("op_4846_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_4850_cast_fp16 = mul(x = x_121_cast_fp16, y = var_859_cast_fp16)[name = string("op_4850_cast_fp16")];
            tensor<int32, [2]> var_4851_split_sizes_0 = const()[name = string("op_4851_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4851_axis_0 = const()[name = string("op_4851_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_4851_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_4851_cast_fp16_1 = split(axis = var_4851_axis_0, split_sizes = var_4851_split_sizes_0, x = x_121_cast_fp16)[name = string("op_4851_cast_fp16")];
            fp16 const_124_promoted_to_fp16 = const()[name = string("const_124_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_4853_cast_fp16 = mul(x = var_4851_cast_fp16_1, y = const_124_promoted_to_fp16)[name = string("op_4853_cast_fp16")];
            int32 var_4855 = const()[name = string("op_4855"), val = int32(-2)];
            bool var_4856_interleave_0 = const()[name = string("op_4856_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_4856_cast_fp16 = concat(axis = var_4855, interleave = var_4856_interleave_0, values = (var_4853_cast_fp16, var_4851_cast_fp16_0))[name = string("op_4856_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_4857_cast_fp16 = mul(x = var_4856_cast_fp16, y = var_866_cast_fp16)[name = string("op_4857_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_75_cast_fp16 = add(x = var_4850_cast_fp16, y = var_4857_cast_fp16)[name = string("query_states_75_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_4863_cast_fp16 = mul(x = var_4839_cast_fp16, y = var_859_cast_fp16)[name = string("op_4863_cast_fp16")];
            tensor<int32, [2]> var_4864_split_sizes_0 = const()[name = string("op_4864_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4864_axis_0 = const()[name = string("op_4864_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_4864_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_4864_cast_fp16_1 = split(axis = var_4864_axis_0, split_sizes = var_4864_split_sizes_0, x = var_4839_cast_fp16)[name = string("op_4864_cast_fp16")];
            fp16 const_125_promoted_to_fp16 = const()[name = string("const_125_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_4866_cast_fp16 = mul(x = var_4864_cast_fp16_1, y = const_125_promoted_to_fp16)[name = string("op_4866_cast_fp16")];
            int32 var_4868 = const()[name = string("op_4868"), val = int32(-2)];
            bool var_4869_interleave_0 = const()[name = string("op_4869_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_4869_cast_fp16 = concat(axis = var_4868, interleave = var_4869_interleave_0, values = (var_4866_cast_fp16, var_4864_cast_fp16_0))[name = string("op_4869_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_4870_cast_fp16 = mul(x = var_4869_cast_fp16, y = var_866_cast_fp16)[name = string("op_4870_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_125_cast_fp16 = add(x = var_4863_cast_fp16, y = var_4870_cast_fp16)[name = string("key_states_125_cast_fp16")];
            tensor<int32, [1]> expand_dims_144 = const()[name = string("expand_dims_144"), val = tensor<int32, [1]>([12])];
            tensor<int32, [1]> expand_dims_145 = const()[name = string("expand_dims_145"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_147 = const()[name = string("expand_dims_147"), val = tensor<int32, [1]>([0])];
            int32 concat_149_axis_0 = const()[name = string("concat_149_axis_0"), val = int32(0)];
            bool concat_149_interleave_0 = const()[name = string("concat_149_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_149 = concat(axis = concat_149_axis_0, interleave = concat_149_interleave_0, values = (expand_dims_144, expand_dims_145, position_id, expand_dims_147))[name = string("concat_149")];
            tensor<int32, [1]> expand_dims_148 = const()[name = string("expand_dims_148"), val = tensor<int32, [1]>([13])];
            tensor<int32, [1]> concat_150_values1_0 = const()[name = string("concat_150_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_150_values3_0 = const()[name = string("concat_150_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_150_axis_0 = const()[name = string("concat_150_axis_0"), val = int32(0)];
            bool concat_150_interleave_0 = const()[name = string("concat_150_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_150 = concat(axis = concat_150_axis_0, interleave = concat_150_interleave_0, values = (expand_dims_148, concat_150_values1_0, cache_position_end, concat_150_values3_0))[name = string("concat_150")];
            tensor<int32, [4]> key_states_127_perm_0 = const()[name = string("key_states_127_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_13_stride_0 = const()[name = string("key_cache_internal_tensor_assign_13_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_13_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_13_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_13_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_13_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_13_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_13_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_127_cast_fp16 = transpose(perm = key_states_127_perm_0, x = key_states_125_cast_fp16)[name = string("transpose_395")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_13_cast_fp16 = slice_update(begin = concat_149, begin_mask = key_cache_internal_tensor_assign_13_begin_mask_0, end = concat_150, end_mask = key_cache_internal_tensor_assign_13_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_13_squeeze_mask_0, stride = key_cache_internal_tensor_assign_13_stride_0, update = key_states_127_cast_fp16, x = coreml_update_state_246)[name = string("key_cache_internal_tensor_assign_13_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_13_cast_fp16, input = key_cache)[name = string("coreml_update_state_248_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_248 = read_state(input = key_cache)[name = string("coreml_update_state_248")];
            tensor<int32, [4]> value_states_75_perm_0 = const()[name = string("value_states_75_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_13_stride_0 = const()[name = string("value_cache_internal_tensor_assign_13_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_13_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_13_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_13_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_13_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_13_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_13_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_75_cast_fp16 = transpose(perm = value_states_75_perm_0, x = var_4846_cast_fp16)[name = string("transpose_394")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_13_cast_fp16 = slice_update(begin = concat_149, begin_mask = value_cache_internal_tensor_assign_13_begin_mask_0, end = concat_150, end_mask = value_cache_internal_tensor_assign_13_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_13_squeeze_mask_0, stride = value_cache_internal_tensor_assign_13_stride_0, update = value_states_75_cast_fp16, x = coreml_update_state_247)[name = string("value_cache_internal_tensor_assign_13_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_13_cast_fp16, input = value_cache)[name = string("coreml_update_state_249_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_249 = read_state(input = value_cache)[name = string("coreml_update_state_249")];
            tensor<int32, [4]> var_4940_begin_0 = const()[name = string("op_4940_begin_0"), val = tensor<int32, [4]>([12, 0, 0, 0])];
            tensor<int32, [4]> var_4940_end_0 = const()[name = string("op_4940_end_0"), val = tensor<int32, [4]>([13, 2, 2048, 128])];
            tensor<bool, [4]> var_4940_end_mask_0 = const()[name = string("op_4940_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4940_cast_fp16 = slice_by_index(begin = var_4940_begin_0, end = var_4940_end_0, end_mask = var_4940_end_mask_0, x = coreml_update_state_248)[name = string("op_4940_cast_fp16")];
            tensor<int32, [2]> tile_24 = const()[name = string("tile_24"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4943_axis_0 = const()[name = string("op_4943_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4943_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4943_cast_fp16_1 = split(axis = var_4943_axis_0, split_sizes = tile_24, x = var_4940_cast_fp16)[name = string("op_4943_cast_fp16")];
            tensor<int32, [4]> var_4950_begin_0 = const()[name = string("op_4950_begin_0"), val = tensor<int32, [4]>([12, 0, 0, 0])];
            tensor<int32, [4]> var_4950_end_0 = const()[name = string("op_4950_end_0"), val = tensor<int32, [4]>([13, 2, 2048, 128])];
            tensor<bool, [4]> var_4950_end_mask_0 = const()[name = string("op_4950_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4950_cast_fp16 = slice_by_index(begin = var_4950_begin_0, end = var_4950_end_0, end_mask = var_4950_end_mask_0, x = coreml_update_state_249)[name = string("op_4950_cast_fp16")];
            tensor<int32, [2]> tile_25 = const()[name = string("tile_25"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4953_axis_0 = const()[name = string("op_4953_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4953_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4953_cast_fp16_1 = split(axis = var_4953_axis_0, split_sizes = tile_25, x = var_4950_cast_fp16)[name = string("op_4953_cast_fp16")];
            tensor<int32, [2]> var_4956_split_sizes_0 = const()[name = string("op_4956_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_4956_axis_0 = const()[name = string("op_4956_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_4956_0, tensor<fp16, [1, 8, 128, 32]> var_4956_1 = split(axis = var_4956_axis_0, split_sizes = var_4956_split_sizes_0, x = query_states_75_cast_fp16)[name = string("op_4956")];
            bool attn_weights_193_transpose_x_0 = const()[name = string("attn_weights_193_transpose_x_0"), val = bool(false)];
            bool attn_weights_193_transpose_y_0 = const()[name = string("attn_weights_193_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_193_cast_fp16 = matmul(transpose_x = attn_weights_193_transpose_x_0, transpose_y = attn_weights_193_transpose_y_0, x = var_4943_cast_fp16_0, y = var_4956_0)[name = string("attn_weights_193_cast_fp16")];
            fp16 var_4959_to_fp16 = const()[name = string("op_4959_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_195_cast_fp16 = mul(x = attn_weights_193_cast_fp16, y = var_4959_to_fp16)[name = string("attn_weights_195_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_197_cast_fp16 = add(x = attn_weights_195_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_197_cast_fp16")];
            int32 var_4963 = const()[name = string("op_4963"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_199_cast_fp16 = softmax(axis = var_4963, x = attn_weights_197_cast_fp16)[name = string("attn_weights_199_cast_fp16")];
            bool var_4969_transpose_x_1 = const()[name = string("op_4969_transpose_x_1"), val = bool(true)];
            bool var_4969_transpose_y_1 = const()[name = string("op_4969_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_4969_cast_fp16 = matmul(transpose_x = var_4969_transpose_x_1, transpose_y = var_4969_transpose_y_1, x = attn_weights_199_cast_fp16, y = var_4953_cast_fp16_0)[name = string("op_4969_cast_fp16")];
            bool attn_weights_201_transpose_x_0 = const()[name = string("attn_weights_201_transpose_x_0"), val = bool(false)];
            bool attn_weights_201_transpose_y_0 = const()[name = string("attn_weights_201_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_201_cast_fp16 = matmul(transpose_x = attn_weights_201_transpose_x_0, transpose_y = attn_weights_201_transpose_y_0, x = var_4943_cast_fp16_1, y = var_4956_1)[name = string("attn_weights_201_cast_fp16")];
            fp16 var_4971_to_fp16 = const()[name = string("op_4971_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_203_cast_fp16 = mul(x = attn_weights_201_cast_fp16, y = var_4971_to_fp16)[name = string("attn_weights_203_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_205_cast_fp16 = add(x = attn_weights_203_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_205_cast_fp16")];
            int32 var_4975 = const()[name = string("op_4975"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_207_cast_fp16 = softmax(axis = var_4975, x = attn_weights_205_cast_fp16)[name = string("attn_weights_207_cast_fp16")];
            bool attn_output_97_transpose_x_1 = const()[name = string("attn_output_97_transpose_x_1"), val = bool(true)];
            bool attn_output_97_transpose_y_1 = const()[name = string("attn_output_97_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_97_cast_fp16 = matmul(transpose_x = attn_output_97_transpose_x_1, transpose_y = attn_output_97_transpose_y_1, x = attn_weights_207_cast_fp16, y = var_4953_cast_fp16_1)[name = string("attn_output_97_cast_fp16")];
            int32 var_4983 = const()[name = string("op_4983"), val = int32(1)];
            bool attn_output_99_interleave_0 = const()[name = string("attn_output_99_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_99_cast_fp16 = concat(axis = var_4983, interleave = attn_output_99_interleave_0, values = (var_4969_cast_fp16, attn_output_97_cast_fp16))[name = string("attn_output_99_cast_fp16")];
            tensor<int32, [4]> var_4987_perm_0 = const()[name = string("op_4987_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_155x = const()[name = string("concat_155x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_4987_cast_fp16 = transpose(perm = var_4987_perm_0, x = attn_output_99_cast_fp16)[name = string("transpose_393")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_103_cast_fp16 = reshape(shape = concat_155x, x = var_4987_cast_fp16)[name = string("attn_output_103_cast_fp16")];
            tensor<int32, [2]> hidden_states_123_strides_0 = const()[name = string("hidden_states_123_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_123_pad_type_0 = const()[name = string("hidden_states_123_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_123_pad_0 = const()[name = string("hidden_states_123_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_123_dilations_0 = const()[name = string("hidden_states_123_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_123_groups_0 = const()[name = string("hidden_states_123_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_123_cast_fp16 = conv(dilations = hidden_states_123_dilations_0, groups = hidden_states_123_groups_0, pad = hidden_states_123_pad_0, pad_type = hidden_states_123_pad_type_0, strides = hidden_states_123_strides_0, weight = layers_12_self_attn_o_proj_weight_cast_fp16, x = attn_output_103_cast_fp16)[name = string("hidden_states_123_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_125_cast_fp16 = add(x = hidden_states_119_cast_fp16, y = hidden_states_123_cast_fp16)[name = string("hidden_states_125_cast_fp16")];
            fp16 const_130_promoted_to_fp16 = const()[name = string("const_130_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_5020_cast_fp16 = mul(x = hidden_states_125_cast_fp16, y = const_130_promoted_to_fp16)[name = string("op_5020_cast_fp16")];
            int32 var_5018 = const()[name = string("op_5018"), val = int32(1)];
            bool doubled_101_interleave_0 = const()[name = string("doubled_101_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_101_cast_fp16 = concat(axis = var_5018, interleave = doubled_101_interleave_0, values = (hidden_states_125_cast_fp16, var_5020_cast_fp16))[name = string("doubled_101_cast_fp16")];
            tensor<int32, [1]> out_51_axes_0 = const()[name = string("out_51_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_51_gamma_0_to_fp16 = const()[name = string("out_51_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407002496)))];
            fp16 var_5030_to_fp16 = const()[name = string("op_5030_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_51_cast_fp16 = layer_norm(axes = out_51_axes_0, epsilon = var_5030_to_fp16, gamma = out_51_gamma_0_to_fp16, x = doubled_101_cast_fp16)[name = string("out_51_cast_fp16")];
            tensor<int32, [2]> var_5041_split_sizes_0 = const()[name = string("op_5041_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5041_axis_0 = const()[name = string("op_5041_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_5041_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_5041_cast_fp16_1 = split(axis = var_5041_axis_0, split_sizes = var_5041_split_sizes_0, x = out_51_cast_fp16)[name = string("op_5041_cast_fp16")];
            tensor<int32, [2]> input_25_strides_0 = const()[name = string("input_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_25_pad_type_0 = const()[name = string("input_25_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_25_pad_0 = const()[name = string("input_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_25_dilations_0 = const()[name = string("input_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_25_groups_0 = const()[name = string("input_25_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_25_cast_fp16 = conv(dilations = input_25_dilations_0, groups = input_25_groups_0, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = input_25_strides_0, weight = layers_12_mlp_gate_proj_weight_cast_fp16, x = var_5041_cast_fp16_0)[name = string("input_25_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_5058_cast_fp16 = silu(x = input_25_cast_fp16)[name = string("op_5058_cast_fp16")];
            tensor<int32, [2]> var_5064_strides_0 = const()[name = string("op_5064_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_5064_pad_type_0 = const()[name = string("op_5064_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_5064_pad_0 = const()[name = string("op_5064_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_5064_dilations_0 = const()[name = string("op_5064_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5064_groups_0 = const()[name = string("op_5064_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_5064_cast_fp16 = conv(dilations = var_5064_dilations_0, groups = var_5064_groups_0, pad = var_5064_pad_0, pad_type = var_5064_pad_type_0, strides = var_5064_strides_0, weight = layers_12_mlp_up_proj_weight_cast_fp16, x = var_5041_cast_fp16_0)[name = string("op_5064_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_129_cast_fp16 = mul(x = var_5058_cast_fp16, y = var_5064_cast_fp16)[name = string("x_129_cast_fp16")];
            tensor<int32, [2]> hidden_states_127_strides_0 = const()[name = string("hidden_states_127_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_127_pad_type_0 = const()[name = string("hidden_states_127_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_127_pad_0 = const()[name = string("hidden_states_127_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_127_dilations_0 = const()[name = string("hidden_states_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_127_groups_0 = const()[name = string("hidden_states_127_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_127_cast_fp16 = conv(dilations = hidden_states_127_dilations_0, groups = hidden_states_127_groups_0, pad = hidden_states_127_pad_0, pad_type = hidden_states_127_pad_type_0, strides = hidden_states_127_strides_0, weight = layers_12_mlp_down_proj_weight_cast_fp16, x = x_129_cast_fp16)[name = string("hidden_states_127_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_129_cast_fp16 = add(x = hidden_states_125_cast_fp16, y = hidden_states_127_cast_fp16)[name = string("hidden_states_129_cast_fp16")];
            fp16 const_132_promoted_to_fp16 = const()[name = string("const_132_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_5082_cast_fp16 = mul(x = hidden_states_129_cast_fp16, y = const_132_promoted_to_fp16)[name = string("op_5082_cast_fp16")];
            int32 var_5080 = const()[name = string("op_5080"), val = int32(1)];
            bool doubled_105_interleave_0 = const()[name = string("doubled_105_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_105_cast_fp16 = concat(axis = var_5080, interleave = doubled_105_interleave_0, values = (hidden_states_129_cast_fp16, var_5082_cast_fp16))[name = string("doubled_105_cast_fp16")];
            tensor<int32, [1]> out_53_axes_0 = const()[name = string("out_53_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_53_gamma_0_to_fp16 = const()[name = string("out_53_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407010752)))];
            fp16 var_5092_to_fp16 = const()[name = string("op_5092_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_53_cast_fp16 = layer_norm(axes = out_53_axes_0, epsilon = var_5092_to_fp16, gamma = out_53_gamma_0_to_fp16, x = doubled_105_cast_fp16)[name = string("out_53_cast_fp16")];
            tensor<int32, [2]> var_5103_split_sizes_0 = const()[name = string("op_5103_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5103_axis_0 = const()[name = string("op_5103_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_5103_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_5103_cast_fp16_1 = split(axis = var_5103_axis_0, split_sizes = var_5103_split_sizes_0, x = out_53_cast_fp16)[name = string("op_5103_cast_fp16")];
            tensor<int32, [2]> query_states_79_strides_0 = const()[name = string("query_states_79_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_79_pad_type_0 = const()[name = string("query_states_79_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_79_pad_0 = const()[name = string("query_states_79_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_79_dilations_0 = const()[name = string("query_states_79_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_79_groups_0 = const()[name = string("query_states_79_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_79_cast_fp16 = conv(dilations = query_states_79_dilations_0, groups = query_states_79_groups_0, pad = query_states_79_pad_0, pad_type = query_states_79_pad_type_0, strides = query_states_79_strides_0, weight = layers_13_self_attn_q_proj_weight_cast_fp16, x = var_5103_cast_fp16_0)[name = string("query_states_79_cast_fp16")];
            tensor<int32, [2]> key_states_131_strides_0 = const()[name = string("key_states_131_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_131_pad_type_0 = const()[name = string("key_states_131_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_131_pad_0 = const()[name = string("key_states_131_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_131_dilations_0 = const()[name = string("key_states_131_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_131_groups_0 = const()[name = string("key_states_131_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_131_cast_fp16 = conv(dilations = key_states_131_dilations_0, groups = key_states_131_groups_0, pad = key_states_131_pad_0, pad_type = key_states_131_pad_type_0, strides = key_states_131_strides_0, weight = layers_13_self_attn_k_proj_weight_cast_fp16, x = var_5103_cast_fp16_0)[name = string("key_states_131_cast_fp16")];
            tensor<int32, [2]> value_states_79_strides_0 = const()[name = string("value_states_79_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_79_pad_type_0 = const()[name = string("value_states_79_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_79_pad_0 = const()[name = string("value_states_79_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_79_dilations_0 = const()[name = string("value_states_79_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_79_groups_0 = const()[name = string("value_states_79_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_79_cast_fp16 = conv(dilations = value_states_79_dilations_0, groups = value_states_79_groups_0, pad = value_states_79_pad_0, pad_type = value_states_79_pad_type_0, strides = value_states_79_strides_0, weight = layers_13_self_attn_v_proj_weight_cast_fp16, x = var_5103_cast_fp16_0)[name = string("value_states_79_cast_fp16")];
            tensor<int32, [4]> concat_156x = const()[name = string("concat_156x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_131_cast_fp16 = reshape(shape = concat_156x, x = query_states_79_cast_fp16)[name = string("x_131_cast_fp16")];
            tensor<int32, [4]> concat_157x = const()[name = string("concat_157x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_5160_cast_fp16 = reshape(shape = concat_157x, x = key_states_131_cast_fp16)[name = string("op_5160_cast_fp16")];
            tensor<int32, [4]> concat_158x = const()[name = string("concat_158x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_5167_cast_fp16 = reshape(shape = concat_158x, x = value_states_79_cast_fp16)[name = string("op_5167_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_5171_cast_fp16 = mul(x = x_131_cast_fp16, y = var_859_cast_fp16)[name = string("op_5171_cast_fp16")];
            tensor<int32, [2]> var_5172_split_sizes_0 = const()[name = string("op_5172_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5172_axis_0 = const()[name = string("op_5172_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_5172_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_5172_cast_fp16_1 = split(axis = var_5172_axis_0, split_sizes = var_5172_split_sizes_0, x = x_131_cast_fp16)[name = string("op_5172_cast_fp16")];
            fp16 const_134_promoted_to_fp16 = const()[name = string("const_134_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_5174_cast_fp16 = mul(x = var_5172_cast_fp16_1, y = const_134_promoted_to_fp16)[name = string("op_5174_cast_fp16")];
            int32 var_5176 = const()[name = string("op_5176"), val = int32(-2)];
            bool var_5177_interleave_0 = const()[name = string("op_5177_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_5177_cast_fp16 = concat(axis = var_5176, interleave = var_5177_interleave_0, values = (var_5174_cast_fp16, var_5172_cast_fp16_0))[name = string("op_5177_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_5178_cast_fp16 = mul(x = var_5177_cast_fp16, y = var_866_cast_fp16)[name = string("op_5178_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_81_cast_fp16 = add(x = var_5171_cast_fp16, y = var_5178_cast_fp16)[name = string("query_states_81_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_5184_cast_fp16 = mul(x = var_5160_cast_fp16, y = var_859_cast_fp16)[name = string("op_5184_cast_fp16")];
            tensor<int32, [2]> var_5185_split_sizes_0 = const()[name = string("op_5185_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5185_axis_0 = const()[name = string("op_5185_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_5185_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_5185_cast_fp16_1 = split(axis = var_5185_axis_0, split_sizes = var_5185_split_sizes_0, x = var_5160_cast_fp16)[name = string("op_5185_cast_fp16")];
            fp16 const_135_promoted_to_fp16 = const()[name = string("const_135_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_5187_cast_fp16 = mul(x = var_5185_cast_fp16_1, y = const_135_promoted_to_fp16)[name = string("op_5187_cast_fp16")];
            int32 var_5189 = const()[name = string("op_5189"), val = int32(-2)];
            bool var_5190_interleave_0 = const()[name = string("op_5190_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_5190_cast_fp16 = concat(axis = var_5189, interleave = var_5190_interleave_0, values = (var_5187_cast_fp16, var_5185_cast_fp16_0))[name = string("op_5190_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_5191_cast_fp16 = mul(x = var_5190_cast_fp16, y = var_866_cast_fp16)[name = string("op_5191_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_135_cast_fp16 = add(x = var_5184_cast_fp16, y = var_5191_cast_fp16)[name = string("key_states_135_cast_fp16")];
            tensor<int32, [1]> expand_dims_156 = const()[name = string("expand_dims_156"), val = tensor<int32, [1]>([13])];
            tensor<int32, [1]> expand_dims_157 = const()[name = string("expand_dims_157"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_159 = const()[name = string("expand_dims_159"), val = tensor<int32, [1]>([0])];
            int32 concat_161_axis_0 = const()[name = string("concat_161_axis_0"), val = int32(0)];
            bool concat_161_interleave_0 = const()[name = string("concat_161_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_161 = concat(axis = concat_161_axis_0, interleave = concat_161_interleave_0, values = (expand_dims_156, expand_dims_157, position_id, expand_dims_159))[name = string("concat_161")];
            tensor<int32, [1]> expand_dims_160 = const()[name = string("expand_dims_160"), val = tensor<int32, [1]>([14])];
            tensor<int32, [1]> concat_162_values1_0 = const()[name = string("concat_162_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_162_values3_0 = const()[name = string("concat_162_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_162_axis_0 = const()[name = string("concat_162_axis_0"), val = int32(0)];
            bool concat_162_interleave_0 = const()[name = string("concat_162_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_162 = concat(axis = concat_162_axis_0, interleave = concat_162_interleave_0, values = (expand_dims_160, concat_162_values1_0, cache_position_end, concat_162_values3_0))[name = string("concat_162")];
            tensor<int32, [4]> key_states_137_perm_0 = const()[name = string("key_states_137_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_14_stride_0 = const()[name = string("key_cache_internal_tensor_assign_14_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_14_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_14_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_14_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_14_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_14_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_14_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_137_cast_fp16 = transpose(perm = key_states_137_perm_0, x = key_states_135_cast_fp16)[name = string("transpose_392")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_14_cast_fp16 = slice_update(begin = concat_161, begin_mask = key_cache_internal_tensor_assign_14_begin_mask_0, end = concat_162, end_mask = key_cache_internal_tensor_assign_14_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_14_squeeze_mask_0, stride = key_cache_internal_tensor_assign_14_stride_0, update = key_states_137_cast_fp16, x = coreml_update_state_248)[name = string("key_cache_internal_tensor_assign_14_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_14_cast_fp16, input = key_cache)[name = string("coreml_update_state_250_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_250 = read_state(input = key_cache)[name = string("coreml_update_state_250")];
            tensor<int32, [4]> value_states_81_perm_0 = const()[name = string("value_states_81_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_14_stride_0 = const()[name = string("value_cache_internal_tensor_assign_14_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_14_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_14_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_14_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_14_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_14_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_14_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_81_cast_fp16 = transpose(perm = value_states_81_perm_0, x = var_5167_cast_fp16)[name = string("transpose_391")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_14_cast_fp16 = slice_update(begin = concat_161, begin_mask = value_cache_internal_tensor_assign_14_begin_mask_0, end = concat_162, end_mask = value_cache_internal_tensor_assign_14_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_14_squeeze_mask_0, stride = value_cache_internal_tensor_assign_14_stride_0, update = value_states_81_cast_fp16, x = coreml_update_state_249)[name = string("value_cache_internal_tensor_assign_14_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_14_cast_fp16, input = value_cache)[name = string("coreml_update_state_251_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_251 = read_state(input = value_cache)[name = string("coreml_update_state_251")];
            tensor<int32, [4]> var_5261_begin_0 = const()[name = string("op_5261_begin_0"), val = tensor<int32, [4]>([13, 0, 0, 0])];
            tensor<int32, [4]> var_5261_end_0 = const()[name = string("op_5261_end_0"), val = tensor<int32, [4]>([14, 2, 2048, 128])];
            tensor<bool, [4]> var_5261_end_mask_0 = const()[name = string("op_5261_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5261_cast_fp16 = slice_by_index(begin = var_5261_begin_0, end = var_5261_end_0, end_mask = var_5261_end_mask_0, x = coreml_update_state_250)[name = string("op_5261_cast_fp16")];
            tensor<int32, [2]> tile_26 = const()[name = string("tile_26"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5264_axis_0 = const()[name = string("op_5264_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5264_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5264_cast_fp16_1 = split(axis = var_5264_axis_0, split_sizes = tile_26, x = var_5261_cast_fp16)[name = string("op_5264_cast_fp16")];
            tensor<int32, [4]> var_5271_begin_0 = const()[name = string("op_5271_begin_0"), val = tensor<int32, [4]>([13, 0, 0, 0])];
            tensor<int32, [4]> var_5271_end_0 = const()[name = string("op_5271_end_0"), val = tensor<int32, [4]>([14, 2, 2048, 128])];
            tensor<bool, [4]> var_5271_end_mask_0 = const()[name = string("op_5271_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5271_cast_fp16 = slice_by_index(begin = var_5271_begin_0, end = var_5271_end_0, end_mask = var_5271_end_mask_0, x = coreml_update_state_251)[name = string("op_5271_cast_fp16")];
            tensor<int32, [2]> tile_27 = const()[name = string("tile_27"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5274_axis_0 = const()[name = string("op_5274_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5274_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5274_cast_fp16_1 = split(axis = var_5274_axis_0, split_sizes = tile_27, x = var_5271_cast_fp16)[name = string("op_5274_cast_fp16")];
            tensor<int32, [2]> var_5277_split_sizes_0 = const()[name = string("op_5277_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_5277_axis_0 = const()[name = string("op_5277_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_5277_0, tensor<fp16, [1, 8, 128, 32]> var_5277_1 = split(axis = var_5277_axis_0, split_sizes = var_5277_split_sizes_0, x = query_states_81_cast_fp16)[name = string("op_5277")];
            bool attn_weights_209_transpose_x_0 = const()[name = string("attn_weights_209_transpose_x_0"), val = bool(false)];
            bool attn_weights_209_transpose_y_0 = const()[name = string("attn_weights_209_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_209_cast_fp16 = matmul(transpose_x = attn_weights_209_transpose_x_0, transpose_y = attn_weights_209_transpose_y_0, x = var_5264_cast_fp16_0, y = var_5277_0)[name = string("attn_weights_209_cast_fp16")];
            fp16 var_5280_to_fp16 = const()[name = string("op_5280_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_211_cast_fp16 = mul(x = attn_weights_209_cast_fp16, y = var_5280_to_fp16)[name = string("attn_weights_211_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_213_cast_fp16 = add(x = attn_weights_211_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_213_cast_fp16")];
            int32 var_5284 = const()[name = string("op_5284"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_215_cast_fp16 = softmax(axis = var_5284, x = attn_weights_213_cast_fp16)[name = string("attn_weights_215_cast_fp16")];
            bool var_5290_transpose_x_1 = const()[name = string("op_5290_transpose_x_1"), val = bool(true)];
            bool var_5290_transpose_y_1 = const()[name = string("op_5290_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_5290_cast_fp16 = matmul(transpose_x = var_5290_transpose_x_1, transpose_y = var_5290_transpose_y_1, x = attn_weights_215_cast_fp16, y = var_5274_cast_fp16_0)[name = string("op_5290_cast_fp16")];
            bool attn_weights_217_transpose_x_0 = const()[name = string("attn_weights_217_transpose_x_0"), val = bool(false)];
            bool attn_weights_217_transpose_y_0 = const()[name = string("attn_weights_217_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_217_cast_fp16 = matmul(transpose_x = attn_weights_217_transpose_x_0, transpose_y = attn_weights_217_transpose_y_0, x = var_5264_cast_fp16_1, y = var_5277_1)[name = string("attn_weights_217_cast_fp16")];
            fp16 var_5292_to_fp16 = const()[name = string("op_5292_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_219_cast_fp16 = mul(x = attn_weights_217_cast_fp16, y = var_5292_to_fp16)[name = string("attn_weights_219_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_221_cast_fp16 = add(x = attn_weights_219_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_221_cast_fp16")];
            int32 var_5296 = const()[name = string("op_5296"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_223_cast_fp16 = softmax(axis = var_5296, x = attn_weights_221_cast_fp16)[name = string("attn_weights_223_cast_fp16")];
            bool attn_output_105_transpose_x_1 = const()[name = string("attn_output_105_transpose_x_1"), val = bool(true)];
            bool attn_output_105_transpose_y_1 = const()[name = string("attn_output_105_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_105_cast_fp16 = matmul(transpose_x = attn_output_105_transpose_x_1, transpose_y = attn_output_105_transpose_y_1, x = attn_weights_223_cast_fp16, y = var_5274_cast_fp16_1)[name = string("attn_output_105_cast_fp16")];
            int32 var_5304 = const()[name = string("op_5304"), val = int32(1)];
            bool attn_output_107_interleave_0 = const()[name = string("attn_output_107_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_107_cast_fp16 = concat(axis = var_5304, interleave = attn_output_107_interleave_0, values = (var_5290_cast_fp16, attn_output_105_cast_fp16))[name = string("attn_output_107_cast_fp16")];
            tensor<int32, [4]> var_5308_perm_0 = const()[name = string("op_5308_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_167x = const()[name = string("concat_167x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_5308_cast_fp16 = transpose(perm = var_5308_perm_0, x = attn_output_107_cast_fp16)[name = string("transpose_390")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_111_cast_fp16 = reshape(shape = concat_167x, x = var_5308_cast_fp16)[name = string("attn_output_111_cast_fp16")];
            tensor<int32, [2]> hidden_states_133_strides_0 = const()[name = string("hidden_states_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_133_pad_type_0 = const()[name = string("hidden_states_133_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_133_pad_0 = const()[name = string("hidden_states_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_133_dilations_0 = const()[name = string("hidden_states_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_133_groups_0 = const()[name = string("hidden_states_133_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_133_cast_fp16 = conv(dilations = hidden_states_133_dilations_0, groups = hidden_states_133_groups_0, pad = hidden_states_133_pad_0, pad_type = hidden_states_133_pad_type_0, strides = hidden_states_133_strides_0, weight = layers_13_self_attn_o_proj_weight_cast_fp16, x = attn_output_111_cast_fp16)[name = string("hidden_states_133_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_135_cast_fp16 = add(x = hidden_states_129_cast_fp16, y = hidden_states_133_cast_fp16)[name = string("hidden_states_135_cast_fp16")];
            fp16 const_140_promoted_to_fp16 = const()[name = string("const_140_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_5341_cast_fp16 = mul(x = hidden_states_135_cast_fp16, y = const_140_promoted_to_fp16)[name = string("op_5341_cast_fp16")];
            int32 var_5339 = const()[name = string("op_5339"), val = int32(1)];
            bool doubled_109_interleave_0 = const()[name = string("doubled_109_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_109_cast_fp16 = concat(axis = var_5339, interleave = doubled_109_interleave_0, values = (hidden_states_135_cast_fp16, var_5341_cast_fp16))[name = string("doubled_109_cast_fp16")];
            tensor<int32, [1]> out_55_axes_0 = const()[name = string("out_55_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_55_gamma_0_to_fp16 = const()[name = string("out_55_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407019008)))];
            fp16 var_5351_to_fp16 = const()[name = string("op_5351_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_55_cast_fp16 = layer_norm(axes = out_55_axes_0, epsilon = var_5351_to_fp16, gamma = out_55_gamma_0_to_fp16, x = doubled_109_cast_fp16)[name = string("out_55_cast_fp16")];
            tensor<int32, [2]> var_5362_split_sizes_0 = const()[name = string("op_5362_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5362_axis_0 = const()[name = string("op_5362_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_5362_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_5362_cast_fp16_1 = split(axis = var_5362_axis_0, split_sizes = var_5362_split_sizes_0, x = out_55_cast_fp16)[name = string("op_5362_cast_fp16")];
            tensor<int32, [2]> input_27_strides_0 = const()[name = string("input_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_27_pad_type_0 = const()[name = string("input_27_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_27_pad_0 = const()[name = string("input_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_27_dilations_0 = const()[name = string("input_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_27_groups_0 = const()[name = string("input_27_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_27_cast_fp16 = conv(dilations = input_27_dilations_0, groups = input_27_groups_0, pad = input_27_pad_0, pad_type = input_27_pad_type_0, strides = input_27_strides_0, weight = layers_13_mlp_gate_proj_weight_cast_fp16, x = var_5362_cast_fp16_0)[name = string("input_27_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_5379_cast_fp16 = silu(x = input_27_cast_fp16)[name = string("op_5379_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_13_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_13_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407027264)))];
            tensor<int32, [2]> var_5385_strides_0 = const()[name = string("op_5385_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_5385_pad_type_0 = const()[name = string("op_5385_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_5385_pad_0 = const()[name = string("op_5385_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_5385_dilations_0 = const()[name = string("op_5385_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5385_groups_0 = const()[name = string("op_5385_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_5385_cast_fp16 = conv(dilations = var_5385_dilations_0, groups = var_5385_groups_0, pad = var_5385_pad_0, pad_type = var_5385_pad_type_0, strides = var_5385_strides_0, weight = layers_13_mlp_up_proj_weight_to_fp16, x = var_5362_cast_fp16_0)[name = string("op_5385_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_139_cast_fp16 = mul(x = var_5379_cast_fp16, y = var_5385_cast_fp16)[name = string("x_139_cast_fp16")];
            tensor<int32, [2]> hidden_states_137_strides_0 = const()[name = string("hidden_states_137_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_137_pad_type_0 = const()[name = string("hidden_states_137_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_137_pad_0 = const()[name = string("hidden_states_137_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_137_dilations_0 = const()[name = string("hidden_states_137_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_137_groups_0 = const()[name = string("hidden_states_137_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_137_cast_fp16 = conv(dilations = hidden_states_137_dilations_0, groups = hidden_states_137_groups_0, pad = hidden_states_137_pad_0, pad_type = hidden_states_137_pad_type_0, strides = hidden_states_137_strides_0, weight = layers_13_mlp_down_proj_weight_cast_fp16, x = x_139_cast_fp16)[name = string("hidden_states_137_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_139_cast_fp16 = add(x = hidden_states_135_cast_fp16, y = hidden_states_137_cast_fp16)[name = string("hidden_states_139_cast_fp16")];
            fp16 const_142_promoted_to_fp16 = const()[name = string("const_142_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_5403_cast_fp16 = mul(x = hidden_states_139_cast_fp16, y = const_142_promoted_to_fp16)[name = string("op_5403_cast_fp16")];
            int32 var_5401 = const()[name = string("op_5401"), val = int32(1)];
            bool doubled_113_interleave_0 = const()[name = string("doubled_113_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_113_cast_fp16 = concat(axis = var_5401, interleave = doubled_113_interleave_0, values = (hidden_states_139_cast_fp16, var_5403_cast_fp16))[name = string("doubled_113_cast_fp16")];
            tensor<int32, [1]> out_57_axes_0 = const()[name = string("out_57_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_57_gamma_0_to_fp16 = const()[name = string("out_57_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1432193152)))];
            fp16 var_5413_to_fp16 = const()[name = string("op_5413_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_57_cast_fp16 = layer_norm(axes = out_57_axes_0, epsilon = var_5413_to_fp16, gamma = out_57_gamma_0_to_fp16, x = doubled_113_cast_fp16)[name = string("out_57_cast_fp16")];
            tensor<int32, [2]> var_5424_split_sizes_0 = const()[name = string("op_5424_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5424_axis_0 = const()[name = string("op_5424_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_5424_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_5424_cast_fp16_1 = split(axis = var_5424_axis_0, split_sizes = var_5424_split_sizes_0, x = out_57_cast_fp16)[name = string("op_5424_cast_fp16")];
            tensor<int32, [2]> query_states_85_strides_0 = const()[name = string("query_states_85_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_85_pad_type_0 = const()[name = string("query_states_85_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_85_pad_0 = const()[name = string("query_states_85_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_85_dilations_0 = const()[name = string("query_states_85_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_85_groups_0 = const()[name = string("query_states_85_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_85_cast_fp16 = conv(dilations = query_states_85_dilations_0, groups = query_states_85_groups_0, pad = query_states_85_pad_0, pad_type = query_states_85_pad_type_0, strides = query_states_85_strides_0, weight = layers_14_self_attn_q_proj_weight_cast_fp16, x = var_5424_cast_fp16_0)[name = string("query_states_85_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_14_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_14_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1432201408)))];
            tensor<int32, [2]> key_states_141_strides_0 = const()[name = string("key_states_141_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_141_pad_type_0 = const()[name = string("key_states_141_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_141_pad_0 = const()[name = string("key_states_141_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_141_dilations_0 = const()[name = string("key_states_141_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_141_groups_0 = const()[name = string("key_states_141_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_141_cast_fp16 = conv(dilations = key_states_141_dilations_0, groups = key_states_141_groups_0, pad = key_states_141_pad_0, pad_type = key_states_141_pad_type_0, strides = key_states_141_strides_0, weight = layers_14_self_attn_k_proj_weight_to_fp16, x = var_5424_cast_fp16_0)[name = string("key_states_141_cast_fp16")];
            tensor<int32, [2]> value_states_85_strides_0 = const()[name = string("value_states_85_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_85_pad_type_0 = const()[name = string("value_states_85_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_85_pad_0 = const()[name = string("value_states_85_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_85_dilations_0 = const()[name = string("value_states_85_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_85_groups_0 = const()[name = string("value_states_85_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_85_cast_fp16 = conv(dilations = value_states_85_dilations_0, groups = value_states_85_groups_0, pad = value_states_85_pad_0, pad_type = value_states_85_pad_type_0, strides = value_states_85_strides_0, weight = layers_14_self_attn_v_proj_weight_cast_fp16, x = var_5424_cast_fp16_0)[name = string("value_states_85_cast_fp16")];
            tensor<int32, [4]> concat_168x = const()[name = string("concat_168x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_141_cast_fp16 = reshape(shape = concat_168x, x = query_states_85_cast_fp16)[name = string("x_141_cast_fp16")];
            tensor<int32, [4]> concat_169x = const()[name = string("concat_169x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_5481_cast_fp16 = reshape(shape = concat_169x, x = key_states_141_cast_fp16)[name = string("op_5481_cast_fp16")];
            tensor<int32, [4]> concat_170x = const()[name = string("concat_170x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_5488_cast_fp16 = reshape(shape = concat_170x, x = value_states_85_cast_fp16)[name = string("op_5488_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_5492_cast_fp16 = mul(x = x_141_cast_fp16, y = var_859_cast_fp16)[name = string("op_5492_cast_fp16")];
            tensor<int32, [2]> var_5493_split_sizes_0 = const()[name = string("op_5493_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5493_axis_0 = const()[name = string("op_5493_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_5493_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_5493_cast_fp16_1 = split(axis = var_5493_axis_0, split_sizes = var_5493_split_sizes_0, x = x_141_cast_fp16)[name = string("op_5493_cast_fp16")];
            fp16 const_144_promoted_to_fp16 = const()[name = string("const_144_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_5495_cast_fp16 = mul(x = var_5493_cast_fp16_1, y = const_144_promoted_to_fp16)[name = string("op_5495_cast_fp16")];
            int32 var_5497 = const()[name = string("op_5497"), val = int32(-2)];
            bool var_5498_interleave_0 = const()[name = string("op_5498_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_5498_cast_fp16 = concat(axis = var_5497, interleave = var_5498_interleave_0, values = (var_5495_cast_fp16, var_5493_cast_fp16_0))[name = string("op_5498_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_5499_cast_fp16 = mul(x = var_5498_cast_fp16, y = var_866_cast_fp16)[name = string("op_5499_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_87_cast_fp16 = add(x = var_5492_cast_fp16, y = var_5499_cast_fp16)[name = string("query_states_87_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_5505_cast_fp16 = mul(x = var_5481_cast_fp16, y = var_859_cast_fp16)[name = string("op_5505_cast_fp16")];
            tensor<int32, [2]> var_5506_split_sizes_0 = const()[name = string("op_5506_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5506_axis_0 = const()[name = string("op_5506_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_5506_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_5506_cast_fp16_1 = split(axis = var_5506_axis_0, split_sizes = var_5506_split_sizes_0, x = var_5481_cast_fp16)[name = string("op_5506_cast_fp16")];
            fp16 const_145_promoted_to_fp16 = const()[name = string("const_145_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_5508_cast_fp16 = mul(x = var_5506_cast_fp16_1, y = const_145_promoted_to_fp16)[name = string("op_5508_cast_fp16")];
            int32 var_5510 = const()[name = string("op_5510"), val = int32(-2)];
            bool var_5511_interleave_0 = const()[name = string("op_5511_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_5511_cast_fp16 = concat(axis = var_5510, interleave = var_5511_interleave_0, values = (var_5508_cast_fp16, var_5506_cast_fp16_0))[name = string("op_5511_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_5512_cast_fp16 = mul(x = var_5511_cast_fp16, y = var_866_cast_fp16)[name = string("op_5512_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_145_cast_fp16 = add(x = var_5505_cast_fp16, y = var_5512_cast_fp16)[name = string("key_states_145_cast_fp16")];
            tensor<int32, [1]> expand_dims_168 = const()[name = string("expand_dims_168"), val = tensor<int32, [1]>([14])];
            tensor<int32, [1]> expand_dims_169 = const()[name = string("expand_dims_169"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_171 = const()[name = string("expand_dims_171"), val = tensor<int32, [1]>([0])];
            int32 concat_173_axis_0 = const()[name = string("concat_173_axis_0"), val = int32(0)];
            bool concat_173_interleave_0 = const()[name = string("concat_173_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_173 = concat(axis = concat_173_axis_0, interleave = concat_173_interleave_0, values = (expand_dims_168, expand_dims_169, position_id, expand_dims_171))[name = string("concat_173")];
            tensor<int32, [1]> expand_dims_172 = const()[name = string("expand_dims_172"), val = tensor<int32, [1]>([15])];
            tensor<int32, [1]> concat_174_values1_0 = const()[name = string("concat_174_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_174_values3_0 = const()[name = string("concat_174_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_174_axis_0 = const()[name = string("concat_174_axis_0"), val = int32(0)];
            bool concat_174_interleave_0 = const()[name = string("concat_174_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_174 = concat(axis = concat_174_axis_0, interleave = concat_174_interleave_0, values = (expand_dims_172, concat_174_values1_0, cache_position_end, concat_174_values3_0))[name = string("concat_174")];
            tensor<int32, [4]> key_states_147_perm_0 = const()[name = string("key_states_147_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_15_stride_0 = const()[name = string("key_cache_internal_tensor_assign_15_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_15_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_15_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_15_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_15_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_15_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_15_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_147_cast_fp16 = transpose(perm = key_states_147_perm_0, x = key_states_145_cast_fp16)[name = string("transpose_389")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_15_cast_fp16 = slice_update(begin = concat_173, begin_mask = key_cache_internal_tensor_assign_15_begin_mask_0, end = concat_174, end_mask = key_cache_internal_tensor_assign_15_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_15_squeeze_mask_0, stride = key_cache_internal_tensor_assign_15_stride_0, update = key_states_147_cast_fp16, x = coreml_update_state_250)[name = string("key_cache_internal_tensor_assign_15_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_15_cast_fp16, input = key_cache)[name = string("coreml_update_state_252_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_252 = read_state(input = key_cache)[name = string("coreml_update_state_252")];
            tensor<int32, [4]> value_states_87_perm_0 = const()[name = string("value_states_87_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_15_stride_0 = const()[name = string("value_cache_internal_tensor_assign_15_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_15_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_15_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_15_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_15_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_15_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_15_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_87_cast_fp16 = transpose(perm = value_states_87_perm_0, x = var_5488_cast_fp16)[name = string("transpose_388")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_15_cast_fp16 = slice_update(begin = concat_173, begin_mask = value_cache_internal_tensor_assign_15_begin_mask_0, end = concat_174, end_mask = value_cache_internal_tensor_assign_15_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_15_squeeze_mask_0, stride = value_cache_internal_tensor_assign_15_stride_0, update = value_states_87_cast_fp16, x = coreml_update_state_251)[name = string("value_cache_internal_tensor_assign_15_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_15_cast_fp16, input = value_cache)[name = string("coreml_update_state_253_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_253 = read_state(input = value_cache)[name = string("coreml_update_state_253")];
            tensor<int32, [4]> var_5582_begin_0 = const()[name = string("op_5582_begin_0"), val = tensor<int32, [4]>([14, 0, 0, 0])];
            tensor<int32, [4]> var_5582_end_0 = const()[name = string("op_5582_end_0"), val = tensor<int32, [4]>([15, 2, 2048, 128])];
            tensor<bool, [4]> var_5582_end_mask_0 = const()[name = string("op_5582_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5582_cast_fp16 = slice_by_index(begin = var_5582_begin_0, end = var_5582_end_0, end_mask = var_5582_end_mask_0, x = coreml_update_state_252)[name = string("op_5582_cast_fp16")];
            tensor<int32, [2]> tile_28 = const()[name = string("tile_28"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5585_axis_0 = const()[name = string("op_5585_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5585_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5585_cast_fp16_1 = split(axis = var_5585_axis_0, split_sizes = tile_28, x = var_5582_cast_fp16)[name = string("op_5585_cast_fp16")];
            tensor<int32, [4]> var_5592_begin_0 = const()[name = string("op_5592_begin_0"), val = tensor<int32, [4]>([14, 0, 0, 0])];
            tensor<int32, [4]> var_5592_end_0 = const()[name = string("op_5592_end_0"), val = tensor<int32, [4]>([15, 2, 2048, 128])];
            tensor<bool, [4]> var_5592_end_mask_0 = const()[name = string("op_5592_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5592_cast_fp16 = slice_by_index(begin = var_5592_begin_0, end = var_5592_end_0, end_mask = var_5592_end_mask_0, x = coreml_update_state_253)[name = string("op_5592_cast_fp16")];
            tensor<int32, [2]> tile_29 = const()[name = string("tile_29"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5595_axis_0 = const()[name = string("op_5595_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5595_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5595_cast_fp16_1 = split(axis = var_5595_axis_0, split_sizes = tile_29, x = var_5592_cast_fp16)[name = string("op_5595_cast_fp16")];
            tensor<int32, [2]> var_5598_split_sizes_0 = const()[name = string("op_5598_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_5598_axis_0 = const()[name = string("op_5598_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_5598_0, tensor<fp16, [1, 8, 128, 32]> var_5598_1 = split(axis = var_5598_axis_0, split_sizes = var_5598_split_sizes_0, x = query_states_87_cast_fp16)[name = string("op_5598")];
            bool attn_weights_225_transpose_x_0 = const()[name = string("attn_weights_225_transpose_x_0"), val = bool(false)];
            bool attn_weights_225_transpose_y_0 = const()[name = string("attn_weights_225_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_225_cast_fp16 = matmul(transpose_x = attn_weights_225_transpose_x_0, transpose_y = attn_weights_225_transpose_y_0, x = var_5585_cast_fp16_0, y = var_5598_0)[name = string("attn_weights_225_cast_fp16")];
            fp16 var_5601_to_fp16 = const()[name = string("op_5601_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_227_cast_fp16 = mul(x = attn_weights_225_cast_fp16, y = var_5601_to_fp16)[name = string("attn_weights_227_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_229_cast_fp16 = add(x = attn_weights_227_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_229_cast_fp16")];
            int32 var_5605 = const()[name = string("op_5605"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_231_cast_fp16 = softmax(axis = var_5605, x = attn_weights_229_cast_fp16)[name = string("attn_weights_231_cast_fp16")];
            bool var_5611_transpose_x_1 = const()[name = string("op_5611_transpose_x_1"), val = bool(true)];
            bool var_5611_transpose_y_1 = const()[name = string("op_5611_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_5611_cast_fp16 = matmul(transpose_x = var_5611_transpose_x_1, transpose_y = var_5611_transpose_y_1, x = attn_weights_231_cast_fp16, y = var_5595_cast_fp16_0)[name = string("op_5611_cast_fp16")];
            bool attn_weights_233_transpose_x_0 = const()[name = string("attn_weights_233_transpose_x_0"), val = bool(false)];
            bool attn_weights_233_transpose_y_0 = const()[name = string("attn_weights_233_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_233_cast_fp16 = matmul(transpose_x = attn_weights_233_transpose_x_0, transpose_y = attn_weights_233_transpose_y_0, x = var_5585_cast_fp16_1, y = var_5598_1)[name = string("attn_weights_233_cast_fp16")];
            fp16 var_5613_to_fp16 = const()[name = string("op_5613_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_235_cast_fp16 = mul(x = attn_weights_233_cast_fp16, y = var_5613_to_fp16)[name = string("attn_weights_235_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_237_cast_fp16 = add(x = attn_weights_235_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_237_cast_fp16")];
            int32 var_5617 = const()[name = string("op_5617"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_239_cast_fp16 = softmax(axis = var_5617, x = attn_weights_237_cast_fp16)[name = string("attn_weights_239_cast_fp16")];
            bool attn_output_113_transpose_x_1 = const()[name = string("attn_output_113_transpose_x_1"), val = bool(true)];
            bool attn_output_113_transpose_y_1 = const()[name = string("attn_output_113_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_113_cast_fp16 = matmul(transpose_x = attn_output_113_transpose_x_1, transpose_y = attn_output_113_transpose_y_1, x = attn_weights_239_cast_fp16, y = var_5595_cast_fp16_1)[name = string("attn_output_113_cast_fp16")];
            int32 var_5625 = const()[name = string("op_5625"), val = int32(1)];
            bool attn_output_115_interleave_0 = const()[name = string("attn_output_115_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_115_cast_fp16 = concat(axis = var_5625, interleave = attn_output_115_interleave_0, values = (var_5611_cast_fp16, attn_output_113_cast_fp16))[name = string("attn_output_115_cast_fp16")];
            tensor<int32, [4]> var_5629_perm_0 = const()[name = string("op_5629_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_179x = const()[name = string("concat_179x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_5629_cast_fp16 = transpose(perm = var_5629_perm_0, x = attn_output_115_cast_fp16)[name = string("transpose_387")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_119_cast_fp16 = reshape(shape = concat_179x, x = var_5629_cast_fp16)[name = string("attn_output_119_cast_fp16")];
            tensor<int32, [2]> hidden_states_143_strides_0 = const()[name = string("hidden_states_143_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_143_pad_type_0 = const()[name = string("hidden_states_143_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_143_pad_0 = const()[name = string("hidden_states_143_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_143_dilations_0 = const()[name = string("hidden_states_143_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_143_groups_0 = const()[name = string("hidden_states_143_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_143_cast_fp16 = conv(dilations = hidden_states_143_dilations_0, groups = hidden_states_143_groups_0, pad = hidden_states_143_pad_0, pad_type = hidden_states_143_pad_type_0, strides = hidden_states_143_strides_0, weight = layers_14_self_attn_o_proj_weight_cast_fp16, x = attn_output_119_cast_fp16)[name = string("hidden_states_143_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_145_cast_fp16 = add(x = hidden_states_139_cast_fp16, y = hidden_states_143_cast_fp16)[name = string("hidden_states_145_cast_fp16")];
            fp16 const_150_promoted_to_fp16 = const()[name = string("const_150_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_5662_cast_fp16 = mul(x = hidden_states_145_cast_fp16, y = const_150_promoted_to_fp16)[name = string("op_5662_cast_fp16")];
            int32 var_5660 = const()[name = string("op_5660"), val = int32(1)];
            bool doubled_117_interleave_0 = const()[name = string("doubled_117_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_117_cast_fp16 = concat(axis = var_5660, interleave = doubled_117_interleave_0, values = (hidden_states_145_cast_fp16, var_5662_cast_fp16))[name = string("doubled_117_cast_fp16")];
            tensor<int32, [1]> out_59_axes_0 = const()[name = string("out_59_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_59_gamma_0_to_fp16 = const()[name = string("out_59_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433250048)))];
            fp16 var_5672_to_fp16 = const()[name = string("op_5672_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_59_cast_fp16 = layer_norm(axes = out_59_axes_0, epsilon = var_5672_to_fp16, gamma = out_59_gamma_0_to_fp16, x = doubled_117_cast_fp16)[name = string("out_59_cast_fp16")];
            tensor<int32, [2]> var_5683_split_sizes_0 = const()[name = string("op_5683_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5683_axis_0 = const()[name = string("op_5683_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_5683_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_5683_cast_fp16_1 = split(axis = var_5683_axis_0, split_sizes = var_5683_split_sizes_0, x = out_59_cast_fp16)[name = string("op_5683_cast_fp16")];
            tensor<int32, [2]> input_29_strides_0 = const()[name = string("input_29_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_29_pad_type_0 = const()[name = string("input_29_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_29_pad_0 = const()[name = string("input_29_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_29_dilations_0 = const()[name = string("input_29_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_29_groups_0 = const()[name = string("input_29_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_29_cast_fp16 = conv(dilations = input_29_dilations_0, groups = input_29_groups_0, pad = input_29_pad_0, pad_type = input_29_pad_type_0, strides = input_29_strides_0, weight = layers_14_mlp_gate_proj_weight_cast_fp16, x = var_5683_cast_fp16_0)[name = string("input_29_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_5700_cast_fp16 = silu(x = input_29_cast_fp16)[name = string("op_5700_cast_fp16")];
            tensor<int32, [2]> var_5706_strides_0 = const()[name = string("op_5706_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_5706_pad_type_0 = const()[name = string("op_5706_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_5706_pad_0 = const()[name = string("op_5706_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_5706_dilations_0 = const()[name = string("op_5706_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5706_groups_0 = const()[name = string("op_5706_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_5706_cast_fp16 = conv(dilations = var_5706_dilations_0, groups = var_5706_groups_0, pad = var_5706_pad_0, pad_type = var_5706_pad_type_0, strides = var_5706_strides_0, weight = layers_14_mlp_up_proj_weight_cast_fp16, x = var_5683_cast_fp16_0)[name = string("op_5706_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_149_cast_fp16 = mul(x = var_5700_cast_fp16, y = var_5706_cast_fp16)[name = string("x_149_cast_fp16")];
            tensor<int32, [2]> hidden_states_147_strides_0 = const()[name = string("hidden_states_147_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_147_pad_type_0 = const()[name = string("hidden_states_147_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_147_pad_0 = const()[name = string("hidden_states_147_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_147_dilations_0 = const()[name = string("hidden_states_147_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_147_groups_0 = const()[name = string("hidden_states_147_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_147_cast_fp16 = conv(dilations = hidden_states_147_dilations_0, groups = hidden_states_147_groups_0, pad = hidden_states_147_pad_0, pad_type = hidden_states_147_pad_type_0, strides = hidden_states_147_strides_0, weight = layers_14_mlp_down_proj_weight_cast_fp16, x = x_149_cast_fp16)[name = string("hidden_states_147_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_149_cast_fp16 = add(x = hidden_states_145_cast_fp16, y = hidden_states_147_cast_fp16)[name = string("hidden_states_149_cast_fp16")];
            fp16 const_152_promoted_to_fp16 = const()[name = string("const_152_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_5724_cast_fp16 = mul(x = hidden_states_149_cast_fp16, y = const_152_promoted_to_fp16)[name = string("op_5724_cast_fp16")];
            int32 var_5722 = const()[name = string("op_5722"), val = int32(1)];
            bool doubled_121_interleave_0 = const()[name = string("doubled_121_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_121_cast_fp16 = concat(axis = var_5722, interleave = doubled_121_interleave_0, values = (hidden_states_149_cast_fp16, var_5724_cast_fp16))[name = string("doubled_121_cast_fp16")];
            tensor<int32, [1]> out_61_axes_0 = const()[name = string("out_61_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_61_gamma_0_to_fp16 = const()[name = string("out_61_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433258304)))];
            fp16 var_5734_to_fp16 = const()[name = string("op_5734_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_61_cast_fp16 = layer_norm(axes = out_61_axes_0, epsilon = var_5734_to_fp16, gamma = out_61_gamma_0_to_fp16, x = doubled_121_cast_fp16)[name = string("out_61_cast_fp16")];
            tensor<int32, [2]> var_5745_split_sizes_0 = const()[name = string("op_5745_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5745_axis_0 = const()[name = string("op_5745_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_5745_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_5745_cast_fp16_1 = split(axis = var_5745_axis_0, split_sizes = var_5745_split_sizes_0, x = out_61_cast_fp16)[name = string("op_5745_cast_fp16")];
            tensor<int32, [2]> query_states_91_strides_0 = const()[name = string("query_states_91_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_91_pad_type_0 = const()[name = string("query_states_91_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_91_pad_0 = const()[name = string("query_states_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_91_dilations_0 = const()[name = string("query_states_91_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_91_groups_0 = const()[name = string("query_states_91_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_91_cast_fp16 = conv(dilations = query_states_91_dilations_0, groups = query_states_91_groups_0, pad = query_states_91_pad_0, pad_type = query_states_91_pad_type_0, strides = query_states_91_strides_0, weight = layers_15_self_attn_q_proj_weight_cast_fp16, x = var_5745_cast_fp16_0)[name = string("query_states_91_cast_fp16")];
            tensor<int32, [2]> key_states_151_strides_0 = const()[name = string("key_states_151_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_151_pad_type_0 = const()[name = string("key_states_151_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_151_pad_0 = const()[name = string("key_states_151_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_151_dilations_0 = const()[name = string("key_states_151_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_151_groups_0 = const()[name = string("key_states_151_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_151_cast_fp16 = conv(dilations = key_states_151_dilations_0, groups = key_states_151_groups_0, pad = key_states_151_pad_0, pad_type = key_states_151_pad_type_0, strides = key_states_151_strides_0, weight = layers_15_self_attn_k_proj_weight_cast_fp16, x = var_5745_cast_fp16_0)[name = string("key_states_151_cast_fp16")];
            tensor<int32, [2]> value_states_91_strides_0 = const()[name = string("value_states_91_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_91_pad_type_0 = const()[name = string("value_states_91_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_91_pad_0 = const()[name = string("value_states_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_91_dilations_0 = const()[name = string("value_states_91_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_91_groups_0 = const()[name = string("value_states_91_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_91_cast_fp16 = conv(dilations = value_states_91_dilations_0, groups = value_states_91_groups_0, pad = value_states_91_pad_0, pad_type = value_states_91_pad_type_0, strides = value_states_91_strides_0, weight = layers_15_self_attn_v_proj_weight_cast_fp16, x = var_5745_cast_fp16_0)[name = string("value_states_91_cast_fp16")];
            tensor<int32, [4]> concat_180x = const()[name = string("concat_180x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_151_cast_fp16 = reshape(shape = concat_180x, x = query_states_91_cast_fp16)[name = string("x_151_cast_fp16")];
            tensor<int32, [4]> concat_181x = const()[name = string("concat_181x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_5802_cast_fp16 = reshape(shape = concat_181x, x = key_states_151_cast_fp16)[name = string("op_5802_cast_fp16")];
            tensor<int32, [4]> concat_182x = const()[name = string("concat_182x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_5809_cast_fp16 = reshape(shape = concat_182x, x = value_states_91_cast_fp16)[name = string("op_5809_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_5813_cast_fp16 = mul(x = x_151_cast_fp16, y = var_859_cast_fp16)[name = string("op_5813_cast_fp16")];
            tensor<int32, [2]> var_5814_split_sizes_0 = const()[name = string("op_5814_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5814_axis_0 = const()[name = string("op_5814_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_5814_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_5814_cast_fp16_1 = split(axis = var_5814_axis_0, split_sizes = var_5814_split_sizes_0, x = x_151_cast_fp16)[name = string("op_5814_cast_fp16")];
            fp16 const_154_promoted_to_fp16 = const()[name = string("const_154_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_5816_cast_fp16 = mul(x = var_5814_cast_fp16_1, y = const_154_promoted_to_fp16)[name = string("op_5816_cast_fp16")];
            int32 var_5818 = const()[name = string("op_5818"), val = int32(-2)];
            bool var_5819_interleave_0 = const()[name = string("op_5819_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_5819_cast_fp16 = concat(axis = var_5818, interleave = var_5819_interleave_0, values = (var_5816_cast_fp16, var_5814_cast_fp16_0))[name = string("op_5819_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_5820_cast_fp16 = mul(x = var_5819_cast_fp16, y = var_866_cast_fp16)[name = string("op_5820_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_93_cast_fp16 = add(x = var_5813_cast_fp16, y = var_5820_cast_fp16)[name = string("query_states_93_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_5826_cast_fp16 = mul(x = var_5802_cast_fp16, y = var_859_cast_fp16)[name = string("op_5826_cast_fp16")];
            tensor<int32, [2]> var_5827_split_sizes_0 = const()[name = string("op_5827_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5827_axis_0 = const()[name = string("op_5827_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_5827_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_5827_cast_fp16_1 = split(axis = var_5827_axis_0, split_sizes = var_5827_split_sizes_0, x = var_5802_cast_fp16)[name = string("op_5827_cast_fp16")];
            fp16 const_155_promoted_to_fp16 = const()[name = string("const_155_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_5829_cast_fp16 = mul(x = var_5827_cast_fp16_1, y = const_155_promoted_to_fp16)[name = string("op_5829_cast_fp16")];
            int32 var_5831 = const()[name = string("op_5831"), val = int32(-2)];
            bool var_5832_interleave_0 = const()[name = string("op_5832_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_5832_cast_fp16 = concat(axis = var_5831, interleave = var_5832_interleave_0, values = (var_5829_cast_fp16, var_5827_cast_fp16_0))[name = string("op_5832_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_5833_cast_fp16 = mul(x = var_5832_cast_fp16, y = var_866_cast_fp16)[name = string("op_5833_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_155_cast_fp16 = add(x = var_5826_cast_fp16, y = var_5833_cast_fp16)[name = string("key_states_155_cast_fp16")];
            tensor<int32, [1]> expand_dims_180 = const()[name = string("expand_dims_180"), val = tensor<int32, [1]>([15])];
            tensor<int32, [1]> expand_dims_181 = const()[name = string("expand_dims_181"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_183 = const()[name = string("expand_dims_183"), val = tensor<int32, [1]>([0])];
            int32 concat_185_axis_0 = const()[name = string("concat_185_axis_0"), val = int32(0)];
            bool concat_185_interleave_0 = const()[name = string("concat_185_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_185 = concat(axis = concat_185_axis_0, interleave = concat_185_interleave_0, values = (expand_dims_180, expand_dims_181, position_id, expand_dims_183))[name = string("concat_185")];
            tensor<int32, [1]> expand_dims_184 = const()[name = string("expand_dims_184"), val = tensor<int32, [1]>([16])];
            tensor<int32, [1]> concat_186_values1_0 = const()[name = string("concat_186_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_186_values3_0 = const()[name = string("concat_186_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_186_axis_0 = const()[name = string("concat_186_axis_0"), val = int32(0)];
            bool concat_186_interleave_0 = const()[name = string("concat_186_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_186 = concat(axis = concat_186_axis_0, interleave = concat_186_interleave_0, values = (expand_dims_184, concat_186_values1_0, cache_position_end, concat_186_values3_0))[name = string("concat_186")];
            tensor<int32, [4]> key_states_157_perm_0 = const()[name = string("key_states_157_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_16_stride_0 = const()[name = string("key_cache_internal_tensor_assign_16_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_16_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_16_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_16_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_16_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_16_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_16_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_157_cast_fp16 = transpose(perm = key_states_157_perm_0, x = key_states_155_cast_fp16)[name = string("transpose_386")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_16_cast_fp16 = slice_update(begin = concat_185, begin_mask = key_cache_internal_tensor_assign_16_begin_mask_0, end = concat_186, end_mask = key_cache_internal_tensor_assign_16_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_16_squeeze_mask_0, stride = key_cache_internal_tensor_assign_16_stride_0, update = key_states_157_cast_fp16, x = coreml_update_state_252)[name = string("key_cache_internal_tensor_assign_16_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_16_cast_fp16, input = key_cache)[name = string("coreml_update_state_254_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_254 = read_state(input = key_cache)[name = string("coreml_update_state_254")];
            tensor<int32, [4]> value_states_93_perm_0 = const()[name = string("value_states_93_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_16_stride_0 = const()[name = string("value_cache_internal_tensor_assign_16_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_16_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_16_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_16_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_16_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_16_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_16_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_93_cast_fp16 = transpose(perm = value_states_93_perm_0, x = var_5809_cast_fp16)[name = string("transpose_385")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_16_cast_fp16 = slice_update(begin = concat_185, begin_mask = value_cache_internal_tensor_assign_16_begin_mask_0, end = concat_186, end_mask = value_cache_internal_tensor_assign_16_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_16_squeeze_mask_0, stride = value_cache_internal_tensor_assign_16_stride_0, update = value_states_93_cast_fp16, x = coreml_update_state_253)[name = string("value_cache_internal_tensor_assign_16_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_16_cast_fp16, input = value_cache)[name = string("coreml_update_state_255_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_255 = read_state(input = value_cache)[name = string("coreml_update_state_255")];
            tensor<int32, [4]> var_5903_begin_0 = const()[name = string("op_5903_begin_0"), val = tensor<int32, [4]>([15, 0, 0, 0])];
            tensor<int32, [4]> var_5903_end_0 = const()[name = string("op_5903_end_0"), val = tensor<int32, [4]>([16, 2, 2048, 128])];
            tensor<bool, [4]> var_5903_end_mask_0 = const()[name = string("op_5903_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5903_cast_fp16 = slice_by_index(begin = var_5903_begin_0, end = var_5903_end_0, end_mask = var_5903_end_mask_0, x = coreml_update_state_254)[name = string("op_5903_cast_fp16")];
            tensor<int32, [2]> tile_30 = const()[name = string("tile_30"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5906_axis_0 = const()[name = string("op_5906_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5906_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5906_cast_fp16_1 = split(axis = var_5906_axis_0, split_sizes = tile_30, x = var_5903_cast_fp16)[name = string("op_5906_cast_fp16")];
            tensor<int32, [4]> var_5913_begin_0 = const()[name = string("op_5913_begin_0"), val = tensor<int32, [4]>([15, 0, 0, 0])];
            tensor<int32, [4]> var_5913_end_0 = const()[name = string("op_5913_end_0"), val = tensor<int32, [4]>([16, 2, 2048, 128])];
            tensor<bool, [4]> var_5913_end_mask_0 = const()[name = string("op_5913_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5913_cast_fp16 = slice_by_index(begin = var_5913_begin_0, end = var_5913_end_0, end_mask = var_5913_end_mask_0, x = coreml_update_state_255)[name = string("op_5913_cast_fp16")];
            tensor<int32, [2]> tile_31 = const()[name = string("tile_31"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5916_axis_0 = const()[name = string("op_5916_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5916_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5916_cast_fp16_1 = split(axis = var_5916_axis_0, split_sizes = tile_31, x = var_5913_cast_fp16)[name = string("op_5916_cast_fp16")];
            tensor<int32, [2]> var_5919_split_sizes_0 = const()[name = string("op_5919_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_5919_axis_0 = const()[name = string("op_5919_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_5919_0, tensor<fp16, [1, 8, 128, 32]> var_5919_1 = split(axis = var_5919_axis_0, split_sizes = var_5919_split_sizes_0, x = query_states_93_cast_fp16)[name = string("op_5919")];
            bool attn_weights_241_transpose_x_0 = const()[name = string("attn_weights_241_transpose_x_0"), val = bool(false)];
            bool attn_weights_241_transpose_y_0 = const()[name = string("attn_weights_241_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_241_cast_fp16 = matmul(transpose_x = attn_weights_241_transpose_x_0, transpose_y = attn_weights_241_transpose_y_0, x = var_5906_cast_fp16_0, y = var_5919_0)[name = string("attn_weights_241_cast_fp16")];
            fp16 var_5922_to_fp16 = const()[name = string("op_5922_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_243_cast_fp16 = mul(x = attn_weights_241_cast_fp16, y = var_5922_to_fp16)[name = string("attn_weights_243_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_245_cast_fp16 = add(x = attn_weights_243_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_245_cast_fp16")];
            int32 var_5926 = const()[name = string("op_5926"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_247_cast_fp16 = softmax(axis = var_5926, x = attn_weights_245_cast_fp16)[name = string("attn_weights_247_cast_fp16")];
            bool var_5932_transpose_x_1 = const()[name = string("op_5932_transpose_x_1"), val = bool(true)];
            bool var_5932_transpose_y_1 = const()[name = string("op_5932_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_5932_cast_fp16 = matmul(transpose_x = var_5932_transpose_x_1, transpose_y = var_5932_transpose_y_1, x = attn_weights_247_cast_fp16, y = var_5916_cast_fp16_0)[name = string("op_5932_cast_fp16")];
            bool attn_weights_249_transpose_x_0 = const()[name = string("attn_weights_249_transpose_x_0"), val = bool(false)];
            bool attn_weights_249_transpose_y_0 = const()[name = string("attn_weights_249_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_249_cast_fp16 = matmul(transpose_x = attn_weights_249_transpose_x_0, transpose_y = attn_weights_249_transpose_y_0, x = var_5906_cast_fp16_1, y = var_5919_1)[name = string("attn_weights_249_cast_fp16")];
            fp16 var_5934_to_fp16 = const()[name = string("op_5934_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_251_cast_fp16 = mul(x = attn_weights_249_cast_fp16, y = var_5934_to_fp16)[name = string("attn_weights_251_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_253_cast_fp16 = add(x = attn_weights_251_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_253_cast_fp16")];
            int32 var_5938 = const()[name = string("op_5938"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_255_cast_fp16 = softmax(axis = var_5938, x = attn_weights_253_cast_fp16)[name = string("attn_weights_255_cast_fp16")];
            bool attn_output_121_transpose_x_1 = const()[name = string("attn_output_121_transpose_x_1"), val = bool(true)];
            bool attn_output_121_transpose_y_1 = const()[name = string("attn_output_121_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_121_cast_fp16 = matmul(transpose_x = attn_output_121_transpose_x_1, transpose_y = attn_output_121_transpose_y_1, x = attn_weights_255_cast_fp16, y = var_5916_cast_fp16_1)[name = string("attn_output_121_cast_fp16")];
            int32 var_5946 = const()[name = string("op_5946"), val = int32(1)];
            bool attn_output_123_interleave_0 = const()[name = string("attn_output_123_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_123_cast_fp16 = concat(axis = var_5946, interleave = attn_output_123_interleave_0, values = (var_5932_cast_fp16, attn_output_121_cast_fp16))[name = string("attn_output_123_cast_fp16")];
            tensor<int32, [4]> var_5950_perm_0 = const()[name = string("op_5950_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_191x = const()[name = string("concat_191x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_5950_cast_fp16 = transpose(perm = var_5950_perm_0, x = attn_output_123_cast_fp16)[name = string("transpose_384")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_127_cast_fp16 = reshape(shape = concat_191x, x = var_5950_cast_fp16)[name = string("attn_output_127_cast_fp16")];
            tensor<int32, [2]> hidden_states_153_strides_0 = const()[name = string("hidden_states_153_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_153_pad_type_0 = const()[name = string("hidden_states_153_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_153_pad_0 = const()[name = string("hidden_states_153_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_153_dilations_0 = const()[name = string("hidden_states_153_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_153_groups_0 = const()[name = string("hidden_states_153_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_153_cast_fp16 = conv(dilations = hidden_states_153_dilations_0, groups = hidden_states_153_groups_0, pad = hidden_states_153_pad_0, pad_type = hidden_states_153_pad_type_0, strides = hidden_states_153_strides_0, weight = layers_15_self_attn_o_proj_weight_cast_fp16, x = attn_output_127_cast_fp16)[name = string("hidden_states_153_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_155_cast_fp16 = add(x = hidden_states_149_cast_fp16, y = hidden_states_153_cast_fp16)[name = string("hidden_states_155_cast_fp16")];
            fp16 const_160_promoted_to_fp16 = const()[name = string("const_160_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_5983_cast_fp16 = mul(x = hidden_states_155_cast_fp16, y = const_160_promoted_to_fp16)[name = string("op_5983_cast_fp16")];
            int32 var_5981 = const()[name = string("op_5981"), val = int32(1)];
            bool doubled_125_interleave_0 = const()[name = string("doubled_125_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_125_cast_fp16 = concat(axis = var_5981, interleave = doubled_125_interleave_0, values = (hidden_states_155_cast_fp16, var_5983_cast_fp16))[name = string("doubled_125_cast_fp16")];
            tensor<int32, [1]> out_63_axes_0 = const()[name = string("out_63_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_63_gamma_0_to_fp16 = const()[name = string("out_63_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433266560)))];
            fp16 var_5993_to_fp16 = const()[name = string("op_5993_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_63_cast_fp16 = layer_norm(axes = out_63_axes_0, epsilon = var_5993_to_fp16, gamma = out_63_gamma_0_to_fp16, x = doubled_125_cast_fp16)[name = string("out_63_cast_fp16")];
            tensor<int32, [2]> var_6004_split_sizes_0 = const()[name = string("op_6004_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6004_axis_0 = const()[name = string("op_6004_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_6004_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_6004_cast_fp16_1 = split(axis = var_6004_axis_0, split_sizes = var_6004_split_sizes_0, x = out_63_cast_fp16)[name = string("op_6004_cast_fp16")];
            tensor<int32, [2]> input_31_strides_0 = const()[name = string("input_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_31_pad_type_0 = const()[name = string("input_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_31_pad_0 = const()[name = string("input_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_31_dilations_0 = const()[name = string("input_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_31_groups_0 = const()[name = string("input_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_31_cast_fp16 = conv(dilations = input_31_dilations_0, groups = input_31_groups_0, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = input_31_strides_0, weight = layers_15_mlp_gate_proj_weight_cast_fp16, x = var_6004_cast_fp16_0)[name = string("input_31_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_6021_cast_fp16 = silu(x = input_31_cast_fp16)[name = string("op_6021_cast_fp16")];
            tensor<int32, [2]> var_6027_strides_0 = const()[name = string("op_6027_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6027_pad_type_0 = const()[name = string("op_6027_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6027_pad_0 = const()[name = string("op_6027_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6027_dilations_0 = const()[name = string("op_6027_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6027_groups_0 = const()[name = string("op_6027_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_6027_cast_fp16 = conv(dilations = var_6027_dilations_0, groups = var_6027_groups_0, pad = var_6027_pad_0, pad_type = var_6027_pad_type_0, strides = var_6027_strides_0, weight = layers_15_mlp_up_proj_weight_cast_fp16, x = var_6004_cast_fp16_0)[name = string("op_6027_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_159_cast_fp16 = mul(x = var_6021_cast_fp16, y = var_6027_cast_fp16)[name = string("x_159_cast_fp16")];
            tensor<int32, [2]> hidden_states_157_strides_0 = const()[name = string("hidden_states_157_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_157_pad_type_0 = const()[name = string("hidden_states_157_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_157_pad_0 = const()[name = string("hidden_states_157_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_157_dilations_0 = const()[name = string("hidden_states_157_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_157_groups_0 = const()[name = string("hidden_states_157_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_157_cast_fp16 = conv(dilations = hidden_states_157_dilations_0, groups = hidden_states_157_groups_0, pad = hidden_states_157_pad_0, pad_type = hidden_states_157_pad_type_0, strides = hidden_states_157_strides_0, weight = layers_15_mlp_down_proj_weight_cast_fp16, x = x_159_cast_fp16)[name = string("hidden_states_157_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_159_cast_fp16 = add(x = hidden_states_155_cast_fp16, y = hidden_states_157_cast_fp16)[name = string("hidden_states_159_cast_fp16")];
            fp16 const_162_promoted_to_fp16 = const()[name = string("const_162_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_6045_cast_fp16 = mul(x = hidden_states_159_cast_fp16, y = const_162_promoted_to_fp16)[name = string("op_6045_cast_fp16")];
            int32 var_6043 = const()[name = string("op_6043"), val = int32(1)];
            bool doubled_129_interleave_0 = const()[name = string("doubled_129_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_129_cast_fp16 = concat(axis = var_6043, interleave = doubled_129_interleave_0, values = (hidden_states_159_cast_fp16, var_6045_cast_fp16))[name = string("doubled_129_cast_fp16")];
            tensor<int32, [1]> out_65_axes_0 = const()[name = string("out_65_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_65_gamma_0_to_fp16 = const()[name = string("out_65_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433274816)))];
            fp16 var_6055_to_fp16 = const()[name = string("op_6055_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_65_cast_fp16 = layer_norm(axes = out_65_axes_0, epsilon = var_6055_to_fp16, gamma = out_65_gamma_0_to_fp16, x = doubled_129_cast_fp16)[name = string("out_65_cast_fp16")];
            tensor<int32, [2]> var_6066_split_sizes_0 = const()[name = string("op_6066_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6066_axis_0 = const()[name = string("op_6066_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_6066_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_6066_cast_fp16_1 = split(axis = var_6066_axis_0, split_sizes = var_6066_split_sizes_0, x = out_65_cast_fp16)[name = string("op_6066_cast_fp16")];
            tensor<int32, [2]> query_states_97_strides_0 = const()[name = string("query_states_97_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_97_pad_type_0 = const()[name = string("query_states_97_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_97_pad_0 = const()[name = string("query_states_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_97_dilations_0 = const()[name = string("query_states_97_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_97_groups_0 = const()[name = string("query_states_97_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_97_cast_fp16 = conv(dilations = query_states_97_dilations_0, groups = query_states_97_groups_0, pad = query_states_97_pad_0, pad_type = query_states_97_pad_type_0, strides = query_states_97_strides_0, weight = layers_16_self_attn_q_proj_weight_cast_fp16, x = var_6066_cast_fp16_0)[name = string("query_states_97_cast_fp16")];
            tensor<int32, [2]> key_states_161_strides_0 = const()[name = string("key_states_161_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_161_pad_type_0 = const()[name = string("key_states_161_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_161_pad_0 = const()[name = string("key_states_161_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_161_dilations_0 = const()[name = string("key_states_161_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_161_groups_0 = const()[name = string("key_states_161_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_161_cast_fp16 = conv(dilations = key_states_161_dilations_0, groups = key_states_161_groups_0, pad = key_states_161_pad_0, pad_type = key_states_161_pad_type_0, strides = key_states_161_strides_0, weight = layers_16_self_attn_k_proj_weight_cast_fp16, x = var_6066_cast_fp16_0)[name = string("key_states_161_cast_fp16")];
            tensor<int32, [2]> value_states_97_strides_0 = const()[name = string("value_states_97_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_97_pad_type_0 = const()[name = string("value_states_97_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_97_pad_0 = const()[name = string("value_states_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_97_dilations_0 = const()[name = string("value_states_97_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_97_groups_0 = const()[name = string("value_states_97_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_97_cast_fp16 = conv(dilations = value_states_97_dilations_0, groups = value_states_97_groups_0, pad = value_states_97_pad_0, pad_type = value_states_97_pad_type_0, strides = value_states_97_strides_0, weight = layers_16_self_attn_v_proj_weight_cast_fp16, x = var_6066_cast_fp16_0)[name = string("value_states_97_cast_fp16")];
            tensor<int32, [4]> concat_192x = const()[name = string("concat_192x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_161_cast_fp16 = reshape(shape = concat_192x, x = query_states_97_cast_fp16)[name = string("x_161_cast_fp16")];
            tensor<int32, [4]> concat_193x = const()[name = string("concat_193x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_6123_cast_fp16 = reshape(shape = concat_193x, x = key_states_161_cast_fp16)[name = string("op_6123_cast_fp16")];
            tensor<int32, [4]> concat_194x = const()[name = string("concat_194x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_6130_cast_fp16 = reshape(shape = concat_194x, x = value_states_97_cast_fp16)[name = string("op_6130_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_6134_cast_fp16 = mul(x = x_161_cast_fp16, y = var_859_cast_fp16)[name = string("op_6134_cast_fp16")];
            tensor<int32, [2]> var_6135_split_sizes_0 = const()[name = string("op_6135_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6135_axis_0 = const()[name = string("op_6135_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_6135_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_6135_cast_fp16_1 = split(axis = var_6135_axis_0, split_sizes = var_6135_split_sizes_0, x = x_161_cast_fp16)[name = string("op_6135_cast_fp16")];
            fp16 const_164_promoted_to_fp16 = const()[name = string("const_164_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_6137_cast_fp16 = mul(x = var_6135_cast_fp16_1, y = const_164_promoted_to_fp16)[name = string("op_6137_cast_fp16")];
            int32 var_6139 = const()[name = string("op_6139"), val = int32(-2)];
            bool var_6140_interleave_0 = const()[name = string("op_6140_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_6140_cast_fp16 = concat(axis = var_6139, interleave = var_6140_interleave_0, values = (var_6137_cast_fp16, var_6135_cast_fp16_0))[name = string("op_6140_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_6141_cast_fp16 = mul(x = var_6140_cast_fp16, y = var_866_cast_fp16)[name = string("op_6141_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_99_cast_fp16 = add(x = var_6134_cast_fp16, y = var_6141_cast_fp16)[name = string("query_states_99_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_6147_cast_fp16 = mul(x = var_6123_cast_fp16, y = var_859_cast_fp16)[name = string("op_6147_cast_fp16")];
            tensor<int32, [2]> var_6148_split_sizes_0 = const()[name = string("op_6148_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6148_axis_0 = const()[name = string("op_6148_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_6148_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_6148_cast_fp16_1 = split(axis = var_6148_axis_0, split_sizes = var_6148_split_sizes_0, x = var_6123_cast_fp16)[name = string("op_6148_cast_fp16")];
            fp16 const_165_promoted_to_fp16 = const()[name = string("const_165_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_6150_cast_fp16 = mul(x = var_6148_cast_fp16_1, y = const_165_promoted_to_fp16)[name = string("op_6150_cast_fp16")];
            int32 var_6152 = const()[name = string("op_6152"), val = int32(-2)];
            bool var_6153_interleave_0 = const()[name = string("op_6153_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_6153_cast_fp16 = concat(axis = var_6152, interleave = var_6153_interleave_0, values = (var_6150_cast_fp16, var_6148_cast_fp16_0))[name = string("op_6153_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_6154_cast_fp16 = mul(x = var_6153_cast_fp16, y = var_866_cast_fp16)[name = string("op_6154_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_165_cast_fp16 = add(x = var_6147_cast_fp16, y = var_6154_cast_fp16)[name = string("key_states_165_cast_fp16")];
            tensor<int32, [1]> expand_dims_192 = const()[name = string("expand_dims_192"), val = tensor<int32, [1]>([16])];
            tensor<int32, [1]> expand_dims_193 = const()[name = string("expand_dims_193"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_195 = const()[name = string("expand_dims_195"), val = tensor<int32, [1]>([0])];
            int32 concat_197_axis_0 = const()[name = string("concat_197_axis_0"), val = int32(0)];
            bool concat_197_interleave_0 = const()[name = string("concat_197_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_197 = concat(axis = concat_197_axis_0, interleave = concat_197_interleave_0, values = (expand_dims_192, expand_dims_193, position_id, expand_dims_195))[name = string("concat_197")];
            tensor<int32, [1]> expand_dims_196 = const()[name = string("expand_dims_196"), val = tensor<int32, [1]>([17])];
            tensor<int32, [1]> concat_198_values1_0 = const()[name = string("concat_198_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_198_values3_0 = const()[name = string("concat_198_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_198_axis_0 = const()[name = string("concat_198_axis_0"), val = int32(0)];
            bool concat_198_interleave_0 = const()[name = string("concat_198_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_198 = concat(axis = concat_198_axis_0, interleave = concat_198_interleave_0, values = (expand_dims_196, concat_198_values1_0, cache_position_end, concat_198_values3_0))[name = string("concat_198")];
            tensor<int32, [4]> key_states_167_perm_0 = const()[name = string("key_states_167_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_17_stride_0 = const()[name = string("key_cache_internal_tensor_assign_17_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_17_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_17_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_17_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_17_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_17_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_17_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_167_cast_fp16 = transpose(perm = key_states_167_perm_0, x = key_states_165_cast_fp16)[name = string("transpose_383")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_17_cast_fp16 = slice_update(begin = concat_197, begin_mask = key_cache_internal_tensor_assign_17_begin_mask_0, end = concat_198, end_mask = key_cache_internal_tensor_assign_17_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_17_squeeze_mask_0, stride = key_cache_internal_tensor_assign_17_stride_0, update = key_states_167_cast_fp16, x = coreml_update_state_254)[name = string("key_cache_internal_tensor_assign_17_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_17_cast_fp16, input = key_cache)[name = string("coreml_update_state_256_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_256 = read_state(input = key_cache)[name = string("coreml_update_state_256")];
            tensor<int32, [4]> value_states_99_perm_0 = const()[name = string("value_states_99_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_17_stride_0 = const()[name = string("value_cache_internal_tensor_assign_17_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_17_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_17_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_17_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_17_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_17_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_17_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_99_cast_fp16 = transpose(perm = value_states_99_perm_0, x = var_6130_cast_fp16)[name = string("transpose_382")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_17_cast_fp16 = slice_update(begin = concat_197, begin_mask = value_cache_internal_tensor_assign_17_begin_mask_0, end = concat_198, end_mask = value_cache_internal_tensor_assign_17_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_17_squeeze_mask_0, stride = value_cache_internal_tensor_assign_17_stride_0, update = value_states_99_cast_fp16, x = coreml_update_state_255)[name = string("value_cache_internal_tensor_assign_17_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_17_cast_fp16, input = value_cache)[name = string("coreml_update_state_257_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_257 = read_state(input = value_cache)[name = string("coreml_update_state_257")];
            tensor<int32, [4]> var_6224_begin_0 = const()[name = string("op_6224_begin_0"), val = tensor<int32, [4]>([16, 0, 0, 0])];
            tensor<int32, [4]> var_6224_end_0 = const()[name = string("op_6224_end_0"), val = tensor<int32, [4]>([17, 2, 2048, 128])];
            tensor<bool, [4]> var_6224_end_mask_0 = const()[name = string("op_6224_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6224_cast_fp16 = slice_by_index(begin = var_6224_begin_0, end = var_6224_end_0, end_mask = var_6224_end_mask_0, x = coreml_update_state_256)[name = string("op_6224_cast_fp16")];
            tensor<int32, [2]> tile_32 = const()[name = string("tile_32"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6227_axis_0 = const()[name = string("op_6227_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6227_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6227_cast_fp16_1 = split(axis = var_6227_axis_0, split_sizes = tile_32, x = var_6224_cast_fp16)[name = string("op_6227_cast_fp16")];
            tensor<int32, [4]> var_6234_begin_0 = const()[name = string("op_6234_begin_0"), val = tensor<int32, [4]>([16, 0, 0, 0])];
            tensor<int32, [4]> var_6234_end_0 = const()[name = string("op_6234_end_0"), val = tensor<int32, [4]>([17, 2, 2048, 128])];
            tensor<bool, [4]> var_6234_end_mask_0 = const()[name = string("op_6234_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6234_cast_fp16 = slice_by_index(begin = var_6234_begin_0, end = var_6234_end_0, end_mask = var_6234_end_mask_0, x = coreml_update_state_257)[name = string("op_6234_cast_fp16")];
            tensor<int32, [2]> tile_33 = const()[name = string("tile_33"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6237_axis_0 = const()[name = string("op_6237_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6237_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6237_cast_fp16_1 = split(axis = var_6237_axis_0, split_sizes = tile_33, x = var_6234_cast_fp16)[name = string("op_6237_cast_fp16")];
            tensor<int32, [2]> var_6240_split_sizes_0 = const()[name = string("op_6240_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_6240_axis_0 = const()[name = string("op_6240_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_6240_0, tensor<fp16, [1, 8, 128, 32]> var_6240_1 = split(axis = var_6240_axis_0, split_sizes = var_6240_split_sizes_0, x = query_states_99_cast_fp16)[name = string("op_6240")];
            bool attn_weights_257_transpose_x_0 = const()[name = string("attn_weights_257_transpose_x_0"), val = bool(false)];
            bool attn_weights_257_transpose_y_0 = const()[name = string("attn_weights_257_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_257_cast_fp16 = matmul(transpose_x = attn_weights_257_transpose_x_0, transpose_y = attn_weights_257_transpose_y_0, x = var_6227_cast_fp16_0, y = var_6240_0)[name = string("attn_weights_257_cast_fp16")];
            fp16 var_6243_to_fp16 = const()[name = string("op_6243_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_259_cast_fp16 = mul(x = attn_weights_257_cast_fp16, y = var_6243_to_fp16)[name = string("attn_weights_259_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_261_cast_fp16 = add(x = attn_weights_259_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_261_cast_fp16")];
            int32 var_6247 = const()[name = string("op_6247"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_263_cast_fp16 = softmax(axis = var_6247, x = attn_weights_261_cast_fp16)[name = string("attn_weights_263_cast_fp16")];
            bool var_6253_transpose_x_1 = const()[name = string("op_6253_transpose_x_1"), val = bool(true)];
            bool var_6253_transpose_y_1 = const()[name = string("op_6253_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_6253_cast_fp16 = matmul(transpose_x = var_6253_transpose_x_1, transpose_y = var_6253_transpose_y_1, x = attn_weights_263_cast_fp16, y = var_6237_cast_fp16_0)[name = string("op_6253_cast_fp16")];
            bool attn_weights_265_transpose_x_0 = const()[name = string("attn_weights_265_transpose_x_0"), val = bool(false)];
            bool attn_weights_265_transpose_y_0 = const()[name = string("attn_weights_265_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_265_cast_fp16 = matmul(transpose_x = attn_weights_265_transpose_x_0, transpose_y = attn_weights_265_transpose_y_0, x = var_6227_cast_fp16_1, y = var_6240_1)[name = string("attn_weights_265_cast_fp16")];
            fp16 var_6255_to_fp16 = const()[name = string("op_6255_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_267_cast_fp16 = mul(x = attn_weights_265_cast_fp16, y = var_6255_to_fp16)[name = string("attn_weights_267_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_269_cast_fp16 = add(x = attn_weights_267_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_269_cast_fp16")];
            int32 var_6259 = const()[name = string("op_6259"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_271_cast_fp16 = softmax(axis = var_6259, x = attn_weights_269_cast_fp16)[name = string("attn_weights_271_cast_fp16")];
            bool attn_output_129_transpose_x_1 = const()[name = string("attn_output_129_transpose_x_1"), val = bool(true)];
            bool attn_output_129_transpose_y_1 = const()[name = string("attn_output_129_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_129_cast_fp16 = matmul(transpose_x = attn_output_129_transpose_x_1, transpose_y = attn_output_129_transpose_y_1, x = attn_weights_271_cast_fp16, y = var_6237_cast_fp16_1)[name = string("attn_output_129_cast_fp16")];
            int32 var_6267 = const()[name = string("op_6267"), val = int32(1)];
            bool attn_output_131_interleave_0 = const()[name = string("attn_output_131_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_131_cast_fp16 = concat(axis = var_6267, interleave = attn_output_131_interleave_0, values = (var_6253_cast_fp16, attn_output_129_cast_fp16))[name = string("attn_output_131_cast_fp16")];
            tensor<int32, [4]> var_6271_perm_0 = const()[name = string("op_6271_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_203x = const()[name = string("concat_203x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_6271_cast_fp16 = transpose(perm = var_6271_perm_0, x = attn_output_131_cast_fp16)[name = string("transpose_381")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_135_cast_fp16 = reshape(shape = concat_203x, x = var_6271_cast_fp16)[name = string("attn_output_135_cast_fp16")];
            tensor<int32, [2]> hidden_states_163_strides_0 = const()[name = string("hidden_states_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_163_pad_type_0 = const()[name = string("hidden_states_163_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_163_pad_0 = const()[name = string("hidden_states_163_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_163_dilations_0 = const()[name = string("hidden_states_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_163_groups_0 = const()[name = string("hidden_states_163_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_163_cast_fp16 = conv(dilations = hidden_states_163_dilations_0, groups = hidden_states_163_groups_0, pad = hidden_states_163_pad_0, pad_type = hidden_states_163_pad_type_0, strides = hidden_states_163_strides_0, weight = layers_16_self_attn_o_proj_weight_cast_fp16, x = attn_output_135_cast_fp16)[name = string("hidden_states_163_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_165_cast_fp16 = add(x = hidden_states_159_cast_fp16, y = hidden_states_163_cast_fp16)[name = string("hidden_states_165_cast_fp16")];
            fp16 const_170_promoted_to_fp16 = const()[name = string("const_170_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_6304_cast_fp16 = mul(x = hidden_states_165_cast_fp16, y = const_170_promoted_to_fp16)[name = string("op_6304_cast_fp16")];
            int32 var_6302 = const()[name = string("op_6302"), val = int32(1)];
            bool doubled_133_interleave_0 = const()[name = string("doubled_133_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_133_cast_fp16 = concat(axis = var_6302, interleave = doubled_133_interleave_0, values = (hidden_states_165_cast_fp16, var_6304_cast_fp16))[name = string("doubled_133_cast_fp16")];
            tensor<int32, [1]> out_67_axes_0 = const()[name = string("out_67_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_67_gamma_0_to_fp16 = const()[name = string("out_67_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433283072)))];
            fp16 var_6314_to_fp16 = const()[name = string("op_6314_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_67_cast_fp16 = layer_norm(axes = out_67_axes_0, epsilon = var_6314_to_fp16, gamma = out_67_gamma_0_to_fp16, x = doubled_133_cast_fp16)[name = string("out_67_cast_fp16")];
            tensor<int32, [2]> var_6325_split_sizes_0 = const()[name = string("op_6325_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6325_axis_0 = const()[name = string("op_6325_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_6325_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_6325_cast_fp16_1 = split(axis = var_6325_axis_0, split_sizes = var_6325_split_sizes_0, x = out_67_cast_fp16)[name = string("op_6325_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_16_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_16_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433291328)))];
            tensor<int32, [2]> input_33_strides_0 = const()[name = string("input_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_33_pad_type_0 = const()[name = string("input_33_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_33_pad_0 = const()[name = string("input_33_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_33_dilations_0 = const()[name = string("input_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_33_groups_0 = const()[name = string("input_33_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_33_cast_fp16 = conv(dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = layers_16_mlp_gate_proj_weight_to_fp16, x = var_6325_cast_fp16_0)[name = string("input_33_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_6342_cast_fp16 = silu(x = input_33_cast_fp16)[name = string("op_6342_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_16_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_16_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1458457216)))];
            tensor<int32, [2]> var_6348_strides_0 = const()[name = string("op_6348_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6348_pad_type_0 = const()[name = string("op_6348_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6348_pad_0 = const()[name = string("op_6348_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6348_dilations_0 = const()[name = string("op_6348_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6348_groups_0 = const()[name = string("op_6348_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_6348_cast_fp16 = conv(dilations = var_6348_dilations_0, groups = var_6348_groups_0, pad = var_6348_pad_0, pad_type = var_6348_pad_type_0, strides = var_6348_strides_0, weight = layers_16_mlp_up_proj_weight_to_fp16, x = var_6325_cast_fp16_0)[name = string("op_6348_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_169_cast_fp16 = mul(x = var_6342_cast_fp16, y = var_6348_cast_fp16)[name = string("x_169_cast_fp16")];
            tensor<int32, [2]> hidden_states_167_strides_0 = const()[name = string("hidden_states_167_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_167_pad_type_0 = const()[name = string("hidden_states_167_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_167_pad_0 = const()[name = string("hidden_states_167_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_167_dilations_0 = const()[name = string("hidden_states_167_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_167_groups_0 = const()[name = string("hidden_states_167_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_167_cast_fp16 = conv(dilations = hidden_states_167_dilations_0, groups = hidden_states_167_groups_0, pad = hidden_states_167_pad_0, pad_type = hidden_states_167_pad_type_0, strides = hidden_states_167_strides_0, weight = layers_16_mlp_down_proj_weight_cast_fp16, x = x_169_cast_fp16)[name = string("hidden_states_167_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_169_cast_fp16 = add(x = hidden_states_165_cast_fp16, y = hidden_states_167_cast_fp16)[name = string("hidden_states_169_cast_fp16")];
            fp16 const_172_promoted_to_fp16 = const()[name = string("const_172_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_6366_cast_fp16 = mul(x = hidden_states_169_cast_fp16, y = const_172_promoted_to_fp16)[name = string("op_6366_cast_fp16")];
            int32 var_6364 = const()[name = string("op_6364"), val = int32(1)];
            bool doubled_137_interleave_0 = const()[name = string("doubled_137_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_137_cast_fp16 = concat(axis = var_6364, interleave = doubled_137_interleave_0, values = (hidden_states_169_cast_fp16, var_6366_cast_fp16))[name = string("doubled_137_cast_fp16")];
            tensor<int32, [1]> out_69_axes_0 = const()[name = string("out_69_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_69_gamma_0_to_fp16 = const()[name = string("out_69_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483623104)))];
            fp16 var_6376_to_fp16 = const()[name = string("op_6376_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_69_cast_fp16 = layer_norm(axes = out_69_axes_0, epsilon = var_6376_to_fp16, gamma = out_69_gamma_0_to_fp16, x = doubled_137_cast_fp16)[name = string("out_69_cast_fp16")];
            tensor<int32, [2]> var_6387_split_sizes_0 = const()[name = string("op_6387_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6387_axis_0 = const()[name = string("op_6387_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_6387_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_6387_cast_fp16_1 = split(axis = var_6387_axis_0, split_sizes = var_6387_split_sizes_0, x = out_69_cast_fp16)[name = string("op_6387_cast_fp16")];
            tensor<int32, [2]> query_states_103_strides_0 = const()[name = string("query_states_103_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_103_pad_type_0 = const()[name = string("query_states_103_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_103_pad_0 = const()[name = string("query_states_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_103_dilations_0 = const()[name = string("query_states_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_103_groups_0 = const()[name = string("query_states_103_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_103_cast_fp16 = conv(dilations = query_states_103_dilations_0, groups = query_states_103_groups_0, pad = query_states_103_pad_0, pad_type = query_states_103_pad_type_0, strides = query_states_103_strides_0, weight = layers_17_self_attn_q_proj_weight_cast_fp16, x = var_6387_cast_fp16_0)[name = string("query_states_103_cast_fp16")];
            tensor<int32, [2]> key_states_171_strides_0 = const()[name = string("key_states_171_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_171_pad_type_0 = const()[name = string("key_states_171_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_171_pad_0 = const()[name = string("key_states_171_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_171_dilations_0 = const()[name = string("key_states_171_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_171_groups_0 = const()[name = string("key_states_171_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_171_cast_fp16 = conv(dilations = key_states_171_dilations_0, groups = key_states_171_groups_0, pad = key_states_171_pad_0, pad_type = key_states_171_pad_type_0, strides = key_states_171_strides_0, weight = layers_17_self_attn_k_proj_weight_cast_fp16, x = var_6387_cast_fp16_0)[name = string("key_states_171_cast_fp16")];
            tensor<int32, [2]> value_states_103_strides_0 = const()[name = string("value_states_103_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_103_pad_type_0 = const()[name = string("value_states_103_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_103_pad_0 = const()[name = string("value_states_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_103_dilations_0 = const()[name = string("value_states_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_103_groups_0 = const()[name = string("value_states_103_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_103_cast_fp16 = conv(dilations = value_states_103_dilations_0, groups = value_states_103_groups_0, pad = value_states_103_pad_0, pad_type = value_states_103_pad_type_0, strides = value_states_103_strides_0, weight = layers_17_self_attn_v_proj_weight_cast_fp16, x = var_6387_cast_fp16_0)[name = string("value_states_103_cast_fp16")];
            tensor<int32, [4]> concat_204x = const()[name = string("concat_204x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_171_cast_fp16 = reshape(shape = concat_204x, x = query_states_103_cast_fp16)[name = string("x_171_cast_fp16")];
            tensor<int32, [4]> concat_205x = const()[name = string("concat_205x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_6444_cast_fp16 = reshape(shape = concat_205x, x = key_states_171_cast_fp16)[name = string("op_6444_cast_fp16")];
            tensor<int32, [4]> concat_206x = const()[name = string("concat_206x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_6451_cast_fp16 = reshape(shape = concat_206x, x = value_states_103_cast_fp16)[name = string("op_6451_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_6455_cast_fp16 = mul(x = x_171_cast_fp16, y = var_859_cast_fp16)[name = string("op_6455_cast_fp16")];
            tensor<int32, [2]> var_6456_split_sizes_0 = const()[name = string("op_6456_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6456_axis_0 = const()[name = string("op_6456_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_6456_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_6456_cast_fp16_1 = split(axis = var_6456_axis_0, split_sizes = var_6456_split_sizes_0, x = x_171_cast_fp16)[name = string("op_6456_cast_fp16")];
            fp16 const_174_promoted_to_fp16 = const()[name = string("const_174_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_6458_cast_fp16 = mul(x = var_6456_cast_fp16_1, y = const_174_promoted_to_fp16)[name = string("op_6458_cast_fp16")];
            int32 var_6460 = const()[name = string("op_6460"), val = int32(-2)];
            bool var_6461_interleave_0 = const()[name = string("op_6461_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_6461_cast_fp16 = concat(axis = var_6460, interleave = var_6461_interleave_0, values = (var_6458_cast_fp16, var_6456_cast_fp16_0))[name = string("op_6461_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_6462_cast_fp16 = mul(x = var_6461_cast_fp16, y = var_866_cast_fp16)[name = string("op_6462_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_105_cast_fp16 = add(x = var_6455_cast_fp16, y = var_6462_cast_fp16)[name = string("query_states_105_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_6468_cast_fp16 = mul(x = var_6444_cast_fp16, y = var_859_cast_fp16)[name = string("op_6468_cast_fp16")];
            tensor<int32, [2]> var_6469_split_sizes_0 = const()[name = string("op_6469_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6469_axis_0 = const()[name = string("op_6469_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_6469_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_6469_cast_fp16_1 = split(axis = var_6469_axis_0, split_sizes = var_6469_split_sizes_0, x = var_6444_cast_fp16)[name = string("op_6469_cast_fp16")];
            fp16 const_175_promoted_to_fp16 = const()[name = string("const_175_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_6471_cast_fp16 = mul(x = var_6469_cast_fp16_1, y = const_175_promoted_to_fp16)[name = string("op_6471_cast_fp16")];
            int32 var_6473 = const()[name = string("op_6473"), val = int32(-2)];
            bool var_6474_interleave_0 = const()[name = string("op_6474_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_6474_cast_fp16 = concat(axis = var_6473, interleave = var_6474_interleave_0, values = (var_6471_cast_fp16, var_6469_cast_fp16_0))[name = string("op_6474_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_6475_cast_fp16 = mul(x = var_6474_cast_fp16, y = var_866_cast_fp16)[name = string("op_6475_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_175_cast_fp16 = add(x = var_6468_cast_fp16, y = var_6475_cast_fp16)[name = string("key_states_175_cast_fp16")];
            tensor<int32, [1]> expand_dims_204 = const()[name = string("expand_dims_204"), val = tensor<int32, [1]>([17])];
            tensor<int32, [1]> expand_dims_205 = const()[name = string("expand_dims_205"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_207 = const()[name = string("expand_dims_207"), val = tensor<int32, [1]>([0])];
            int32 concat_209_axis_0 = const()[name = string("concat_209_axis_0"), val = int32(0)];
            bool concat_209_interleave_0 = const()[name = string("concat_209_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_209 = concat(axis = concat_209_axis_0, interleave = concat_209_interleave_0, values = (expand_dims_204, expand_dims_205, position_id, expand_dims_207))[name = string("concat_209")];
            tensor<int32, [1]> expand_dims_208 = const()[name = string("expand_dims_208"), val = tensor<int32, [1]>([18])];
            tensor<int32, [1]> concat_210_values1_0 = const()[name = string("concat_210_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_210_values3_0 = const()[name = string("concat_210_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_210_axis_0 = const()[name = string("concat_210_axis_0"), val = int32(0)];
            bool concat_210_interleave_0 = const()[name = string("concat_210_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_210 = concat(axis = concat_210_axis_0, interleave = concat_210_interleave_0, values = (expand_dims_208, concat_210_values1_0, cache_position_end, concat_210_values3_0))[name = string("concat_210")];
            tensor<int32, [4]> key_states_177_perm_0 = const()[name = string("key_states_177_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_18_stride_0 = const()[name = string("key_cache_internal_tensor_assign_18_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_18_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_18_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_18_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_18_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_18_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_18_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_177_cast_fp16 = transpose(perm = key_states_177_perm_0, x = key_states_175_cast_fp16)[name = string("transpose_380")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_18_cast_fp16 = slice_update(begin = concat_209, begin_mask = key_cache_internal_tensor_assign_18_begin_mask_0, end = concat_210, end_mask = key_cache_internal_tensor_assign_18_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_18_squeeze_mask_0, stride = key_cache_internal_tensor_assign_18_stride_0, update = key_states_177_cast_fp16, x = coreml_update_state_256)[name = string("key_cache_internal_tensor_assign_18_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_18_cast_fp16, input = key_cache)[name = string("coreml_update_state_258_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_258 = read_state(input = key_cache)[name = string("coreml_update_state_258")];
            tensor<int32, [4]> value_states_105_perm_0 = const()[name = string("value_states_105_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_18_stride_0 = const()[name = string("value_cache_internal_tensor_assign_18_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_18_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_18_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_18_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_18_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_18_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_18_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_105_cast_fp16 = transpose(perm = value_states_105_perm_0, x = var_6451_cast_fp16)[name = string("transpose_379")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_18_cast_fp16 = slice_update(begin = concat_209, begin_mask = value_cache_internal_tensor_assign_18_begin_mask_0, end = concat_210, end_mask = value_cache_internal_tensor_assign_18_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_18_squeeze_mask_0, stride = value_cache_internal_tensor_assign_18_stride_0, update = value_states_105_cast_fp16, x = coreml_update_state_257)[name = string("value_cache_internal_tensor_assign_18_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_18_cast_fp16, input = value_cache)[name = string("coreml_update_state_259_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_259 = read_state(input = value_cache)[name = string("coreml_update_state_259")];
            tensor<int32, [4]> var_6545_begin_0 = const()[name = string("op_6545_begin_0"), val = tensor<int32, [4]>([17, 0, 0, 0])];
            tensor<int32, [4]> var_6545_end_0 = const()[name = string("op_6545_end_0"), val = tensor<int32, [4]>([18, 2, 2048, 128])];
            tensor<bool, [4]> var_6545_end_mask_0 = const()[name = string("op_6545_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6545_cast_fp16 = slice_by_index(begin = var_6545_begin_0, end = var_6545_end_0, end_mask = var_6545_end_mask_0, x = coreml_update_state_258)[name = string("op_6545_cast_fp16")];
            tensor<int32, [2]> tile_34 = const()[name = string("tile_34"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6548_axis_0 = const()[name = string("op_6548_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6548_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6548_cast_fp16_1 = split(axis = var_6548_axis_0, split_sizes = tile_34, x = var_6545_cast_fp16)[name = string("op_6548_cast_fp16")];
            tensor<int32, [4]> var_6555_begin_0 = const()[name = string("op_6555_begin_0"), val = tensor<int32, [4]>([17, 0, 0, 0])];
            tensor<int32, [4]> var_6555_end_0 = const()[name = string("op_6555_end_0"), val = tensor<int32, [4]>([18, 2, 2048, 128])];
            tensor<bool, [4]> var_6555_end_mask_0 = const()[name = string("op_6555_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6555_cast_fp16 = slice_by_index(begin = var_6555_begin_0, end = var_6555_end_0, end_mask = var_6555_end_mask_0, x = coreml_update_state_259)[name = string("op_6555_cast_fp16")];
            tensor<int32, [2]> tile_35 = const()[name = string("tile_35"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6558_axis_0 = const()[name = string("op_6558_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6558_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6558_cast_fp16_1 = split(axis = var_6558_axis_0, split_sizes = tile_35, x = var_6555_cast_fp16)[name = string("op_6558_cast_fp16")];
            tensor<int32, [2]> var_6561_split_sizes_0 = const()[name = string("op_6561_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_6561_axis_0 = const()[name = string("op_6561_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_6561_0, tensor<fp16, [1, 8, 128, 32]> var_6561_1 = split(axis = var_6561_axis_0, split_sizes = var_6561_split_sizes_0, x = query_states_105_cast_fp16)[name = string("op_6561")];
            bool attn_weights_273_transpose_x_0 = const()[name = string("attn_weights_273_transpose_x_0"), val = bool(false)];
            bool attn_weights_273_transpose_y_0 = const()[name = string("attn_weights_273_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_273_cast_fp16 = matmul(transpose_x = attn_weights_273_transpose_x_0, transpose_y = attn_weights_273_transpose_y_0, x = var_6548_cast_fp16_0, y = var_6561_0)[name = string("attn_weights_273_cast_fp16")];
            fp16 var_6564_to_fp16 = const()[name = string("op_6564_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_275_cast_fp16 = mul(x = attn_weights_273_cast_fp16, y = var_6564_to_fp16)[name = string("attn_weights_275_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_277_cast_fp16 = add(x = attn_weights_275_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_277_cast_fp16")];
            int32 var_6568 = const()[name = string("op_6568"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_279_cast_fp16 = softmax(axis = var_6568, x = attn_weights_277_cast_fp16)[name = string("attn_weights_279_cast_fp16")];
            bool var_6574_transpose_x_1 = const()[name = string("op_6574_transpose_x_1"), val = bool(true)];
            bool var_6574_transpose_y_1 = const()[name = string("op_6574_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_6574_cast_fp16 = matmul(transpose_x = var_6574_transpose_x_1, transpose_y = var_6574_transpose_y_1, x = attn_weights_279_cast_fp16, y = var_6558_cast_fp16_0)[name = string("op_6574_cast_fp16")];
            bool attn_weights_281_transpose_x_0 = const()[name = string("attn_weights_281_transpose_x_0"), val = bool(false)];
            bool attn_weights_281_transpose_y_0 = const()[name = string("attn_weights_281_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_281_cast_fp16 = matmul(transpose_x = attn_weights_281_transpose_x_0, transpose_y = attn_weights_281_transpose_y_0, x = var_6548_cast_fp16_1, y = var_6561_1)[name = string("attn_weights_281_cast_fp16")];
            fp16 var_6576_to_fp16 = const()[name = string("op_6576_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_283_cast_fp16 = mul(x = attn_weights_281_cast_fp16, y = var_6576_to_fp16)[name = string("attn_weights_283_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_285_cast_fp16 = add(x = attn_weights_283_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_285_cast_fp16")];
            int32 var_6580 = const()[name = string("op_6580"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_287_cast_fp16 = softmax(axis = var_6580, x = attn_weights_285_cast_fp16)[name = string("attn_weights_287_cast_fp16")];
            bool attn_output_137_transpose_x_1 = const()[name = string("attn_output_137_transpose_x_1"), val = bool(true)];
            bool attn_output_137_transpose_y_1 = const()[name = string("attn_output_137_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_137_cast_fp16 = matmul(transpose_x = attn_output_137_transpose_x_1, transpose_y = attn_output_137_transpose_y_1, x = attn_weights_287_cast_fp16, y = var_6558_cast_fp16_1)[name = string("attn_output_137_cast_fp16")];
            int32 var_6588 = const()[name = string("op_6588"), val = int32(1)];
            bool attn_output_139_interleave_0 = const()[name = string("attn_output_139_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_139_cast_fp16 = concat(axis = var_6588, interleave = attn_output_139_interleave_0, values = (var_6574_cast_fp16, attn_output_137_cast_fp16))[name = string("attn_output_139_cast_fp16")];
            tensor<int32, [4]> var_6592_perm_0 = const()[name = string("op_6592_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_215x = const()[name = string("concat_215x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_6592_cast_fp16 = transpose(perm = var_6592_perm_0, x = attn_output_139_cast_fp16)[name = string("transpose_378")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_143_cast_fp16 = reshape(shape = concat_215x, x = var_6592_cast_fp16)[name = string("attn_output_143_cast_fp16")];
            tensor<int32, [2]> hidden_states_173_strides_0 = const()[name = string("hidden_states_173_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_173_pad_type_0 = const()[name = string("hidden_states_173_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_173_pad_0 = const()[name = string("hidden_states_173_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_173_dilations_0 = const()[name = string("hidden_states_173_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_173_groups_0 = const()[name = string("hidden_states_173_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_173_cast_fp16 = conv(dilations = hidden_states_173_dilations_0, groups = hidden_states_173_groups_0, pad = hidden_states_173_pad_0, pad_type = hidden_states_173_pad_type_0, strides = hidden_states_173_strides_0, weight = layers_17_self_attn_o_proj_weight_cast_fp16, x = attn_output_143_cast_fp16)[name = string("hidden_states_173_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_175_cast_fp16 = add(x = hidden_states_169_cast_fp16, y = hidden_states_173_cast_fp16)[name = string("hidden_states_175_cast_fp16")];
            fp16 const_180_promoted_to_fp16 = const()[name = string("const_180_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_6625_cast_fp16 = mul(x = hidden_states_175_cast_fp16, y = const_180_promoted_to_fp16)[name = string("op_6625_cast_fp16")];
            int32 var_6623 = const()[name = string("op_6623"), val = int32(1)];
            bool doubled_141_interleave_0 = const()[name = string("doubled_141_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_141_cast_fp16 = concat(axis = var_6623, interleave = doubled_141_interleave_0, values = (hidden_states_175_cast_fp16, var_6625_cast_fp16))[name = string("doubled_141_cast_fp16")];
            tensor<int32, [1]> out_71_axes_0 = const()[name = string("out_71_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_71_gamma_0_to_fp16 = const()[name = string("out_71_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483631360)))];
            fp16 var_6635_to_fp16 = const()[name = string("op_6635_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_71_cast_fp16 = layer_norm(axes = out_71_axes_0, epsilon = var_6635_to_fp16, gamma = out_71_gamma_0_to_fp16, x = doubled_141_cast_fp16)[name = string("out_71_cast_fp16")];
            tensor<int32, [2]> var_6646_split_sizes_0 = const()[name = string("op_6646_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6646_axis_0 = const()[name = string("op_6646_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_6646_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_6646_cast_fp16_1 = split(axis = var_6646_axis_0, split_sizes = var_6646_split_sizes_0, x = out_71_cast_fp16)[name = string("op_6646_cast_fp16")];
            tensor<int32, [2]> input_35_strides_0 = const()[name = string("input_35_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_35_pad_type_0 = const()[name = string("input_35_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_35_pad_0 = const()[name = string("input_35_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_35_dilations_0 = const()[name = string("input_35_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_35_groups_0 = const()[name = string("input_35_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_35_cast_fp16 = conv(dilations = input_35_dilations_0, groups = input_35_groups_0, pad = input_35_pad_0, pad_type = input_35_pad_type_0, strides = input_35_strides_0, weight = layers_17_mlp_gate_proj_weight_cast_fp16, x = var_6646_cast_fp16_0)[name = string("input_35_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_6663_cast_fp16 = silu(x = input_35_cast_fp16)[name = string("op_6663_cast_fp16")];
            tensor<int32, [2]> var_6669_strides_0 = const()[name = string("op_6669_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6669_pad_type_0 = const()[name = string("op_6669_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6669_pad_0 = const()[name = string("op_6669_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6669_dilations_0 = const()[name = string("op_6669_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6669_groups_0 = const()[name = string("op_6669_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_6669_cast_fp16 = conv(dilations = var_6669_dilations_0, groups = var_6669_groups_0, pad = var_6669_pad_0, pad_type = var_6669_pad_type_0, strides = var_6669_strides_0, weight = layers_17_mlp_up_proj_weight_cast_fp16, x = var_6646_cast_fp16_0)[name = string("op_6669_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_179_cast_fp16 = mul(x = var_6663_cast_fp16, y = var_6669_cast_fp16)[name = string("x_179_cast_fp16")];
            tensor<int32, [2]> hidden_states_177_strides_0 = const()[name = string("hidden_states_177_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_177_pad_type_0 = const()[name = string("hidden_states_177_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_177_pad_0 = const()[name = string("hidden_states_177_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_177_dilations_0 = const()[name = string("hidden_states_177_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_177_groups_0 = const()[name = string("hidden_states_177_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_177_cast_fp16 = conv(dilations = hidden_states_177_dilations_0, groups = hidden_states_177_groups_0, pad = hidden_states_177_pad_0, pad_type = hidden_states_177_pad_type_0, strides = hidden_states_177_strides_0, weight = layers_17_mlp_down_proj_weight_cast_fp16, x = x_179_cast_fp16)[name = string("hidden_states_177_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_179_cast_fp16 = add(x = hidden_states_175_cast_fp16, y = hidden_states_177_cast_fp16)[name = string("hidden_states_179_cast_fp16")];
            fp16 const_182_promoted_to_fp16 = const()[name = string("const_182_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_6687_cast_fp16 = mul(x = hidden_states_179_cast_fp16, y = const_182_promoted_to_fp16)[name = string("op_6687_cast_fp16")];
            int32 var_6685 = const()[name = string("op_6685"), val = int32(1)];
            bool doubled_145_interleave_0 = const()[name = string("doubled_145_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_145_cast_fp16 = concat(axis = var_6685, interleave = doubled_145_interleave_0, values = (hidden_states_179_cast_fp16, var_6687_cast_fp16))[name = string("doubled_145_cast_fp16")];
            tensor<int32, [1]> out_73_axes_0 = const()[name = string("out_73_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_73_gamma_0_to_fp16 = const()[name = string("out_73_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483639616)))];
            fp16 var_6697_to_fp16 = const()[name = string("op_6697_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_73_cast_fp16 = layer_norm(axes = out_73_axes_0, epsilon = var_6697_to_fp16, gamma = out_73_gamma_0_to_fp16, x = doubled_145_cast_fp16)[name = string("out_73_cast_fp16")];
            tensor<int32, [2]> var_6708_split_sizes_0 = const()[name = string("op_6708_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6708_axis_0 = const()[name = string("op_6708_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_6708_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_6708_cast_fp16_1 = split(axis = var_6708_axis_0, split_sizes = var_6708_split_sizes_0, x = out_73_cast_fp16)[name = string("op_6708_cast_fp16")];
            tensor<int32, [2]> query_states_109_strides_0 = const()[name = string("query_states_109_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_109_pad_type_0 = const()[name = string("query_states_109_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_109_pad_0 = const()[name = string("query_states_109_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_109_dilations_0 = const()[name = string("query_states_109_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_109_groups_0 = const()[name = string("query_states_109_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_109_cast_fp16 = conv(dilations = query_states_109_dilations_0, groups = query_states_109_groups_0, pad = query_states_109_pad_0, pad_type = query_states_109_pad_type_0, strides = query_states_109_strides_0, weight = layers_18_self_attn_q_proj_weight_cast_fp16, x = var_6708_cast_fp16_0)[name = string("query_states_109_cast_fp16")];
            tensor<int32, [2]> key_states_181_strides_0 = const()[name = string("key_states_181_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_181_pad_type_0 = const()[name = string("key_states_181_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_181_pad_0 = const()[name = string("key_states_181_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_181_dilations_0 = const()[name = string("key_states_181_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_181_groups_0 = const()[name = string("key_states_181_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_181_cast_fp16 = conv(dilations = key_states_181_dilations_0, groups = key_states_181_groups_0, pad = key_states_181_pad_0, pad_type = key_states_181_pad_type_0, strides = key_states_181_strides_0, weight = layers_18_self_attn_k_proj_weight_cast_fp16, x = var_6708_cast_fp16_0)[name = string("key_states_181_cast_fp16")];
            tensor<int32, [2]> value_states_109_strides_0 = const()[name = string("value_states_109_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_109_pad_type_0 = const()[name = string("value_states_109_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_109_pad_0 = const()[name = string("value_states_109_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_109_dilations_0 = const()[name = string("value_states_109_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_109_groups_0 = const()[name = string("value_states_109_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_109_cast_fp16 = conv(dilations = value_states_109_dilations_0, groups = value_states_109_groups_0, pad = value_states_109_pad_0, pad_type = value_states_109_pad_type_0, strides = value_states_109_strides_0, weight = layers_18_self_attn_v_proj_weight_cast_fp16, x = var_6708_cast_fp16_0)[name = string("value_states_109_cast_fp16")];
            tensor<int32, [4]> concat_216x = const()[name = string("concat_216x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_181_cast_fp16 = reshape(shape = concat_216x, x = query_states_109_cast_fp16)[name = string("x_181_cast_fp16")];
            tensor<int32, [4]> concat_217x = const()[name = string("concat_217x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_6765_cast_fp16 = reshape(shape = concat_217x, x = key_states_181_cast_fp16)[name = string("op_6765_cast_fp16")];
            tensor<int32, [4]> concat_218x = const()[name = string("concat_218x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_6772_cast_fp16 = reshape(shape = concat_218x, x = value_states_109_cast_fp16)[name = string("op_6772_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_6776_cast_fp16 = mul(x = x_181_cast_fp16, y = var_859_cast_fp16)[name = string("op_6776_cast_fp16")];
            tensor<int32, [2]> var_6777_split_sizes_0 = const()[name = string("op_6777_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6777_axis_0 = const()[name = string("op_6777_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_6777_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_6777_cast_fp16_1 = split(axis = var_6777_axis_0, split_sizes = var_6777_split_sizes_0, x = x_181_cast_fp16)[name = string("op_6777_cast_fp16")];
            fp16 const_184_promoted_to_fp16 = const()[name = string("const_184_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_6779_cast_fp16 = mul(x = var_6777_cast_fp16_1, y = const_184_promoted_to_fp16)[name = string("op_6779_cast_fp16")];
            int32 var_6781 = const()[name = string("op_6781"), val = int32(-2)];
            bool var_6782_interleave_0 = const()[name = string("op_6782_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_6782_cast_fp16 = concat(axis = var_6781, interleave = var_6782_interleave_0, values = (var_6779_cast_fp16, var_6777_cast_fp16_0))[name = string("op_6782_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_6783_cast_fp16 = mul(x = var_6782_cast_fp16, y = var_866_cast_fp16)[name = string("op_6783_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_111_cast_fp16 = add(x = var_6776_cast_fp16, y = var_6783_cast_fp16)[name = string("query_states_111_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_6789_cast_fp16 = mul(x = var_6765_cast_fp16, y = var_859_cast_fp16)[name = string("op_6789_cast_fp16")];
            tensor<int32, [2]> var_6790_split_sizes_0 = const()[name = string("op_6790_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6790_axis_0 = const()[name = string("op_6790_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_6790_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_6790_cast_fp16_1 = split(axis = var_6790_axis_0, split_sizes = var_6790_split_sizes_0, x = var_6765_cast_fp16)[name = string("op_6790_cast_fp16")];
            fp16 const_185_promoted_to_fp16 = const()[name = string("const_185_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_6792_cast_fp16 = mul(x = var_6790_cast_fp16_1, y = const_185_promoted_to_fp16)[name = string("op_6792_cast_fp16")];
            int32 var_6794 = const()[name = string("op_6794"), val = int32(-2)];
            bool var_6795_interleave_0 = const()[name = string("op_6795_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_6795_cast_fp16 = concat(axis = var_6794, interleave = var_6795_interleave_0, values = (var_6792_cast_fp16, var_6790_cast_fp16_0))[name = string("op_6795_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_6796_cast_fp16 = mul(x = var_6795_cast_fp16, y = var_866_cast_fp16)[name = string("op_6796_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_185_cast_fp16 = add(x = var_6789_cast_fp16, y = var_6796_cast_fp16)[name = string("key_states_185_cast_fp16")];
            tensor<int32, [1]> expand_dims_216 = const()[name = string("expand_dims_216"), val = tensor<int32, [1]>([18])];
            tensor<int32, [1]> expand_dims_217 = const()[name = string("expand_dims_217"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_219 = const()[name = string("expand_dims_219"), val = tensor<int32, [1]>([0])];
            int32 concat_221_axis_0 = const()[name = string("concat_221_axis_0"), val = int32(0)];
            bool concat_221_interleave_0 = const()[name = string("concat_221_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_221 = concat(axis = concat_221_axis_0, interleave = concat_221_interleave_0, values = (expand_dims_216, expand_dims_217, position_id, expand_dims_219))[name = string("concat_221")];
            tensor<int32, [1]> expand_dims_220 = const()[name = string("expand_dims_220"), val = tensor<int32, [1]>([19])];
            tensor<int32, [1]> concat_222_values1_0 = const()[name = string("concat_222_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_222_values3_0 = const()[name = string("concat_222_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_222_axis_0 = const()[name = string("concat_222_axis_0"), val = int32(0)];
            bool concat_222_interleave_0 = const()[name = string("concat_222_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_222 = concat(axis = concat_222_axis_0, interleave = concat_222_interleave_0, values = (expand_dims_220, concat_222_values1_0, cache_position_end, concat_222_values3_0))[name = string("concat_222")];
            tensor<int32, [4]> key_states_187_perm_0 = const()[name = string("key_states_187_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_19_stride_0 = const()[name = string("key_cache_internal_tensor_assign_19_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_19_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_19_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_19_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_19_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_19_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_19_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_187_cast_fp16 = transpose(perm = key_states_187_perm_0, x = key_states_185_cast_fp16)[name = string("transpose_377")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_19_cast_fp16 = slice_update(begin = concat_221, begin_mask = key_cache_internal_tensor_assign_19_begin_mask_0, end = concat_222, end_mask = key_cache_internal_tensor_assign_19_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_19_squeeze_mask_0, stride = key_cache_internal_tensor_assign_19_stride_0, update = key_states_187_cast_fp16, x = coreml_update_state_258)[name = string("key_cache_internal_tensor_assign_19_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_19_cast_fp16, input = key_cache)[name = string("coreml_update_state_260_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_260 = read_state(input = key_cache)[name = string("coreml_update_state_260")];
            tensor<int32, [4]> value_states_111_perm_0 = const()[name = string("value_states_111_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_19_stride_0 = const()[name = string("value_cache_internal_tensor_assign_19_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_19_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_19_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_19_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_19_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_19_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_19_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_111_cast_fp16 = transpose(perm = value_states_111_perm_0, x = var_6772_cast_fp16)[name = string("transpose_376")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_19_cast_fp16 = slice_update(begin = concat_221, begin_mask = value_cache_internal_tensor_assign_19_begin_mask_0, end = concat_222, end_mask = value_cache_internal_tensor_assign_19_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_19_squeeze_mask_0, stride = value_cache_internal_tensor_assign_19_stride_0, update = value_states_111_cast_fp16, x = coreml_update_state_259)[name = string("value_cache_internal_tensor_assign_19_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_19_cast_fp16, input = value_cache)[name = string("coreml_update_state_261_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_261 = read_state(input = value_cache)[name = string("coreml_update_state_261")];
            tensor<int32, [4]> var_6866_begin_0 = const()[name = string("op_6866_begin_0"), val = tensor<int32, [4]>([18, 0, 0, 0])];
            tensor<int32, [4]> var_6866_end_0 = const()[name = string("op_6866_end_0"), val = tensor<int32, [4]>([19, 2, 2048, 128])];
            tensor<bool, [4]> var_6866_end_mask_0 = const()[name = string("op_6866_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6866_cast_fp16 = slice_by_index(begin = var_6866_begin_0, end = var_6866_end_0, end_mask = var_6866_end_mask_0, x = coreml_update_state_260)[name = string("op_6866_cast_fp16")];
            tensor<int32, [2]> tile_36 = const()[name = string("tile_36"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6869_axis_0 = const()[name = string("op_6869_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6869_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6869_cast_fp16_1 = split(axis = var_6869_axis_0, split_sizes = tile_36, x = var_6866_cast_fp16)[name = string("op_6869_cast_fp16")];
            tensor<int32, [4]> var_6876_begin_0 = const()[name = string("op_6876_begin_0"), val = tensor<int32, [4]>([18, 0, 0, 0])];
            tensor<int32, [4]> var_6876_end_0 = const()[name = string("op_6876_end_0"), val = tensor<int32, [4]>([19, 2, 2048, 128])];
            tensor<bool, [4]> var_6876_end_mask_0 = const()[name = string("op_6876_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6876_cast_fp16 = slice_by_index(begin = var_6876_begin_0, end = var_6876_end_0, end_mask = var_6876_end_mask_0, x = coreml_update_state_261)[name = string("op_6876_cast_fp16")];
            tensor<int32, [2]> tile_37 = const()[name = string("tile_37"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6879_axis_0 = const()[name = string("op_6879_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6879_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6879_cast_fp16_1 = split(axis = var_6879_axis_0, split_sizes = tile_37, x = var_6876_cast_fp16)[name = string("op_6879_cast_fp16")];
            tensor<int32, [2]> var_6882_split_sizes_0 = const()[name = string("op_6882_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_6882_axis_0 = const()[name = string("op_6882_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_6882_0, tensor<fp16, [1, 8, 128, 32]> var_6882_1 = split(axis = var_6882_axis_0, split_sizes = var_6882_split_sizes_0, x = query_states_111_cast_fp16)[name = string("op_6882")];
            bool attn_weights_289_transpose_x_0 = const()[name = string("attn_weights_289_transpose_x_0"), val = bool(false)];
            bool attn_weights_289_transpose_y_0 = const()[name = string("attn_weights_289_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_289_cast_fp16 = matmul(transpose_x = attn_weights_289_transpose_x_0, transpose_y = attn_weights_289_transpose_y_0, x = var_6869_cast_fp16_0, y = var_6882_0)[name = string("attn_weights_289_cast_fp16")];
            fp16 var_6885_to_fp16 = const()[name = string("op_6885_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_291_cast_fp16 = mul(x = attn_weights_289_cast_fp16, y = var_6885_to_fp16)[name = string("attn_weights_291_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_293_cast_fp16 = add(x = attn_weights_291_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_293_cast_fp16")];
            int32 var_6889 = const()[name = string("op_6889"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_295_cast_fp16 = softmax(axis = var_6889, x = attn_weights_293_cast_fp16)[name = string("attn_weights_295_cast_fp16")];
            bool var_6895_transpose_x_1 = const()[name = string("op_6895_transpose_x_1"), val = bool(true)];
            bool var_6895_transpose_y_1 = const()[name = string("op_6895_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_6895_cast_fp16 = matmul(transpose_x = var_6895_transpose_x_1, transpose_y = var_6895_transpose_y_1, x = attn_weights_295_cast_fp16, y = var_6879_cast_fp16_0)[name = string("op_6895_cast_fp16")];
            bool attn_weights_297_transpose_x_0 = const()[name = string("attn_weights_297_transpose_x_0"), val = bool(false)];
            bool attn_weights_297_transpose_y_0 = const()[name = string("attn_weights_297_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_297_cast_fp16 = matmul(transpose_x = attn_weights_297_transpose_x_0, transpose_y = attn_weights_297_transpose_y_0, x = var_6869_cast_fp16_1, y = var_6882_1)[name = string("attn_weights_297_cast_fp16")];
            fp16 var_6897_to_fp16 = const()[name = string("op_6897_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_299_cast_fp16 = mul(x = attn_weights_297_cast_fp16, y = var_6897_to_fp16)[name = string("attn_weights_299_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_301_cast_fp16 = add(x = attn_weights_299_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_301_cast_fp16")];
            int32 var_6901 = const()[name = string("op_6901"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_303_cast_fp16 = softmax(axis = var_6901, x = attn_weights_301_cast_fp16)[name = string("attn_weights_303_cast_fp16")];
            bool attn_output_145_transpose_x_1 = const()[name = string("attn_output_145_transpose_x_1"), val = bool(true)];
            bool attn_output_145_transpose_y_1 = const()[name = string("attn_output_145_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_145_cast_fp16 = matmul(transpose_x = attn_output_145_transpose_x_1, transpose_y = attn_output_145_transpose_y_1, x = attn_weights_303_cast_fp16, y = var_6879_cast_fp16_1)[name = string("attn_output_145_cast_fp16")];
            int32 var_6909 = const()[name = string("op_6909"), val = int32(1)];
            bool attn_output_147_interleave_0 = const()[name = string("attn_output_147_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_147_cast_fp16 = concat(axis = var_6909, interleave = attn_output_147_interleave_0, values = (var_6895_cast_fp16, attn_output_145_cast_fp16))[name = string("attn_output_147_cast_fp16")];
            tensor<int32, [4]> var_6913_perm_0 = const()[name = string("op_6913_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_227x = const()[name = string("concat_227x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_6913_cast_fp16 = transpose(perm = var_6913_perm_0, x = attn_output_147_cast_fp16)[name = string("transpose_375")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_151_cast_fp16 = reshape(shape = concat_227x, x = var_6913_cast_fp16)[name = string("attn_output_151_cast_fp16")];
            tensor<int32, [2]> hidden_states_183_strides_0 = const()[name = string("hidden_states_183_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_183_pad_type_0 = const()[name = string("hidden_states_183_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_183_pad_0 = const()[name = string("hidden_states_183_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_183_dilations_0 = const()[name = string("hidden_states_183_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_183_groups_0 = const()[name = string("hidden_states_183_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_183_cast_fp16 = conv(dilations = hidden_states_183_dilations_0, groups = hidden_states_183_groups_0, pad = hidden_states_183_pad_0, pad_type = hidden_states_183_pad_type_0, strides = hidden_states_183_strides_0, weight = layers_18_self_attn_o_proj_weight_cast_fp16, x = attn_output_151_cast_fp16)[name = string("hidden_states_183_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_185_cast_fp16 = add(x = hidden_states_179_cast_fp16, y = hidden_states_183_cast_fp16)[name = string("hidden_states_185_cast_fp16")];
            fp16 const_190_promoted_to_fp16 = const()[name = string("const_190_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_6946_cast_fp16 = mul(x = hidden_states_185_cast_fp16, y = const_190_promoted_to_fp16)[name = string("op_6946_cast_fp16")];
            int32 var_6944 = const()[name = string("op_6944"), val = int32(1)];
            bool doubled_149_interleave_0 = const()[name = string("doubled_149_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_149_cast_fp16 = concat(axis = var_6944, interleave = doubled_149_interleave_0, values = (hidden_states_185_cast_fp16, var_6946_cast_fp16))[name = string("doubled_149_cast_fp16")];
            tensor<int32, [1]> out_75_axes_0 = const()[name = string("out_75_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_75_gamma_0_to_fp16 = const()[name = string("out_75_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483647872)))];
            fp16 var_6956_to_fp16 = const()[name = string("op_6956_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_75_cast_fp16 = layer_norm(axes = out_75_axes_0, epsilon = var_6956_to_fp16, gamma = out_75_gamma_0_to_fp16, x = doubled_149_cast_fp16)[name = string("out_75_cast_fp16")];
            tensor<int32, [2]> var_6967_split_sizes_0 = const()[name = string("op_6967_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6967_axis_0 = const()[name = string("op_6967_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_6967_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_6967_cast_fp16_1 = split(axis = var_6967_axis_0, split_sizes = var_6967_split_sizes_0, x = out_75_cast_fp16)[name = string("op_6967_cast_fp16")];
            tensor<int32, [2]> input_37_strides_0 = const()[name = string("input_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_37_pad_type_0 = const()[name = string("input_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_37_pad_0 = const()[name = string("input_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_37_dilations_0 = const()[name = string("input_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_37_groups_0 = const()[name = string("input_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_37_cast_fp16 = conv(dilations = input_37_dilations_0, groups = input_37_groups_0, pad = input_37_pad_0, pad_type = input_37_pad_type_0, strides = input_37_strides_0, weight = layers_18_mlp_gate_proj_weight_cast_fp16, x = var_6967_cast_fp16_0)[name = string("input_37_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_6984_cast_fp16 = silu(x = input_37_cast_fp16)[name = string("op_6984_cast_fp16")];
            tensor<int32, [2]> var_6990_strides_0 = const()[name = string("op_6990_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6990_pad_type_0 = const()[name = string("op_6990_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6990_pad_0 = const()[name = string("op_6990_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6990_dilations_0 = const()[name = string("op_6990_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6990_groups_0 = const()[name = string("op_6990_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_6990_cast_fp16 = conv(dilations = var_6990_dilations_0, groups = var_6990_groups_0, pad = var_6990_pad_0, pad_type = var_6990_pad_type_0, strides = var_6990_strides_0, weight = layers_18_mlp_up_proj_weight_cast_fp16, x = var_6967_cast_fp16_0)[name = string("op_6990_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_189_cast_fp16 = mul(x = var_6984_cast_fp16, y = var_6990_cast_fp16)[name = string("x_189_cast_fp16")];
            tensor<int32, [2]> hidden_states_187_strides_0 = const()[name = string("hidden_states_187_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_187_pad_type_0 = const()[name = string("hidden_states_187_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_187_pad_0 = const()[name = string("hidden_states_187_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_187_dilations_0 = const()[name = string("hidden_states_187_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_187_groups_0 = const()[name = string("hidden_states_187_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_187_cast_fp16 = conv(dilations = hidden_states_187_dilations_0, groups = hidden_states_187_groups_0, pad = hidden_states_187_pad_0, pad_type = hidden_states_187_pad_type_0, strides = hidden_states_187_strides_0, weight = layers_18_mlp_down_proj_weight_cast_fp16, x = x_189_cast_fp16)[name = string("hidden_states_187_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_189_cast_fp16 = add(x = hidden_states_185_cast_fp16, y = hidden_states_187_cast_fp16)[name = string("hidden_states_189_cast_fp16")];
            fp16 const_192_promoted_to_fp16 = const()[name = string("const_192_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_7008_cast_fp16 = mul(x = hidden_states_189_cast_fp16, y = const_192_promoted_to_fp16)[name = string("op_7008_cast_fp16")];
            int32 var_7006 = const()[name = string("op_7006"), val = int32(1)];
            bool doubled_153_interleave_0 = const()[name = string("doubled_153_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_153_cast_fp16 = concat(axis = var_7006, interleave = doubled_153_interleave_0, values = (hidden_states_189_cast_fp16, var_7008_cast_fp16))[name = string("doubled_153_cast_fp16")];
            tensor<int32, [1]> out_77_axes_0 = const()[name = string("out_77_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_77_gamma_0_to_fp16 = const()[name = string("out_77_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483656128)))];
            fp16 var_7018_to_fp16 = const()[name = string("op_7018_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_77_cast_fp16 = layer_norm(axes = out_77_axes_0, epsilon = var_7018_to_fp16, gamma = out_77_gamma_0_to_fp16, x = doubled_153_cast_fp16)[name = string("out_77_cast_fp16")];
            tensor<int32, [2]> var_7029_split_sizes_0 = const()[name = string("op_7029_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7029_axis_0 = const()[name = string("op_7029_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_7029_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_7029_cast_fp16_1 = split(axis = var_7029_axis_0, split_sizes = var_7029_split_sizes_0, x = out_77_cast_fp16)[name = string("op_7029_cast_fp16")];
            tensor<int32, [2]> query_states_115_strides_0 = const()[name = string("query_states_115_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_115_pad_type_0 = const()[name = string("query_states_115_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_115_pad_0 = const()[name = string("query_states_115_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_115_dilations_0 = const()[name = string("query_states_115_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_115_groups_0 = const()[name = string("query_states_115_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_115_cast_fp16 = conv(dilations = query_states_115_dilations_0, groups = query_states_115_groups_0, pad = query_states_115_pad_0, pad_type = query_states_115_pad_type_0, strides = query_states_115_strides_0, weight = layers_19_self_attn_q_proj_weight_cast_fp16, x = var_7029_cast_fp16_0)[name = string("query_states_115_cast_fp16")];
            tensor<int32, [2]> key_states_191_strides_0 = const()[name = string("key_states_191_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_191_pad_type_0 = const()[name = string("key_states_191_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_191_pad_0 = const()[name = string("key_states_191_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_191_dilations_0 = const()[name = string("key_states_191_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_191_groups_0 = const()[name = string("key_states_191_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_191_cast_fp16 = conv(dilations = key_states_191_dilations_0, groups = key_states_191_groups_0, pad = key_states_191_pad_0, pad_type = key_states_191_pad_type_0, strides = key_states_191_strides_0, weight = layers_19_self_attn_k_proj_weight_cast_fp16, x = var_7029_cast_fp16_0)[name = string("key_states_191_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_19_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_19_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483664384)))];
            tensor<int32, [2]> value_states_115_strides_0 = const()[name = string("value_states_115_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_115_pad_type_0 = const()[name = string("value_states_115_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_115_pad_0 = const()[name = string("value_states_115_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_115_dilations_0 = const()[name = string("value_states_115_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_115_groups_0 = const()[name = string("value_states_115_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_115_cast_fp16 = conv(dilations = value_states_115_dilations_0, groups = value_states_115_groups_0, pad = value_states_115_pad_0, pad_type = value_states_115_pad_type_0, strides = value_states_115_strides_0, weight = layers_19_self_attn_v_proj_weight_to_fp16, x = var_7029_cast_fp16_0)[name = string("value_states_115_cast_fp16")];
            tensor<int32, [4]> concat_228x = const()[name = string("concat_228x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_191_cast_fp16 = reshape(shape = concat_228x, x = query_states_115_cast_fp16)[name = string("x_191_cast_fp16")];
            tensor<int32, [4]> concat_229x = const()[name = string("concat_229x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_7086_cast_fp16 = reshape(shape = concat_229x, x = key_states_191_cast_fp16)[name = string("op_7086_cast_fp16")];
            tensor<int32, [4]> concat_230x = const()[name = string("concat_230x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_7093_cast_fp16 = reshape(shape = concat_230x, x = value_states_115_cast_fp16)[name = string("op_7093_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_7097_cast_fp16 = mul(x = x_191_cast_fp16, y = var_859_cast_fp16)[name = string("op_7097_cast_fp16")];
            tensor<int32, [2]> var_7098_split_sizes_0 = const()[name = string("op_7098_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7098_axis_0 = const()[name = string("op_7098_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_7098_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_7098_cast_fp16_1 = split(axis = var_7098_axis_0, split_sizes = var_7098_split_sizes_0, x = x_191_cast_fp16)[name = string("op_7098_cast_fp16")];
            fp16 const_194_promoted_to_fp16 = const()[name = string("const_194_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_7100_cast_fp16 = mul(x = var_7098_cast_fp16_1, y = const_194_promoted_to_fp16)[name = string("op_7100_cast_fp16")];
            int32 var_7102 = const()[name = string("op_7102"), val = int32(-2)];
            bool var_7103_interleave_0 = const()[name = string("op_7103_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_7103_cast_fp16 = concat(axis = var_7102, interleave = var_7103_interleave_0, values = (var_7100_cast_fp16, var_7098_cast_fp16_0))[name = string("op_7103_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_7104_cast_fp16 = mul(x = var_7103_cast_fp16, y = var_866_cast_fp16)[name = string("op_7104_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_117_cast_fp16 = add(x = var_7097_cast_fp16, y = var_7104_cast_fp16)[name = string("query_states_117_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_7110_cast_fp16 = mul(x = var_7086_cast_fp16, y = var_859_cast_fp16)[name = string("op_7110_cast_fp16")];
            tensor<int32, [2]> var_7111_split_sizes_0 = const()[name = string("op_7111_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7111_axis_0 = const()[name = string("op_7111_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_7111_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_7111_cast_fp16_1 = split(axis = var_7111_axis_0, split_sizes = var_7111_split_sizes_0, x = var_7086_cast_fp16)[name = string("op_7111_cast_fp16")];
            fp16 const_195_promoted_to_fp16 = const()[name = string("const_195_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_7113_cast_fp16 = mul(x = var_7111_cast_fp16_1, y = const_195_promoted_to_fp16)[name = string("op_7113_cast_fp16")];
            int32 var_7115 = const()[name = string("op_7115"), val = int32(-2)];
            bool var_7116_interleave_0 = const()[name = string("op_7116_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_7116_cast_fp16 = concat(axis = var_7115, interleave = var_7116_interleave_0, values = (var_7113_cast_fp16, var_7111_cast_fp16_0))[name = string("op_7116_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_7117_cast_fp16 = mul(x = var_7116_cast_fp16, y = var_866_cast_fp16)[name = string("op_7117_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_195_cast_fp16 = add(x = var_7110_cast_fp16, y = var_7117_cast_fp16)[name = string("key_states_195_cast_fp16")];
            tensor<int32, [1]> expand_dims_228 = const()[name = string("expand_dims_228"), val = tensor<int32, [1]>([19])];
            tensor<int32, [1]> expand_dims_229 = const()[name = string("expand_dims_229"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_231 = const()[name = string("expand_dims_231"), val = tensor<int32, [1]>([0])];
            int32 concat_233_axis_0 = const()[name = string("concat_233_axis_0"), val = int32(0)];
            bool concat_233_interleave_0 = const()[name = string("concat_233_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_233 = concat(axis = concat_233_axis_0, interleave = concat_233_interleave_0, values = (expand_dims_228, expand_dims_229, position_id, expand_dims_231))[name = string("concat_233")];
            tensor<int32, [1]> expand_dims_232 = const()[name = string("expand_dims_232"), val = tensor<int32, [1]>([20])];
            tensor<int32, [1]> concat_234_values1_0 = const()[name = string("concat_234_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_234_values3_0 = const()[name = string("concat_234_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_234_axis_0 = const()[name = string("concat_234_axis_0"), val = int32(0)];
            bool concat_234_interleave_0 = const()[name = string("concat_234_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_234 = concat(axis = concat_234_axis_0, interleave = concat_234_interleave_0, values = (expand_dims_232, concat_234_values1_0, cache_position_end, concat_234_values3_0))[name = string("concat_234")];
            tensor<int32, [4]> key_states_197_perm_0 = const()[name = string("key_states_197_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_20_stride_0 = const()[name = string("key_cache_internal_tensor_assign_20_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_20_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_20_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_20_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_20_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_20_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_20_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_197_cast_fp16 = transpose(perm = key_states_197_perm_0, x = key_states_195_cast_fp16)[name = string("transpose_374")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_20_cast_fp16 = slice_update(begin = concat_233, begin_mask = key_cache_internal_tensor_assign_20_begin_mask_0, end = concat_234, end_mask = key_cache_internal_tensor_assign_20_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_20_squeeze_mask_0, stride = key_cache_internal_tensor_assign_20_stride_0, update = key_states_197_cast_fp16, x = coreml_update_state_260)[name = string("key_cache_internal_tensor_assign_20_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_20_cast_fp16, input = key_cache)[name = string("coreml_update_state_262_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_262 = read_state(input = key_cache)[name = string("coreml_update_state_262")];
            tensor<int32, [4]> value_states_117_perm_0 = const()[name = string("value_states_117_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_20_stride_0 = const()[name = string("value_cache_internal_tensor_assign_20_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_20_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_20_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_20_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_20_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_20_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_20_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_117_cast_fp16 = transpose(perm = value_states_117_perm_0, x = var_7093_cast_fp16)[name = string("transpose_373")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_20_cast_fp16 = slice_update(begin = concat_233, begin_mask = value_cache_internal_tensor_assign_20_begin_mask_0, end = concat_234, end_mask = value_cache_internal_tensor_assign_20_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_20_squeeze_mask_0, stride = value_cache_internal_tensor_assign_20_stride_0, update = value_states_117_cast_fp16, x = coreml_update_state_261)[name = string("value_cache_internal_tensor_assign_20_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_20_cast_fp16, input = value_cache)[name = string("coreml_update_state_263_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_263 = read_state(input = value_cache)[name = string("coreml_update_state_263")];
            tensor<int32, [4]> var_7187_begin_0 = const()[name = string("op_7187_begin_0"), val = tensor<int32, [4]>([19, 0, 0, 0])];
            tensor<int32, [4]> var_7187_end_0 = const()[name = string("op_7187_end_0"), val = tensor<int32, [4]>([20, 2, 2048, 128])];
            tensor<bool, [4]> var_7187_end_mask_0 = const()[name = string("op_7187_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7187_cast_fp16 = slice_by_index(begin = var_7187_begin_0, end = var_7187_end_0, end_mask = var_7187_end_mask_0, x = coreml_update_state_262)[name = string("op_7187_cast_fp16")];
            tensor<int32, [2]> tile_38 = const()[name = string("tile_38"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7190_axis_0 = const()[name = string("op_7190_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7190_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7190_cast_fp16_1 = split(axis = var_7190_axis_0, split_sizes = tile_38, x = var_7187_cast_fp16)[name = string("op_7190_cast_fp16")];
            tensor<int32, [4]> var_7197_begin_0 = const()[name = string("op_7197_begin_0"), val = tensor<int32, [4]>([19, 0, 0, 0])];
            tensor<int32, [4]> var_7197_end_0 = const()[name = string("op_7197_end_0"), val = tensor<int32, [4]>([20, 2, 2048, 128])];
            tensor<bool, [4]> var_7197_end_mask_0 = const()[name = string("op_7197_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7197_cast_fp16 = slice_by_index(begin = var_7197_begin_0, end = var_7197_end_0, end_mask = var_7197_end_mask_0, x = coreml_update_state_263)[name = string("op_7197_cast_fp16")];
            tensor<int32, [2]> tile_39 = const()[name = string("tile_39"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7200_axis_0 = const()[name = string("op_7200_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7200_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7200_cast_fp16_1 = split(axis = var_7200_axis_0, split_sizes = tile_39, x = var_7197_cast_fp16)[name = string("op_7200_cast_fp16")];
            tensor<int32, [2]> var_7203_split_sizes_0 = const()[name = string("op_7203_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_7203_axis_0 = const()[name = string("op_7203_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_7203_0, tensor<fp16, [1, 8, 128, 32]> var_7203_1 = split(axis = var_7203_axis_0, split_sizes = var_7203_split_sizes_0, x = query_states_117_cast_fp16)[name = string("op_7203")];
            bool attn_weights_305_transpose_x_0 = const()[name = string("attn_weights_305_transpose_x_0"), val = bool(false)];
            bool attn_weights_305_transpose_y_0 = const()[name = string("attn_weights_305_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_305_cast_fp16 = matmul(transpose_x = attn_weights_305_transpose_x_0, transpose_y = attn_weights_305_transpose_y_0, x = var_7190_cast_fp16_0, y = var_7203_0)[name = string("attn_weights_305_cast_fp16")];
            fp16 var_7206_to_fp16 = const()[name = string("op_7206_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_307_cast_fp16 = mul(x = attn_weights_305_cast_fp16, y = var_7206_to_fp16)[name = string("attn_weights_307_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_309_cast_fp16 = add(x = attn_weights_307_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_309_cast_fp16")];
            int32 var_7210 = const()[name = string("op_7210"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_311_cast_fp16 = softmax(axis = var_7210, x = attn_weights_309_cast_fp16)[name = string("attn_weights_311_cast_fp16")];
            bool var_7216_transpose_x_1 = const()[name = string("op_7216_transpose_x_1"), val = bool(true)];
            bool var_7216_transpose_y_1 = const()[name = string("op_7216_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_7216_cast_fp16 = matmul(transpose_x = var_7216_transpose_x_1, transpose_y = var_7216_transpose_y_1, x = attn_weights_311_cast_fp16, y = var_7200_cast_fp16_0)[name = string("op_7216_cast_fp16")];
            bool attn_weights_313_transpose_x_0 = const()[name = string("attn_weights_313_transpose_x_0"), val = bool(false)];
            bool attn_weights_313_transpose_y_0 = const()[name = string("attn_weights_313_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_313_cast_fp16 = matmul(transpose_x = attn_weights_313_transpose_x_0, transpose_y = attn_weights_313_transpose_y_0, x = var_7190_cast_fp16_1, y = var_7203_1)[name = string("attn_weights_313_cast_fp16")];
            fp16 var_7218_to_fp16 = const()[name = string("op_7218_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_315_cast_fp16 = mul(x = attn_weights_313_cast_fp16, y = var_7218_to_fp16)[name = string("attn_weights_315_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_317_cast_fp16 = add(x = attn_weights_315_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_317_cast_fp16")];
            int32 var_7222 = const()[name = string("op_7222"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_319_cast_fp16 = softmax(axis = var_7222, x = attn_weights_317_cast_fp16)[name = string("attn_weights_319_cast_fp16")];
            bool attn_output_153_transpose_x_1 = const()[name = string("attn_output_153_transpose_x_1"), val = bool(true)];
            bool attn_output_153_transpose_y_1 = const()[name = string("attn_output_153_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_153_cast_fp16 = matmul(transpose_x = attn_output_153_transpose_x_1, transpose_y = attn_output_153_transpose_y_1, x = attn_weights_319_cast_fp16, y = var_7200_cast_fp16_1)[name = string("attn_output_153_cast_fp16")];
            int32 var_7230 = const()[name = string("op_7230"), val = int32(1)];
            bool attn_output_155_interleave_0 = const()[name = string("attn_output_155_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_155_cast_fp16 = concat(axis = var_7230, interleave = attn_output_155_interleave_0, values = (var_7216_cast_fp16, attn_output_153_cast_fp16))[name = string("attn_output_155_cast_fp16")];
            tensor<int32, [4]> var_7234_perm_0 = const()[name = string("op_7234_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_239x = const()[name = string("concat_239x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_7234_cast_fp16 = transpose(perm = var_7234_perm_0, x = attn_output_155_cast_fp16)[name = string("transpose_372")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_159_cast_fp16 = reshape(shape = concat_239x, x = var_7234_cast_fp16)[name = string("attn_output_159_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_19_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_19_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1484713024)))];
            tensor<int32, [2]> hidden_states_193_strides_0 = const()[name = string("hidden_states_193_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_193_pad_type_0 = const()[name = string("hidden_states_193_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_193_pad_0 = const()[name = string("hidden_states_193_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_193_dilations_0 = const()[name = string("hidden_states_193_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_193_groups_0 = const()[name = string("hidden_states_193_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_193_cast_fp16 = conv(dilations = hidden_states_193_dilations_0, groups = hidden_states_193_groups_0, pad = hidden_states_193_pad_0, pad_type = hidden_states_193_pad_type_0, strides = hidden_states_193_strides_0, weight = layers_19_self_attn_o_proj_weight_to_fp16, x = attn_output_159_cast_fp16)[name = string("hidden_states_193_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_195_cast_fp16 = add(x = hidden_states_189_cast_fp16, y = hidden_states_193_cast_fp16)[name = string("hidden_states_195_cast_fp16")];
            fp16 const_200_promoted_to_fp16 = const()[name = string("const_200_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_7267_cast_fp16 = mul(x = hidden_states_195_cast_fp16, y = const_200_promoted_to_fp16)[name = string("op_7267_cast_fp16")];
            int32 var_7265 = const()[name = string("op_7265"), val = int32(1)];
            bool doubled_157_interleave_0 = const()[name = string("doubled_157_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_157_cast_fp16 = concat(axis = var_7265, interleave = doubled_157_interleave_0, values = (hidden_states_195_cast_fp16, var_7267_cast_fp16))[name = string("doubled_157_cast_fp16")];
            tensor<int32, [1]> out_79_axes_0 = const()[name = string("out_79_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_79_gamma_0_to_fp16 = const()[name = string("out_79_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1493101696)))];
            fp16 var_7277_to_fp16 = const()[name = string("op_7277_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_79_cast_fp16 = layer_norm(axes = out_79_axes_0, epsilon = var_7277_to_fp16, gamma = out_79_gamma_0_to_fp16, x = doubled_157_cast_fp16)[name = string("out_79_cast_fp16")];
            tensor<int32, [2]> var_7288_split_sizes_0 = const()[name = string("op_7288_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7288_axis_0 = const()[name = string("op_7288_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_7288_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_7288_cast_fp16_1 = split(axis = var_7288_axis_0, split_sizes = var_7288_split_sizes_0, x = out_79_cast_fp16)[name = string("op_7288_cast_fp16")];
            tensor<int32, [2]> input_39_strides_0 = const()[name = string("input_39_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_39_pad_type_0 = const()[name = string("input_39_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_39_pad_0 = const()[name = string("input_39_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_39_dilations_0 = const()[name = string("input_39_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_39_groups_0 = const()[name = string("input_39_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_39_cast_fp16 = conv(dilations = input_39_dilations_0, groups = input_39_groups_0, pad = input_39_pad_0, pad_type = input_39_pad_type_0, strides = input_39_strides_0, weight = layers_19_mlp_gate_proj_weight_cast_fp16, x = var_7288_cast_fp16_0)[name = string("input_39_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_7305_cast_fp16 = silu(x = input_39_cast_fp16)[name = string("op_7305_cast_fp16")];
            tensor<int32, [2]> var_7311_strides_0 = const()[name = string("op_7311_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_7311_pad_type_0 = const()[name = string("op_7311_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_7311_pad_0 = const()[name = string("op_7311_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_7311_dilations_0 = const()[name = string("op_7311_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7311_groups_0 = const()[name = string("op_7311_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_7311_cast_fp16 = conv(dilations = var_7311_dilations_0, groups = var_7311_groups_0, pad = var_7311_pad_0, pad_type = var_7311_pad_type_0, strides = var_7311_strides_0, weight = layers_19_mlp_up_proj_weight_cast_fp16, x = var_7288_cast_fp16_0)[name = string("op_7311_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_199_cast_fp16 = mul(x = var_7305_cast_fp16, y = var_7311_cast_fp16)[name = string("x_199_cast_fp16")];
            tensor<int32, [2]> hidden_states_197_strides_0 = const()[name = string("hidden_states_197_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_197_pad_type_0 = const()[name = string("hidden_states_197_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_197_pad_0 = const()[name = string("hidden_states_197_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_197_dilations_0 = const()[name = string("hidden_states_197_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_197_groups_0 = const()[name = string("hidden_states_197_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_197_cast_fp16 = conv(dilations = hidden_states_197_dilations_0, groups = hidden_states_197_groups_0, pad = hidden_states_197_pad_0, pad_type = hidden_states_197_pad_type_0, strides = hidden_states_197_strides_0, weight = layers_19_mlp_down_proj_weight_cast_fp16, x = x_199_cast_fp16)[name = string("hidden_states_197_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_199_cast_fp16 = add(x = hidden_states_195_cast_fp16, y = hidden_states_197_cast_fp16)[name = string("hidden_states_199_cast_fp16")];
            fp16 const_202_promoted_to_fp16 = const()[name = string("const_202_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_7329_cast_fp16 = mul(x = hidden_states_199_cast_fp16, y = const_202_promoted_to_fp16)[name = string("op_7329_cast_fp16")];
            int32 var_7327 = const()[name = string("op_7327"), val = int32(1)];
            bool doubled_161_interleave_0 = const()[name = string("doubled_161_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_161_cast_fp16 = concat(axis = var_7327, interleave = doubled_161_interleave_0, values = (hidden_states_199_cast_fp16, var_7329_cast_fp16))[name = string("doubled_161_cast_fp16")];
            tensor<int32, [1]> out_81_axes_0 = const()[name = string("out_81_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_81_gamma_0_to_fp16 = const()[name = string("out_81_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1493109952)))];
            fp16 var_7339_to_fp16 = const()[name = string("op_7339_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_81_cast_fp16 = layer_norm(axes = out_81_axes_0, epsilon = var_7339_to_fp16, gamma = out_81_gamma_0_to_fp16, x = doubled_161_cast_fp16)[name = string("out_81_cast_fp16")];
            tensor<int32, [2]> var_7350_split_sizes_0 = const()[name = string("op_7350_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7350_axis_0 = const()[name = string("op_7350_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_7350_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_7350_cast_fp16_1 = split(axis = var_7350_axis_0, split_sizes = var_7350_split_sizes_0, x = out_81_cast_fp16)[name = string("op_7350_cast_fp16")];
            tensor<int32, [2]> query_states_121_strides_0 = const()[name = string("query_states_121_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_121_pad_type_0 = const()[name = string("query_states_121_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_121_pad_0 = const()[name = string("query_states_121_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_121_dilations_0 = const()[name = string("query_states_121_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_121_groups_0 = const()[name = string("query_states_121_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_121_cast_fp16 = conv(dilations = query_states_121_dilations_0, groups = query_states_121_groups_0, pad = query_states_121_pad_0, pad_type = query_states_121_pad_type_0, strides = query_states_121_strides_0, weight = layers_20_self_attn_q_proj_weight_cast_fp16, x = var_7350_cast_fp16_0)[name = string("query_states_121_cast_fp16")];
            tensor<int32, [2]> key_states_201_strides_0 = const()[name = string("key_states_201_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_201_pad_type_0 = const()[name = string("key_states_201_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_201_pad_0 = const()[name = string("key_states_201_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_201_dilations_0 = const()[name = string("key_states_201_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_201_groups_0 = const()[name = string("key_states_201_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_201_cast_fp16 = conv(dilations = key_states_201_dilations_0, groups = key_states_201_groups_0, pad = key_states_201_pad_0, pad_type = key_states_201_pad_type_0, strides = key_states_201_strides_0, weight = layers_20_self_attn_k_proj_weight_cast_fp16, x = var_7350_cast_fp16_0)[name = string("key_states_201_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_20_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_20_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1493118208)))];
            tensor<int32, [2]> value_states_121_strides_0 = const()[name = string("value_states_121_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_121_pad_type_0 = const()[name = string("value_states_121_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_121_pad_0 = const()[name = string("value_states_121_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_121_dilations_0 = const()[name = string("value_states_121_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_121_groups_0 = const()[name = string("value_states_121_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_121_cast_fp16 = conv(dilations = value_states_121_dilations_0, groups = value_states_121_groups_0, pad = value_states_121_pad_0, pad_type = value_states_121_pad_type_0, strides = value_states_121_strides_0, weight = layers_20_self_attn_v_proj_weight_to_fp16, x = var_7350_cast_fp16_0)[name = string("value_states_121_cast_fp16")];
            tensor<int32, [4]> concat_240x = const()[name = string("concat_240x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_201_cast_fp16 = reshape(shape = concat_240x, x = query_states_121_cast_fp16)[name = string("x_201_cast_fp16")];
            tensor<int32, [4]> concat_241x = const()[name = string("concat_241x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_7407_cast_fp16 = reshape(shape = concat_241x, x = key_states_201_cast_fp16)[name = string("op_7407_cast_fp16")];
            tensor<int32, [4]> concat_242x = const()[name = string("concat_242x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_7414_cast_fp16 = reshape(shape = concat_242x, x = value_states_121_cast_fp16)[name = string("op_7414_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_7418_cast_fp16 = mul(x = x_201_cast_fp16, y = var_859_cast_fp16)[name = string("op_7418_cast_fp16")];
            tensor<int32, [2]> var_7419_split_sizes_0 = const()[name = string("op_7419_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7419_axis_0 = const()[name = string("op_7419_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_7419_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_7419_cast_fp16_1 = split(axis = var_7419_axis_0, split_sizes = var_7419_split_sizes_0, x = x_201_cast_fp16)[name = string("op_7419_cast_fp16")];
            fp16 const_204_promoted_to_fp16 = const()[name = string("const_204_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_7421_cast_fp16 = mul(x = var_7419_cast_fp16_1, y = const_204_promoted_to_fp16)[name = string("op_7421_cast_fp16")];
            int32 var_7423 = const()[name = string("op_7423"), val = int32(-2)];
            bool var_7424_interleave_0 = const()[name = string("op_7424_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_7424_cast_fp16 = concat(axis = var_7423, interleave = var_7424_interleave_0, values = (var_7421_cast_fp16, var_7419_cast_fp16_0))[name = string("op_7424_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_7425_cast_fp16 = mul(x = var_7424_cast_fp16, y = var_866_cast_fp16)[name = string("op_7425_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_123_cast_fp16 = add(x = var_7418_cast_fp16, y = var_7425_cast_fp16)[name = string("query_states_123_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_7431_cast_fp16 = mul(x = var_7407_cast_fp16, y = var_859_cast_fp16)[name = string("op_7431_cast_fp16")];
            tensor<int32, [2]> var_7432_split_sizes_0 = const()[name = string("op_7432_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7432_axis_0 = const()[name = string("op_7432_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_7432_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_7432_cast_fp16_1 = split(axis = var_7432_axis_0, split_sizes = var_7432_split_sizes_0, x = var_7407_cast_fp16)[name = string("op_7432_cast_fp16")];
            fp16 const_205_promoted_to_fp16 = const()[name = string("const_205_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_7434_cast_fp16 = mul(x = var_7432_cast_fp16_1, y = const_205_promoted_to_fp16)[name = string("op_7434_cast_fp16")];
            int32 var_7436 = const()[name = string("op_7436"), val = int32(-2)];
            bool var_7437_interleave_0 = const()[name = string("op_7437_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_7437_cast_fp16 = concat(axis = var_7436, interleave = var_7437_interleave_0, values = (var_7434_cast_fp16, var_7432_cast_fp16_0))[name = string("op_7437_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_7438_cast_fp16 = mul(x = var_7437_cast_fp16, y = var_866_cast_fp16)[name = string("op_7438_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_205_cast_fp16 = add(x = var_7431_cast_fp16, y = var_7438_cast_fp16)[name = string("key_states_205_cast_fp16")];
            tensor<int32, [1]> expand_dims_240 = const()[name = string("expand_dims_240"), val = tensor<int32, [1]>([20])];
            tensor<int32, [1]> expand_dims_241 = const()[name = string("expand_dims_241"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_243 = const()[name = string("expand_dims_243"), val = tensor<int32, [1]>([0])];
            int32 concat_245_axis_0 = const()[name = string("concat_245_axis_0"), val = int32(0)];
            bool concat_245_interleave_0 = const()[name = string("concat_245_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_245 = concat(axis = concat_245_axis_0, interleave = concat_245_interleave_0, values = (expand_dims_240, expand_dims_241, position_id, expand_dims_243))[name = string("concat_245")];
            tensor<int32, [1]> expand_dims_244 = const()[name = string("expand_dims_244"), val = tensor<int32, [1]>([21])];
            tensor<int32, [1]> concat_246_values1_0 = const()[name = string("concat_246_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_246_values3_0 = const()[name = string("concat_246_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_246_axis_0 = const()[name = string("concat_246_axis_0"), val = int32(0)];
            bool concat_246_interleave_0 = const()[name = string("concat_246_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_246 = concat(axis = concat_246_axis_0, interleave = concat_246_interleave_0, values = (expand_dims_244, concat_246_values1_0, cache_position_end, concat_246_values3_0))[name = string("concat_246")];
            tensor<int32, [4]> key_states_207_perm_0 = const()[name = string("key_states_207_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_21_stride_0 = const()[name = string("key_cache_internal_tensor_assign_21_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_21_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_21_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_21_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_21_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_21_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_21_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_207_cast_fp16 = transpose(perm = key_states_207_perm_0, x = key_states_205_cast_fp16)[name = string("transpose_371")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_21_cast_fp16 = slice_update(begin = concat_245, begin_mask = key_cache_internal_tensor_assign_21_begin_mask_0, end = concat_246, end_mask = key_cache_internal_tensor_assign_21_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_21_squeeze_mask_0, stride = key_cache_internal_tensor_assign_21_stride_0, update = key_states_207_cast_fp16, x = coreml_update_state_262)[name = string("key_cache_internal_tensor_assign_21_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_21_cast_fp16, input = key_cache)[name = string("coreml_update_state_264_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_264 = read_state(input = key_cache)[name = string("coreml_update_state_264")];
            tensor<int32, [4]> value_states_123_perm_0 = const()[name = string("value_states_123_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_21_stride_0 = const()[name = string("value_cache_internal_tensor_assign_21_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_21_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_21_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_21_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_21_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_21_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_21_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_123_cast_fp16 = transpose(perm = value_states_123_perm_0, x = var_7414_cast_fp16)[name = string("transpose_370")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_21_cast_fp16 = slice_update(begin = concat_245, begin_mask = value_cache_internal_tensor_assign_21_begin_mask_0, end = concat_246, end_mask = value_cache_internal_tensor_assign_21_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_21_squeeze_mask_0, stride = value_cache_internal_tensor_assign_21_stride_0, update = value_states_123_cast_fp16, x = coreml_update_state_263)[name = string("value_cache_internal_tensor_assign_21_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_21_cast_fp16, input = value_cache)[name = string("coreml_update_state_265_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_265 = read_state(input = value_cache)[name = string("coreml_update_state_265")];
            tensor<int32, [4]> var_7508_begin_0 = const()[name = string("op_7508_begin_0"), val = tensor<int32, [4]>([20, 0, 0, 0])];
            tensor<int32, [4]> var_7508_end_0 = const()[name = string("op_7508_end_0"), val = tensor<int32, [4]>([21, 2, 2048, 128])];
            tensor<bool, [4]> var_7508_end_mask_0 = const()[name = string("op_7508_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7508_cast_fp16 = slice_by_index(begin = var_7508_begin_0, end = var_7508_end_0, end_mask = var_7508_end_mask_0, x = coreml_update_state_264)[name = string("op_7508_cast_fp16")];
            tensor<int32, [2]> tile_40 = const()[name = string("tile_40"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7511_axis_0 = const()[name = string("op_7511_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7511_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7511_cast_fp16_1 = split(axis = var_7511_axis_0, split_sizes = tile_40, x = var_7508_cast_fp16)[name = string("op_7511_cast_fp16")];
            tensor<int32, [4]> var_7518_begin_0 = const()[name = string("op_7518_begin_0"), val = tensor<int32, [4]>([20, 0, 0, 0])];
            tensor<int32, [4]> var_7518_end_0 = const()[name = string("op_7518_end_0"), val = tensor<int32, [4]>([21, 2, 2048, 128])];
            tensor<bool, [4]> var_7518_end_mask_0 = const()[name = string("op_7518_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7518_cast_fp16 = slice_by_index(begin = var_7518_begin_0, end = var_7518_end_0, end_mask = var_7518_end_mask_0, x = coreml_update_state_265)[name = string("op_7518_cast_fp16")];
            tensor<int32, [2]> tile_41 = const()[name = string("tile_41"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7521_axis_0 = const()[name = string("op_7521_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7521_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7521_cast_fp16_1 = split(axis = var_7521_axis_0, split_sizes = tile_41, x = var_7518_cast_fp16)[name = string("op_7521_cast_fp16")];
            tensor<int32, [2]> var_7524_split_sizes_0 = const()[name = string("op_7524_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_7524_axis_0 = const()[name = string("op_7524_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_7524_0, tensor<fp16, [1, 8, 128, 32]> var_7524_1 = split(axis = var_7524_axis_0, split_sizes = var_7524_split_sizes_0, x = query_states_123_cast_fp16)[name = string("op_7524")];
            bool attn_weights_321_transpose_x_0 = const()[name = string("attn_weights_321_transpose_x_0"), val = bool(false)];
            bool attn_weights_321_transpose_y_0 = const()[name = string("attn_weights_321_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_321_cast_fp16 = matmul(transpose_x = attn_weights_321_transpose_x_0, transpose_y = attn_weights_321_transpose_y_0, x = var_7511_cast_fp16_0, y = var_7524_0)[name = string("attn_weights_321_cast_fp16")];
            fp16 var_7527_to_fp16 = const()[name = string("op_7527_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_323_cast_fp16 = mul(x = attn_weights_321_cast_fp16, y = var_7527_to_fp16)[name = string("attn_weights_323_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_325_cast_fp16 = add(x = attn_weights_323_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_325_cast_fp16")];
            int32 var_7531 = const()[name = string("op_7531"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_327_cast_fp16 = softmax(axis = var_7531, x = attn_weights_325_cast_fp16)[name = string("attn_weights_327_cast_fp16")];
            bool var_7537_transpose_x_1 = const()[name = string("op_7537_transpose_x_1"), val = bool(true)];
            bool var_7537_transpose_y_1 = const()[name = string("op_7537_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_7537_cast_fp16 = matmul(transpose_x = var_7537_transpose_x_1, transpose_y = var_7537_transpose_y_1, x = attn_weights_327_cast_fp16, y = var_7521_cast_fp16_0)[name = string("op_7537_cast_fp16")];
            bool attn_weights_329_transpose_x_0 = const()[name = string("attn_weights_329_transpose_x_0"), val = bool(false)];
            bool attn_weights_329_transpose_y_0 = const()[name = string("attn_weights_329_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_329_cast_fp16 = matmul(transpose_x = attn_weights_329_transpose_x_0, transpose_y = attn_weights_329_transpose_y_0, x = var_7511_cast_fp16_1, y = var_7524_1)[name = string("attn_weights_329_cast_fp16")];
            fp16 var_7539_to_fp16 = const()[name = string("op_7539_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_331_cast_fp16 = mul(x = attn_weights_329_cast_fp16, y = var_7539_to_fp16)[name = string("attn_weights_331_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_333_cast_fp16 = add(x = attn_weights_331_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_333_cast_fp16")];
            int32 var_7543 = const()[name = string("op_7543"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_335_cast_fp16 = softmax(axis = var_7543, x = attn_weights_333_cast_fp16)[name = string("attn_weights_335_cast_fp16")];
            bool attn_output_161_transpose_x_1 = const()[name = string("attn_output_161_transpose_x_1"), val = bool(true)];
            bool attn_output_161_transpose_y_1 = const()[name = string("attn_output_161_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_161_cast_fp16 = matmul(transpose_x = attn_output_161_transpose_x_1, transpose_y = attn_output_161_transpose_y_1, x = attn_weights_335_cast_fp16, y = var_7521_cast_fp16_1)[name = string("attn_output_161_cast_fp16")];
            int32 var_7551 = const()[name = string("op_7551"), val = int32(1)];
            bool attn_output_163_interleave_0 = const()[name = string("attn_output_163_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_163_cast_fp16 = concat(axis = var_7551, interleave = attn_output_163_interleave_0, values = (var_7537_cast_fp16, attn_output_161_cast_fp16))[name = string("attn_output_163_cast_fp16")];
            tensor<int32, [4]> var_7555_perm_0 = const()[name = string("op_7555_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_251x = const()[name = string("concat_251x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_7555_cast_fp16 = transpose(perm = var_7555_perm_0, x = attn_output_163_cast_fp16)[name = string("transpose_369")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_167_cast_fp16 = reshape(shape = concat_251x, x = var_7555_cast_fp16)[name = string("attn_output_167_cast_fp16")];
            tensor<int32, [2]> hidden_states_203_strides_0 = const()[name = string("hidden_states_203_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_203_pad_type_0 = const()[name = string("hidden_states_203_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_203_pad_0 = const()[name = string("hidden_states_203_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_203_dilations_0 = const()[name = string("hidden_states_203_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_203_groups_0 = const()[name = string("hidden_states_203_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_203_cast_fp16 = conv(dilations = hidden_states_203_dilations_0, groups = hidden_states_203_groups_0, pad = hidden_states_203_pad_0, pad_type = hidden_states_203_pad_type_0, strides = hidden_states_203_strides_0, weight = layers_20_self_attn_o_proj_weight_cast_fp16, x = attn_output_167_cast_fp16)[name = string("hidden_states_203_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_205_cast_fp16 = add(x = hidden_states_199_cast_fp16, y = hidden_states_203_cast_fp16)[name = string("hidden_states_205_cast_fp16")];
            fp16 const_210_promoted_to_fp16 = const()[name = string("const_210_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_7588_cast_fp16 = mul(x = hidden_states_205_cast_fp16, y = const_210_promoted_to_fp16)[name = string("op_7588_cast_fp16")];
            int32 var_7586 = const()[name = string("op_7586"), val = int32(1)];
            bool doubled_165_interleave_0 = const()[name = string("doubled_165_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_165_cast_fp16 = concat(axis = var_7586, interleave = doubled_165_interleave_0, values = (hidden_states_205_cast_fp16, var_7588_cast_fp16))[name = string("doubled_165_cast_fp16")];
            tensor<int32, [1]> out_83_axes_0 = const()[name = string("out_83_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_83_gamma_0_to_fp16 = const()[name = string("out_83_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1494166848)))];
            fp16 var_7598_to_fp16 = const()[name = string("op_7598_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_83_cast_fp16 = layer_norm(axes = out_83_axes_0, epsilon = var_7598_to_fp16, gamma = out_83_gamma_0_to_fp16, x = doubled_165_cast_fp16)[name = string("out_83_cast_fp16")];
            tensor<int32, [2]> var_7609_split_sizes_0 = const()[name = string("op_7609_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7609_axis_0 = const()[name = string("op_7609_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_7609_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_7609_cast_fp16_1 = split(axis = var_7609_axis_0, split_sizes = var_7609_split_sizes_0, x = out_83_cast_fp16)[name = string("op_7609_cast_fp16")];
            tensor<int32, [2]> input_41_strides_0 = const()[name = string("input_41_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_41_pad_type_0 = const()[name = string("input_41_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_41_pad_0 = const()[name = string("input_41_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_41_dilations_0 = const()[name = string("input_41_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_41_groups_0 = const()[name = string("input_41_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_41_cast_fp16 = conv(dilations = input_41_dilations_0, groups = input_41_groups_0, pad = input_41_pad_0, pad_type = input_41_pad_type_0, strides = input_41_strides_0, weight = layers_20_mlp_gate_proj_weight_cast_fp16, x = var_7609_cast_fp16_0)[name = string("input_41_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_7626_cast_fp16 = silu(x = input_41_cast_fp16)[name = string("op_7626_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_20_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_20_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1494175104)))];
            tensor<int32, [2]> var_7632_strides_0 = const()[name = string("op_7632_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_7632_pad_type_0 = const()[name = string("op_7632_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_7632_pad_0 = const()[name = string("op_7632_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_7632_dilations_0 = const()[name = string("op_7632_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7632_groups_0 = const()[name = string("op_7632_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_7632_cast_fp16 = conv(dilations = var_7632_dilations_0, groups = var_7632_groups_0, pad = var_7632_pad_0, pad_type = var_7632_pad_type_0, strides = var_7632_strides_0, weight = layers_20_mlp_up_proj_weight_to_fp16, x = var_7609_cast_fp16_0)[name = string("op_7632_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_209_cast_fp16 = mul(x = var_7626_cast_fp16, y = var_7632_cast_fp16)[name = string("x_209_cast_fp16")];
            tensor<int32, [2]> hidden_states_207_strides_0 = const()[name = string("hidden_states_207_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_207_pad_type_0 = const()[name = string("hidden_states_207_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_207_pad_0 = const()[name = string("hidden_states_207_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_207_dilations_0 = const()[name = string("hidden_states_207_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_207_groups_0 = const()[name = string("hidden_states_207_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_207_cast_fp16 = conv(dilations = hidden_states_207_dilations_0, groups = hidden_states_207_groups_0, pad = hidden_states_207_pad_0, pad_type = hidden_states_207_pad_type_0, strides = hidden_states_207_strides_0, weight = layers_20_mlp_down_proj_weight_cast_fp16, x = x_209_cast_fp16)[name = string("hidden_states_207_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_209_cast_fp16 = add(x = hidden_states_205_cast_fp16, y = hidden_states_207_cast_fp16)[name = string("hidden_states_209_cast_fp16")];
            fp16 const_212_promoted_to_fp16 = const()[name = string("const_212_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_7650_cast_fp16 = mul(x = hidden_states_209_cast_fp16, y = const_212_promoted_to_fp16)[name = string("op_7650_cast_fp16")];
            int32 var_7648 = const()[name = string("op_7648"), val = int32(1)];
            bool doubled_169_interleave_0 = const()[name = string("doubled_169_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_169_cast_fp16 = concat(axis = var_7648, interleave = doubled_169_interleave_0, values = (hidden_states_209_cast_fp16, var_7650_cast_fp16))[name = string("doubled_169_cast_fp16")];
            tensor<int32, [1]> out_85_axes_0 = const()[name = string("out_85_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_85_gamma_0_to_fp16 = const()[name = string("out_85_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1519340992)))];
            fp16 var_7660_to_fp16 = const()[name = string("op_7660_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_85_cast_fp16 = layer_norm(axes = out_85_axes_0, epsilon = var_7660_to_fp16, gamma = out_85_gamma_0_to_fp16, x = doubled_169_cast_fp16)[name = string("out_85_cast_fp16")];
            tensor<int32, [2]> var_7671_split_sizes_0 = const()[name = string("op_7671_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7671_axis_0 = const()[name = string("op_7671_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_7671_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_7671_cast_fp16_1 = split(axis = var_7671_axis_0, split_sizes = var_7671_split_sizes_0, x = out_85_cast_fp16)[name = string("op_7671_cast_fp16")];
            tensor<int32, [2]> query_states_127_strides_0 = const()[name = string("query_states_127_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_127_pad_type_0 = const()[name = string("query_states_127_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_127_pad_0 = const()[name = string("query_states_127_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_127_dilations_0 = const()[name = string("query_states_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_127_groups_0 = const()[name = string("query_states_127_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_127_cast_fp16 = conv(dilations = query_states_127_dilations_0, groups = query_states_127_groups_0, pad = query_states_127_pad_0, pad_type = query_states_127_pad_type_0, strides = query_states_127_strides_0, weight = layers_21_self_attn_q_proj_weight_cast_fp16, x = var_7671_cast_fp16_0)[name = string("query_states_127_cast_fp16")];
            tensor<int32, [2]> key_states_211_strides_0 = const()[name = string("key_states_211_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_211_pad_type_0 = const()[name = string("key_states_211_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_211_pad_0 = const()[name = string("key_states_211_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_211_dilations_0 = const()[name = string("key_states_211_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_211_groups_0 = const()[name = string("key_states_211_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_211_cast_fp16 = conv(dilations = key_states_211_dilations_0, groups = key_states_211_groups_0, pad = key_states_211_pad_0, pad_type = key_states_211_pad_type_0, strides = key_states_211_strides_0, weight = layers_21_self_attn_k_proj_weight_cast_fp16, x = var_7671_cast_fp16_0)[name = string("key_states_211_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_21_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_21_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1519349248)))];
            tensor<int32, [2]> value_states_127_strides_0 = const()[name = string("value_states_127_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_127_pad_type_0 = const()[name = string("value_states_127_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_127_pad_0 = const()[name = string("value_states_127_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_127_dilations_0 = const()[name = string("value_states_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_127_groups_0 = const()[name = string("value_states_127_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_127_cast_fp16 = conv(dilations = value_states_127_dilations_0, groups = value_states_127_groups_0, pad = value_states_127_pad_0, pad_type = value_states_127_pad_type_0, strides = value_states_127_strides_0, weight = layers_21_self_attn_v_proj_weight_to_fp16, x = var_7671_cast_fp16_0)[name = string("value_states_127_cast_fp16")];
            tensor<int32, [4]> concat_252x = const()[name = string("concat_252x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_211_cast_fp16 = reshape(shape = concat_252x, x = query_states_127_cast_fp16)[name = string("x_211_cast_fp16")];
            tensor<int32, [4]> concat_253x = const()[name = string("concat_253x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_7728_cast_fp16 = reshape(shape = concat_253x, x = key_states_211_cast_fp16)[name = string("op_7728_cast_fp16")];
            tensor<int32, [4]> concat_254x = const()[name = string("concat_254x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_7735_cast_fp16 = reshape(shape = concat_254x, x = value_states_127_cast_fp16)[name = string("op_7735_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_7739_cast_fp16 = mul(x = x_211_cast_fp16, y = var_859_cast_fp16)[name = string("op_7739_cast_fp16")];
            tensor<int32, [2]> var_7740_split_sizes_0 = const()[name = string("op_7740_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7740_axis_0 = const()[name = string("op_7740_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_7740_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_7740_cast_fp16_1 = split(axis = var_7740_axis_0, split_sizes = var_7740_split_sizes_0, x = x_211_cast_fp16)[name = string("op_7740_cast_fp16")];
            fp16 const_214_promoted_to_fp16 = const()[name = string("const_214_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_7742_cast_fp16 = mul(x = var_7740_cast_fp16_1, y = const_214_promoted_to_fp16)[name = string("op_7742_cast_fp16")];
            int32 var_7744 = const()[name = string("op_7744"), val = int32(-2)];
            bool var_7745_interleave_0 = const()[name = string("op_7745_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_7745_cast_fp16 = concat(axis = var_7744, interleave = var_7745_interleave_0, values = (var_7742_cast_fp16, var_7740_cast_fp16_0))[name = string("op_7745_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_7746_cast_fp16 = mul(x = var_7745_cast_fp16, y = var_866_cast_fp16)[name = string("op_7746_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_129_cast_fp16 = add(x = var_7739_cast_fp16, y = var_7746_cast_fp16)[name = string("query_states_129_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_7752_cast_fp16 = mul(x = var_7728_cast_fp16, y = var_859_cast_fp16)[name = string("op_7752_cast_fp16")];
            tensor<int32, [2]> var_7753_split_sizes_0 = const()[name = string("op_7753_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7753_axis_0 = const()[name = string("op_7753_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_7753_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_7753_cast_fp16_1 = split(axis = var_7753_axis_0, split_sizes = var_7753_split_sizes_0, x = var_7728_cast_fp16)[name = string("op_7753_cast_fp16")];
            fp16 const_215_promoted_to_fp16 = const()[name = string("const_215_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_7755_cast_fp16 = mul(x = var_7753_cast_fp16_1, y = const_215_promoted_to_fp16)[name = string("op_7755_cast_fp16")];
            int32 var_7757 = const()[name = string("op_7757"), val = int32(-2)];
            bool var_7758_interleave_0 = const()[name = string("op_7758_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_7758_cast_fp16 = concat(axis = var_7757, interleave = var_7758_interleave_0, values = (var_7755_cast_fp16, var_7753_cast_fp16_0))[name = string("op_7758_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_7759_cast_fp16 = mul(x = var_7758_cast_fp16, y = var_866_cast_fp16)[name = string("op_7759_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_215_cast_fp16 = add(x = var_7752_cast_fp16, y = var_7759_cast_fp16)[name = string("key_states_215_cast_fp16")];
            tensor<int32, [1]> expand_dims_252 = const()[name = string("expand_dims_252"), val = tensor<int32, [1]>([21])];
            tensor<int32, [1]> expand_dims_253 = const()[name = string("expand_dims_253"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_255 = const()[name = string("expand_dims_255"), val = tensor<int32, [1]>([0])];
            int32 concat_257_axis_0 = const()[name = string("concat_257_axis_0"), val = int32(0)];
            bool concat_257_interleave_0 = const()[name = string("concat_257_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_257 = concat(axis = concat_257_axis_0, interleave = concat_257_interleave_0, values = (expand_dims_252, expand_dims_253, position_id, expand_dims_255))[name = string("concat_257")];
            tensor<int32, [1]> expand_dims_256 = const()[name = string("expand_dims_256"), val = tensor<int32, [1]>([22])];
            tensor<int32, [1]> concat_258_values1_0 = const()[name = string("concat_258_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_258_values3_0 = const()[name = string("concat_258_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_258_axis_0 = const()[name = string("concat_258_axis_0"), val = int32(0)];
            bool concat_258_interleave_0 = const()[name = string("concat_258_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_258 = concat(axis = concat_258_axis_0, interleave = concat_258_interleave_0, values = (expand_dims_256, concat_258_values1_0, cache_position_end, concat_258_values3_0))[name = string("concat_258")];
            tensor<int32, [4]> key_states_217_perm_0 = const()[name = string("key_states_217_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_22_stride_0 = const()[name = string("key_cache_internal_tensor_assign_22_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_22_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_22_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_22_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_22_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_22_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_22_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_217_cast_fp16 = transpose(perm = key_states_217_perm_0, x = key_states_215_cast_fp16)[name = string("transpose_368")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_22_cast_fp16 = slice_update(begin = concat_257, begin_mask = key_cache_internal_tensor_assign_22_begin_mask_0, end = concat_258, end_mask = key_cache_internal_tensor_assign_22_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_22_squeeze_mask_0, stride = key_cache_internal_tensor_assign_22_stride_0, update = key_states_217_cast_fp16, x = coreml_update_state_264)[name = string("key_cache_internal_tensor_assign_22_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_22_cast_fp16, input = key_cache)[name = string("coreml_update_state_266_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_266 = read_state(input = key_cache)[name = string("coreml_update_state_266")];
            tensor<int32, [4]> value_states_129_perm_0 = const()[name = string("value_states_129_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_22_stride_0 = const()[name = string("value_cache_internal_tensor_assign_22_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_22_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_22_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_22_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_22_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_22_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_22_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_129_cast_fp16 = transpose(perm = value_states_129_perm_0, x = var_7735_cast_fp16)[name = string("transpose_367")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_22_cast_fp16 = slice_update(begin = concat_257, begin_mask = value_cache_internal_tensor_assign_22_begin_mask_0, end = concat_258, end_mask = value_cache_internal_tensor_assign_22_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_22_squeeze_mask_0, stride = value_cache_internal_tensor_assign_22_stride_0, update = value_states_129_cast_fp16, x = coreml_update_state_265)[name = string("value_cache_internal_tensor_assign_22_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_22_cast_fp16, input = value_cache)[name = string("coreml_update_state_267_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_267 = read_state(input = value_cache)[name = string("coreml_update_state_267")];
            tensor<int32, [4]> var_7829_begin_0 = const()[name = string("op_7829_begin_0"), val = tensor<int32, [4]>([21, 0, 0, 0])];
            tensor<int32, [4]> var_7829_end_0 = const()[name = string("op_7829_end_0"), val = tensor<int32, [4]>([22, 2, 2048, 128])];
            tensor<bool, [4]> var_7829_end_mask_0 = const()[name = string("op_7829_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7829_cast_fp16 = slice_by_index(begin = var_7829_begin_0, end = var_7829_end_0, end_mask = var_7829_end_mask_0, x = coreml_update_state_266)[name = string("op_7829_cast_fp16")];
            tensor<int32, [2]> tile_42 = const()[name = string("tile_42"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7832_axis_0 = const()[name = string("op_7832_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7832_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7832_cast_fp16_1 = split(axis = var_7832_axis_0, split_sizes = tile_42, x = var_7829_cast_fp16)[name = string("op_7832_cast_fp16")];
            tensor<int32, [4]> var_7839_begin_0 = const()[name = string("op_7839_begin_0"), val = tensor<int32, [4]>([21, 0, 0, 0])];
            tensor<int32, [4]> var_7839_end_0 = const()[name = string("op_7839_end_0"), val = tensor<int32, [4]>([22, 2, 2048, 128])];
            tensor<bool, [4]> var_7839_end_mask_0 = const()[name = string("op_7839_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7839_cast_fp16 = slice_by_index(begin = var_7839_begin_0, end = var_7839_end_0, end_mask = var_7839_end_mask_0, x = coreml_update_state_267)[name = string("op_7839_cast_fp16")];
            tensor<int32, [2]> tile_43 = const()[name = string("tile_43"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7842_axis_0 = const()[name = string("op_7842_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7842_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7842_cast_fp16_1 = split(axis = var_7842_axis_0, split_sizes = tile_43, x = var_7839_cast_fp16)[name = string("op_7842_cast_fp16")];
            tensor<int32, [2]> var_7845_split_sizes_0 = const()[name = string("op_7845_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_7845_axis_0 = const()[name = string("op_7845_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_7845_0, tensor<fp16, [1, 8, 128, 32]> var_7845_1 = split(axis = var_7845_axis_0, split_sizes = var_7845_split_sizes_0, x = query_states_129_cast_fp16)[name = string("op_7845")];
            bool attn_weights_337_transpose_x_0 = const()[name = string("attn_weights_337_transpose_x_0"), val = bool(false)];
            bool attn_weights_337_transpose_y_0 = const()[name = string("attn_weights_337_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_337_cast_fp16 = matmul(transpose_x = attn_weights_337_transpose_x_0, transpose_y = attn_weights_337_transpose_y_0, x = var_7832_cast_fp16_0, y = var_7845_0)[name = string("attn_weights_337_cast_fp16")];
            fp16 var_7848_to_fp16 = const()[name = string("op_7848_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_339_cast_fp16 = mul(x = attn_weights_337_cast_fp16, y = var_7848_to_fp16)[name = string("attn_weights_339_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_341_cast_fp16 = add(x = attn_weights_339_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_341_cast_fp16")];
            int32 var_7852 = const()[name = string("op_7852"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_343_cast_fp16 = softmax(axis = var_7852, x = attn_weights_341_cast_fp16)[name = string("attn_weights_343_cast_fp16")];
            bool var_7858_transpose_x_1 = const()[name = string("op_7858_transpose_x_1"), val = bool(true)];
            bool var_7858_transpose_y_1 = const()[name = string("op_7858_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_7858_cast_fp16 = matmul(transpose_x = var_7858_transpose_x_1, transpose_y = var_7858_transpose_y_1, x = attn_weights_343_cast_fp16, y = var_7842_cast_fp16_0)[name = string("op_7858_cast_fp16")];
            bool attn_weights_345_transpose_x_0 = const()[name = string("attn_weights_345_transpose_x_0"), val = bool(false)];
            bool attn_weights_345_transpose_y_0 = const()[name = string("attn_weights_345_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_345_cast_fp16 = matmul(transpose_x = attn_weights_345_transpose_x_0, transpose_y = attn_weights_345_transpose_y_0, x = var_7832_cast_fp16_1, y = var_7845_1)[name = string("attn_weights_345_cast_fp16")];
            fp16 var_7860_to_fp16 = const()[name = string("op_7860_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_347_cast_fp16 = mul(x = attn_weights_345_cast_fp16, y = var_7860_to_fp16)[name = string("attn_weights_347_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_349_cast_fp16 = add(x = attn_weights_347_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_349_cast_fp16")];
            int32 var_7864 = const()[name = string("op_7864"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_351_cast_fp16 = softmax(axis = var_7864, x = attn_weights_349_cast_fp16)[name = string("attn_weights_351_cast_fp16")];
            bool attn_output_169_transpose_x_1 = const()[name = string("attn_output_169_transpose_x_1"), val = bool(true)];
            bool attn_output_169_transpose_y_1 = const()[name = string("attn_output_169_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_169_cast_fp16 = matmul(transpose_x = attn_output_169_transpose_x_1, transpose_y = attn_output_169_transpose_y_1, x = attn_weights_351_cast_fp16, y = var_7842_cast_fp16_1)[name = string("attn_output_169_cast_fp16")];
            int32 var_7872 = const()[name = string("op_7872"), val = int32(1)];
            bool attn_output_171_interleave_0 = const()[name = string("attn_output_171_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_171_cast_fp16 = concat(axis = var_7872, interleave = attn_output_171_interleave_0, values = (var_7858_cast_fp16, attn_output_169_cast_fp16))[name = string("attn_output_171_cast_fp16")];
            tensor<int32, [4]> var_7876_perm_0 = const()[name = string("op_7876_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_263x = const()[name = string("concat_263x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_7876_cast_fp16 = transpose(perm = var_7876_perm_0, x = attn_output_171_cast_fp16)[name = string("transpose_366")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_175_cast_fp16 = reshape(shape = concat_263x, x = var_7876_cast_fp16)[name = string("attn_output_175_cast_fp16")];
            tensor<int32, [2]> hidden_states_213_strides_0 = const()[name = string("hidden_states_213_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_213_pad_type_0 = const()[name = string("hidden_states_213_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_213_pad_0 = const()[name = string("hidden_states_213_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_213_dilations_0 = const()[name = string("hidden_states_213_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_213_groups_0 = const()[name = string("hidden_states_213_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_213_cast_fp16 = conv(dilations = hidden_states_213_dilations_0, groups = hidden_states_213_groups_0, pad = hidden_states_213_pad_0, pad_type = hidden_states_213_pad_type_0, strides = hidden_states_213_strides_0, weight = layers_21_self_attn_o_proj_weight_cast_fp16, x = attn_output_175_cast_fp16)[name = string("hidden_states_213_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_215_cast_fp16 = add(x = hidden_states_209_cast_fp16, y = hidden_states_213_cast_fp16)[name = string("hidden_states_215_cast_fp16")];
            fp16 const_220_promoted_to_fp16 = const()[name = string("const_220_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_7909_cast_fp16 = mul(x = hidden_states_215_cast_fp16, y = const_220_promoted_to_fp16)[name = string("op_7909_cast_fp16")];
            int32 var_7907 = const()[name = string("op_7907"), val = int32(1)];
            bool doubled_173_interleave_0 = const()[name = string("doubled_173_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_173_cast_fp16 = concat(axis = var_7907, interleave = doubled_173_interleave_0, values = (hidden_states_215_cast_fp16, var_7909_cast_fp16))[name = string("doubled_173_cast_fp16")];
            tensor<int32, [1]> out_87_axes_0 = const()[name = string("out_87_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_87_gamma_0_to_fp16 = const()[name = string("out_87_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1520397888)))];
            fp16 var_7919_to_fp16 = const()[name = string("op_7919_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_87_cast_fp16 = layer_norm(axes = out_87_axes_0, epsilon = var_7919_to_fp16, gamma = out_87_gamma_0_to_fp16, x = doubled_173_cast_fp16)[name = string("out_87_cast_fp16")];
            tensor<int32, [2]> var_7930_split_sizes_0 = const()[name = string("op_7930_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7930_axis_0 = const()[name = string("op_7930_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_7930_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_7930_cast_fp16_1 = split(axis = var_7930_axis_0, split_sizes = var_7930_split_sizes_0, x = out_87_cast_fp16)[name = string("op_7930_cast_fp16")];
            tensor<int32, [2]> input_43_strides_0 = const()[name = string("input_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_43_pad_type_0 = const()[name = string("input_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_43_pad_0 = const()[name = string("input_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_43_dilations_0 = const()[name = string("input_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_43_groups_0 = const()[name = string("input_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_43_cast_fp16 = conv(dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = layers_21_mlp_gate_proj_weight_cast_fp16, x = var_7930_cast_fp16_0)[name = string("input_43_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_7947_cast_fp16 = silu(x = input_43_cast_fp16)[name = string("op_7947_cast_fp16")];
            tensor<int32, [2]> var_7953_strides_0 = const()[name = string("op_7953_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_7953_pad_type_0 = const()[name = string("op_7953_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_7953_pad_0 = const()[name = string("op_7953_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_7953_dilations_0 = const()[name = string("op_7953_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7953_groups_0 = const()[name = string("op_7953_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_7953_cast_fp16 = conv(dilations = var_7953_dilations_0, groups = var_7953_groups_0, pad = var_7953_pad_0, pad_type = var_7953_pad_type_0, strides = var_7953_strides_0, weight = layers_21_mlp_up_proj_weight_cast_fp16, x = var_7930_cast_fp16_0)[name = string("op_7953_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_219_cast_fp16 = mul(x = var_7947_cast_fp16, y = var_7953_cast_fp16)[name = string("x_219_cast_fp16")];
            tensor<int32, [2]> hidden_states_217_strides_0 = const()[name = string("hidden_states_217_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_217_pad_type_0 = const()[name = string("hidden_states_217_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_217_pad_0 = const()[name = string("hidden_states_217_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_217_dilations_0 = const()[name = string("hidden_states_217_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_217_groups_0 = const()[name = string("hidden_states_217_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_217_cast_fp16 = conv(dilations = hidden_states_217_dilations_0, groups = hidden_states_217_groups_0, pad = hidden_states_217_pad_0, pad_type = hidden_states_217_pad_type_0, strides = hidden_states_217_strides_0, weight = layers_21_mlp_down_proj_weight_cast_fp16, x = x_219_cast_fp16)[name = string("hidden_states_217_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_219_cast_fp16 = add(x = hidden_states_215_cast_fp16, y = hidden_states_217_cast_fp16)[name = string("hidden_states_219_cast_fp16")];
            fp16 const_222_promoted_to_fp16 = const()[name = string("const_222_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_7971_cast_fp16 = mul(x = hidden_states_219_cast_fp16, y = const_222_promoted_to_fp16)[name = string("op_7971_cast_fp16")];
            int32 var_7969 = const()[name = string("op_7969"), val = int32(1)];
            bool doubled_177_interleave_0 = const()[name = string("doubled_177_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_177_cast_fp16 = concat(axis = var_7969, interleave = doubled_177_interleave_0, values = (hidden_states_219_cast_fp16, var_7971_cast_fp16))[name = string("doubled_177_cast_fp16")];
            tensor<int32, [1]> out_89_axes_0 = const()[name = string("out_89_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_89_gamma_0_to_fp16 = const()[name = string("out_89_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1520406144)))];
            fp16 var_7981_to_fp16 = const()[name = string("op_7981_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_89_cast_fp16 = layer_norm(axes = out_89_axes_0, epsilon = var_7981_to_fp16, gamma = out_89_gamma_0_to_fp16, x = doubled_177_cast_fp16)[name = string("out_89_cast_fp16")];
            tensor<int32, [2]> var_7992_split_sizes_0 = const()[name = string("op_7992_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7992_axis_0 = const()[name = string("op_7992_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_7992_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_7992_cast_fp16_1 = split(axis = var_7992_axis_0, split_sizes = var_7992_split_sizes_0, x = out_89_cast_fp16)[name = string("op_7992_cast_fp16")];
            tensor<int32, [2]> query_states_133_strides_0 = const()[name = string("query_states_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_133_pad_type_0 = const()[name = string("query_states_133_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_133_pad_0 = const()[name = string("query_states_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_133_dilations_0 = const()[name = string("query_states_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_133_groups_0 = const()[name = string("query_states_133_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_133_cast_fp16 = conv(dilations = query_states_133_dilations_0, groups = query_states_133_groups_0, pad = query_states_133_pad_0, pad_type = query_states_133_pad_type_0, strides = query_states_133_strides_0, weight = layers_22_self_attn_q_proj_weight_cast_fp16, x = var_7992_cast_fp16_0)[name = string("query_states_133_cast_fp16")];
            tensor<int32, [2]> key_states_221_strides_0 = const()[name = string("key_states_221_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_221_pad_type_0 = const()[name = string("key_states_221_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_221_pad_0 = const()[name = string("key_states_221_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_221_dilations_0 = const()[name = string("key_states_221_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_221_groups_0 = const()[name = string("key_states_221_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_221_cast_fp16 = conv(dilations = key_states_221_dilations_0, groups = key_states_221_groups_0, pad = key_states_221_pad_0, pad_type = key_states_221_pad_type_0, strides = key_states_221_strides_0, weight = layers_22_self_attn_k_proj_weight_cast_fp16, x = var_7992_cast_fp16_0)[name = string("key_states_221_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_22_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_22_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1520414400)))];
            tensor<int32, [2]> value_states_133_strides_0 = const()[name = string("value_states_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_133_pad_type_0 = const()[name = string("value_states_133_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_133_pad_0 = const()[name = string("value_states_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_133_dilations_0 = const()[name = string("value_states_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_133_groups_0 = const()[name = string("value_states_133_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_133_cast_fp16 = conv(dilations = value_states_133_dilations_0, groups = value_states_133_groups_0, pad = value_states_133_pad_0, pad_type = value_states_133_pad_type_0, strides = value_states_133_strides_0, weight = layers_22_self_attn_v_proj_weight_to_fp16, x = var_7992_cast_fp16_0)[name = string("value_states_133_cast_fp16")];
            tensor<int32, [4]> concat_264x = const()[name = string("concat_264x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_221_cast_fp16 = reshape(shape = concat_264x, x = query_states_133_cast_fp16)[name = string("x_221_cast_fp16")];
            tensor<int32, [4]> concat_265x = const()[name = string("concat_265x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_8049_cast_fp16 = reshape(shape = concat_265x, x = key_states_221_cast_fp16)[name = string("op_8049_cast_fp16")];
            tensor<int32, [4]> concat_266x = const()[name = string("concat_266x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_8056_cast_fp16 = reshape(shape = concat_266x, x = value_states_133_cast_fp16)[name = string("op_8056_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_8060_cast_fp16 = mul(x = x_221_cast_fp16, y = var_859_cast_fp16)[name = string("op_8060_cast_fp16")];
            tensor<int32, [2]> var_8061_split_sizes_0 = const()[name = string("op_8061_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8061_axis_0 = const()[name = string("op_8061_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_8061_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_8061_cast_fp16_1 = split(axis = var_8061_axis_0, split_sizes = var_8061_split_sizes_0, x = x_221_cast_fp16)[name = string("op_8061_cast_fp16")];
            fp16 const_224_promoted_to_fp16 = const()[name = string("const_224_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_8063_cast_fp16 = mul(x = var_8061_cast_fp16_1, y = const_224_promoted_to_fp16)[name = string("op_8063_cast_fp16")];
            int32 var_8065 = const()[name = string("op_8065"), val = int32(-2)];
            bool var_8066_interleave_0 = const()[name = string("op_8066_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_8066_cast_fp16 = concat(axis = var_8065, interleave = var_8066_interleave_0, values = (var_8063_cast_fp16, var_8061_cast_fp16_0))[name = string("op_8066_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_8067_cast_fp16 = mul(x = var_8066_cast_fp16, y = var_866_cast_fp16)[name = string("op_8067_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_135_cast_fp16 = add(x = var_8060_cast_fp16, y = var_8067_cast_fp16)[name = string("query_states_135_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_8073_cast_fp16 = mul(x = var_8049_cast_fp16, y = var_859_cast_fp16)[name = string("op_8073_cast_fp16")];
            tensor<int32, [2]> var_8074_split_sizes_0 = const()[name = string("op_8074_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8074_axis_0 = const()[name = string("op_8074_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_8074_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_8074_cast_fp16_1 = split(axis = var_8074_axis_0, split_sizes = var_8074_split_sizes_0, x = var_8049_cast_fp16)[name = string("op_8074_cast_fp16")];
            fp16 const_225_promoted_to_fp16 = const()[name = string("const_225_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_8076_cast_fp16 = mul(x = var_8074_cast_fp16_1, y = const_225_promoted_to_fp16)[name = string("op_8076_cast_fp16")];
            int32 var_8078 = const()[name = string("op_8078"), val = int32(-2)];
            bool var_8079_interleave_0 = const()[name = string("op_8079_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_8079_cast_fp16 = concat(axis = var_8078, interleave = var_8079_interleave_0, values = (var_8076_cast_fp16, var_8074_cast_fp16_0))[name = string("op_8079_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_8080_cast_fp16 = mul(x = var_8079_cast_fp16, y = var_866_cast_fp16)[name = string("op_8080_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_225_cast_fp16 = add(x = var_8073_cast_fp16, y = var_8080_cast_fp16)[name = string("key_states_225_cast_fp16")];
            tensor<int32, [1]> expand_dims_264 = const()[name = string("expand_dims_264"), val = tensor<int32, [1]>([22])];
            tensor<int32, [1]> expand_dims_265 = const()[name = string("expand_dims_265"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_267 = const()[name = string("expand_dims_267"), val = tensor<int32, [1]>([0])];
            int32 concat_269_axis_0 = const()[name = string("concat_269_axis_0"), val = int32(0)];
            bool concat_269_interleave_0 = const()[name = string("concat_269_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_269 = concat(axis = concat_269_axis_0, interleave = concat_269_interleave_0, values = (expand_dims_264, expand_dims_265, position_id, expand_dims_267))[name = string("concat_269")];
            tensor<int32, [1]> expand_dims_268 = const()[name = string("expand_dims_268"), val = tensor<int32, [1]>([23])];
            tensor<int32, [1]> concat_270_values1_0 = const()[name = string("concat_270_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_270_values3_0 = const()[name = string("concat_270_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_270_axis_0 = const()[name = string("concat_270_axis_0"), val = int32(0)];
            bool concat_270_interleave_0 = const()[name = string("concat_270_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_270 = concat(axis = concat_270_axis_0, interleave = concat_270_interleave_0, values = (expand_dims_268, concat_270_values1_0, cache_position_end, concat_270_values3_0))[name = string("concat_270")];
            tensor<int32, [4]> key_states_227_perm_0 = const()[name = string("key_states_227_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_23_stride_0 = const()[name = string("key_cache_internal_tensor_assign_23_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_23_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_23_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_23_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_23_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_23_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_23_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_227_cast_fp16 = transpose(perm = key_states_227_perm_0, x = key_states_225_cast_fp16)[name = string("transpose_365")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_23_cast_fp16 = slice_update(begin = concat_269, begin_mask = key_cache_internal_tensor_assign_23_begin_mask_0, end = concat_270, end_mask = key_cache_internal_tensor_assign_23_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_23_squeeze_mask_0, stride = key_cache_internal_tensor_assign_23_stride_0, update = key_states_227_cast_fp16, x = coreml_update_state_266)[name = string("key_cache_internal_tensor_assign_23_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_23_cast_fp16, input = key_cache)[name = string("coreml_update_state_268_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_268 = read_state(input = key_cache)[name = string("coreml_update_state_268")];
            tensor<int32, [4]> value_states_135_perm_0 = const()[name = string("value_states_135_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_23_stride_0 = const()[name = string("value_cache_internal_tensor_assign_23_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_23_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_23_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_23_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_23_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_23_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_23_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_135_cast_fp16 = transpose(perm = value_states_135_perm_0, x = var_8056_cast_fp16)[name = string("transpose_364")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_23_cast_fp16 = slice_update(begin = concat_269, begin_mask = value_cache_internal_tensor_assign_23_begin_mask_0, end = concat_270, end_mask = value_cache_internal_tensor_assign_23_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_23_squeeze_mask_0, stride = value_cache_internal_tensor_assign_23_stride_0, update = value_states_135_cast_fp16, x = coreml_update_state_267)[name = string("value_cache_internal_tensor_assign_23_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_23_cast_fp16, input = value_cache)[name = string("coreml_update_state_269_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_269 = read_state(input = value_cache)[name = string("coreml_update_state_269")];
            tensor<int32, [4]> var_8150_begin_0 = const()[name = string("op_8150_begin_0"), val = tensor<int32, [4]>([22, 0, 0, 0])];
            tensor<int32, [4]> var_8150_end_0 = const()[name = string("op_8150_end_0"), val = tensor<int32, [4]>([23, 2, 2048, 128])];
            tensor<bool, [4]> var_8150_end_mask_0 = const()[name = string("op_8150_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8150_cast_fp16 = slice_by_index(begin = var_8150_begin_0, end = var_8150_end_0, end_mask = var_8150_end_mask_0, x = coreml_update_state_268)[name = string("op_8150_cast_fp16")];
            tensor<int32, [2]> tile_44 = const()[name = string("tile_44"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8153_axis_0 = const()[name = string("op_8153_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8153_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8153_cast_fp16_1 = split(axis = var_8153_axis_0, split_sizes = tile_44, x = var_8150_cast_fp16)[name = string("op_8153_cast_fp16")];
            tensor<int32, [4]> var_8160_begin_0 = const()[name = string("op_8160_begin_0"), val = tensor<int32, [4]>([22, 0, 0, 0])];
            tensor<int32, [4]> var_8160_end_0 = const()[name = string("op_8160_end_0"), val = tensor<int32, [4]>([23, 2, 2048, 128])];
            tensor<bool, [4]> var_8160_end_mask_0 = const()[name = string("op_8160_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8160_cast_fp16 = slice_by_index(begin = var_8160_begin_0, end = var_8160_end_0, end_mask = var_8160_end_mask_0, x = coreml_update_state_269)[name = string("op_8160_cast_fp16")];
            tensor<int32, [2]> tile_45 = const()[name = string("tile_45"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8163_axis_0 = const()[name = string("op_8163_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8163_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8163_cast_fp16_1 = split(axis = var_8163_axis_0, split_sizes = tile_45, x = var_8160_cast_fp16)[name = string("op_8163_cast_fp16")];
            tensor<int32, [2]> var_8166_split_sizes_0 = const()[name = string("op_8166_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_8166_axis_0 = const()[name = string("op_8166_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_8166_0, tensor<fp16, [1, 8, 128, 32]> var_8166_1 = split(axis = var_8166_axis_0, split_sizes = var_8166_split_sizes_0, x = query_states_135_cast_fp16)[name = string("op_8166")];
            bool attn_weights_353_transpose_x_0 = const()[name = string("attn_weights_353_transpose_x_0"), val = bool(false)];
            bool attn_weights_353_transpose_y_0 = const()[name = string("attn_weights_353_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_353_cast_fp16 = matmul(transpose_x = attn_weights_353_transpose_x_0, transpose_y = attn_weights_353_transpose_y_0, x = var_8153_cast_fp16_0, y = var_8166_0)[name = string("attn_weights_353_cast_fp16")];
            fp16 var_8169_to_fp16 = const()[name = string("op_8169_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_355_cast_fp16 = mul(x = attn_weights_353_cast_fp16, y = var_8169_to_fp16)[name = string("attn_weights_355_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_357_cast_fp16 = add(x = attn_weights_355_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_357_cast_fp16")];
            int32 var_8173 = const()[name = string("op_8173"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_359_cast_fp16 = softmax(axis = var_8173, x = attn_weights_357_cast_fp16)[name = string("attn_weights_359_cast_fp16")];
            bool var_8179_transpose_x_1 = const()[name = string("op_8179_transpose_x_1"), val = bool(true)];
            bool var_8179_transpose_y_1 = const()[name = string("op_8179_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_8179_cast_fp16 = matmul(transpose_x = var_8179_transpose_x_1, transpose_y = var_8179_transpose_y_1, x = attn_weights_359_cast_fp16, y = var_8163_cast_fp16_0)[name = string("op_8179_cast_fp16")];
            bool attn_weights_361_transpose_x_0 = const()[name = string("attn_weights_361_transpose_x_0"), val = bool(false)];
            bool attn_weights_361_transpose_y_0 = const()[name = string("attn_weights_361_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_361_cast_fp16 = matmul(transpose_x = attn_weights_361_transpose_x_0, transpose_y = attn_weights_361_transpose_y_0, x = var_8153_cast_fp16_1, y = var_8166_1)[name = string("attn_weights_361_cast_fp16")];
            fp16 var_8181_to_fp16 = const()[name = string("op_8181_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_363_cast_fp16 = mul(x = attn_weights_361_cast_fp16, y = var_8181_to_fp16)[name = string("attn_weights_363_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_365_cast_fp16 = add(x = attn_weights_363_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_365_cast_fp16")];
            int32 var_8185 = const()[name = string("op_8185"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_367_cast_fp16 = softmax(axis = var_8185, x = attn_weights_365_cast_fp16)[name = string("attn_weights_367_cast_fp16")];
            bool attn_output_177_transpose_x_1 = const()[name = string("attn_output_177_transpose_x_1"), val = bool(true)];
            bool attn_output_177_transpose_y_1 = const()[name = string("attn_output_177_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_177_cast_fp16 = matmul(transpose_x = attn_output_177_transpose_x_1, transpose_y = attn_output_177_transpose_y_1, x = attn_weights_367_cast_fp16, y = var_8163_cast_fp16_1)[name = string("attn_output_177_cast_fp16")];
            int32 var_8193 = const()[name = string("op_8193"), val = int32(1)];
            bool attn_output_179_interleave_0 = const()[name = string("attn_output_179_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_179_cast_fp16 = concat(axis = var_8193, interleave = attn_output_179_interleave_0, values = (var_8179_cast_fp16, attn_output_177_cast_fp16))[name = string("attn_output_179_cast_fp16")];
            tensor<int32, [4]> var_8197_perm_0 = const()[name = string("op_8197_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_275x = const()[name = string("concat_275x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_8197_cast_fp16 = transpose(perm = var_8197_perm_0, x = attn_output_179_cast_fp16)[name = string("transpose_363")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_183_cast_fp16 = reshape(shape = concat_275x, x = var_8197_cast_fp16)[name = string("attn_output_183_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_22_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_22_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1521463040)))];
            tensor<int32, [2]> hidden_states_223_strides_0 = const()[name = string("hidden_states_223_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_223_pad_type_0 = const()[name = string("hidden_states_223_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_223_pad_0 = const()[name = string("hidden_states_223_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_223_dilations_0 = const()[name = string("hidden_states_223_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_223_groups_0 = const()[name = string("hidden_states_223_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_223_cast_fp16 = conv(dilations = hidden_states_223_dilations_0, groups = hidden_states_223_groups_0, pad = hidden_states_223_pad_0, pad_type = hidden_states_223_pad_type_0, strides = hidden_states_223_strides_0, weight = layers_22_self_attn_o_proj_weight_to_fp16, x = attn_output_183_cast_fp16)[name = string("hidden_states_223_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_225_cast_fp16 = add(x = hidden_states_219_cast_fp16, y = hidden_states_223_cast_fp16)[name = string("hidden_states_225_cast_fp16")];
            fp16 const_230_promoted_to_fp16 = const()[name = string("const_230_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_8230_cast_fp16 = mul(x = hidden_states_225_cast_fp16, y = const_230_promoted_to_fp16)[name = string("op_8230_cast_fp16")];
            int32 var_8228 = const()[name = string("op_8228"), val = int32(1)];
            bool doubled_181_interleave_0 = const()[name = string("doubled_181_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_181_cast_fp16 = concat(axis = var_8228, interleave = doubled_181_interleave_0, values = (hidden_states_225_cast_fp16, var_8230_cast_fp16))[name = string("doubled_181_cast_fp16")];
            tensor<int32, [1]> out_91_axes_0 = const()[name = string("out_91_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_91_gamma_0_to_fp16 = const()[name = string("out_91_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1529851712)))];
            fp16 var_8240_to_fp16 = const()[name = string("op_8240_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_91_cast_fp16 = layer_norm(axes = out_91_axes_0, epsilon = var_8240_to_fp16, gamma = out_91_gamma_0_to_fp16, x = doubled_181_cast_fp16)[name = string("out_91_cast_fp16")];
            tensor<int32, [2]> var_8251_split_sizes_0 = const()[name = string("op_8251_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8251_axis_0 = const()[name = string("op_8251_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_8251_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_8251_cast_fp16_1 = split(axis = var_8251_axis_0, split_sizes = var_8251_split_sizes_0, x = out_91_cast_fp16)[name = string("op_8251_cast_fp16")];
            tensor<int32, [2]> input_45_strides_0 = const()[name = string("input_45_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_45_pad_type_0 = const()[name = string("input_45_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_45_pad_0 = const()[name = string("input_45_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_45_dilations_0 = const()[name = string("input_45_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_45_groups_0 = const()[name = string("input_45_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_45_cast_fp16 = conv(dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = layers_22_mlp_gate_proj_weight_cast_fp16, x = var_8251_cast_fp16_0)[name = string("input_45_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_8268_cast_fp16 = silu(x = input_45_cast_fp16)[name = string("op_8268_cast_fp16")];
            tensor<int32, [2]> var_8274_strides_0 = const()[name = string("op_8274_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_8274_pad_type_0 = const()[name = string("op_8274_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_8274_pad_0 = const()[name = string("op_8274_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_8274_dilations_0 = const()[name = string("op_8274_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8274_groups_0 = const()[name = string("op_8274_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_8274_cast_fp16 = conv(dilations = var_8274_dilations_0, groups = var_8274_groups_0, pad = var_8274_pad_0, pad_type = var_8274_pad_type_0, strides = var_8274_strides_0, weight = layers_22_mlp_up_proj_weight_cast_fp16, x = var_8251_cast_fp16_0)[name = string("op_8274_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_229_cast_fp16 = mul(x = var_8268_cast_fp16, y = var_8274_cast_fp16)[name = string("x_229_cast_fp16")];
            tensor<int32, [2]> hidden_states_227_strides_0 = const()[name = string("hidden_states_227_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_227_pad_type_0 = const()[name = string("hidden_states_227_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_227_pad_0 = const()[name = string("hidden_states_227_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_227_dilations_0 = const()[name = string("hidden_states_227_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_227_groups_0 = const()[name = string("hidden_states_227_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_227_cast_fp16 = conv(dilations = hidden_states_227_dilations_0, groups = hidden_states_227_groups_0, pad = hidden_states_227_pad_0, pad_type = hidden_states_227_pad_type_0, strides = hidden_states_227_strides_0, weight = layers_22_mlp_down_proj_weight_cast_fp16, x = x_229_cast_fp16)[name = string("hidden_states_227_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_229_cast_fp16 = add(x = hidden_states_225_cast_fp16, y = hidden_states_227_cast_fp16)[name = string("hidden_states_229_cast_fp16")];
            fp16 const_232_promoted_to_fp16 = const()[name = string("const_232_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_8292_cast_fp16 = mul(x = hidden_states_229_cast_fp16, y = const_232_promoted_to_fp16)[name = string("op_8292_cast_fp16")];
            int32 var_8290 = const()[name = string("op_8290"), val = int32(1)];
            bool doubled_185_interleave_0 = const()[name = string("doubled_185_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_185_cast_fp16 = concat(axis = var_8290, interleave = doubled_185_interleave_0, values = (hidden_states_229_cast_fp16, var_8292_cast_fp16))[name = string("doubled_185_cast_fp16")];
            tensor<int32, [1]> out_93_axes_0 = const()[name = string("out_93_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_93_gamma_0_to_fp16 = const()[name = string("out_93_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1529859968)))];
            fp16 var_8302_to_fp16 = const()[name = string("op_8302_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_93_cast_fp16 = layer_norm(axes = out_93_axes_0, epsilon = var_8302_to_fp16, gamma = out_93_gamma_0_to_fp16, x = doubled_185_cast_fp16)[name = string("out_93_cast_fp16")];
            tensor<int32, [2]> var_8313_split_sizes_0 = const()[name = string("op_8313_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8313_axis_0 = const()[name = string("op_8313_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_8313_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_8313_cast_fp16_1 = split(axis = var_8313_axis_0, split_sizes = var_8313_split_sizes_0, x = out_93_cast_fp16)[name = string("op_8313_cast_fp16")];
            tensor<int32, [2]> query_states_139_strides_0 = const()[name = string("query_states_139_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_139_pad_type_0 = const()[name = string("query_states_139_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_139_pad_0 = const()[name = string("query_states_139_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_139_dilations_0 = const()[name = string("query_states_139_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_139_groups_0 = const()[name = string("query_states_139_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_139_cast_fp16 = conv(dilations = query_states_139_dilations_0, groups = query_states_139_groups_0, pad = query_states_139_pad_0, pad_type = query_states_139_pad_type_0, strides = query_states_139_strides_0, weight = layers_23_self_attn_q_proj_weight_cast_fp16, x = var_8313_cast_fp16_0)[name = string("query_states_139_cast_fp16")];
            tensor<int32, [2]> key_states_231_strides_0 = const()[name = string("key_states_231_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_231_pad_type_0 = const()[name = string("key_states_231_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_231_pad_0 = const()[name = string("key_states_231_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_231_dilations_0 = const()[name = string("key_states_231_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_231_groups_0 = const()[name = string("key_states_231_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_231_cast_fp16 = conv(dilations = key_states_231_dilations_0, groups = key_states_231_groups_0, pad = key_states_231_pad_0, pad_type = key_states_231_pad_type_0, strides = key_states_231_strides_0, weight = layers_23_self_attn_k_proj_weight_cast_fp16, x = var_8313_cast_fp16_0)[name = string("key_states_231_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_23_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_23_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1529868224)))];
            tensor<int32, [2]> value_states_139_strides_0 = const()[name = string("value_states_139_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_139_pad_type_0 = const()[name = string("value_states_139_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_139_pad_0 = const()[name = string("value_states_139_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_139_dilations_0 = const()[name = string("value_states_139_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_139_groups_0 = const()[name = string("value_states_139_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_139_cast_fp16 = conv(dilations = value_states_139_dilations_0, groups = value_states_139_groups_0, pad = value_states_139_pad_0, pad_type = value_states_139_pad_type_0, strides = value_states_139_strides_0, weight = layers_23_self_attn_v_proj_weight_to_fp16, x = var_8313_cast_fp16_0)[name = string("value_states_139_cast_fp16")];
            tensor<int32, [4]> concat_276x = const()[name = string("concat_276x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_231_cast_fp16 = reshape(shape = concat_276x, x = query_states_139_cast_fp16)[name = string("x_231_cast_fp16")];
            tensor<int32, [4]> concat_277x = const()[name = string("concat_277x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_8370_cast_fp16 = reshape(shape = concat_277x, x = key_states_231_cast_fp16)[name = string("op_8370_cast_fp16")];
            tensor<int32, [4]> concat_278x = const()[name = string("concat_278x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_8377_cast_fp16 = reshape(shape = concat_278x, x = value_states_139_cast_fp16)[name = string("op_8377_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_8381_cast_fp16 = mul(x = x_231_cast_fp16, y = var_859_cast_fp16)[name = string("op_8381_cast_fp16")];
            tensor<int32, [2]> var_8382_split_sizes_0 = const()[name = string("op_8382_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8382_axis_0 = const()[name = string("op_8382_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_8382_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_8382_cast_fp16_1 = split(axis = var_8382_axis_0, split_sizes = var_8382_split_sizes_0, x = x_231_cast_fp16)[name = string("op_8382_cast_fp16")];
            fp16 const_234_promoted_to_fp16 = const()[name = string("const_234_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_8384_cast_fp16 = mul(x = var_8382_cast_fp16_1, y = const_234_promoted_to_fp16)[name = string("op_8384_cast_fp16")];
            int32 var_8386 = const()[name = string("op_8386"), val = int32(-2)];
            bool var_8387_interleave_0 = const()[name = string("op_8387_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_8387_cast_fp16 = concat(axis = var_8386, interleave = var_8387_interleave_0, values = (var_8384_cast_fp16, var_8382_cast_fp16_0))[name = string("op_8387_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_8388_cast_fp16 = mul(x = var_8387_cast_fp16, y = var_866_cast_fp16)[name = string("op_8388_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_141_cast_fp16 = add(x = var_8381_cast_fp16, y = var_8388_cast_fp16)[name = string("query_states_141_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_8394_cast_fp16 = mul(x = var_8370_cast_fp16, y = var_859_cast_fp16)[name = string("op_8394_cast_fp16")];
            tensor<int32, [2]> var_8395_split_sizes_0 = const()[name = string("op_8395_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8395_axis_0 = const()[name = string("op_8395_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_8395_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_8395_cast_fp16_1 = split(axis = var_8395_axis_0, split_sizes = var_8395_split_sizes_0, x = var_8370_cast_fp16)[name = string("op_8395_cast_fp16")];
            fp16 const_235_promoted_to_fp16 = const()[name = string("const_235_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_8397_cast_fp16 = mul(x = var_8395_cast_fp16_1, y = const_235_promoted_to_fp16)[name = string("op_8397_cast_fp16")];
            int32 var_8399 = const()[name = string("op_8399"), val = int32(-2)];
            bool var_8400_interleave_0 = const()[name = string("op_8400_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_8400_cast_fp16 = concat(axis = var_8399, interleave = var_8400_interleave_0, values = (var_8397_cast_fp16, var_8395_cast_fp16_0))[name = string("op_8400_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_8401_cast_fp16 = mul(x = var_8400_cast_fp16, y = var_866_cast_fp16)[name = string("op_8401_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_235_cast_fp16 = add(x = var_8394_cast_fp16, y = var_8401_cast_fp16)[name = string("key_states_235_cast_fp16")];
            tensor<int32, [1]> expand_dims_276 = const()[name = string("expand_dims_276"), val = tensor<int32, [1]>([23])];
            tensor<int32, [1]> expand_dims_277 = const()[name = string("expand_dims_277"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_279 = const()[name = string("expand_dims_279"), val = tensor<int32, [1]>([0])];
            int32 concat_281_axis_0 = const()[name = string("concat_281_axis_0"), val = int32(0)];
            bool concat_281_interleave_0 = const()[name = string("concat_281_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_281 = concat(axis = concat_281_axis_0, interleave = concat_281_interleave_0, values = (expand_dims_276, expand_dims_277, position_id, expand_dims_279))[name = string("concat_281")];
            tensor<int32, [1]> expand_dims_280 = const()[name = string("expand_dims_280"), val = tensor<int32, [1]>([24])];
            tensor<int32, [1]> concat_282_values1_0 = const()[name = string("concat_282_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_282_values3_0 = const()[name = string("concat_282_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_282_axis_0 = const()[name = string("concat_282_axis_0"), val = int32(0)];
            bool concat_282_interleave_0 = const()[name = string("concat_282_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_282 = concat(axis = concat_282_axis_0, interleave = concat_282_interleave_0, values = (expand_dims_280, concat_282_values1_0, cache_position_end, concat_282_values3_0))[name = string("concat_282")];
            tensor<int32, [4]> key_states_237_perm_0 = const()[name = string("key_states_237_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_24_stride_0 = const()[name = string("key_cache_internal_tensor_assign_24_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_24_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_24_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_24_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_24_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_24_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_24_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_237_cast_fp16 = transpose(perm = key_states_237_perm_0, x = key_states_235_cast_fp16)[name = string("transpose_362")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_24_cast_fp16 = slice_update(begin = concat_281, begin_mask = key_cache_internal_tensor_assign_24_begin_mask_0, end = concat_282, end_mask = key_cache_internal_tensor_assign_24_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_24_squeeze_mask_0, stride = key_cache_internal_tensor_assign_24_stride_0, update = key_states_237_cast_fp16, x = coreml_update_state_268)[name = string("key_cache_internal_tensor_assign_24_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_24_cast_fp16, input = key_cache)[name = string("coreml_update_state_270_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_270 = read_state(input = key_cache)[name = string("coreml_update_state_270")];
            tensor<int32, [4]> value_states_141_perm_0 = const()[name = string("value_states_141_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_24_stride_0 = const()[name = string("value_cache_internal_tensor_assign_24_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_24_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_24_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_24_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_24_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_24_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_24_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_141_cast_fp16 = transpose(perm = value_states_141_perm_0, x = var_8377_cast_fp16)[name = string("transpose_361")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_24_cast_fp16 = slice_update(begin = concat_281, begin_mask = value_cache_internal_tensor_assign_24_begin_mask_0, end = concat_282, end_mask = value_cache_internal_tensor_assign_24_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_24_squeeze_mask_0, stride = value_cache_internal_tensor_assign_24_stride_0, update = value_states_141_cast_fp16, x = coreml_update_state_269)[name = string("value_cache_internal_tensor_assign_24_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_24_cast_fp16, input = value_cache)[name = string("coreml_update_state_271_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_271 = read_state(input = value_cache)[name = string("coreml_update_state_271")];
            tensor<int32, [4]> var_8471_begin_0 = const()[name = string("op_8471_begin_0"), val = tensor<int32, [4]>([23, 0, 0, 0])];
            tensor<int32, [4]> var_8471_end_0 = const()[name = string("op_8471_end_0"), val = tensor<int32, [4]>([24, 2, 2048, 128])];
            tensor<bool, [4]> var_8471_end_mask_0 = const()[name = string("op_8471_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8471_cast_fp16 = slice_by_index(begin = var_8471_begin_0, end = var_8471_end_0, end_mask = var_8471_end_mask_0, x = coreml_update_state_270)[name = string("op_8471_cast_fp16")];
            tensor<int32, [2]> tile_46 = const()[name = string("tile_46"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8474_axis_0 = const()[name = string("op_8474_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8474_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8474_cast_fp16_1 = split(axis = var_8474_axis_0, split_sizes = tile_46, x = var_8471_cast_fp16)[name = string("op_8474_cast_fp16")];
            tensor<int32, [4]> var_8481_begin_0 = const()[name = string("op_8481_begin_0"), val = tensor<int32, [4]>([23, 0, 0, 0])];
            tensor<int32, [4]> var_8481_end_0 = const()[name = string("op_8481_end_0"), val = tensor<int32, [4]>([24, 2, 2048, 128])];
            tensor<bool, [4]> var_8481_end_mask_0 = const()[name = string("op_8481_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8481_cast_fp16 = slice_by_index(begin = var_8481_begin_0, end = var_8481_end_0, end_mask = var_8481_end_mask_0, x = coreml_update_state_271)[name = string("op_8481_cast_fp16")];
            tensor<int32, [2]> tile_47 = const()[name = string("tile_47"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8484_axis_0 = const()[name = string("op_8484_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8484_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8484_cast_fp16_1 = split(axis = var_8484_axis_0, split_sizes = tile_47, x = var_8481_cast_fp16)[name = string("op_8484_cast_fp16")];
            tensor<int32, [2]> var_8487_split_sizes_0 = const()[name = string("op_8487_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_8487_axis_0 = const()[name = string("op_8487_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_8487_0, tensor<fp16, [1, 8, 128, 32]> var_8487_1 = split(axis = var_8487_axis_0, split_sizes = var_8487_split_sizes_0, x = query_states_141_cast_fp16)[name = string("op_8487")];
            bool attn_weights_369_transpose_x_0 = const()[name = string("attn_weights_369_transpose_x_0"), val = bool(false)];
            bool attn_weights_369_transpose_y_0 = const()[name = string("attn_weights_369_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_369_cast_fp16 = matmul(transpose_x = attn_weights_369_transpose_x_0, transpose_y = attn_weights_369_transpose_y_0, x = var_8474_cast_fp16_0, y = var_8487_0)[name = string("attn_weights_369_cast_fp16")];
            fp16 var_8490_to_fp16 = const()[name = string("op_8490_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_371_cast_fp16 = mul(x = attn_weights_369_cast_fp16, y = var_8490_to_fp16)[name = string("attn_weights_371_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_373_cast_fp16 = add(x = attn_weights_371_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_373_cast_fp16")];
            int32 var_8494 = const()[name = string("op_8494"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_375_cast_fp16 = softmax(axis = var_8494, x = attn_weights_373_cast_fp16)[name = string("attn_weights_375_cast_fp16")];
            bool var_8500_transpose_x_1 = const()[name = string("op_8500_transpose_x_1"), val = bool(true)];
            bool var_8500_transpose_y_1 = const()[name = string("op_8500_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_8500_cast_fp16 = matmul(transpose_x = var_8500_transpose_x_1, transpose_y = var_8500_transpose_y_1, x = attn_weights_375_cast_fp16, y = var_8484_cast_fp16_0)[name = string("op_8500_cast_fp16")];
            bool attn_weights_377_transpose_x_0 = const()[name = string("attn_weights_377_transpose_x_0"), val = bool(false)];
            bool attn_weights_377_transpose_y_0 = const()[name = string("attn_weights_377_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_377_cast_fp16 = matmul(transpose_x = attn_weights_377_transpose_x_0, transpose_y = attn_weights_377_transpose_y_0, x = var_8474_cast_fp16_1, y = var_8487_1)[name = string("attn_weights_377_cast_fp16")];
            fp16 var_8502_to_fp16 = const()[name = string("op_8502_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_379_cast_fp16 = mul(x = attn_weights_377_cast_fp16, y = var_8502_to_fp16)[name = string("attn_weights_379_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_381_cast_fp16 = add(x = attn_weights_379_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_381_cast_fp16")];
            int32 var_8506 = const()[name = string("op_8506"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_383_cast_fp16 = softmax(axis = var_8506, x = attn_weights_381_cast_fp16)[name = string("attn_weights_383_cast_fp16")];
            bool attn_output_185_transpose_x_1 = const()[name = string("attn_output_185_transpose_x_1"), val = bool(true)];
            bool attn_output_185_transpose_y_1 = const()[name = string("attn_output_185_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_185_cast_fp16 = matmul(transpose_x = attn_output_185_transpose_x_1, transpose_y = attn_output_185_transpose_y_1, x = attn_weights_383_cast_fp16, y = var_8484_cast_fp16_1)[name = string("attn_output_185_cast_fp16")];
            int32 var_8514 = const()[name = string("op_8514"), val = int32(1)];
            bool attn_output_187_interleave_0 = const()[name = string("attn_output_187_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_187_cast_fp16 = concat(axis = var_8514, interleave = attn_output_187_interleave_0, values = (var_8500_cast_fp16, attn_output_185_cast_fp16))[name = string("attn_output_187_cast_fp16")];
            tensor<int32, [4]> var_8518_perm_0 = const()[name = string("op_8518_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_287x = const()[name = string("concat_287x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_8518_cast_fp16 = transpose(perm = var_8518_perm_0, x = attn_output_187_cast_fp16)[name = string("transpose_360")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_191_cast_fp16 = reshape(shape = concat_287x, x = var_8518_cast_fp16)[name = string("attn_output_191_cast_fp16")];
            tensor<int32, [2]> hidden_states_233_strides_0 = const()[name = string("hidden_states_233_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_233_pad_type_0 = const()[name = string("hidden_states_233_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_233_pad_0 = const()[name = string("hidden_states_233_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_233_dilations_0 = const()[name = string("hidden_states_233_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_233_groups_0 = const()[name = string("hidden_states_233_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_233_cast_fp16 = conv(dilations = hidden_states_233_dilations_0, groups = hidden_states_233_groups_0, pad = hidden_states_233_pad_0, pad_type = hidden_states_233_pad_type_0, strides = hidden_states_233_strides_0, weight = layers_23_self_attn_o_proj_weight_cast_fp16, x = attn_output_191_cast_fp16)[name = string("hidden_states_233_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_235_cast_fp16 = add(x = hidden_states_229_cast_fp16, y = hidden_states_233_cast_fp16)[name = string("hidden_states_235_cast_fp16")];
            fp16 const_240_promoted_to_fp16 = const()[name = string("const_240_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_8551_cast_fp16 = mul(x = hidden_states_235_cast_fp16, y = const_240_promoted_to_fp16)[name = string("op_8551_cast_fp16")];
            int32 var_8549 = const()[name = string("op_8549"), val = int32(1)];
            bool doubled_189_interleave_0 = const()[name = string("doubled_189_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_189_cast_fp16 = concat(axis = var_8549, interleave = doubled_189_interleave_0, values = (hidden_states_235_cast_fp16, var_8551_cast_fp16))[name = string("doubled_189_cast_fp16")];
            tensor<int32, [1]> out_95_axes_0 = const()[name = string("out_95_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_95_gamma_0_to_fp16 = const()[name = string("out_95_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1530916864)))];
            fp16 var_8561_to_fp16 = const()[name = string("op_8561_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_95_cast_fp16 = layer_norm(axes = out_95_axes_0, epsilon = var_8561_to_fp16, gamma = out_95_gamma_0_to_fp16, x = doubled_189_cast_fp16)[name = string("out_95_cast_fp16")];
            tensor<int32, [2]> var_8572_split_sizes_0 = const()[name = string("op_8572_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8572_axis_0 = const()[name = string("op_8572_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_8572_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_8572_cast_fp16_1 = split(axis = var_8572_axis_0, split_sizes = var_8572_split_sizes_0, x = out_95_cast_fp16)[name = string("op_8572_cast_fp16")];
            tensor<int32, [2]> input_47_strides_0 = const()[name = string("input_47_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_47_pad_type_0 = const()[name = string("input_47_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_47_dilations_0 = const()[name = string("input_47_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_47_groups_0 = const()[name = string("input_47_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_47_cast_fp16 = conv(dilations = input_47_dilations_0, groups = input_47_groups_0, pad = input_47_pad_0, pad_type = input_47_pad_type_0, strides = input_47_strides_0, weight = layers_23_mlp_gate_proj_weight_cast_fp16, x = var_8572_cast_fp16_0)[name = string("input_47_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_8589_cast_fp16 = silu(x = input_47_cast_fp16)[name = string("op_8589_cast_fp16")];
            tensor<int32, [2]> var_8595_strides_0 = const()[name = string("op_8595_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_8595_pad_type_0 = const()[name = string("op_8595_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_8595_pad_0 = const()[name = string("op_8595_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_8595_dilations_0 = const()[name = string("op_8595_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8595_groups_0 = const()[name = string("op_8595_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_8595_cast_fp16 = conv(dilations = var_8595_dilations_0, groups = var_8595_groups_0, pad = var_8595_pad_0, pad_type = var_8595_pad_type_0, strides = var_8595_strides_0, weight = layers_23_mlp_up_proj_weight_cast_fp16, x = var_8572_cast_fp16_0)[name = string("op_8595_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_239_cast_fp16 = mul(x = var_8589_cast_fp16, y = var_8595_cast_fp16)[name = string("x_239_cast_fp16")];
            tensor<int32, [2]> hidden_states_237_strides_0 = const()[name = string("hidden_states_237_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_237_pad_type_0 = const()[name = string("hidden_states_237_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_237_pad_0 = const()[name = string("hidden_states_237_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_237_dilations_0 = const()[name = string("hidden_states_237_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_237_groups_0 = const()[name = string("hidden_states_237_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_237_cast_fp16 = conv(dilations = hidden_states_237_dilations_0, groups = hidden_states_237_groups_0, pad = hidden_states_237_pad_0, pad_type = hidden_states_237_pad_type_0, strides = hidden_states_237_strides_0, weight = layers_23_mlp_down_proj_weight_cast_fp16, x = x_239_cast_fp16)[name = string("hidden_states_237_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_239_cast_fp16 = add(x = hidden_states_235_cast_fp16, y = hidden_states_237_cast_fp16)[name = string("hidden_states_239_cast_fp16")];
            fp16 const_242_promoted_to_fp16 = const()[name = string("const_242_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_8613_cast_fp16 = mul(x = hidden_states_239_cast_fp16, y = const_242_promoted_to_fp16)[name = string("op_8613_cast_fp16")];
            int32 var_8611 = const()[name = string("op_8611"), val = int32(1)];
            bool doubled_193_interleave_0 = const()[name = string("doubled_193_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_193_cast_fp16 = concat(axis = var_8611, interleave = doubled_193_interleave_0, values = (hidden_states_239_cast_fp16, var_8613_cast_fp16))[name = string("doubled_193_cast_fp16")];
            tensor<int32, [1]> out_97_axes_0 = const()[name = string("out_97_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_97_gamma_0_to_fp16 = const()[name = string("out_97_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1530925120)))];
            fp16 var_8623_to_fp16 = const()[name = string("op_8623_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_97_cast_fp16 = layer_norm(axes = out_97_axes_0, epsilon = var_8623_to_fp16, gamma = out_97_gamma_0_to_fp16, x = doubled_193_cast_fp16)[name = string("out_97_cast_fp16")];
            tensor<int32, [2]> var_8634_split_sizes_0 = const()[name = string("op_8634_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8634_axis_0 = const()[name = string("op_8634_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_8634_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_8634_cast_fp16_1 = split(axis = var_8634_axis_0, split_sizes = var_8634_split_sizes_0, x = out_97_cast_fp16)[name = string("op_8634_cast_fp16")];
            tensor<int32, [2]> query_states_145_strides_0 = const()[name = string("query_states_145_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_145_pad_type_0 = const()[name = string("query_states_145_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_145_pad_0 = const()[name = string("query_states_145_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_145_dilations_0 = const()[name = string("query_states_145_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_145_groups_0 = const()[name = string("query_states_145_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_145_cast_fp16 = conv(dilations = query_states_145_dilations_0, groups = query_states_145_groups_0, pad = query_states_145_pad_0, pad_type = query_states_145_pad_type_0, strides = query_states_145_strides_0, weight = layers_24_self_attn_q_proj_weight_cast_fp16, x = var_8634_cast_fp16_0)[name = string("query_states_145_cast_fp16")];
            tensor<int32, [2]> key_states_241_strides_0 = const()[name = string("key_states_241_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_241_pad_type_0 = const()[name = string("key_states_241_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_241_pad_0 = const()[name = string("key_states_241_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_241_dilations_0 = const()[name = string("key_states_241_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_241_groups_0 = const()[name = string("key_states_241_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_241_cast_fp16 = conv(dilations = key_states_241_dilations_0, groups = key_states_241_groups_0, pad = key_states_241_pad_0, pad_type = key_states_241_pad_type_0, strides = key_states_241_strides_0, weight = layers_24_self_attn_k_proj_weight_cast_fp16, x = var_8634_cast_fp16_0)[name = string("key_states_241_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_24_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_24_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1530933376)))];
            tensor<int32, [2]> value_states_145_strides_0 = const()[name = string("value_states_145_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_145_pad_type_0 = const()[name = string("value_states_145_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_145_pad_0 = const()[name = string("value_states_145_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_145_dilations_0 = const()[name = string("value_states_145_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_145_groups_0 = const()[name = string("value_states_145_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_145_cast_fp16 = conv(dilations = value_states_145_dilations_0, groups = value_states_145_groups_0, pad = value_states_145_pad_0, pad_type = value_states_145_pad_type_0, strides = value_states_145_strides_0, weight = layers_24_self_attn_v_proj_weight_to_fp16, x = var_8634_cast_fp16_0)[name = string("value_states_145_cast_fp16")];
            tensor<int32, [4]> concat_288x = const()[name = string("concat_288x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_241_cast_fp16 = reshape(shape = concat_288x, x = query_states_145_cast_fp16)[name = string("x_241_cast_fp16")];
            tensor<int32, [4]> concat_289x = const()[name = string("concat_289x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_8691_cast_fp16 = reshape(shape = concat_289x, x = key_states_241_cast_fp16)[name = string("op_8691_cast_fp16")];
            tensor<int32, [4]> concat_290x = const()[name = string("concat_290x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_8698_cast_fp16 = reshape(shape = concat_290x, x = value_states_145_cast_fp16)[name = string("op_8698_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_8702_cast_fp16 = mul(x = x_241_cast_fp16, y = var_859_cast_fp16)[name = string("op_8702_cast_fp16")];
            tensor<int32, [2]> var_8703_split_sizes_0 = const()[name = string("op_8703_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8703_axis_0 = const()[name = string("op_8703_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_8703_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_8703_cast_fp16_1 = split(axis = var_8703_axis_0, split_sizes = var_8703_split_sizes_0, x = x_241_cast_fp16)[name = string("op_8703_cast_fp16")];
            fp16 const_244_promoted_to_fp16 = const()[name = string("const_244_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_8705_cast_fp16 = mul(x = var_8703_cast_fp16_1, y = const_244_promoted_to_fp16)[name = string("op_8705_cast_fp16")];
            int32 var_8707 = const()[name = string("op_8707"), val = int32(-2)];
            bool var_8708_interleave_0 = const()[name = string("op_8708_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_8708_cast_fp16 = concat(axis = var_8707, interleave = var_8708_interleave_0, values = (var_8705_cast_fp16, var_8703_cast_fp16_0))[name = string("op_8708_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_8709_cast_fp16 = mul(x = var_8708_cast_fp16, y = var_866_cast_fp16)[name = string("op_8709_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_147_cast_fp16 = add(x = var_8702_cast_fp16, y = var_8709_cast_fp16)[name = string("query_states_147_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_8715_cast_fp16 = mul(x = var_8691_cast_fp16, y = var_859_cast_fp16)[name = string("op_8715_cast_fp16")];
            tensor<int32, [2]> var_8716_split_sizes_0 = const()[name = string("op_8716_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8716_axis_0 = const()[name = string("op_8716_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_8716_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_8716_cast_fp16_1 = split(axis = var_8716_axis_0, split_sizes = var_8716_split_sizes_0, x = var_8691_cast_fp16)[name = string("op_8716_cast_fp16")];
            fp16 const_245_promoted_to_fp16 = const()[name = string("const_245_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_8718_cast_fp16 = mul(x = var_8716_cast_fp16_1, y = const_245_promoted_to_fp16)[name = string("op_8718_cast_fp16")];
            int32 var_8720 = const()[name = string("op_8720"), val = int32(-2)];
            bool var_8721_interleave_0 = const()[name = string("op_8721_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_8721_cast_fp16 = concat(axis = var_8720, interleave = var_8721_interleave_0, values = (var_8718_cast_fp16, var_8716_cast_fp16_0))[name = string("op_8721_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_8722_cast_fp16 = mul(x = var_8721_cast_fp16, y = var_866_cast_fp16)[name = string("op_8722_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_245_cast_fp16 = add(x = var_8715_cast_fp16, y = var_8722_cast_fp16)[name = string("key_states_245_cast_fp16")];
            tensor<int32, [1]> expand_dims_288 = const()[name = string("expand_dims_288"), val = tensor<int32, [1]>([24])];
            tensor<int32, [1]> expand_dims_289 = const()[name = string("expand_dims_289"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_291 = const()[name = string("expand_dims_291"), val = tensor<int32, [1]>([0])];
            int32 concat_293_axis_0 = const()[name = string("concat_293_axis_0"), val = int32(0)];
            bool concat_293_interleave_0 = const()[name = string("concat_293_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_293 = concat(axis = concat_293_axis_0, interleave = concat_293_interleave_0, values = (expand_dims_288, expand_dims_289, position_id, expand_dims_291))[name = string("concat_293")];
            tensor<int32, [1]> expand_dims_292 = const()[name = string("expand_dims_292"), val = tensor<int32, [1]>([25])];
            tensor<int32, [1]> concat_294_values1_0 = const()[name = string("concat_294_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_294_values3_0 = const()[name = string("concat_294_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_294_axis_0 = const()[name = string("concat_294_axis_0"), val = int32(0)];
            bool concat_294_interleave_0 = const()[name = string("concat_294_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_294 = concat(axis = concat_294_axis_0, interleave = concat_294_interleave_0, values = (expand_dims_292, concat_294_values1_0, cache_position_end, concat_294_values3_0))[name = string("concat_294")];
            tensor<int32, [4]> key_states_247_perm_0 = const()[name = string("key_states_247_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_25_stride_0 = const()[name = string("key_cache_internal_tensor_assign_25_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_25_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_25_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_25_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_25_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_25_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_25_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_247_cast_fp16 = transpose(perm = key_states_247_perm_0, x = key_states_245_cast_fp16)[name = string("transpose_359")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_25_cast_fp16 = slice_update(begin = concat_293, begin_mask = key_cache_internal_tensor_assign_25_begin_mask_0, end = concat_294, end_mask = key_cache_internal_tensor_assign_25_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_25_squeeze_mask_0, stride = key_cache_internal_tensor_assign_25_stride_0, update = key_states_247_cast_fp16, x = coreml_update_state_270)[name = string("key_cache_internal_tensor_assign_25_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_25_cast_fp16, input = key_cache)[name = string("coreml_update_state_272_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_272 = read_state(input = key_cache)[name = string("coreml_update_state_272")];
            tensor<int32, [4]> value_states_147_perm_0 = const()[name = string("value_states_147_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_25_stride_0 = const()[name = string("value_cache_internal_tensor_assign_25_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_25_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_25_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_25_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_25_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_25_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_25_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_147_cast_fp16 = transpose(perm = value_states_147_perm_0, x = var_8698_cast_fp16)[name = string("transpose_358")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_25_cast_fp16 = slice_update(begin = concat_293, begin_mask = value_cache_internal_tensor_assign_25_begin_mask_0, end = concat_294, end_mask = value_cache_internal_tensor_assign_25_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_25_squeeze_mask_0, stride = value_cache_internal_tensor_assign_25_stride_0, update = value_states_147_cast_fp16, x = coreml_update_state_271)[name = string("value_cache_internal_tensor_assign_25_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_25_cast_fp16, input = value_cache)[name = string("coreml_update_state_273_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_273 = read_state(input = value_cache)[name = string("coreml_update_state_273")];
            tensor<int32, [4]> var_8792_begin_0 = const()[name = string("op_8792_begin_0"), val = tensor<int32, [4]>([24, 0, 0, 0])];
            tensor<int32, [4]> var_8792_end_0 = const()[name = string("op_8792_end_0"), val = tensor<int32, [4]>([25, 2, 2048, 128])];
            tensor<bool, [4]> var_8792_end_mask_0 = const()[name = string("op_8792_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8792_cast_fp16 = slice_by_index(begin = var_8792_begin_0, end = var_8792_end_0, end_mask = var_8792_end_mask_0, x = coreml_update_state_272)[name = string("op_8792_cast_fp16")];
            tensor<int32, [2]> tile_48 = const()[name = string("tile_48"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8795_axis_0 = const()[name = string("op_8795_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8795_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8795_cast_fp16_1 = split(axis = var_8795_axis_0, split_sizes = tile_48, x = var_8792_cast_fp16)[name = string("op_8795_cast_fp16")];
            tensor<int32, [4]> var_8802_begin_0 = const()[name = string("op_8802_begin_0"), val = tensor<int32, [4]>([24, 0, 0, 0])];
            tensor<int32, [4]> var_8802_end_0 = const()[name = string("op_8802_end_0"), val = tensor<int32, [4]>([25, 2, 2048, 128])];
            tensor<bool, [4]> var_8802_end_mask_0 = const()[name = string("op_8802_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8802_cast_fp16 = slice_by_index(begin = var_8802_begin_0, end = var_8802_end_0, end_mask = var_8802_end_mask_0, x = coreml_update_state_273)[name = string("op_8802_cast_fp16")];
            tensor<int32, [2]> tile_49 = const()[name = string("tile_49"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8805_axis_0 = const()[name = string("op_8805_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8805_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8805_cast_fp16_1 = split(axis = var_8805_axis_0, split_sizes = tile_49, x = var_8802_cast_fp16)[name = string("op_8805_cast_fp16")];
            tensor<int32, [2]> var_8808_split_sizes_0 = const()[name = string("op_8808_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_8808_axis_0 = const()[name = string("op_8808_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_8808_0, tensor<fp16, [1, 8, 128, 32]> var_8808_1 = split(axis = var_8808_axis_0, split_sizes = var_8808_split_sizes_0, x = query_states_147_cast_fp16)[name = string("op_8808")];
            bool attn_weights_385_transpose_x_0 = const()[name = string("attn_weights_385_transpose_x_0"), val = bool(false)];
            bool attn_weights_385_transpose_y_0 = const()[name = string("attn_weights_385_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_385_cast_fp16 = matmul(transpose_x = attn_weights_385_transpose_x_0, transpose_y = attn_weights_385_transpose_y_0, x = var_8795_cast_fp16_0, y = var_8808_0)[name = string("attn_weights_385_cast_fp16")];
            fp16 var_8811_to_fp16 = const()[name = string("op_8811_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_387_cast_fp16 = mul(x = attn_weights_385_cast_fp16, y = var_8811_to_fp16)[name = string("attn_weights_387_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_389_cast_fp16 = add(x = attn_weights_387_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_389_cast_fp16")];
            int32 var_8815 = const()[name = string("op_8815"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_391_cast_fp16 = softmax(axis = var_8815, x = attn_weights_389_cast_fp16)[name = string("attn_weights_391_cast_fp16")];
            bool var_8821_transpose_x_1 = const()[name = string("op_8821_transpose_x_1"), val = bool(true)];
            bool var_8821_transpose_y_1 = const()[name = string("op_8821_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_8821_cast_fp16 = matmul(transpose_x = var_8821_transpose_x_1, transpose_y = var_8821_transpose_y_1, x = attn_weights_391_cast_fp16, y = var_8805_cast_fp16_0)[name = string("op_8821_cast_fp16")];
            bool attn_weights_393_transpose_x_0 = const()[name = string("attn_weights_393_transpose_x_0"), val = bool(false)];
            bool attn_weights_393_transpose_y_0 = const()[name = string("attn_weights_393_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_393_cast_fp16 = matmul(transpose_x = attn_weights_393_transpose_x_0, transpose_y = attn_weights_393_transpose_y_0, x = var_8795_cast_fp16_1, y = var_8808_1)[name = string("attn_weights_393_cast_fp16")];
            fp16 var_8823_to_fp16 = const()[name = string("op_8823_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_395_cast_fp16 = mul(x = attn_weights_393_cast_fp16, y = var_8823_to_fp16)[name = string("attn_weights_395_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_397_cast_fp16 = add(x = attn_weights_395_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_397_cast_fp16")];
            int32 var_8827 = const()[name = string("op_8827"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_399_cast_fp16 = softmax(axis = var_8827, x = attn_weights_397_cast_fp16)[name = string("attn_weights_399_cast_fp16")];
            bool attn_output_193_transpose_x_1 = const()[name = string("attn_output_193_transpose_x_1"), val = bool(true)];
            bool attn_output_193_transpose_y_1 = const()[name = string("attn_output_193_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_193_cast_fp16 = matmul(transpose_x = attn_output_193_transpose_x_1, transpose_y = attn_output_193_transpose_y_1, x = attn_weights_399_cast_fp16, y = var_8805_cast_fp16_1)[name = string("attn_output_193_cast_fp16")];
            int32 var_8835 = const()[name = string("op_8835"), val = int32(1)];
            bool attn_output_195_interleave_0 = const()[name = string("attn_output_195_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_195_cast_fp16 = concat(axis = var_8835, interleave = attn_output_195_interleave_0, values = (var_8821_cast_fp16, attn_output_193_cast_fp16))[name = string("attn_output_195_cast_fp16")];
            tensor<int32, [4]> var_8839_perm_0 = const()[name = string("op_8839_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_299x = const()[name = string("concat_299x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_8839_cast_fp16 = transpose(perm = var_8839_perm_0, x = attn_output_195_cast_fp16)[name = string("transpose_357")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_199_cast_fp16 = reshape(shape = concat_299x, x = var_8839_cast_fp16)[name = string("attn_output_199_cast_fp16")];
            tensor<int32, [2]> hidden_states_243_strides_0 = const()[name = string("hidden_states_243_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_243_pad_type_0 = const()[name = string("hidden_states_243_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_243_pad_0 = const()[name = string("hidden_states_243_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_243_dilations_0 = const()[name = string("hidden_states_243_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_243_groups_0 = const()[name = string("hidden_states_243_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_243_cast_fp16 = conv(dilations = hidden_states_243_dilations_0, groups = hidden_states_243_groups_0, pad = hidden_states_243_pad_0, pad_type = hidden_states_243_pad_type_0, strides = hidden_states_243_strides_0, weight = layers_24_self_attn_o_proj_weight_cast_fp16, x = attn_output_199_cast_fp16)[name = string("hidden_states_243_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_245_cast_fp16 = add(x = hidden_states_239_cast_fp16, y = hidden_states_243_cast_fp16)[name = string("hidden_states_245_cast_fp16")];
            fp16 const_250_promoted_to_fp16 = const()[name = string("const_250_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_8872_cast_fp16 = mul(x = hidden_states_245_cast_fp16, y = const_250_promoted_to_fp16)[name = string("op_8872_cast_fp16")];
            int32 var_8870 = const()[name = string("op_8870"), val = int32(1)];
            bool doubled_197_interleave_0 = const()[name = string("doubled_197_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_197_cast_fp16 = concat(axis = var_8870, interleave = doubled_197_interleave_0, values = (hidden_states_245_cast_fp16, var_8872_cast_fp16))[name = string("doubled_197_cast_fp16")];
            tensor<int32, [1]> out_99_axes_0 = const()[name = string("out_99_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_99_gamma_0_to_fp16 = const()[name = string("out_99_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1531982016)))];
            fp16 var_8882_to_fp16 = const()[name = string("op_8882_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_99_cast_fp16 = layer_norm(axes = out_99_axes_0, epsilon = var_8882_to_fp16, gamma = out_99_gamma_0_to_fp16, x = doubled_197_cast_fp16)[name = string("out_99_cast_fp16")];
            tensor<int32, [2]> var_8893_split_sizes_0 = const()[name = string("op_8893_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8893_axis_0 = const()[name = string("op_8893_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_8893_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_8893_cast_fp16_1 = split(axis = var_8893_axis_0, split_sizes = var_8893_split_sizes_0, x = out_99_cast_fp16)[name = string("op_8893_cast_fp16")];
            tensor<int32, [2]> input_49_strides_0 = const()[name = string("input_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_49_pad_type_0 = const()[name = string("input_49_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_49_pad_0 = const()[name = string("input_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_49_dilations_0 = const()[name = string("input_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_49_groups_0 = const()[name = string("input_49_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_49_cast_fp16 = conv(dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = layers_24_mlp_gate_proj_weight_cast_fp16, x = var_8893_cast_fp16_0)[name = string("input_49_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_8910_cast_fp16 = silu(x = input_49_cast_fp16)[name = string("op_8910_cast_fp16")];
            tensor<int32, [2]> var_8916_strides_0 = const()[name = string("op_8916_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_8916_pad_type_0 = const()[name = string("op_8916_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_8916_pad_0 = const()[name = string("op_8916_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_8916_dilations_0 = const()[name = string("op_8916_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8916_groups_0 = const()[name = string("op_8916_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_8916_cast_fp16 = conv(dilations = var_8916_dilations_0, groups = var_8916_groups_0, pad = var_8916_pad_0, pad_type = var_8916_pad_type_0, strides = var_8916_strides_0, weight = layers_24_mlp_up_proj_weight_cast_fp16, x = var_8893_cast_fp16_0)[name = string("op_8916_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_249_cast_fp16 = mul(x = var_8910_cast_fp16, y = var_8916_cast_fp16)[name = string("x_249_cast_fp16")];
            tensor<int32, [2]> hidden_states_247_strides_0 = const()[name = string("hidden_states_247_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_247_pad_type_0 = const()[name = string("hidden_states_247_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_247_pad_0 = const()[name = string("hidden_states_247_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_247_dilations_0 = const()[name = string("hidden_states_247_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_247_groups_0 = const()[name = string("hidden_states_247_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_247_cast_fp16 = conv(dilations = hidden_states_247_dilations_0, groups = hidden_states_247_groups_0, pad = hidden_states_247_pad_0, pad_type = hidden_states_247_pad_type_0, strides = hidden_states_247_strides_0, weight = layers_24_mlp_down_proj_weight_cast_fp16, x = x_249_cast_fp16)[name = string("hidden_states_247_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_249_cast_fp16 = add(x = hidden_states_245_cast_fp16, y = hidden_states_247_cast_fp16)[name = string("hidden_states_249_cast_fp16")];
            fp16 const_252_promoted_to_fp16 = const()[name = string("const_252_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_8934_cast_fp16 = mul(x = hidden_states_249_cast_fp16, y = const_252_promoted_to_fp16)[name = string("op_8934_cast_fp16")];
            int32 var_8932 = const()[name = string("op_8932"), val = int32(1)];
            bool doubled_201_interleave_0 = const()[name = string("doubled_201_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_201_cast_fp16 = concat(axis = var_8932, interleave = doubled_201_interleave_0, values = (hidden_states_249_cast_fp16, var_8934_cast_fp16))[name = string("doubled_201_cast_fp16")];
            tensor<int32, [1]> out_101_axes_0 = const()[name = string("out_101_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_101_gamma_0_to_fp16 = const()[name = string("out_101_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1531990272)))];
            fp16 var_8944_to_fp16 = const()[name = string("op_8944_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_101_cast_fp16 = layer_norm(axes = out_101_axes_0, epsilon = var_8944_to_fp16, gamma = out_101_gamma_0_to_fp16, x = doubled_201_cast_fp16)[name = string("out_101_cast_fp16")];
            tensor<int32, [2]> var_8955_split_sizes_0 = const()[name = string("op_8955_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8955_axis_0 = const()[name = string("op_8955_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_8955_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_8955_cast_fp16_1 = split(axis = var_8955_axis_0, split_sizes = var_8955_split_sizes_0, x = out_101_cast_fp16)[name = string("op_8955_cast_fp16")];
            tensor<int32, [2]> query_states_151_strides_0 = const()[name = string("query_states_151_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_151_pad_type_0 = const()[name = string("query_states_151_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_151_pad_0 = const()[name = string("query_states_151_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_151_dilations_0 = const()[name = string("query_states_151_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_151_groups_0 = const()[name = string("query_states_151_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_151_cast_fp16 = conv(dilations = query_states_151_dilations_0, groups = query_states_151_groups_0, pad = query_states_151_pad_0, pad_type = query_states_151_pad_type_0, strides = query_states_151_strides_0, weight = layers_25_self_attn_q_proj_weight_cast_fp16, x = var_8955_cast_fp16_0)[name = string("query_states_151_cast_fp16")];
            tensor<int32, [2]> key_states_251_strides_0 = const()[name = string("key_states_251_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_251_pad_type_0 = const()[name = string("key_states_251_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_251_pad_0 = const()[name = string("key_states_251_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_251_dilations_0 = const()[name = string("key_states_251_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_251_groups_0 = const()[name = string("key_states_251_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_251_cast_fp16 = conv(dilations = key_states_251_dilations_0, groups = key_states_251_groups_0, pad = key_states_251_pad_0, pad_type = key_states_251_pad_type_0, strides = key_states_251_strides_0, weight = layers_25_self_attn_k_proj_weight_cast_fp16, x = var_8955_cast_fp16_0)[name = string("key_states_251_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_25_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_25_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1531998528)))];
            tensor<int32, [2]> value_states_151_strides_0 = const()[name = string("value_states_151_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_151_pad_type_0 = const()[name = string("value_states_151_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_151_pad_0 = const()[name = string("value_states_151_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_151_dilations_0 = const()[name = string("value_states_151_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_151_groups_0 = const()[name = string("value_states_151_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_151_cast_fp16 = conv(dilations = value_states_151_dilations_0, groups = value_states_151_groups_0, pad = value_states_151_pad_0, pad_type = value_states_151_pad_type_0, strides = value_states_151_strides_0, weight = layers_25_self_attn_v_proj_weight_to_fp16, x = var_8955_cast_fp16_0)[name = string("value_states_151_cast_fp16")];
            tensor<int32, [4]> concat_300x = const()[name = string("concat_300x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_251_cast_fp16 = reshape(shape = concat_300x, x = query_states_151_cast_fp16)[name = string("x_251_cast_fp16")];
            tensor<int32, [4]> concat_301x = const()[name = string("concat_301x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_9012_cast_fp16 = reshape(shape = concat_301x, x = key_states_251_cast_fp16)[name = string("op_9012_cast_fp16")];
            tensor<int32, [4]> concat_302x = const()[name = string("concat_302x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_9019_cast_fp16 = reshape(shape = concat_302x, x = value_states_151_cast_fp16)[name = string("op_9019_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_9023_cast_fp16 = mul(x = x_251_cast_fp16, y = var_859_cast_fp16)[name = string("op_9023_cast_fp16")];
            tensor<int32, [2]> var_9024_split_sizes_0 = const()[name = string("op_9024_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9024_axis_0 = const()[name = string("op_9024_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_9024_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_9024_cast_fp16_1 = split(axis = var_9024_axis_0, split_sizes = var_9024_split_sizes_0, x = x_251_cast_fp16)[name = string("op_9024_cast_fp16")];
            fp16 const_254_promoted_to_fp16 = const()[name = string("const_254_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_9026_cast_fp16 = mul(x = var_9024_cast_fp16_1, y = const_254_promoted_to_fp16)[name = string("op_9026_cast_fp16")];
            int32 var_9028 = const()[name = string("op_9028"), val = int32(-2)];
            bool var_9029_interleave_0 = const()[name = string("op_9029_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_9029_cast_fp16 = concat(axis = var_9028, interleave = var_9029_interleave_0, values = (var_9026_cast_fp16, var_9024_cast_fp16_0))[name = string("op_9029_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_9030_cast_fp16 = mul(x = var_9029_cast_fp16, y = var_866_cast_fp16)[name = string("op_9030_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_153_cast_fp16 = add(x = var_9023_cast_fp16, y = var_9030_cast_fp16)[name = string("query_states_153_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_9036_cast_fp16 = mul(x = var_9012_cast_fp16, y = var_859_cast_fp16)[name = string("op_9036_cast_fp16")];
            tensor<int32, [2]> var_9037_split_sizes_0 = const()[name = string("op_9037_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9037_axis_0 = const()[name = string("op_9037_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_9037_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_9037_cast_fp16_1 = split(axis = var_9037_axis_0, split_sizes = var_9037_split_sizes_0, x = var_9012_cast_fp16)[name = string("op_9037_cast_fp16")];
            fp16 const_255_promoted_to_fp16 = const()[name = string("const_255_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_9039_cast_fp16 = mul(x = var_9037_cast_fp16_1, y = const_255_promoted_to_fp16)[name = string("op_9039_cast_fp16")];
            int32 var_9041 = const()[name = string("op_9041"), val = int32(-2)];
            bool var_9042_interleave_0 = const()[name = string("op_9042_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_9042_cast_fp16 = concat(axis = var_9041, interleave = var_9042_interleave_0, values = (var_9039_cast_fp16, var_9037_cast_fp16_0))[name = string("op_9042_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_9043_cast_fp16 = mul(x = var_9042_cast_fp16, y = var_866_cast_fp16)[name = string("op_9043_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_255_cast_fp16 = add(x = var_9036_cast_fp16, y = var_9043_cast_fp16)[name = string("key_states_255_cast_fp16")];
            tensor<int32, [1]> expand_dims_300 = const()[name = string("expand_dims_300"), val = tensor<int32, [1]>([25])];
            tensor<int32, [1]> expand_dims_301 = const()[name = string("expand_dims_301"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_303 = const()[name = string("expand_dims_303"), val = tensor<int32, [1]>([0])];
            int32 concat_305_axis_0 = const()[name = string("concat_305_axis_0"), val = int32(0)];
            bool concat_305_interleave_0 = const()[name = string("concat_305_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_305 = concat(axis = concat_305_axis_0, interleave = concat_305_interleave_0, values = (expand_dims_300, expand_dims_301, position_id, expand_dims_303))[name = string("concat_305")];
            tensor<int32, [1]> expand_dims_304 = const()[name = string("expand_dims_304"), val = tensor<int32, [1]>([26])];
            tensor<int32, [1]> concat_306_values1_0 = const()[name = string("concat_306_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_306_values3_0 = const()[name = string("concat_306_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_306_axis_0 = const()[name = string("concat_306_axis_0"), val = int32(0)];
            bool concat_306_interleave_0 = const()[name = string("concat_306_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_306 = concat(axis = concat_306_axis_0, interleave = concat_306_interleave_0, values = (expand_dims_304, concat_306_values1_0, cache_position_end, concat_306_values3_0))[name = string("concat_306")];
            tensor<int32, [4]> key_states_257_perm_0 = const()[name = string("key_states_257_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_26_stride_0 = const()[name = string("key_cache_internal_tensor_assign_26_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_26_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_26_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_26_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_26_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_26_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_26_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_257_cast_fp16 = transpose(perm = key_states_257_perm_0, x = key_states_255_cast_fp16)[name = string("transpose_356")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_26_cast_fp16 = slice_update(begin = concat_305, begin_mask = key_cache_internal_tensor_assign_26_begin_mask_0, end = concat_306, end_mask = key_cache_internal_tensor_assign_26_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_26_squeeze_mask_0, stride = key_cache_internal_tensor_assign_26_stride_0, update = key_states_257_cast_fp16, x = coreml_update_state_272)[name = string("key_cache_internal_tensor_assign_26_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_26_cast_fp16, input = key_cache)[name = string("coreml_update_state_274_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_274 = read_state(input = key_cache)[name = string("coreml_update_state_274")];
            tensor<int32, [4]> value_states_153_perm_0 = const()[name = string("value_states_153_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_26_stride_0 = const()[name = string("value_cache_internal_tensor_assign_26_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_26_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_26_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_26_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_26_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_26_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_26_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_153_cast_fp16 = transpose(perm = value_states_153_perm_0, x = var_9019_cast_fp16)[name = string("transpose_355")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_26_cast_fp16 = slice_update(begin = concat_305, begin_mask = value_cache_internal_tensor_assign_26_begin_mask_0, end = concat_306, end_mask = value_cache_internal_tensor_assign_26_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_26_squeeze_mask_0, stride = value_cache_internal_tensor_assign_26_stride_0, update = value_states_153_cast_fp16, x = coreml_update_state_273)[name = string("value_cache_internal_tensor_assign_26_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_26_cast_fp16, input = value_cache)[name = string("coreml_update_state_275_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_275 = read_state(input = value_cache)[name = string("coreml_update_state_275")];
            tensor<int32, [4]> var_9113_begin_0 = const()[name = string("op_9113_begin_0"), val = tensor<int32, [4]>([25, 0, 0, 0])];
            tensor<int32, [4]> var_9113_end_0 = const()[name = string("op_9113_end_0"), val = tensor<int32, [4]>([26, 2, 2048, 128])];
            tensor<bool, [4]> var_9113_end_mask_0 = const()[name = string("op_9113_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9113_cast_fp16 = slice_by_index(begin = var_9113_begin_0, end = var_9113_end_0, end_mask = var_9113_end_mask_0, x = coreml_update_state_274)[name = string("op_9113_cast_fp16")];
            tensor<int32, [2]> tile_50 = const()[name = string("tile_50"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9116_axis_0 = const()[name = string("op_9116_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9116_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9116_cast_fp16_1 = split(axis = var_9116_axis_0, split_sizes = tile_50, x = var_9113_cast_fp16)[name = string("op_9116_cast_fp16")];
            tensor<int32, [4]> var_9123_begin_0 = const()[name = string("op_9123_begin_0"), val = tensor<int32, [4]>([25, 0, 0, 0])];
            tensor<int32, [4]> var_9123_end_0 = const()[name = string("op_9123_end_0"), val = tensor<int32, [4]>([26, 2, 2048, 128])];
            tensor<bool, [4]> var_9123_end_mask_0 = const()[name = string("op_9123_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9123_cast_fp16 = slice_by_index(begin = var_9123_begin_0, end = var_9123_end_0, end_mask = var_9123_end_mask_0, x = coreml_update_state_275)[name = string("op_9123_cast_fp16")];
            tensor<int32, [2]> tile_51 = const()[name = string("tile_51"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9126_axis_0 = const()[name = string("op_9126_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9126_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9126_cast_fp16_1 = split(axis = var_9126_axis_0, split_sizes = tile_51, x = var_9123_cast_fp16)[name = string("op_9126_cast_fp16")];
            tensor<int32, [2]> var_9129_split_sizes_0 = const()[name = string("op_9129_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_9129_axis_0 = const()[name = string("op_9129_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_9129_0, tensor<fp16, [1, 8, 128, 32]> var_9129_1 = split(axis = var_9129_axis_0, split_sizes = var_9129_split_sizes_0, x = query_states_153_cast_fp16)[name = string("op_9129")];
            bool attn_weights_401_transpose_x_0 = const()[name = string("attn_weights_401_transpose_x_0"), val = bool(false)];
            bool attn_weights_401_transpose_y_0 = const()[name = string("attn_weights_401_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_401_cast_fp16 = matmul(transpose_x = attn_weights_401_transpose_x_0, transpose_y = attn_weights_401_transpose_y_0, x = var_9116_cast_fp16_0, y = var_9129_0)[name = string("attn_weights_401_cast_fp16")];
            fp16 var_9132_to_fp16 = const()[name = string("op_9132_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_403_cast_fp16 = mul(x = attn_weights_401_cast_fp16, y = var_9132_to_fp16)[name = string("attn_weights_403_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_405_cast_fp16 = add(x = attn_weights_403_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_405_cast_fp16")];
            int32 var_9136 = const()[name = string("op_9136"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_407_cast_fp16 = softmax(axis = var_9136, x = attn_weights_405_cast_fp16)[name = string("attn_weights_407_cast_fp16")];
            bool var_9142_transpose_x_1 = const()[name = string("op_9142_transpose_x_1"), val = bool(true)];
            bool var_9142_transpose_y_1 = const()[name = string("op_9142_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_9142_cast_fp16 = matmul(transpose_x = var_9142_transpose_x_1, transpose_y = var_9142_transpose_y_1, x = attn_weights_407_cast_fp16, y = var_9126_cast_fp16_0)[name = string("op_9142_cast_fp16")];
            bool attn_weights_409_transpose_x_0 = const()[name = string("attn_weights_409_transpose_x_0"), val = bool(false)];
            bool attn_weights_409_transpose_y_0 = const()[name = string("attn_weights_409_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_409_cast_fp16 = matmul(transpose_x = attn_weights_409_transpose_x_0, transpose_y = attn_weights_409_transpose_y_0, x = var_9116_cast_fp16_1, y = var_9129_1)[name = string("attn_weights_409_cast_fp16")];
            fp16 var_9144_to_fp16 = const()[name = string("op_9144_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_411_cast_fp16 = mul(x = attn_weights_409_cast_fp16, y = var_9144_to_fp16)[name = string("attn_weights_411_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_413_cast_fp16 = add(x = attn_weights_411_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_413_cast_fp16")];
            int32 var_9148 = const()[name = string("op_9148"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_415_cast_fp16 = softmax(axis = var_9148, x = attn_weights_413_cast_fp16)[name = string("attn_weights_415_cast_fp16")];
            bool attn_output_201_transpose_x_1 = const()[name = string("attn_output_201_transpose_x_1"), val = bool(true)];
            bool attn_output_201_transpose_y_1 = const()[name = string("attn_output_201_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_201_cast_fp16 = matmul(transpose_x = attn_output_201_transpose_x_1, transpose_y = attn_output_201_transpose_y_1, x = attn_weights_415_cast_fp16, y = var_9126_cast_fp16_1)[name = string("attn_output_201_cast_fp16")];
            int32 var_9156 = const()[name = string("op_9156"), val = int32(1)];
            bool attn_output_203_interleave_0 = const()[name = string("attn_output_203_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_203_cast_fp16 = concat(axis = var_9156, interleave = attn_output_203_interleave_0, values = (var_9142_cast_fp16, attn_output_201_cast_fp16))[name = string("attn_output_203_cast_fp16")];
            tensor<int32, [4]> var_9160_perm_0 = const()[name = string("op_9160_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_311x = const()[name = string("concat_311x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_9160_cast_fp16 = transpose(perm = var_9160_perm_0, x = attn_output_203_cast_fp16)[name = string("transpose_354")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_207_cast_fp16 = reshape(shape = concat_311x, x = var_9160_cast_fp16)[name = string("attn_output_207_cast_fp16")];
            tensor<int32, [2]> hidden_states_253_strides_0 = const()[name = string("hidden_states_253_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_253_pad_type_0 = const()[name = string("hidden_states_253_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_253_pad_0 = const()[name = string("hidden_states_253_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_253_dilations_0 = const()[name = string("hidden_states_253_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_253_groups_0 = const()[name = string("hidden_states_253_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_253_cast_fp16 = conv(dilations = hidden_states_253_dilations_0, groups = hidden_states_253_groups_0, pad = hidden_states_253_pad_0, pad_type = hidden_states_253_pad_type_0, strides = hidden_states_253_strides_0, weight = layers_25_self_attn_o_proj_weight_cast_fp16, x = attn_output_207_cast_fp16)[name = string("hidden_states_253_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_255_cast_fp16 = add(x = hidden_states_249_cast_fp16, y = hidden_states_253_cast_fp16)[name = string("hidden_states_255_cast_fp16")];
            fp16 const_260_promoted_to_fp16 = const()[name = string("const_260_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_9193_cast_fp16 = mul(x = hidden_states_255_cast_fp16, y = const_260_promoted_to_fp16)[name = string("op_9193_cast_fp16")];
            int32 var_9191 = const()[name = string("op_9191"), val = int32(1)];
            bool doubled_205_interleave_0 = const()[name = string("doubled_205_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_205_cast_fp16 = concat(axis = var_9191, interleave = doubled_205_interleave_0, values = (hidden_states_255_cast_fp16, var_9193_cast_fp16))[name = string("doubled_205_cast_fp16")];
            tensor<int32, [1]> out_103_axes_0 = const()[name = string("out_103_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_103_gamma_0_to_fp16 = const()[name = string("out_103_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1533047168)))];
            fp16 var_9203_to_fp16 = const()[name = string("op_9203_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_103_cast_fp16 = layer_norm(axes = out_103_axes_0, epsilon = var_9203_to_fp16, gamma = out_103_gamma_0_to_fp16, x = doubled_205_cast_fp16)[name = string("out_103_cast_fp16")];
            tensor<int32, [2]> var_9214_split_sizes_0 = const()[name = string("op_9214_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9214_axis_0 = const()[name = string("op_9214_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_9214_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_9214_cast_fp16_1 = split(axis = var_9214_axis_0, split_sizes = var_9214_split_sizes_0, x = out_103_cast_fp16)[name = string("op_9214_cast_fp16")];
            tensor<int32, [2]> input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_51_cast_fp16 = conv(dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = layers_25_mlp_gate_proj_weight_cast_fp16, x = var_9214_cast_fp16_0)[name = string("input_51_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_9231_cast_fp16 = silu(x = input_51_cast_fp16)[name = string("op_9231_cast_fp16")];
            tensor<int32, [2]> var_9237_strides_0 = const()[name = string("op_9237_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_9237_pad_type_0 = const()[name = string("op_9237_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_9237_pad_0 = const()[name = string("op_9237_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_9237_dilations_0 = const()[name = string("op_9237_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9237_groups_0 = const()[name = string("op_9237_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_9237_cast_fp16 = conv(dilations = var_9237_dilations_0, groups = var_9237_groups_0, pad = var_9237_pad_0, pad_type = var_9237_pad_type_0, strides = var_9237_strides_0, weight = layers_25_mlp_up_proj_weight_cast_fp16, x = var_9214_cast_fp16_0)[name = string("op_9237_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_259_cast_fp16 = mul(x = var_9231_cast_fp16, y = var_9237_cast_fp16)[name = string("x_259_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_25_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_25_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1533055424)))];
            tensor<int32, [2]> hidden_states_257_strides_0 = const()[name = string("hidden_states_257_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_257_pad_type_0 = const()[name = string("hidden_states_257_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_257_pad_0 = const()[name = string("hidden_states_257_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_257_dilations_0 = const()[name = string("hidden_states_257_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_257_groups_0 = const()[name = string("hidden_states_257_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_257_cast_fp16 = conv(dilations = hidden_states_257_dilations_0, groups = hidden_states_257_groups_0, pad = hidden_states_257_pad_0, pad_type = hidden_states_257_pad_type_0, strides = hidden_states_257_strides_0, weight = layers_25_mlp_down_proj_weight_to_fp16, x = x_259_cast_fp16)[name = string("hidden_states_257_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_259_cast_fp16 = add(x = hidden_states_255_cast_fp16, y = hidden_states_257_cast_fp16)[name = string("hidden_states_259_cast_fp16")];
            fp16 const_262_promoted_to_fp16 = const()[name = string("const_262_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_9255_cast_fp16 = mul(x = hidden_states_259_cast_fp16, y = const_262_promoted_to_fp16)[name = string("op_9255_cast_fp16")];
            int32 var_9253 = const()[name = string("op_9253"), val = int32(1)];
            bool doubled_209_interleave_0 = const()[name = string("doubled_209_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_209_cast_fp16 = concat(axis = var_9253, interleave = doubled_209_interleave_0, values = (hidden_states_259_cast_fp16, var_9255_cast_fp16))[name = string("doubled_209_cast_fp16")];
            tensor<int32, [1]> out_105_axes_0 = const()[name = string("out_105_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_105_gamma_0_to_fp16 = const()[name = string("out_105_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1558221312)))];
            fp16 var_9265_to_fp16 = const()[name = string("op_9265_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_105_cast_fp16 = layer_norm(axes = out_105_axes_0, epsilon = var_9265_to_fp16, gamma = out_105_gamma_0_to_fp16, x = doubled_209_cast_fp16)[name = string("out_105_cast_fp16")];
            tensor<int32, [2]> var_9276_split_sizes_0 = const()[name = string("op_9276_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9276_axis_0 = const()[name = string("op_9276_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_9276_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_9276_cast_fp16_1 = split(axis = var_9276_axis_0, split_sizes = var_9276_split_sizes_0, x = out_105_cast_fp16)[name = string("op_9276_cast_fp16")];
            tensor<int32, [2]> query_states_157_strides_0 = const()[name = string("query_states_157_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_157_pad_type_0 = const()[name = string("query_states_157_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_157_pad_0 = const()[name = string("query_states_157_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_157_dilations_0 = const()[name = string("query_states_157_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_157_groups_0 = const()[name = string("query_states_157_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_157_cast_fp16 = conv(dilations = query_states_157_dilations_0, groups = query_states_157_groups_0, pad = query_states_157_pad_0, pad_type = query_states_157_pad_type_0, strides = query_states_157_strides_0, weight = layers_26_self_attn_q_proj_weight_cast_fp16, x = var_9276_cast_fp16_0)[name = string("query_states_157_cast_fp16")];
            tensor<int32, [2]> key_states_261_strides_0 = const()[name = string("key_states_261_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_261_pad_type_0 = const()[name = string("key_states_261_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_261_pad_0 = const()[name = string("key_states_261_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_261_dilations_0 = const()[name = string("key_states_261_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_261_groups_0 = const()[name = string("key_states_261_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_261_cast_fp16 = conv(dilations = key_states_261_dilations_0, groups = key_states_261_groups_0, pad = key_states_261_pad_0, pad_type = key_states_261_pad_type_0, strides = key_states_261_strides_0, weight = layers_26_self_attn_k_proj_weight_cast_fp16, x = var_9276_cast_fp16_0)[name = string("key_states_261_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_26_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_26_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1558229568)))];
            tensor<int32, [2]> value_states_157_strides_0 = const()[name = string("value_states_157_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_157_pad_type_0 = const()[name = string("value_states_157_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_157_pad_0 = const()[name = string("value_states_157_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_157_dilations_0 = const()[name = string("value_states_157_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_157_groups_0 = const()[name = string("value_states_157_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_157_cast_fp16 = conv(dilations = value_states_157_dilations_0, groups = value_states_157_groups_0, pad = value_states_157_pad_0, pad_type = value_states_157_pad_type_0, strides = value_states_157_strides_0, weight = layers_26_self_attn_v_proj_weight_to_fp16, x = var_9276_cast_fp16_0)[name = string("value_states_157_cast_fp16")];
            tensor<int32, [4]> concat_312x = const()[name = string("concat_312x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_261_cast_fp16 = reshape(shape = concat_312x, x = query_states_157_cast_fp16)[name = string("x_261_cast_fp16")];
            tensor<int32, [4]> concat_313x = const()[name = string("concat_313x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_9333_cast_fp16 = reshape(shape = concat_313x, x = key_states_261_cast_fp16)[name = string("op_9333_cast_fp16")];
            tensor<int32, [4]> concat_314x = const()[name = string("concat_314x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_9340_cast_fp16 = reshape(shape = concat_314x, x = value_states_157_cast_fp16)[name = string("op_9340_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_9344_cast_fp16 = mul(x = x_261_cast_fp16, y = var_859_cast_fp16)[name = string("op_9344_cast_fp16")];
            tensor<int32, [2]> var_9345_split_sizes_0 = const()[name = string("op_9345_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9345_axis_0 = const()[name = string("op_9345_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_9345_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_9345_cast_fp16_1 = split(axis = var_9345_axis_0, split_sizes = var_9345_split_sizes_0, x = x_261_cast_fp16)[name = string("op_9345_cast_fp16")];
            fp16 const_264_promoted_to_fp16 = const()[name = string("const_264_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_9347_cast_fp16 = mul(x = var_9345_cast_fp16_1, y = const_264_promoted_to_fp16)[name = string("op_9347_cast_fp16")];
            int32 var_9349 = const()[name = string("op_9349"), val = int32(-2)];
            bool var_9350_interleave_0 = const()[name = string("op_9350_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_9350_cast_fp16 = concat(axis = var_9349, interleave = var_9350_interleave_0, values = (var_9347_cast_fp16, var_9345_cast_fp16_0))[name = string("op_9350_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_9351_cast_fp16 = mul(x = var_9350_cast_fp16, y = var_866_cast_fp16)[name = string("op_9351_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_159_cast_fp16 = add(x = var_9344_cast_fp16, y = var_9351_cast_fp16)[name = string("query_states_159_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_9357_cast_fp16 = mul(x = var_9333_cast_fp16, y = var_859_cast_fp16)[name = string("op_9357_cast_fp16")];
            tensor<int32, [2]> var_9358_split_sizes_0 = const()[name = string("op_9358_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9358_axis_0 = const()[name = string("op_9358_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_9358_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_9358_cast_fp16_1 = split(axis = var_9358_axis_0, split_sizes = var_9358_split_sizes_0, x = var_9333_cast_fp16)[name = string("op_9358_cast_fp16")];
            fp16 const_265_promoted_to_fp16 = const()[name = string("const_265_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_9360_cast_fp16 = mul(x = var_9358_cast_fp16_1, y = const_265_promoted_to_fp16)[name = string("op_9360_cast_fp16")];
            int32 var_9362 = const()[name = string("op_9362"), val = int32(-2)];
            bool var_9363_interleave_0 = const()[name = string("op_9363_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_9363_cast_fp16 = concat(axis = var_9362, interleave = var_9363_interleave_0, values = (var_9360_cast_fp16, var_9358_cast_fp16_0))[name = string("op_9363_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_9364_cast_fp16 = mul(x = var_9363_cast_fp16, y = var_866_cast_fp16)[name = string("op_9364_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_265_cast_fp16 = add(x = var_9357_cast_fp16, y = var_9364_cast_fp16)[name = string("key_states_265_cast_fp16")];
            tensor<int32, [1]> expand_dims_312 = const()[name = string("expand_dims_312"), val = tensor<int32, [1]>([26])];
            tensor<int32, [1]> expand_dims_313 = const()[name = string("expand_dims_313"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_315 = const()[name = string("expand_dims_315"), val = tensor<int32, [1]>([0])];
            int32 concat_317_axis_0 = const()[name = string("concat_317_axis_0"), val = int32(0)];
            bool concat_317_interleave_0 = const()[name = string("concat_317_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_317 = concat(axis = concat_317_axis_0, interleave = concat_317_interleave_0, values = (expand_dims_312, expand_dims_313, position_id, expand_dims_315))[name = string("concat_317")];
            tensor<int32, [1]> expand_dims_316 = const()[name = string("expand_dims_316"), val = tensor<int32, [1]>([27])];
            tensor<int32, [1]> concat_318_values1_0 = const()[name = string("concat_318_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_318_values3_0 = const()[name = string("concat_318_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_318_axis_0 = const()[name = string("concat_318_axis_0"), val = int32(0)];
            bool concat_318_interleave_0 = const()[name = string("concat_318_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_318 = concat(axis = concat_318_axis_0, interleave = concat_318_interleave_0, values = (expand_dims_316, concat_318_values1_0, cache_position_end, concat_318_values3_0))[name = string("concat_318")];
            tensor<int32, [4]> key_states_267_perm_0 = const()[name = string("key_states_267_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_27_stride_0 = const()[name = string("key_cache_internal_tensor_assign_27_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_27_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_27_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_27_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_27_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_27_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_27_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_267_cast_fp16 = transpose(perm = key_states_267_perm_0, x = key_states_265_cast_fp16)[name = string("transpose_353")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_27_cast_fp16 = slice_update(begin = concat_317, begin_mask = key_cache_internal_tensor_assign_27_begin_mask_0, end = concat_318, end_mask = key_cache_internal_tensor_assign_27_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_27_squeeze_mask_0, stride = key_cache_internal_tensor_assign_27_stride_0, update = key_states_267_cast_fp16, x = coreml_update_state_274)[name = string("key_cache_internal_tensor_assign_27_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_27_cast_fp16, input = key_cache)[name = string("coreml_update_state_276_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_276 = read_state(input = key_cache)[name = string("coreml_update_state_276")];
            tensor<int32, [4]> value_states_159_perm_0 = const()[name = string("value_states_159_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_27_stride_0 = const()[name = string("value_cache_internal_tensor_assign_27_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_27_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_27_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_27_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_27_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_27_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_27_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_159_cast_fp16 = transpose(perm = value_states_159_perm_0, x = var_9340_cast_fp16)[name = string("transpose_352")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_27_cast_fp16 = slice_update(begin = concat_317, begin_mask = value_cache_internal_tensor_assign_27_begin_mask_0, end = concat_318, end_mask = value_cache_internal_tensor_assign_27_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_27_squeeze_mask_0, stride = value_cache_internal_tensor_assign_27_stride_0, update = value_states_159_cast_fp16, x = coreml_update_state_275)[name = string("value_cache_internal_tensor_assign_27_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_27_cast_fp16, input = value_cache)[name = string("coreml_update_state_277_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_277 = read_state(input = value_cache)[name = string("coreml_update_state_277")];
            tensor<int32, [4]> var_9434_begin_0 = const()[name = string("op_9434_begin_0"), val = tensor<int32, [4]>([26, 0, 0, 0])];
            tensor<int32, [4]> var_9434_end_0 = const()[name = string("op_9434_end_0"), val = tensor<int32, [4]>([27, 2, 2048, 128])];
            tensor<bool, [4]> var_9434_end_mask_0 = const()[name = string("op_9434_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9434_cast_fp16 = slice_by_index(begin = var_9434_begin_0, end = var_9434_end_0, end_mask = var_9434_end_mask_0, x = coreml_update_state_276)[name = string("op_9434_cast_fp16")];
            tensor<int32, [2]> tile_52 = const()[name = string("tile_52"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9437_axis_0 = const()[name = string("op_9437_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9437_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9437_cast_fp16_1 = split(axis = var_9437_axis_0, split_sizes = tile_52, x = var_9434_cast_fp16)[name = string("op_9437_cast_fp16")];
            tensor<int32, [4]> var_9444_begin_0 = const()[name = string("op_9444_begin_0"), val = tensor<int32, [4]>([26, 0, 0, 0])];
            tensor<int32, [4]> var_9444_end_0 = const()[name = string("op_9444_end_0"), val = tensor<int32, [4]>([27, 2, 2048, 128])];
            tensor<bool, [4]> var_9444_end_mask_0 = const()[name = string("op_9444_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9444_cast_fp16 = slice_by_index(begin = var_9444_begin_0, end = var_9444_end_0, end_mask = var_9444_end_mask_0, x = coreml_update_state_277)[name = string("op_9444_cast_fp16")];
            tensor<int32, [2]> tile_53 = const()[name = string("tile_53"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9447_axis_0 = const()[name = string("op_9447_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9447_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9447_cast_fp16_1 = split(axis = var_9447_axis_0, split_sizes = tile_53, x = var_9444_cast_fp16)[name = string("op_9447_cast_fp16")];
            tensor<int32, [2]> var_9450_split_sizes_0 = const()[name = string("op_9450_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_9450_axis_0 = const()[name = string("op_9450_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_9450_0, tensor<fp16, [1, 8, 128, 32]> var_9450_1 = split(axis = var_9450_axis_0, split_sizes = var_9450_split_sizes_0, x = query_states_159_cast_fp16)[name = string("op_9450")];
            bool attn_weights_417_transpose_x_0 = const()[name = string("attn_weights_417_transpose_x_0"), val = bool(false)];
            bool attn_weights_417_transpose_y_0 = const()[name = string("attn_weights_417_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_417_cast_fp16 = matmul(transpose_x = attn_weights_417_transpose_x_0, transpose_y = attn_weights_417_transpose_y_0, x = var_9437_cast_fp16_0, y = var_9450_0)[name = string("attn_weights_417_cast_fp16")];
            fp16 var_9453_to_fp16 = const()[name = string("op_9453_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_419_cast_fp16 = mul(x = attn_weights_417_cast_fp16, y = var_9453_to_fp16)[name = string("attn_weights_419_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_421_cast_fp16 = add(x = attn_weights_419_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_421_cast_fp16")];
            int32 var_9457 = const()[name = string("op_9457"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_423_cast_fp16 = softmax(axis = var_9457, x = attn_weights_421_cast_fp16)[name = string("attn_weights_423_cast_fp16")];
            bool var_9463_transpose_x_1 = const()[name = string("op_9463_transpose_x_1"), val = bool(true)];
            bool var_9463_transpose_y_1 = const()[name = string("op_9463_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_9463_cast_fp16 = matmul(transpose_x = var_9463_transpose_x_1, transpose_y = var_9463_transpose_y_1, x = attn_weights_423_cast_fp16, y = var_9447_cast_fp16_0)[name = string("op_9463_cast_fp16")];
            bool attn_weights_425_transpose_x_0 = const()[name = string("attn_weights_425_transpose_x_0"), val = bool(false)];
            bool attn_weights_425_transpose_y_0 = const()[name = string("attn_weights_425_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_425_cast_fp16 = matmul(transpose_x = attn_weights_425_transpose_x_0, transpose_y = attn_weights_425_transpose_y_0, x = var_9437_cast_fp16_1, y = var_9450_1)[name = string("attn_weights_425_cast_fp16")];
            fp16 var_9465_to_fp16 = const()[name = string("op_9465_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_427_cast_fp16 = mul(x = attn_weights_425_cast_fp16, y = var_9465_to_fp16)[name = string("attn_weights_427_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_429_cast_fp16 = add(x = attn_weights_427_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_429_cast_fp16")];
            int32 var_9469 = const()[name = string("op_9469"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_431_cast_fp16 = softmax(axis = var_9469, x = attn_weights_429_cast_fp16)[name = string("attn_weights_431_cast_fp16")];
            bool attn_output_209_transpose_x_1 = const()[name = string("attn_output_209_transpose_x_1"), val = bool(true)];
            bool attn_output_209_transpose_y_1 = const()[name = string("attn_output_209_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_209_cast_fp16 = matmul(transpose_x = attn_output_209_transpose_x_1, transpose_y = attn_output_209_transpose_y_1, x = attn_weights_431_cast_fp16, y = var_9447_cast_fp16_1)[name = string("attn_output_209_cast_fp16")];
            int32 var_9477 = const()[name = string("op_9477"), val = int32(1)];
            bool attn_output_211_interleave_0 = const()[name = string("attn_output_211_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_211_cast_fp16 = concat(axis = var_9477, interleave = attn_output_211_interleave_0, values = (var_9463_cast_fp16, attn_output_209_cast_fp16))[name = string("attn_output_211_cast_fp16")];
            tensor<int32, [4]> var_9481_perm_0 = const()[name = string("op_9481_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_323x = const()[name = string("concat_323x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_9481_cast_fp16 = transpose(perm = var_9481_perm_0, x = attn_output_211_cast_fp16)[name = string("transpose_351")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_215_cast_fp16 = reshape(shape = concat_323x, x = var_9481_cast_fp16)[name = string("attn_output_215_cast_fp16")];
            tensor<int32, [2]> hidden_states_263_strides_0 = const()[name = string("hidden_states_263_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_263_pad_type_0 = const()[name = string("hidden_states_263_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_263_pad_0 = const()[name = string("hidden_states_263_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_263_dilations_0 = const()[name = string("hidden_states_263_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_263_groups_0 = const()[name = string("hidden_states_263_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_263_cast_fp16 = conv(dilations = hidden_states_263_dilations_0, groups = hidden_states_263_groups_0, pad = hidden_states_263_pad_0, pad_type = hidden_states_263_pad_type_0, strides = hidden_states_263_strides_0, weight = layers_26_self_attn_o_proj_weight_cast_fp16, x = attn_output_215_cast_fp16)[name = string("hidden_states_263_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_265_cast_fp16 = add(x = hidden_states_259_cast_fp16, y = hidden_states_263_cast_fp16)[name = string("hidden_states_265_cast_fp16")];
            fp16 const_270_promoted_to_fp16 = const()[name = string("const_270_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_9514_cast_fp16 = mul(x = hidden_states_265_cast_fp16, y = const_270_promoted_to_fp16)[name = string("op_9514_cast_fp16")];
            int32 var_9512 = const()[name = string("op_9512"), val = int32(1)];
            bool doubled_213_interleave_0 = const()[name = string("doubled_213_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_213_cast_fp16 = concat(axis = var_9512, interleave = doubled_213_interleave_0, values = (hidden_states_265_cast_fp16, var_9514_cast_fp16))[name = string("doubled_213_cast_fp16")];
            tensor<int32, [1]> out_107_axes_0 = const()[name = string("out_107_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_107_gamma_0_to_fp16 = const()[name = string("out_107_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1559278208)))];
            fp16 var_9524_to_fp16 = const()[name = string("op_9524_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_107_cast_fp16 = layer_norm(axes = out_107_axes_0, epsilon = var_9524_to_fp16, gamma = out_107_gamma_0_to_fp16, x = doubled_213_cast_fp16)[name = string("out_107_cast_fp16")];
            tensor<int32, [2]> var_9535_split_sizes_0 = const()[name = string("op_9535_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9535_axis_0 = const()[name = string("op_9535_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_9535_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_9535_cast_fp16_1 = split(axis = var_9535_axis_0, split_sizes = var_9535_split_sizes_0, x = out_107_cast_fp16)[name = string("op_9535_cast_fp16")];
            tensor<int32, [2]> input_53_strides_0 = const()[name = string("input_53_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_53_pad_type_0 = const()[name = string("input_53_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_53_dilations_0 = const()[name = string("input_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_53_groups_0 = const()[name = string("input_53_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_53_cast_fp16 = conv(dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = layers_26_mlp_gate_proj_weight_cast_fp16, x = var_9535_cast_fp16_0)[name = string("input_53_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_9552_cast_fp16 = silu(x = input_53_cast_fp16)[name = string("op_9552_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_26_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_26_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1559286464)))];
            tensor<int32, [2]> var_9558_strides_0 = const()[name = string("op_9558_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_9558_pad_type_0 = const()[name = string("op_9558_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_9558_pad_0 = const()[name = string("op_9558_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_9558_dilations_0 = const()[name = string("op_9558_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9558_groups_0 = const()[name = string("op_9558_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_9558_cast_fp16 = conv(dilations = var_9558_dilations_0, groups = var_9558_groups_0, pad = var_9558_pad_0, pad_type = var_9558_pad_type_0, strides = var_9558_strides_0, weight = layers_26_mlp_up_proj_weight_to_fp16, x = var_9535_cast_fp16_0)[name = string("op_9558_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_269_cast_fp16 = mul(x = var_9552_cast_fp16, y = var_9558_cast_fp16)[name = string("x_269_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_26_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_26_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1584452352)))];
            tensor<int32, [2]> hidden_states_267_strides_0 = const()[name = string("hidden_states_267_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_267_pad_type_0 = const()[name = string("hidden_states_267_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_267_pad_0 = const()[name = string("hidden_states_267_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_267_dilations_0 = const()[name = string("hidden_states_267_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_267_groups_0 = const()[name = string("hidden_states_267_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_267_cast_fp16 = conv(dilations = hidden_states_267_dilations_0, groups = hidden_states_267_groups_0, pad = hidden_states_267_pad_0, pad_type = hidden_states_267_pad_type_0, strides = hidden_states_267_strides_0, weight = layers_26_mlp_down_proj_weight_to_fp16, x = x_269_cast_fp16)[name = string("hidden_states_267_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_269_cast_fp16 = add(x = hidden_states_265_cast_fp16, y = hidden_states_267_cast_fp16)[name = string("hidden_states_269_cast_fp16")];
            fp16 const_272_promoted_to_fp16 = const()[name = string("const_272_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_9576_cast_fp16 = mul(x = hidden_states_269_cast_fp16, y = const_272_promoted_to_fp16)[name = string("op_9576_cast_fp16")];
            int32 var_9574 = const()[name = string("op_9574"), val = int32(1)];
            bool doubled_217_interleave_0 = const()[name = string("doubled_217_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_217_cast_fp16 = concat(axis = var_9574, interleave = doubled_217_interleave_0, values = (hidden_states_269_cast_fp16, var_9576_cast_fp16))[name = string("doubled_217_cast_fp16")];
            tensor<int32, [1]> out_109_axes_0 = const()[name = string("out_109_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_109_gamma_0_to_fp16 = const()[name = string("out_109_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1609618240)))];
            fp16 var_9586_to_fp16 = const()[name = string("op_9586_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_109_cast_fp16 = layer_norm(axes = out_109_axes_0, epsilon = var_9586_to_fp16, gamma = out_109_gamma_0_to_fp16, x = doubled_217_cast_fp16)[name = string("out_109_cast_fp16")];
            tensor<int32, [2]> var_9597_split_sizes_0 = const()[name = string("op_9597_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9597_axis_0 = const()[name = string("op_9597_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_9597_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_9597_cast_fp16_1 = split(axis = var_9597_axis_0, split_sizes = var_9597_split_sizes_0, x = out_109_cast_fp16)[name = string("op_9597_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_27_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1609626496)))];
            tensor<int32, [2]> query_states_163_strides_0 = const()[name = string("query_states_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_163_pad_type_0 = const()[name = string("query_states_163_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_163_pad_0 = const()[name = string("query_states_163_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_163_dilations_0 = const()[name = string("query_states_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_163_groups_0 = const()[name = string("query_states_163_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> query_states_163_cast_fp16 = conv(dilations = query_states_163_dilations_0, groups = query_states_163_groups_0, pad = query_states_163_pad_0, pad_type = query_states_163_pad_type_0, strides = query_states_163_strides_0, weight = layers_27_self_attn_q_proj_weight_to_fp16, x = var_9597_cast_fp16_0)[name = string("query_states_163_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_27_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1618015168)))];
            tensor<int32, [2]> key_states_271_strides_0 = const()[name = string("key_states_271_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_271_pad_type_0 = const()[name = string("key_states_271_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_271_pad_0 = const()[name = string("key_states_271_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_271_dilations_0 = const()[name = string("key_states_271_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_271_groups_0 = const()[name = string("key_states_271_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> key_states_271_cast_fp16 = conv(dilations = key_states_271_dilations_0, groups = key_states_271_groups_0, pad = key_states_271_pad_0, pad_type = key_states_271_pad_type_0, strides = key_states_271_strides_0, weight = layers_27_self_attn_k_proj_weight_to_fp16, x = var_9597_cast_fp16_0)[name = string("key_states_271_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_27_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1619063808)))];
            tensor<int32, [2]> value_states_163_strides_0 = const()[name = string("value_states_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_163_pad_type_0 = const()[name = string("value_states_163_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_163_pad_0 = const()[name = string("value_states_163_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_163_dilations_0 = const()[name = string("value_states_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_163_groups_0 = const()[name = string("value_states_163_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 32]> value_states_163_cast_fp16 = conv(dilations = value_states_163_dilations_0, groups = value_states_163_groups_0, pad = value_states_163_pad_0, pad_type = value_states_163_pad_type_0, strides = value_states_163_strides_0, weight = layers_27_self_attn_v_proj_weight_to_fp16, x = var_9597_cast_fp16_0)[name = string("value_states_163_cast_fp16")];
            tensor<int32, [4]> concat_324x = const()[name = string("concat_324x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 32]> x_271_cast_fp16 = reshape(shape = concat_324x, x = query_states_163_cast_fp16)[name = string("x_271_cast_fp16")];
            tensor<int32, [4]> concat_325x = const()[name = string("concat_325x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_9654_cast_fp16 = reshape(shape = concat_325x, x = key_states_271_cast_fp16)[name = string("op_9654_cast_fp16")];
            tensor<int32, [4]> concat_326x = const()[name = string("concat_326x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 32]> var_9661_cast_fp16 = reshape(shape = concat_326x, x = value_states_163_cast_fp16)[name = string("op_9661_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_9665_cast_fp16 = mul(x = x_271_cast_fp16, y = var_859_cast_fp16)[name = string("op_9665_cast_fp16")];
            tensor<int32, [2]> var_9666_split_sizes_0 = const()[name = string("op_9666_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9666_axis_0 = const()[name = string("op_9666_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 32]> var_9666_cast_fp16_0, tensor<fp16, [1, 16, 64, 32]> var_9666_cast_fp16_1 = split(axis = var_9666_axis_0, split_sizes = var_9666_split_sizes_0, x = x_271_cast_fp16)[name = string("op_9666_cast_fp16")];
            fp16 const_274_promoted_to_fp16 = const()[name = string("const_274_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 32]> var_9668_cast_fp16 = mul(x = var_9666_cast_fp16_1, y = const_274_promoted_to_fp16)[name = string("op_9668_cast_fp16")];
            int32 var_9670 = const()[name = string("op_9670"), val = int32(-2)];
            bool var_9671_interleave_0 = const()[name = string("op_9671_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 32]> var_9671_cast_fp16 = concat(axis = var_9670, interleave = var_9671_interleave_0, values = (var_9668_cast_fp16, var_9666_cast_fp16_0))[name = string("op_9671_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> var_9672_cast_fp16 = mul(x = var_9671_cast_fp16, y = var_866_cast_fp16)[name = string("op_9672_cast_fp16")];
            tensor<fp16, [1, 16, 128, 32]> query_states_165_cast_fp16 = add(x = var_9665_cast_fp16, y = var_9672_cast_fp16)[name = string("query_states_165_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_9678_cast_fp16 = mul(x = var_9654_cast_fp16, y = var_859_cast_fp16)[name = string("op_9678_cast_fp16")];
            tensor<int32, [2]> var_9679_split_sizes_0 = const()[name = string("op_9679_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9679_axis_0 = const()[name = string("op_9679_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 32]> var_9679_cast_fp16_0, tensor<fp16, [1, 2, 64, 32]> var_9679_cast_fp16_1 = split(axis = var_9679_axis_0, split_sizes = var_9679_split_sizes_0, x = var_9654_cast_fp16)[name = string("op_9679_cast_fp16")];
            fp16 const_275_promoted_to_fp16 = const()[name = string("const_275_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 32]> var_9681_cast_fp16 = mul(x = var_9679_cast_fp16_1, y = const_275_promoted_to_fp16)[name = string("op_9681_cast_fp16")];
            int32 var_9683 = const()[name = string("op_9683"), val = int32(-2)];
            bool var_9684_interleave_0 = const()[name = string("op_9684_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 32]> var_9684_cast_fp16 = concat(axis = var_9683, interleave = var_9684_interleave_0, values = (var_9681_cast_fp16, var_9679_cast_fp16_0))[name = string("op_9684_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> var_9685_cast_fp16 = mul(x = var_9684_cast_fp16, y = var_866_cast_fp16)[name = string("op_9685_cast_fp16")];
            tensor<fp16, [1, 2, 128, 32]> key_states_275_cast_fp16 = add(x = var_9678_cast_fp16, y = var_9685_cast_fp16)[name = string("key_states_275_cast_fp16")];
            tensor<int32, [1]> expand_dims_324 = const()[name = string("expand_dims_324"), val = tensor<int32, [1]>([27])];
            tensor<int32, [1]> expand_dims_325 = const()[name = string("expand_dims_325"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_327 = const()[name = string("expand_dims_327"), val = tensor<int32, [1]>([0])];
            int32 concat_329_axis_0 = const()[name = string("concat_329_axis_0"), val = int32(0)];
            bool concat_329_interleave_0 = const()[name = string("concat_329_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_329 = concat(axis = concat_329_axis_0, interleave = concat_329_interleave_0, values = (expand_dims_324, expand_dims_325, position_id, expand_dims_327))[name = string("concat_329")];
            tensor<int32, [1]> expand_dims_328 = const()[name = string("expand_dims_328"), val = tensor<int32, [1]>([28])];
            tensor<int32, [1]> concat_330_values1_0 = const()[name = string("concat_330_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_330_values3_0 = const()[name = string("concat_330_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_330_axis_0 = const()[name = string("concat_330_axis_0"), val = int32(0)];
            bool concat_330_interleave_0 = const()[name = string("concat_330_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_330 = concat(axis = concat_330_axis_0, interleave = concat_330_interleave_0, values = (expand_dims_328, concat_330_values1_0, cache_position_end, concat_330_values3_0))[name = string("concat_330")];
            tensor<int32, [4]> key_states_277_perm_0 = const()[name = string("key_states_277_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_28_stride_0 = const()[name = string("key_cache_internal_tensor_assign_28_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_28_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_28_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_28_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_28_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_28_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_28_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> key_states_277_cast_fp16 = transpose(perm = key_states_277_perm_0, x = key_states_275_cast_fp16)[name = string("transpose_350")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_28_cast_fp16 = slice_update(begin = concat_329, begin_mask = key_cache_internal_tensor_assign_28_begin_mask_0, end = concat_330, end_mask = key_cache_internal_tensor_assign_28_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_28_squeeze_mask_0, stride = key_cache_internal_tensor_assign_28_stride_0, update = key_states_277_cast_fp16, x = coreml_update_state_276)[name = string("key_cache_internal_tensor_assign_28_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_28_cast_fp16, input = key_cache)[name = string("coreml_update_state_278_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_278 = read_state(input = key_cache)[name = string("coreml_update_state_278")];
            tensor<int32, [4]> value_states_165_perm_0 = const()[name = string("value_states_165_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_28_stride_0 = const()[name = string("value_cache_internal_tensor_assign_28_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_28_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_28_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_28_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_28_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_28_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_28_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 32, 128]> value_states_165_cast_fp16 = transpose(perm = value_states_165_perm_0, x = var_9661_cast_fp16)[name = string("transpose_349")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_28_cast_fp16 = slice_update(begin = concat_329, begin_mask = value_cache_internal_tensor_assign_28_begin_mask_0, end = concat_330, end_mask = value_cache_internal_tensor_assign_28_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_28_squeeze_mask_0, stride = value_cache_internal_tensor_assign_28_stride_0, update = value_states_165_cast_fp16, x = coreml_update_state_277)[name = string("value_cache_internal_tensor_assign_28_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_28_cast_fp16, input = value_cache)[name = string("coreml_update_state_279_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_279 = read_state(input = value_cache)[name = string("coreml_update_state_279")];
            tensor<int32, [4]> var_9755_begin_0 = const()[name = string("op_9755_begin_0"), val = tensor<int32, [4]>([27, 0, 0, 0])];
            tensor<int32, [4]> var_9755_end_0 = const()[name = string("op_9755_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_9755_end_mask_0 = const()[name = string("op_9755_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9755_cast_fp16 = slice_by_index(begin = var_9755_begin_0, end = var_9755_end_0, end_mask = var_9755_end_mask_0, x = coreml_update_state_278)[name = string("op_9755_cast_fp16")];
            tensor<int32, [2]> tile_54 = const()[name = string("tile_54"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9758_axis_0 = const()[name = string("op_9758_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9758_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9758_cast_fp16_1 = split(axis = var_9758_axis_0, split_sizes = tile_54, x = var_9755_cast_fp16)[name = string("op_9758_cast_fp16")];
            tensor<int32, [4]> var_9765_begin_0 = const()[name = string("op_9765_begin_0"), val = tensor<int32, [4]>([27, 0, 0, 0])];
            tensor<int32, [4]> var_9765_end_0 = const()[name = string("op_9765_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_9765_end_mask_0 = const()[name = string("op_9765_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9765_cast_fp16 = slice_by_index(begin = var_9765_begin_0, end = var_9765_end_0, end_mask = var_9765_end_mask_0, x = coreml_update_state_279)[name = string("op_9765_cast_fp16")];
            tensor<int32, [2]> tile_55 = const()[name = string("tile_55"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9768_axis_0 = const()[name = string("op_9768_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9768_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9768_cast_fp16_1 = split(axis = var_9768_axis_0, split_sizes = tile_55, x = var_9765_cast_fp16)[name = string("op_9768_cast_fp16")];
            tensor<int32, [2]> var_9771_split_sizes_0 = const()[name = string("op_9771_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_9771_axis_0 = const()[name = string("op_9771_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 32]> var_9771_0, tensor<fp16, [1, 8, 128, 32]> var_9771_1 = split(axis = var_9771_axis_0, split_sizes = var_9771_split_sizes_0, x = query_states_165_cast_fp16)[name = string("op_9771")];
            bool attn_weights_433_transpose_x_0 = const()[name = string("attn_weights_433_transpose_x_0"), val = bool(false)];
            bool attn_weights_433_transpose_y_0 = const()[name = string("attn_weights_433_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_433_cast_fp16 = matmul(transpose_x = attn_weights_433_transpose_x_0, transpose_y = attn_weights_433_transpose_y_0, x = var_9758_cast_fp16_0, y = var_9771_0)[name = string("attn_weights_433_cast_fp16")];
            fp16 var_9774_to_fp16 = const()[name = string("op_9774_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_435_cast_fp16 = mul(x = attn_weights_433_cast_fp16, y = var_9774_to_fp16)[name = string("attn_weights_435_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_437_cast_fp16 = add(x = attn_weights_435_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_437_cast_fp16")];
            int32 var_9778 = const()[name = string("op_9778"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_439_cast_fp16 = softmax(axis = var_9778, x = attn_weights_437_cast_fp16)[name = string("attn_weights_439_cast_fp16")];
            bool var_9784_transpose_x_1 = const()[name = string("op_9784_transpose_x_1"), val = bool(true)];
            bool var_9784_transpose_y_1 = const()[name = string("op_9784_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> var_9784_cast_fp16 = matmul(transpose_x = var_9784_transpose_x_1, transpose_y = var_9784_transpose_y_1, x = attn_weights_439_cast_fp16, y = var_9768_cast_fp16_0)[name = string("op_9784_cast_fp16")];
            bool attn_weights_441_transpose_x_0 = const()[name = string("attn_weights_441_transpose_x_0"), val = bool(false)];
            bool attn_weights_441_transpose_y_0 = const()[name = string("attn_weights_441_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_441_cast_fp16 = matmul(transpose_x = attn_weights_441_transpose_x_0, transpose_y = attn_weights_441_transpose_y_0, x = var_9758_cast_fp16_1, y = var_9771_1)[name = string("attn_weights_441_cast_fp16")];
            fp16 var_9786_to_fp16 = const()[name = string("op_9786_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_443_cast_fp16 = mul(x = attn_weights_441_cast_fp16, y = var_9786_to_fp16)[name = string("attn_weights_443_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_445_cast_fp16 = add(x = attn_weights_443_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_445_cast_fp16")];
            int32 var_9790 = const()[name = string("op_9790"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 32]> attn_weights_cast_fp16 = softmax(axis = var_9790, x = attn_weights_445_cast_fp16)[name = string("attn_weights_cast_fp16")];
            bool attn_output_217_transpose_x_1 = const()[name = string("attn_output_217_transpose_x_1"), val = bool(true)];
            bool attn_output_217_transpose_y_1 = const()[name = string("attn_output_217_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 32, 128]> attn_output_217_cast_fp16 = matmul(transpose_x = attn_output_217_transpose_x_1, transpose_y = attn_output_217_transpose_y_1, x = attn_weights_cast_fp16, y = var_9768_cast_fp16_1)[name = string("attn_output_217_cast_fp16")];
            int32 var_9798 = const()[name = string("op_9798"), val = int32(1)];
            bool attn_output_219_interleave_0 = const()[name = string("attn_output_219_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 32, 128]> attn_output_219_cast_fp16 = concat(axis = var_9798, interleave = attn_output_219_interleave_0, values = (var_9784_cast_fp16, attn_output_217_cast_fp16))[name = string("attn_output_219_cast_fp16")];
            tensor<int32, [4]> var_9802_perm_0 = const()[name = string("op_9802_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_335x = const()[name = string("concat_335x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 32]> var_9802_cast_fp16 = transpose(perm = var_9802_perm_0, x = attn_output_219_cast_fp16)[name = string("transpose_348")];
            tensor<fp16, [1, 2048, 1, 32]> attn_output_cast_fp16 = reshape(shape = concat_335x, x = var_9802_cast_fp16)[name = string("attn_output_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_27_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1620112448)))];
            tensor<int32, [2]> hidden_states_273_strides_0 = const()[name = string("hidden_states_273_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_273_pad_type_0 = const()[name = string("hidden_states_273_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_273_pad_0 = const()[name = string("hidden_states_273_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_273_dilations_0 = const()[name = string("hidden_states_273_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_273_groups_0 = const()[name = string("hidden_states_273_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_273_cast_fp16 = conv(dilations = hidden_states_273_dilations_0, groups = hidden_states_273_groups_0, pad = hidden_states_273_pad_0, pad_type = hidden_states_273_pad_type_0, strides = hidden_states_273_strides_0, weight = layers_27_self_attn_o_proj_weight_to_fp16, x = attn_output_cast_fp16)[name = string("hidden_states_273_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_275_cast_fp16 = add(x = hidden_states_269_cast_fp16, y = hidden_states_273_cast_fp16)[name = string("hidden_states_275_cast_fp16")];
            fp16 const_280_promoted_to_fp16 = const()[name = string("const_280_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_9835_cast_fp16 = mul(x = hidden_states_275_cast_fp16, y = const_280_promoted_to_fp16)[name = string("op_9835_cast_fp16")];
            int32 var_9833 = const()[name = string("op_9833"), val = int32(1)];
            bool doubled_221_interleave_0 = const()[name = string("doubled_221_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_221_cast_fp16 = concat(axis = var_9833, interleave = doubled_221_interleave_0, values = (hidden_states_275_cast_fp16, var_9835_cast_fp16))[name = string("doubled_221_cast_fp16")];
            tensor<int32, [1]> out_111_axes_0 = const()[name = string("out_111_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_111_gamma_0_to_fp16 = const()[name = string("out_111_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1628501120)))];
            fp16 var_9845_to_fp16 = const()[name = string("op_9845_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_111_cast_fp16 = layer_norm(axes = out_111_axes_0, epsilon = var_9845_to_fp16, gamma = out_111_gamma_0_to_fp16, x = doubled_221_cast_fp16)[name = string("out_111_cast_fp16")];
            tensor<int32, [2]> var_9856_split_sizes_0 = const()[name = string("op_9856_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9856_axis_0 = const()[name = string("op_9856_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> var_9856_cast_fp16_0, tensor<fp16, [1, 2048, 1, 32]> var_9856_cast_fp16_1 = split(axis = var_9856_axis_0, split_sizes = var_9856_split_sizes_0, x = out_111_cast_fp16)[name = string("op_9856_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_27_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_27_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1628509376)))];
            tensor<int32, [2]> input_strides_0 = const()[name = string("input_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_pad_type_0 = const()[name = string("input_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_pad_0 = const()[name = string("input_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_dilations_0 = const()[name = string("input_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_groups_0 = const()[name = string("input_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> input_cast_fp16 = conv(dilations = input_dilations_0, groups = input_groups_0, pad = input_pad_0, pad_type = input_pad_type_0, strides = input_strides_0, weight = layers_27_mlp_gate_proj_weight_to_fp16, x = var_9856_cast_fp16_0)[name = string("input_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> var_9873_cast_fp16 = silu(x = input_cast_fp16)[name = string("op_9873_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_27_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_27_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1653675264)))];
            tensor<int32, [2]> var_9879_strides_0 = const()[name = string("op_9879_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_9879_pad_type_0 = const()[name = string("op_9879_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_9879_pad_0 = const()[name = string("op_9879_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_9879_dilations_0 = const()[name = string("op_9879_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9879_groups_0 = const()[name = string("op_9879_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 32]> var_9879_cast_fp16 = conv(dilations = var_9879_dilations_0, groups = var_9879_groups_0, pad = var_9879_pad_0, pad_type = var_9879_pad_type_0, strides = var_9879_strides_0, weight = layers_27_mlp_up_proj_weight_to_fp16, x = var_9856_cast_fp16_0)[name = string("op_9879_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 32]> x_cast_fp16 = mul(x = var_9873_cast_fp16, y = var_9879_cast_fp16)[name = string("x_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_27_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_27_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1678841152)))];
            tensor<int32, [2]> hidden_states_277_strides_0 = const()[name = string("hidden_states_277_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_277_pad_type_0 = const()[name = string("hidden_states_277_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_277_pad_0 = const()[name = string("hidden_states_277_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_277_dilations_0 = const()[name = string("hidden_states_277_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_277_groups_0 = const()[name = string("hidden_states_277_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_277_cast_fp16 = conv(dilations = hidden_states_277_dilations_0, groups = hidden_states_277_groups_0, pad = hidden_states_277_pad_0, pad_type = hidden_states_277_pad_type_0, strides = hidden_states_277_strides_0, weight = layers_27_mlp_down_proj_weight_to_fp16, x = x_cast_fp16)[name = string("hidden_states_277_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states_cast_fp16 = add(x = hidden_states_275_cast_fp16, y = hidden_states_277_cast_fp16)[name = string("hidden_states_cast_fp16")];
            fp16 const_282_promoted_to_fp16 = const()[name = string("const_282_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 32]> var_9897_cast_fp16 = mul(x = hidden_states_cast_fp16, y = const_282_promoted_to_fp16)[name = string("op_9897_cast_fp16")];
            int32 var_9895 = const()[name = string("op_9895"), val = int32(1)];
            bool doubled_225_interleave_0 = const()[name = string("doubled_225_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 32]> doubled_225_cast_fp16 = concat(axis = var_9895, interleave = doubled_225_interleave_0, values = (hidden_states_cast_fp16, var_9897_cast_fp16))[name = string("doubled_225_cast_fp16")];
            tensor<int32, [1]> out_axes_0 = const()[name = string("out_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_gamma_0_to_fp16 = const()[name = string("out_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1704007040)))];
            fp16 var_9907_to_fp16 = const()[name = string("op_9907_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 32]> out_cast_fp16 = layer_norm(axes = out_axes_0, epsilon = var_9907_to_fp16, gamma = out_gamma_0_to_fp16, x = doubled_225_cast_fp16)[name = string("out_cast_fp16")];
            tensor<int32, [2]> var_9918_split_sizes_0 = const()[name = string("op_9918_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9918_axis_0 = const()[name = string("op_9918_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 32]> hidden_states, tensor<fp16, [1, 2048, 1, 32]> var_9918_cast_fp16_1 = split(axis = var_9918_axis_0, split_sizes = var_9918_split_sizes_0, x = out_cast_fp16)[name = string("op_9918_cast_fp16")];
        } -> (hidden_states);
    func length_64<ios18>(tensor<fp32, [1, 2048, 1, 64]> inputs_embeds, state<tensor<fp16, [28, 2, 2048, 128]>> key_cache, tensor<int32, [1]> position_id, tensor<int32, [64]> position_index_seed, state<tensor<fp16, [28, 2, 2048, 128]>> value_cache) {
            tensor<fp16, [256, 2048, 1, 1]> layers_1_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524992))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524416))))[name = string("layers_1_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_1_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(525312))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13120640))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13108288))))[name = string("layers_1_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_2_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13126848))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13651776))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13651200))))[name = string("layers_2_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_2_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13652096))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26247424))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26235072))))[name = string("layers_2_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_3_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26253632))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26778560))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26777984))))[name = string("layers_3_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_3_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26778880))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30977408))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30973248))))[name = string("layers_3_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_3_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30979520))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43566656))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43562496))))[name = string("layers_3_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_4_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43568768))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44093696))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44093120))))[name = string("layers_4_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_4_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44094016))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48292544))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48288384))))[name = string("layers_4_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_4_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48294656))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60889984))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60877632))))[name = string("layers_4_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_4_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60896192))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73491520))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73479168))))[name = string("layers_4_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_4_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73497728))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86084864))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86080704))))[name = string("layers_4_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_5_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86086976))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86611904))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86611328))))[name = string("layers_5_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_5_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86612224))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90810752))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90806592))))[name = string("layers_5_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_5_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90812864))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103408192))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103395840))))[name = string("layers_5_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_5_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103414400))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116001536))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115997376))))[name = string("layers_5_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_6_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116003648))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116528576))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116528000))))[name = string("layers_6_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_6_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116528896))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120727424))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120723264))))[name = string("layers_6_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_6_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120729536))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133324864))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133312512))))[name = string("layers_6_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_6_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133331072))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145926400))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145914048))))[name = string("layers_6_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_6_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145932608))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158519744))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158515584))))[name = string("layers_6_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_7_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158521856))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159046784))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159046208))))[name = string("layers_7_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_7_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159047104))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163245632))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163241472))))[name = string("layers_7_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_7_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163247744))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175843072))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175830720))))[name = string("layers_7_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_8_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175849280))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176374208))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176373632))))[name = string("layers_8_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_8_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176374528))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180573056))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180568896))))[name = string("layers_8_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_8_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180575168))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193170496))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193158144))))[name = string("layers_8_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_8_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193176704))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205772032))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205759680))))[name = string("layers_8_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_8_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205778240))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218365376))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218361216))))[name = string("layers_8_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_9_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218367488))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218892416))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218891840))))[name = string("layers_9_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_9_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218892736))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223091264))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223087104))))[name = string("layers_9_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_9_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223093376))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235688704))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235676352))))[name = string("layers_9_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_9_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235694912))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248290240))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248277888))))[name = string("layers_9_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_9_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248296448))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260883584))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260879424))))[name = string("layers_9_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_10_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260885696))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261410624))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261410048))))[name = string("layers_10_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_10_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261410944))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265609472))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265605312))))[name = string("layers_10_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_10_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265611584))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278206912))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278194560))))[name = string("layers_10_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_10_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278213120))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290808448))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290796096))))[name = string("layers_10_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_10_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290814656))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303401792))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303397632))))[name = string("layers_10_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_11_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303403904))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307602432))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307598272))))[name = string("layers_11_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_11_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307604544))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308129472))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308128896))))[name = string("layers_11_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_11_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308129792))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308654720))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308654144))))[name = string("layers_11_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_11_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308655040))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312853568))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312849408))))[name = string("layers_11_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_11_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312855680))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325451008))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325438656))))[name = string("layers_11_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_11_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325457216))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338052544))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338040192))))[name = string("layers_11_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_11_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338058752))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350645888))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350641728))))[name = string("layers_11_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_12_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350648000))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354846528))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354842368))))[name = string("layers_12_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_12_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354848640))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355373568))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355372992))))[name = string("layers_12_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_12_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355373888))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355898816))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355898240))))[name = string("layers_12_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_12_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355899136))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360097664))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360093504))))[name = string("layers_12_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_12_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360099776))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372695104))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372682752))))[name = string("layers_12_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_12_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372701312))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385296640))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385284288))))[name = string("layers_12_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_12_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385302848))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397889984))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397885824))))[name = string("layers_12_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_13_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397892096))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402090624))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402086464))))[name = string("layers_13_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_13_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402092736))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402617664))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402617088))))[name = string("layers_13_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_13_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402617984))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403142912))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403142336))))[name = string("layers_13_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_13_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403143232))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407341760))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407337600))))[name = string("layers_13_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_13_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407343872))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419939200))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419926848))))[name = string("layers_13_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_13_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419945408))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432532544))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432528384))))[name = string("layers_13_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_14_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432534656))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436733184))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436729024))))[name = string("layers_14_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_14_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436735296))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437260224))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437259648))))[name = string("layers_14_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_14_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437260544))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441459072))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441454912))))[name = string("layers_14_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_14_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441461184))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454056512))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454044160))))[name = string("layers_14_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_14_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454062720))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(466658048))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(466645696))))[name = string("layers_14_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_14_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(466664256))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(479251392))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(479247232))))[name = string("layers_14_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_15_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(479253504))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483452032))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483447872))))[name = string("layers_15_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_15_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483454144))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483979072))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483978496))))[name = string("layers_15_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_15_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483979392))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484504320))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484503744))))[name = string("layers_15_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_15_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484504640))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488703168))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488699008))))[name = string("layers_15_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_15_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488705280))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(501300608))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(501288256))))[name = string("layers_15_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_15_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(501306816))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513902144))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513889792))))[name = string("layers_15_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_15_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513908352))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526495488))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526491328))))[name = string("layers_15_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_16_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526497600))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530696128))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530691968))))[name = string("layers_16_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_16_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530698240))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531223168))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531222592))))[name = string("layers_16_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_16_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531223488))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531748416))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531747840))))[name = string("layers_16_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_16_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531748736))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535947264))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535943104))))[name = string("layers_16_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_16_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535949376))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(548536512))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(548532352))))[name = string("layers_16_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_17_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(548538624))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552737152))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552732992))))[name = string("layers_17_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_17_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552739264))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553264192))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553263616))))[name = string("layers_17_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_17_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553264512))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553789440))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553788864))))[name = string("layers_17_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_17_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553789760))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557988288))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557984128))))[name = string("layers_17_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_17_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557990400))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570585728))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570573376))))[name = string("layers_17_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_17_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570591936))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583187264))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583174912))))[name = string("layers_17_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_17_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583193472))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(595780608))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(595776448))))[name = string("layers_17_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_18_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(595782720))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(599981248))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(599977088))))[name = string("layers_18_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_18_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(599983360))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(600508288))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(600507712))))[name = string("layers_18_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_18_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(600508608))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(601033536))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(601032960))))[name = string("layers_18_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_18_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(601033856))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(605232384))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(605228224))))[name = string("layers_18_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_18_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(605234496))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(617829824))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(617817472))))[name = string("layers_18_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_18_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(617836032))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(630431360))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(630419008))))[name = string("layers_18_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_18_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(630437568))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(643024704))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(643020544))))[name = string("layers_18_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_19_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(643026816))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647225344))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647221184))))[name = string("layers_19_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_19_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647227456))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647752384))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647751808))))[name = string("layers_19_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_19_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647752704))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(660348032))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(660335680))))[name = string("layers_19_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_19_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(660354240))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(672949568))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(672937216))))[name = string("layers_19_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_19_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(672955776))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(685542912))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(685538752))))[name = string("layers_19_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_20_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(685545024))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(689743552))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(689739392))))[name = string("layers_20_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_20_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(689745664))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(690270592))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(690270016))))[name = string("layers_20_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_20_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(690270912))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(694469440))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(694465280))))[name = string("layers_20_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_20_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(694471552))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(707066880))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(707054528))))[name = string("layers_20_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_20_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(707073088))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(719660224))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(719656064))))[name = string("layers_20_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_21_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(719662336))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(723860864))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(723856704))))[name = string("layers_21_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_21_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(723862976))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724387904))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724387328))))[name = string("layers_21_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_21_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724388224))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(728586752))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(728582592))))[name = string("layers_21_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_21_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(728588864))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(741184192))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(741171840))))[name = string("layers_21_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_21_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(741190400))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(753785728))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(753773376))))[name = string("layers_21_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_21_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(753791936))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(766379072))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(766374912))))[name = string("layers_21_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_22_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(766381184))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(770579712))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(770575552))))[name = string("layers_22_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_22_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(770581824))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(771106752))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(771106176))))[name = string("layers_22_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_22_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(771107072))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783702400))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783690048))))[name = string("layers_22_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_22_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783708608))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(796303936))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(796291584))))[name = string("layers_22_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_22_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(796310144))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808897280))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808893120))))[name = string("layers_22_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_23_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808899392))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813097920))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813093760))))[name = string("layers_23_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_23_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813100032))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813624960))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813624384))))[name = string("layers_23_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_23_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813625280))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(817823808))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(817819648))))[name = string("layers_23_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_23_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(817825920))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(830421248))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(830408896))))[name = string("layers_23_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_23_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(830427456))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(843022784))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(843010432))))[name = string("layers_23_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_23_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(843028992))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855616128))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855611968))))[name = string("layers_23_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_24_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855618240))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(859816768))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(859812608))))[name = string("layers_24_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_24_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(859818880))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(860343808))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(860343232))))[name = string("layers_24_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_24_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(860344128))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(864542656))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(864538496))))[name = string("layers_24_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_24_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(864544768))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(877140096))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(877127744))))[name = string("layers_24_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_24_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(877146304))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889741632))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889729280))))[name = string("layers_24_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_24_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889747840))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(902334976))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(902330816))))[name = string("layers_24_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_25_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(902337088))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(906535616))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(906531456))))[name = string("layers_25_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_25_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(906537728))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(907062656))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(907062080))))[name = string("layers_25_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_25_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(907062976))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(911261504))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(911257344))))[name = string("layers_25_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_25_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(911263616))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(923858944))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(923846592))))[name = string("layers_25_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_25_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(923865152))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936460480))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936448128))))[name = string("layers_25_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_26_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936466688))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(940665216))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(940661056))))[name = string("layers_26_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_26_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(940667328))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(941192256))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(941191680))))[name = string("layers_26_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_26_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(941192576))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(945391104))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(945386944))))[name = string("layers_26_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_26_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(945393216))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(957988544))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(957976192))))[name = string("layers_26_mlp_gate_proj_weight_cast_fp16")];
            int32 gather_0_cast_uint16_to_int32 = const()[name = string("gather_0_cast_uint16_to_int32"), val = int32(64)];
            tensor<int32, [1]> cache_position_end = add(x = position_id, y = gather_0_cast_uint16_to_int32)[name = string("cache_position_end")];
            fp16 fill_like_0_value_0_to_fp16 = const()[name = string("fill_like_0_value_0_to_fp16"), val = fp16(0x1p+0)];
            tensor<fp16, [64]> fill_like_0_cast_fp16 = fill_like(ref_tensor = position_index_seed, value = fill_like_0_value_0_to_fp16)[name = string("fill_like_0_cast_fp16")];
            int32 var_831 = const()[name = string("op_831"), val = int32(0)];
            bool var_833_exclusive_0 = const()[name = string("op_833_exclusive_0"), val = bool(false)];
            bool var_833_reverse_0 = const()[name = string("op_833_reverse_0"), val = bool(false)];
            tensor<fp16, [64]> var_833_cast_fp16 = cumsum(axis = var_831, exclusive = var_833_exclusive_0, reverse = var_833_reverse_0, x = fill_like_0_cast_fp16)[name = string("op_833_cast_fp16")];
            fp16 var_835_promoted_to_fp16 = const()[name = string("op_835_promoted_to_fp16"), val = fp16(0x1p+0)];
            tensor<fp16, [64]> position_offsets_cast_fp16 = sub(x = var_833_cast_fp16, y = var_835_promoted_to_fp16)[name = string("position_offsets_cast_fp16")];
            tensor<int32, [1]> var_838_axes_0 = const()[name = string("op_838_axes_0"), val = tensor<int32, [1]>([0])];
            tensor<fp16, [1, 64]> var_838_cast_fp16 = expand_dims(axes = var_838_axes_0, x = position_offsets_cast_fp16)[name = string("op_838_cast_fp16")];
            string position_id_promoted_to_fp16_dtype_0 = const()[name = string("position_id_promoted_to_fp16_dtype_0"), val = string("fp16")];
            tensor<fp16, [1]> position_id_to_fp16 = cast(dtype = position_id_promoted_to_fp16_dtype_0, x = position_id)[name = string("cast_23")];
            tensor<fp16, [1, 64]> position_ids_1_cast_fp16 = add(x = var_838_cast_fp16, y = position_id_to_fp16)[name = string("position_ids_1_cast_fp16")];
            string position_ids_dtype_0 = const()[name = string("position_ids_dtype_0"), val = string("int32")];
            int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)];
            tensor<int32, [1, 64]> position_ids_1_cast_fp16_to_int32 = cast(dtype = position_ids_dtype_0, x = position_ids_1_cast_fp16)[name = string("cast_22")];
            tensor<bool, [1, 64]> greater_equal_0 = greater_equal(x = position_ids_1_cast_fp16_to_int32, y = greater_equal_0_y_0)[name = string("greater_equal_0")];
            int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(32768)];
            tensor<int32, [1, 64]> add_0 = add(x = position_ids_1_cast_fp16_to_int32, y = slice_by_index_0)[name = string("add_0")];
            tensor<int32, [1, 64]> select_0 = select(a = position_ids_1_cast_fp16_to_int32, b = add_0, cond = greater_equal_0)[name = string("select_0")];
            tensor<fp16, [32768, 128]> rope_emb_cos_cached_to_fp16 = const()[name = string("rope_emb_cos_cached_to_fp16"), val = tensor<fp16, [32768, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(957994752)))];
            int32 cos_1_batch_dims_0 = const()[name = string("cos_1_batch_dims_0"), val = int32(0)];
            bool cos_1_validate_indices_0 = const()[name = string("cos_1_validate_indices_0"), val = bool(false)];
            int32 greater_equal_10_y_0 = const()[name = string("greater_equal_10_y_0"), val = int32(0)];
            tensor<bool, [1, 64]> greater_equal_10 = greater_equal(x = select_0, y = greater_equal_10_y_0)[name = string("greater_equal_10")];
            int32 slice_by_index_10 = const()[name = string("slice_by_index_10"), val = int32(32768)];
            tensor<int32, [1, 64]> add_10 = add(x = select_0, y = slice_by_index_10)[name = string("add_10")];
            tensor<int32, [1, 64]> select_10 = select(a = select_0, b = add_10, cond = greater_equal_10)[name = string("select_10")];
            int32 cos_1_cast_fp16_axis_5 = const()[name = string("cos_1_cast_fp16_axis_5"), val = int32(0)];
            tensor<fp16, [1, 64, 128]> cos_1_cast_fp16 = gather(axis = cos_1_cast_fp16_axis_5, batch_dims = cos_1_batch_dims_0, indices = select_10, validate_indices = cos_1_validate_indices_0, x = rope_emb_cos_cached_to_fp16)[name = string("cos_1_cast_fp16")];
            tensor<fp16, [32768, 128]> rope_emb_sin_cached_to_fp16 = const()[name = string("rope_emb_sin_cached_to_fp16"), val = tensor<fp16, [32768, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(966383424)))];
            int32 sin_1_batch_dims_0 = const()[name = string("sin_1_batch_dims_0"), val = int32(0)];
            bool sin_1_validate_indices_0 = const()[name = string("sin_1_validate_indices_0"), val = bool(false)];
            int32 sin_1_cast_fp16_axis_5 = const()[name = string("sin_1_cast_fp16_axis_5"), val = int32(0)];
            tensor<fp16, [1, 64, 128]> sin_1_cast_fp16 = gather(axis = sin_1_cast_fp16_axis_5, batch_dims = sin_1_batch_dims_0, indices = select_10, validate_indices = sin_1_validate_indices_0, x = rope_emb_sin_cached_to_fp16)[name = string("sin_1_cast_fp16")];
            tensor<int32, [3]> var_857_perm_0 = const()[name = string("op_857_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<int32, [1]> var_859_axes_0 = const()[name = string("op_859_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 128, 64]> var_857_cast_fp16 = transpose(perm = var_857_perm_0, x = cos_1_cast_fp16)[name = string("transpose_521")];
            tensor<fp16, [1, 1, 128, 64]> var_859_cast_fp16 = expand_dims(axes = var_859_axes_0, x = var_857_cast_fp16)[name = string("op_859_cast_fp16")];
            tensor<int32, [3]> var_864_perm_0 = const()[name = string("op_864_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<int32, [1]> var_866_axes_0 = const()[name = string("op_866_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 128, 64]> var_864_cast_fp16 = transpose(perm = var_864_perm_0, x = sin_1_cast_fp16)[name = string("transpose_520")];
            tensor<fp16, [1, 1, 128, 64]> var_866_cast_fp16 = expand_dims(axes = var_866_axes_0, x = var_864_cast_fp16)[name = string("op_866_cast_fp16")];
            tensor<int32, [1]> var_885_axes_0 = const()[name = string("op_885_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1, 64, 1]> var_885 = expand_dims(axes = var_885_axes_0, x = position_ids_1_cast_fp16_to_int32)[name = string("op_885")];
            tensor<int32, [1, 1, 2048]> var_878 = const()[name = string("op_878"), val = tensor<int32, [1, 1, 2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(974772096)))];
            tensor<bool, [1, 64, 2048]> var_886 = greater(x = var_878, y = var_885)[name = string("op_886")];
            string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")];
            tensor<int32, [1]> var_893_axes_0 = const()[name = string("op_893_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 64, 2048]> var_886_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = var_886)[name = string("cast_21")];
            tensor<fp16, [1, 1, 64, 2048]> var_893_cast_fp16 = expand_dims(axes = var_893_axes_0, x = var_886_to_fp16)[name = string("op_893_cast_fp16")];
            tensor<int32, [4]> mask_perm_0 = const()[name = string("mask_perm_0"), val = tensor<int32, [4]>([0, 1, -1, -2])];
            fp16 var_897_promoted_to_fp16 = const()[name = string("op_897_promoted_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1, 2048, 64]> mask_cast_fp16 = transpose(perm = mask_perm_0, x = var_893_cast_fp16)[name = string("transpose_519")];
            tensor<bool, [1, 1, 2048, 64]> var_898_cast_fp16 = equal(x = mask_cast_fp16, y = var_897_promoted_to_fp16)[name = string("op_898_cast_fp16")];
            fp16 var_899_to_fp16 = const()[name = string("op_899_to_fp16"), val = fp16(-inf)];
            tensor<fp16, [1, 1, 2048, 64]> attn_mask_1_cast_fp16 = select(a = mask_cast_fp16, b = var_899_to_fp16, cond = var_898_cast_fp16)[name = string("attn_mask_1_cast_fp16")];
            string inputs_embeds_to_fp16_dtype_0 = const()[name = string("inputs_embeds_to_fp16_dtype_0"), val = string("fp16")];
            fp16 const_2_promoted_to_fp16 = const()[name = string("const_2_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> inputs_embeds_to_fp16 = cast(dtype = inputs_embeds_to_fp16_dtype_0, x = inputs_embeds)[name = string("cast_20")];
            tensor<fp16, [1, 2048, 1, 64]> var_909_cast_fp16 = mul(x = inputs_embeds_to_fp16, y = const_2_promoted_to_fp16)[name = string("op_909_cast_fp16")];
            int32 var_907 = const()[name = string("op_907"), val = int32(1)];
            bool doubled_1_interleave_0 = const()[name = string("doubled_1_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_1_cast_fp16 = concat(axis = var_907, interleave = doubled_1_interleave_0, values = (inputs_embeds_to_fp16, var_909_cast_fp16))[name = string("doubled_1_cast_fp16")];
            tensor<int32, [1]> out_1_axes_0 = const()[name = string("out_1_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_1_gamma_0_to_fp16 = const()[name = string("out_1_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(974780352)))];
            fp16 var_919_to_fp16 = const()[name = string("op_919_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_1_cast_fp16 = layer_norm(axes = out_1_axes_0, epsilon = var_919_to_fp16, gamma = out_1_gamma_0_to_fp16, x = doubled_1_cast_fp16)[name = string("out_1_cast_fp16")];
            tensor<int32, [2]> var_930_split_sizes_0 = const()[name = string("op_930_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_930_axis_0 = const()[name = string("op_930_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_930_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_930_cast_fp16_1 = split(axis = var_930_axis_0, split_sizes = var_930_split_sizes_0, x = out_1_cast_fp16)[name = string("op_930_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_0_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(974788608)))];
            tensor<int32, [2]> query_states_1_strides_0 = const()[name = string("query_states_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_1_pad_type_0 = const()[name = string("query_states_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_1_pad_0 = const()[name = string("query_states_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_1_dilations_0 = const()[name = string("query_states_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_1_groups_0 = const()[name = string("query_states_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_1_cast_fp16 = conv(dilations = query_states_1_dilations_0, groups = query_states_1_groups_0, pad = query_states_1_pad_0, pad_type = query_states_1_pad_type_0, strides = query_states_1_strides_0, weight = layers_0_self_attn_q_proj_weight_to_fp16, x = var_930_cast_fp16_0)[name = string("query_states_1_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_0_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(983177280)))];
            tensor<int32, [2]> key_states_1_strides_0 = const()[name = string("key_states_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_1_pad_type_0 = const()[name = string("key_states_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_1_pad_0 = const()[name = string("key_states_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_1_dilations_0 = const()[name = string("key_states_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_1_groups_0 = const()[name = string("key_states_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_1_cast_fp16 = conv(dilations = key_states_1_dilations_0, groups = key_states_1_groups_0, pad = key_states_1_pad_0, pad_type = key_states_1_pad_type_0, strides = key_states_1_strides_0, weight = layers_0_self_attn_k_proj_weight_to_fp16, x = var_930_cast_fp16_0)[name = string("key_states_1_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_0_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(984225920)))];
            tensor<int32, [2]> value_states_1_strides_0 = const()[name = string("value_states_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_1_pad_type_0 = const()[name = string("value_states_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_1_pad_0 = const()[name = string("value_states_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_1_dilations_0 = const()[name = string("value_states_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_1_groups_0 = const()[name = string("value_states_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_1_cast_fp16 = conv(dilations = value_states_1_dilations_0, groups = value_states_1_groups_0, pad = value_states_1_pad_0, pad_type = value_states_1_pad_type_0, strides = value_states_1_strides_0, weight = layers_0_self_attn_v_proj_weight_to_fp16, x = var_930_cast_fp16_0)[name = string("value_states_1_cast_fp16")];
            tensor<int32, [4]> concat_0x = const()[name = string("concat_0x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_1_cast_fp16 = reshape(shape = concat_0x, x = query_states_1_cast_fp16)[name = string("x_1_cast_fp16")];
            tensor<int32, [4]> concat_1x = const()[name = string("concat_1x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_987_cast_fp16 = reshape(shape = concat_1x, x = key_states_1_cast_fp16)[name = string("op_987_cast_fp16")];
            tensor<int32, [4]> concat_2x = const()[name = string("concat_2x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_994_cast_fp16 = reshape(shape = concat_2x, x = value_states_1_cast_fp16)[name = string("op_994_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_998_cast_fp16 = mul(x = x_1_cast_fp16, y = var_859_cast_fp16)[name = string("op_998_cast_fp16")];
            tensor<int32, [2]> var_999_split_sizes_0 = const()[name = string("op_999_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_999_axis_0 = const()[name = string("op_999_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_999_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_999_cast_fp16_1 = split(axis = var_999_axis_0, split_sizes = var_999_split_sizes_0, x = x_1_cast_fp16)[name = string("op_999_cast_fp16")];
            fp16 const_4_promoted_to_fp16 = const()[name = string("const_4_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_1001_cast_fp16 = mul(x = var_999_cast_fp16_1, y = const_4_promoted_to_fp16)[name = string("op_1001_cast_fp16")];
            int32 var_1003 = const()[name = string("op_1003"), val = int32(-2)];
            bool var_1004_interleave_0 = const()[name = string("op_1004_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_1004_cast_fp16 = concat(axis = var_1003, interleave = var_1004_interleave_0, values = (var_1001_cast_fp16, var_999_cast_fp16_0))[name = string("op_1004_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_1005_cast_fp16 = mul(x = var_1004_cast_fp16, y = var_866_cast_fp16)[name = string("op_1005_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_3_cast_fp16 = add(x = var_998_cast_fp16, y = var_1005_cast_fp16)[name = string("query_states_3_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_1011_cast_fp16 = mul(x = var_987_cast_fp16, y = var_859_cast_fp16)[name = string("op_1011_cast_fp16")];
            tensor<int32, [2]> var_1012_split_sizes_0 = const()[name = string("op_1012_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1012_axis_0 = const()[name = string("op_1012_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_1012_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_1012_cast_fp16_1 = split(axis = var_1012_axis_0, split_sizes = var_1012_split_sizes_0, x = var_987_cast_fp16)[name = string("op_1012_cast_fp16")];
            fp16 const_5_promoted_to_fp16 = const()[name = string("const_5_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_1014_cast_fp16 = mul(x = var_1012_cast_fp16_1, y = const_5_promoted_to_fp16)[name = string("op_1014_cast_fp16")];
            int32 var_1016 = const()[name = string("op_1016"), val = int32(-2)];
            bool var_1017_interleave_0 = const()[name = string("op_1017_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_1017_cast_fp16 = concat(axis = var_1016, interleave = var_1017_interleave_0, values = (var_1014_cast_fp16, var_1012_cast_fp16_0))[name = string("op_1017_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_1018_cast_fp16 = mul(x = var_1017_cast_fp16, y = var_866_cast_fp16)[name = string("op_1018_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_5_cast_fp16 = add(x = var_1011_cast_fp16, y = var_1018_cast_fp16)[name = string("key_states_5_cast_fp16")];
            tensor<fp16, [28, 2, 2048, 128]> read_state_0 = read_state(input = key_cache)[name = string("read_state_0")];
            tensor<int32, [1]> expand_dims_0 = const()[name = string("expand_dims_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_1 = const()[name = string("expand_dims_1"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_3 = const()[name = string("expand_dims_3"), val = tensor<int32, [1]>([0])];
            int32 concat_5_axis_0 = const()[name = string("concat_5_axis_0"), val = int32(0)];
            bool concat_5_interleave_0 = const()[name = string("concat_5_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_5 = concat(axis = concat_5_axis_0, interleave = concat_5_interleave_0, values = (expand_dims_0, expand_dims_1, position_id, expand_dims_3))[name = string("concat_5")];
            tensor<int32, [1]> expand_dims_4 = const()[name = string("expand_dims_4"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> concat_6_values1_0 = const()[name = string("concat_6_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_6_values3_0 = const()[name = string("concat_6_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_6_axis_0 = const()[name = string("concat_6_axis_0"), val = int32(0)];
            bool concat_6_interleave_0 = const()[name = string("concat_6_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_6 = concat(axis = concat_6_axis_0, interleave = concat_6_interleave_0, values = (expand_dims_4, concat_6_values1_0, cache_position_end, concat_6_values3_0))[name = string("concat_6")];
            tensor<int32, [4]> key_states_7_perm_0 = const()[name = string("key_states_7_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_1_stride_0 = const()[name = string("key_cache_internal_tensor_assign_1_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_1_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_1_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_1_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_1_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_1_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_1_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_7_cast_fp16 = transpose(perm = key_states_7_perm_0, x = key_states_5_cast_fp16)[name = string("transpose_518")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_1_cast_fp16 = slice_update(begin = concat_5, begin_mask = key_cache_internal_tensor_assign_1_begin_mask_0, end = concat_6, end_mask = key_cache_internal_tensor_assign_1_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_1_squeeze_mask_0, stride = key_cache_internal_tensor_assign_1_stride_0, update = key_states_7_cast_fp16, x = read_state_0)[name = string("key_cache_internal_tensor_assign_1_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_1_cast_fp16, input = key_cache)[name = string("coreml_update_state_280_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_280 = read_state(input = key_cache)[name = string("coreml_update_state_280")];
            tensor<fp16, [28, 2, 2048, 128]> read_state_1 = read_state(input = value_cache)[name = string("read_state_1")];
            tensor<int32, [4]> value_states_3_perm_0 = const()[name = string("value_states_3_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_1_stride_0 = const()[name = string("value_cache_internal_tensor_assign_1_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_1_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_1_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_1_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_1_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_1_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_1_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_3_cast_fp16 = transpose(perm = value_states_3_perm_0, x = var_994_cast_fp16)[name = string("transpose_517")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_1_cast_fp16 = slice_update(begin = concat_5, begin_mask = value_cache_internal_tensor_assign_1_begin_mask_0, end = concat_6, end_mask = value_cache_internal_tensor_assign_1_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_1_squeeze_mask_0, stride = value_cache_internal_tensor_assign_1_stride_0, update = value_states_3_cast_fp16, x = read_state_1)[name = string("value_cache_internal_tensor_assign_1_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_1_cast_fp16, input = value_cache)[name = string("coreml_update_state_281_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_281 = read_state(input = value_cache)[name = string("coreml_update_state_281")];
            tensor<int32, [4]> var_1088_begin_0 = const()[name = string("op_1088_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> var_1088_end_0 = const()[name = string("op_1088_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_1088_end_mask_0 = const()[name = string("op_1088_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1088_cast_fp16 = slice_by_index(begin = var_1088_begin_0, end = var_1088_end_0, end_mask = var_1088_end_mask_0, x = coreml_update_state_280)[name = string("op_1088_cast_fp16")];
            tensor<int32, [2]> tile_0 = const()[name = string("tile_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1091_axis_0 = const()[name = string("op_1091_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1091_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1091_cast_fp16_1 = split(axis = var_1091_axis_0, split_sizes = tile_0, x = var_1088_cast_fp16)[name = string("op_1091_cast_fp16")];
            tensor<int32, [4]> var_1098_begin_0 = const()[name = string("op_1098_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> var_1098_end_0 = const()[name = string("op_1098_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_1098_end_mask_0 = const()[name = string("op_1098_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1098_cast_fp16 = slice_by_index(begin = var_1098_begin_0, end = var_1098_end_0, end_mask = var_1098_end_mask_0, x = coreml_update_state_281)[name = string("op_1098_cast_fp16")];
            tensor<int32, [2]> tile_1 = const()[name = string("tile_1"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1101_axis_0 = const()[name = string("op_1101_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1101_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1101_cast_fp16_1 = split(axis = var_1101_axis_0, split_sizes = tile_1, x = var_1098_cast_fp16)[name = string("op_1101_cast_fp16")];
            tensor<int32, [2]> var_1104_split_sizes_0 = const()[name = string("op_1104_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_1104_axis_0 = const()[name = string("op_1104_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_1104_0, tensor<fp16, [1, 8, 128, 64]> var_1104_1 = split(axis = var_1104_axis_0, split_sizes = var_1104_split_sizes_0, x = query_states_3_cast_fp16)[name = string("op_1104")];
            bool attn_weights_1_transpose_x_0 = const()[name = string("attn_weights_1_transpose_x_0"), val = bool(false)];
            bool attn_weights_1_transpose_y_0 = const()[name = string("attn_weights_1_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_1_cast_fp16 = matmul(transpose_x = attn_weights_1_transpose_x_0, transpose_y = attn_weights_1_transpose_y_0, x = var_1091_cast_fp16_0, y = var_1104_0)[name = string("attn_weights_1_cast_fp16")];
            fp16 var_1107_to_fp16 = const()[name = string("op_1107_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_3_cast_fp16 = mul(x = attn_weights_1_cast_fp16, y = var_1107_to_fp16)[name = string("attn_weights_3_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_5_cast_fp16 = add(x = attn_weights_3_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_5_cast_fp16")];
            int32 var_1111 = const()[name = string("op_1111"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_7_cast_fp16 = softmax(axis = var_1111, x = attn_weights_5_cast_fp16)[name = string("attn_weights_7_cast_fp16")];
            bool var_1117_transpose_x_1 = const()[name = string("op_1117_transpose_x_1"), val = bool(true)];
            bool var_1117_transpose_y_1 = const()[name = string("op_1117_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_1117_cast_fp16 = matmul(transpose_x = var_1117_transpose_x_1, transpose_y = var_1117_transpose_y_1, x = attn_weights_7_cast_fp16, y = var_1101_cast_fp16_0)[name = string("op_1117_cast_fp16")];
            bool attn_weights_9_transpose_x_0 = const()[name = string("attn_weights_9_transpose_x_0"), val = bool(false)];
            bool attn_weights_9_transpose_y_0 = const()[name = string("attn_weights_9_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_9_cast_fp16 = matmul(transpose_x = attn_weights_9_transpose_x_0, transpose_y = attn_weights_9_transpose_y_0, x = var_1091_cast_fp16_1, y = var_1104_1)[name = string("attn_weights_9_cast_fp16")];
            fp16 var_1119_to_fp16 = const()[name = string("op_1119_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_11_cast_fp16 = mul(x = attn_weights_9_cast_fp16, y = var_1119_to_fp16)[name = string("attn_weights_11_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_13_cast_fp16 = add(x = attn_weights_11_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_13_cast_fp16")];
            int32 var_1123 = const()[name = string("op_1123"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_15_cast_fp16 = softmax(axis = var_1123, x = attn_weights_13_cast_fp16)[name = string("attn_weights_15_cast_fp16")];
            bool attn_output_1_transpose_x_1 = const()[name = string("attn_output_1_transpose_x_1"), val = bool(true)];
            bool attn_output_1_transpose_y_1 = const()[name = string("attn_output_1_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_1_cast_fp16 = matmul(transpose_x = attn_output_1_transpose_x_1, transpose_y = attn_output_1_transpose_y_1, x = attn_weights_15_cast_fp16, y = var_1101_cast_fp16_1)[name = string("attn_output_1_cast_fp16")];
            int32 var_1131 = const()[name = string("op_1131"), val = int32(1)];
            bool attn_output_3_interleave_0 = const()[name = string("attn_output_3_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_3_cast_fp16 = concat(axis = var_1131, interleave = attn_output_3_interleave_0, values = (var_1117_cast_fp16, attn_output_1_cast_fp16))[name = string("attn_output_3_cast_fp16")];
            tensor<int32, [4]> var_1135_perm_0 = const()[name = string("op_1135_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_11x = const()[name = string("concat_11x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_1135_cast_fp16 = transpose(perm = var_1135_perm_0, x = attn_output_3_cast_fp16)[name = string("transpose_516")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_7_cast_fp16 = reshape(shape = concat_11x, x = var_1135_cast_fp16)[name = string("attn_output_7_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_0_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(985274560)))];
            tensor<int32, [2]> hidden_states_3_strides_0 = const()[name = string("hidden_states_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_3_pad_type_0 = const()[name = string("hidden_states_3_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_3_pad_0 = const()[name = string("hidden_states_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_3_dilations_0 = const()[name = string("hidden_states_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_3_groups_0 = const()[name = string("hidden_states_3_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_3_cast_fp16 = conv(dilations = hidden_states_3_dilations_0, groups = hidden_states_3_groups_0, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = hidden_states_3_strides_0, weight = layers_0_self_attn_o_proj_weight_to_fp16, x = attn_output_7_cast_fp16)[name = string("hidden_states_3_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_5_cast_fp16 = add(x = inputs_embeds_to_fp16, y = hidden_states_3_cast_fp16)[name = string("hidden_states_5_cast_fp16")];
            fp16 const_10_promoted_to_fp16 = const()[name = string("const_10_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_1168_cast_fp16 = mul(x = hidden_states_5_cast_fp16, y = const_10_promoted_to_fp16)[name = string("op_1168_cast_fp16")];
            int32 var_1166 = const()[name = string("op_1166"), val = int32(1)];
            bool doubled_5_interleave_0 = const()[name = string("doubled_5_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_5_cast_fp16 = concat(axis = var_1166, interleave = doubled_5_interleave_0, values = (hidden_states_5_cast_fp16, var_1168_cast_fp16))[name = string("doubled_5_cast_fp16")];
            tensor<int32, [1]> out_3_axes_0 = const()[name = string("out_3_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_3_gamma_0_to_fp16 = const()[name = string("out_3_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(993663232)))];
            fp16 var_1178_to_fp16 = const()[name = string("op_1178_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_3_cast_fp16 = layer_norm(axes = out_3_axes_0, epsilon = var_1178_to_fp16, gamma = out_3_gamma_0_to_fp16, x = doubled_5_cast_fp16)[name = string("out_3_cast_fp16")];
            tensor<int32, [2]> var_1189_split_sizes_0 = const()[name = string("op_1189_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1189_axis_0 = const()[name = string("op_1189_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_1189_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_1189_cast_fp16_1 = split(axis = var_1189_axis_0, split_sizes = var_1189_split_sizes_0, x = out_3_cast_fp16)[name = string("op_1189_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_0_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_0_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(993671488)))];
            tensor<int32, [2]> input_1_strides_0 = const()[name = string("input_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_1_pad_type_0 = const()[name = string("input_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_1_pad_0 = const()[name = string("input_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_1_dilations_0 = const()[name = string("input_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_1_groups_0 = const()[name = string("input_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_1_cast_fp16 = conv(dilations = input_1_dilations_0, groups = input_1_groups_0, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = input_1_strides_0, weight = layers_0_mlp_gate_proj_weight_to_fp16, x = var_1189_cast_fp16_0)[name = string("input_1_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_1206_cast_fp16 = silu(x = input_1_cast_fp16)[name = string("op_1206_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_0_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_0_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1018837376)))];
            tensor<int32, [2]> var_1212_strides_0 = const()[name = string("op_1212_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_1212_pad_type_0 = const()[name = string("op_1212_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_1212_pad_0 = const()[name = string("op_1212_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_1212_dilations_0 = const()[name = string("op_1212_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1212_groups_0 = const()[name = string("op_1212_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_1212_cast_fp16 = conv(dilations = var_1212_dilations_0, groups = var_1212_groups_0, pad = var_1212_pad_0, pad_type = var_1212_pad_type_0, strides = var_1212_strides_0, weight = layers_0_mlp_up_proj_weight_to_fp16, x = var_1189_cast_fp16_0)[name = string("op_1212_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_9_cast_fp16 = mul(x = var_1206_cast_fp16, y = var_1212_cast_fp16)[name = string("x_9_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_0_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_0_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1044003264)))];
            tensor<int32, [2]> hidden_states_7_strides_0 = const()[name = string("hidden_states_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_7_pad_type_0 = const()[name = string("hidden_states_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_7_pad_0 = const()[name = string("hidden_states_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_7_dilations_0 = const()[name = string("hidden_states_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_7_groups_0 = const()[name = string("hidden_states_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_7_cast_fp16 = conv(dilations = hidden_states_7_dilations_0, groups = hidden_states_7_groups_0, pad = hidden_states_7_pad_0, pad_type = hidden_states_7_pad_type_0, strides = hidden_states_7_strides_0, weight = layers_0_mlp_down_proj_weight_to_fp16, x = x_9_cast_fp16)[name = string("hidden_states_7_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_9_cast_fp16 = add(x = hidden_states_5_cast_fp16, y = hidden_states_7_cast_fp16)[name = string("hidden_states_9_cast_fp16")];
            fp16 const_12_promoted_to_fp16 = const()[name = string("const_12_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_1230_cast_fp16 = mul(x = hidden_states_9_cast_fp16, y = const_12_promoted_to_fp16)[name = string("op_1230_cast_fp16")];
            int32 var_1228 = const()[name = string("op_1228"), val = int32(1)];
            bool doubled_9_interleave_0 = const()[name = string("doubled_9_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_9_cast_fp16 = concat(axis = var_1228, interleave = doubled_9_interleave_0, values = (hidden_states_9_cast_fp16, var_1230_cast_fp16))[name = string("doubled_9_cast_fp16")];
            tensor<int32, [1]> out_5_axes_0 = const()[name = string("out_5_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_5_gamma_0_to_fp16 = const()[name = string("out_5_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1069169152)))];
            fp16 var_1240_to_fp16 = const()[name = string("op_1240_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_5_cast_fp16 = layer_norm(axes = out_5_axes_0, epsilon = var_1240_to_fp16, gamma = out_5_gamma_0_to_fp16, x = doubled_9_cast_fp16)[name = string("out_5_cast_fp16")];
            tensor<int32, [2]> var_1251_split_sizes_0 = const()[name = string("op_1251_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1251_axis_0 = const()[name = string("op_1251_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_1251_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_1251_cast_fp16_1 = split(axis = var_1251_axis_0, split_sizes = var_1251_split_sizes_0, x = out_5_cast_fp16)[name = string("op_1251_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_1_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_1_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1069177408)))];
            tensor<int32, [2]> query_states_7_strides_0 = const()[name = string("query_states_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_7_pad_type_0 = const()[name = string("query_states_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_7_pad_0 = const()[name = string("query_states_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_7_dilations_0 = const()[name = string("query_states_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_7_groups_0 = const()[name = string("query_states_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_7_cast_fp16 = conv(dilations = query_states_7_dilations_0, groups = query_states_7_groups_0, pad = query_states_7_pad_0, pad_type = query_states_7_pad_type_0, strides = query_states_7_strides_0, weight = layers_1_self_attn_q_proj_weight_to_fp16, x = var_1251_cast_fp16_0)[name = string("query_states_7_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_1_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_1_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1077566080)))];
            tensor<int32, [2]> key_states_11_strides_0 = const()[name = string("key_states_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_11_pad_type_0 = const()[name = string("key_states_11_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_11_pad_0 = const()[name = string("key_states_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_11_dilations_0 = const()[name = string("key_states_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_11_groups_0 = const()[name = string("key_states_11_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_11_cast_fp16 = conv(dilations = key_states_11_dilations_0, groups = key_states_11_groups_0, pad = key_states_11_pad_0, pad_type = key_states_11_pad_type_0, strides = key_states_11_strides_0, weight = layers_1_self_attn_k_proj_weight_to_fp16, x = var_1251_cast_fp16_0)[name = string("key_states_11_cast_fp16")];
            tensor<int32, [2]> value_states_7_strides_0 = const()[name = string("value_states_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_7_pad_type_0 = const()[name = string("value_states_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_7_pad_0 = const()[name = string("value_states_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_7_dilations_0 = const()[name = string("value_states_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_7_groups_0 = const()[name = string("value_states_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_7_cast_fp16 = conv(dilations = value_states_7_dilations_0, groups = value_states_7_groups_0, pad = value_states_7_pad_0, pad_type = value_states_7_pad_type_0, strides = value_states_7_strides_0, weight = layers_1_self_attn_v_proj_weight_cast_fp16, x = var_1251_cast_fp16_0)[name = string("value_states_7_cast_fp16")];
            tensor<int32, [4]> concat_12x = const()[name = string("concat_12x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_11_cast_fp16 = reshape(shape = concat_12x, x = query_states_7_cast_fp16)[name = string("x_11_cast_fp16")];
            tensor<int32, [4]> concat_13x = const()[name = string("concat_13x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_1308_cast_fp16 = reshape(shape = concat_13x, x = key_states_11_cast_fp16)[name = string("op_1308_cast_fp16")];
            tensor<int32, [4]> concat_14x = const()[name = string("concat_14x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_1315_cast_fp16 = reshape(shape = concat_14x, x = value_states_7_cast_fp16)[name = string("op_1315_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_1319_cast_fp16 = mul(x = x_11_cast_fp16, y = var_859_cast_fp16)[name = string("op_1319_cast_fp16")];
            tensor<int32, [2]> var_1320_split_sizes_0 = const()[name = string("op_1320_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1320_axis_0 = const()[name = string("op_1320_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_1320_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_1320_cast_fp16_1 = split(axis = var_1320_axis_0, split_sizes = var_1320_split_sizes_0, x = x_11_cast_fp16)[name = string("op_1320_cast_fp16")];
            fp16 const_14_promoted_to_fp16 = const()[name = string("const_14_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_1322_cast_fp16 = mul(x = var_1320_cast_fp16_1, y = const_14_promoted_to_fp16)[name = string("op_1322_cast_fp16")];
            int32 var_1324 = const()[name = string("op_1324"), val = int32(-2)];
            bool var_1325_interleave_0 = const()[name = string("op_1325_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_1325_cast_fp16 = concat(axis = var_1324, interleave = var_1325_interleave_0, values = (var_1322_cast_fp16, var_1320_cast_fp16_0))[name = string("op_1325_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_1326_cast_fp16 = mul(x = var_1325_cast_fp16, y = var_866_cast_fp16)[name = string("op_1326_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_9_cast_fp16 = add(x = var_1319_cast_fp16, y = var_1326_cast_fp16)[name = string("query_states_9_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_1332_cast_fp16 = mul(x = var_1308_cast_fp16, y = var_859_cast_fp16)[name = string("op_1332_cast_fp16")];
            tensor<int32, [2]> var_1333_split_sizes_0 = const()[name = string("op_1333_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1333_axis_0 = const()[name = string("op_1333_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_1333_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_1333_cast_fp16_1 = split(axis = var_1333_axis_0, split_sizes = var_1333_split_sizes_0, x = var_1308_cast_fp16)[name = string("op_1333_cast_fp16")];
            fp16 const_15_promoted_to_fp16 = const()[name = string("const_15_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_1335_cast_fp16 = mul(x = var_1333_cast_fp16_1, y = const_15_promoted_to_fp16)[name = string("op_1335_cast_fp16")];
            int32 var_1337 = const()[name = string("op_1337"), val = int32(-2)];
            bool var_1338_interleave_0 = const()[name = string("op_1338_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_1338_cast_fp16 = concat(axis = var_1337, interleave = var_1338_interleave_0, values = (var_1335_cast_fp16, var_1333_cast_fp16_0))[name = string("op_1338_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_1339_cast_fp16 = mul(x = var_1338_cast_fp16, y = var_866_cast_fp16)[name = string("op_1339_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_15_cast_fp16 = add(x = var_1332_cast_fp16, y = var_1339_cast_fp16)[name = string("key_states_15_cast_fp16")];
            tensor<int32, [1]> expand_dims_12 = const()[name = string("expand_dims_12"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> expand_dims_13 = const()[name = string("expand_dims_13"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_15 = const()[name = string("expand_dims_15"), val = tensor<int32, [1]>([0])];
            int32 concat_17_axis_0 = const()[name = string("concat_17_axis_0"), val = int32(0)];
            bool concat_17_interleave_0 = const()[name = string("concat_17_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_17 = concat(axis = concat_17_axis_0, interleave = concat_17_interleave_0, values = (expand_dims_12, expand_dims_13, position_id, expand_dims_15))[name = string("concat_17")];
            tensor<int32, [1]> expand_dims_16 = const()[name = string("expand_dims_16"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> concat_18_values1_0 = const()[name = string("concat_18_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_18_values3_0 = const()[name = string("concat_18_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_18_axis_0 = const()[name = string("concat_18_axis_0"), val = int32(0)];
            bool concat_18_interleave_0 = const()[name = string("concat_18_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_18 = concat(axis = concat_18_axis_0, interleave = concat_18_interleave_0, values = (expand_dims_16, concat_18_values1_0, cache_position_end, concat_18_values3_0))[name = string("concat_18")];
            tensor<int32, [4]> key_states_17_perm_0 = const()[name = string("key_states_17_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_2_stride_0 = const()[name = string("key_cache_internal_tensor_assign_2_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_2_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_2_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_2_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_2_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_2_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_2_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_17_cast_fp16 = transpose(perm = key_states_17_perm_0, x = key_states_15_cast_fp16)[name = string("transpose_515")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_2_cast_fp16 = slice_update(begin = concat_17, begin_mask = key_cache_internal_tensor_assign_2_begin_mask_0, end = concat_18, end_mask = key_cache_internal_tensor_assign_2_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_2_squeeze_mask_0, stride = key_cache_internal_tensor_assign_2_stride_0, update = key_states_17_cast_fp16, x = coreml_update_state_280)[name = string("key_cache_internal_tensor_assign_2_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_2_cast_fp16, input = key_cache)[name = string("coreml_update_state_282_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_282 = read_state(input = key_cache)[name = string("coreml_update_state_282")];
            tensor<int32, [4]> value_states_9_perm_0 = const()[name = string("value_states_9_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_2_stride_0 = const()[name = string("value_cache_internal_tensor_assign_2_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_2_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_2_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_2_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_2_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_2_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_2_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_9_cast_fp16 = transpose(perm = value_states_9_perm_0, x = var_1315_cast_fp16)[name = string("transpose_514")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_2_cast_fp16 = slice_update(begin = concat_17, begin_mask = value_cache_internal_tensor_assign_2_begin_mask_0, end = concat_18, end_mask = value_cache_internal_tensor_assign_2_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_2_squeeze_mask_0, stride = value_cache_internal_tensor_assign_2_stride_0, update = value_states_9_cast_fp16, x = coreml_update_state_281)[name = string("value_cache_internal_tensor_assign_2_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_2_cast_fp16, input = value_cache)[name = string("coreml_update_state_283_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_283 = read_state(input = value_cache)[name = string("coreml_update_state_283")];
            tensor<int32, [4]> var_1409_begin_0 = const()[name = string("op_1409_begin_0"), val = tensor<int32, [4]>([1, 0, 0, 0])];
            tensor<int32, [4]> var_1409_end_0 = const()[name = string("op_1409_end_0"), val = tensor<int32, [4]>([2, 2, 2048, 128])];
            tensor<bool, [4]> var_1409_end_mask_0 = const()[name = string("op_1409_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1409_cast_fp16 = slice_by_index(begin = var_1409_begin_0, end = var_1409_end_0, end_mask = var_1409_end_mask_0, x = coreml_update_state_282)[name = string("op_1409_cast_fp16")];
            tensor<int32, [2]> tile_2 = const()[name = string("tile_2"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1412_axis_0 = const()[name = string("op_1412_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1412_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1412_cast_fp16_1 = split(axis = var_1412_axis_0, split_sizes = tile_2, x = var_1409_cast_fp16)[name = string("op_1412_cast_fp16")];
            tensor<int32, [4]> var_1419_begin_0 = const()[name = string("op_1419_begin_0"), val = tensor<int32, [4]>([1, 0, 0, 0])];
            tensor<int32, [4]> var_1419_end_0 = const()[name = string("op_1419_end_0"), val = tensor<int32, [4]>([2, 2, 2048, 128])];
            tensor<bool, [4]> var_1419_end_mask_0 = const()[name = string("op_1419_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1419_cast_fp16 = slice_by_index(begin = var_1419_begin_0, end = var_1419_end_0, end_mask = var_1419_end_mask_0, x = coreml_update_state_283)[name = string("op_1419_cast_fp16")];
            tensor<int32, [2]> tile_3 = const()[name = string("tile_3"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1422_axis_0 = const()[name = string("op_1422_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1422_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1422_cast_fp16_1 = split(axis = var_1422_axis_0, split_sizes = tile_3, x = var_1419_cast_fp16)[name = string("op_1422_cast_fp16")];
            tensor<int32, [2]> var_1425_split_sizes_0 = const()[name = string("op_1425_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_1425_axis_0 = const()[name = string("op_1425_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_1425_0, tensor<fp16, [1, 8, 128, 64]> var_1425_1 = split(axis = var_1425_axis_0, split_sizes = var_1425_split_sizes_0, x = query_states_9_cast_fp16)[name = string("op_1425")];
            bool attn_weights_17_transpose_x_0 = const()[name = string("attn_weights_17_transpose_x_0"), val = bool(false)];
            bool attn_weights_17_transpose_y_0 = const()[name = string("attn_weights_17_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_17_cast_fp16 = matmul(transpose_x = attn_weights_17_transpose_x_0, transpose_y = attn_weights_17_transpose_y_0, x = var_1412_cast_fp16_0, y = var_1425_0)[name = string("attn_weights_17_cast_fp16")];
            fp16 var_1428_to_fp16 = const()[name = string("op_1428_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_19_cast_fp16 = mul(x = attn_weights_17_cast_fp16, y = var_1428_to_fp16)[name = string("attn_weights_19_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_21_cast_fp16 = add(x = attn_weights_19_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_21_cast_fp16")];
            int32 var_1432 = const()[name = string("op_1432"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_23_cast_fp16 = softmax(axis = var_1432, x = attn_weights_21_cast_fp16)[name = string("attn_weights_23_cast_fp16")];
            bool var_1438_transpose_x_1 = const()[name = string("op_1438_transpose_x_1"), val = bool(true)];
            bool var_1438_transpose_y_1 = const()[name = string("op_1438_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_1438_cast_fp16 = matmul(transpose_x = var_1438_transpose_x_1, transpose_y = var_1438_transpose_y_1, x = attn_weights_23_cast_fp16, y = var_1422_cast_fp16_0)[name = string("op_1438_cast_fp16")];
            bool attn_weights_25_transpose_x_0 = const()[name = string("attn_weights_25_transpose_x_0"), val = bool(false)];
            bool attn_weights_25_transpose_y_0 = const()[name = string("attn_weights_25_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_25_cast_fp16 = matmul(transpose_x = attn_weights_25_transpose_x_0, transpose_y = attn_weights_25_transpose_y_0, x = var_1412_cast_fp16_1, y = var_1425_1)[name = string("attn_weights_25_cast_fp16")];
            fp16 var_1440_to_fp16 = const()[name = string("op_1440_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_27_cast_fp16 = mul(x = attn_weights_25_cast_fp16, y = var_1440_to_fp16)[name = string("attn_weights_27_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_29_cast_fp16 = add(x = attn_weights_27_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_29_cast_fp16")];
            int32 var_1444 = const()[name = string("op_1444"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_31_cast_fp16 = softmax(axis = var_1444, x = attn_weights_29_cast_fp16)[name = string("attn_weights_31_cast_fp16")];
            bool attn_output_9_transpose_x_1 = const()[name = string("attn_output_9_transpose_x_1"), val = bool(true)];
            bool attn_output_9_transpose_y_1 = const()[name = string("attn_output_9_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_9_cast_fp16 = matmul(transpose_x = attn_output_9_transpose_x_1, transpose_y = attn_output_9_transpose_y_1, x = attn_weights_31_cast_fp16, y = var_1422_cast_fp16_1)[name = string("attn_output_9_cast_fp16")];
            int32 var_1452 = const()[name = string("op_1452"), val = int32(1)];
            bool attn_output_11_interleave_0 = const()[name = string("attn_output_11_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_11_cast_fp16 = concat(axis = var_1452, interleave = attn_output_11_interleave_0, values = (var_1438_cast_fp16, attn_output_9_cast_fp16))[name = string("attn_output_11_cast_fp16")];
            tensor<int32, [4]> var_1456_perm_0 = const()[name = string("op_1456_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_23x = const()[name = string("concat_23x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_1456_cast_fp16 = transpose(perm = var_1456_perm_0, x = attn_output_11_cast_fp16)[name = string("transpose_513")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_15_cast_fp16 = reshape(shape = concat_23x, x = var_1456_cast_fp16)[name = string("attn_output_15_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_1_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_1_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1078614720)))];
            tensor<int32, [2]> hidden_states_13_strides_0 = const()[name = string("hidden_states_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_13_pad_type_0 = const()[name = string("hidden_states_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_13_pad_0 = const()[name = string("hidden_states_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_13_dilations_0 = const()[name = string("hidden_states_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_13_groups_0 = const()[name = string("hidden_states_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_13_cast_fp16 = conv(dilations = hidden_states_13_dilations_0, groups = hidden_states_13_groups_0, pad = hidden_states_13_pad_0, pad_type = hidden_states_13_pad_type_0, strides = hidden_states_13_strides_0, weight = layers_1_self_attn_o_proj_weight_to_fp16, x = attn_output_15_cast_fp16)[name = string("hidden_states_13_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_15_cast_fp16 = add(x = hidden_states_9_cast_fp16, y = hidden_states_13_cast_fp16)[name = string("hidden_states_15_cast_fp16")];
            fp16 const_20_promoted_to_fp16 = const()[name = string("const_20_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_1489_cast_fp16 = mul(x = hidden_states_15_cast_fp16, y = const_20_promoted_to_fp16)[name = string("op_1489_cast_fp16")];
            int32 var_1487 = const()[name = string("op_1487"), val = int32(1)];
            bool doubled_13_interleave_0 = const()[name = string("doubled_13_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_13_cast_fp16 = concat(axis = var_1487, interleave = doubled_13_interleave_0, values = (hidden_states_15_cast_fp16, var_1489_cast_fp16))[name = string("doubled_13_cast_fp16")];
            tensor<int32, [1]> out_7_axes_0 = const()[name = string("out_7_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_7_gamma_0_to_fp16 = const()[name = string("out_7_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1087003392)))];
            fp16 var_1499_to_fp16 = const()[name = string("op_1499_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_7_cast_fp16 = layer_norm(axes = out_7_axes_0, epsilon = var_1499_to_fp16, gamma = out_7_gamma_0_to_fp16, x = doubled_13_cast_fp16)[name = string("out_7_cast_fp16")];
            tensor<int32, [2]> var_1510_split_sizes_0 = const()[name = string("op_1510_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1510_axis_0 = const()[name = string("op_1510_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_1510_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_1510_cast_fp16_1 = split(axis = var_1510_axis_0, split_sizes = var_1510_split_sizes_0, x = out_7_cast_fp16)[name = string("op_1510_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_1_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_1_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1087011648)))];
            tensor<int32, [2]> input_3_strides_0 = const()[name = string("input_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_3_dilations_0 = const()[name = string("input_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_3_groups_0 = const()[name = string("input_3_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_3_cast_fp16 = conv(dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = layers_1_mlp_gate_proj_weight_to_fp16, x = var_1510_cast_fp16_0)[name = string("input_3_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_1527_cast_fp16 = silu(x = input_3_cast_fp16)[name = string("op_1527_cast_fp16")];
            tensor<int32, [2]> var_1533_strides_0 = const()[name = string("op_1533_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_1533_pad_type_0 = const()[name = string("op_1533_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_1533_pad_0 = const()[name = string("op_1533_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_1533_dilations_0 = const()[name = string("op_1533_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1533_groups_0 = const()[name = string("op_1533_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_1533_cast_fp16 = conv(dilations = var_1533_dilations_0, groups = var_1533_groups_0, pad = var_1533_pad_0, pad_type = var_1533_pad_type_0, strides = var_1533_strides_0, weight = layers_1_mlp_up_proj_weight_cast_fp16, x = var_1510_cast_fp16_0)[name = string("op_1533_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_19_cast_fp16 = mul(x = var_1527_cast_fp16, y = var_1533_cast_fp16)[name = string("x_19_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_1_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_1_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1112177536)))];
            tensor<int32, [2]> hidden_states_17_strides_0 = const()[name = string("hidden_states_17_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_17_pad_type_0 = const()[name = string("hidden_states_17_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_17_pad_0 = const()[name = string("hidden_states_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_17_dilations_0 = const()[name = string("hidden_states_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_17_groups_0 = const()[name = string("hidden_states_17_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_17_cast_fp16 = conv(dilations = hidden_states_17_dilations_0, groups = hidden_states_17_groups_0, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = hidden_states_17_strides_0, weight = layers_1_mlp_down_proj_weight_to_fp16, x = x_19_cast_fp16)[name = string("hidden_states_17_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_19_cast_fp16 = add(x = hidden_states_15_cast_fp16, y = hidden_states_17_cast_fp16)[name = string("hidden_states_19_cast_fp16")];
            fp16 const_22_promoted_to_fp16 = const()[name = string("const_22_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_1551_cast_fp16 = mul(x = hidden_states_19_cast_fp16, y = const_22_promoted_to_fp16)[name = string("op_1551_cast_fp16")];
            int32 var_1549 = const()[name = string("op_1549"), val = int32(1)];
            bool doubled_17_interleave_0 = const()[name = string("doubled_17_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_17_cast_fp16 = concat(axis = var_1549, interleave = doubled_17_interleave_0, values = (hidden_states_19_cast_fp16, var_1551_cast_fp16))[name = string("doubled_17_cast_fp16")];
            tensor<int32, [1]> out_9_axes_0 = const()[name = string("out_9_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_9_gamma_0_to_fp16 = const()[name = string("out_9_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1137343424)))];
            fp16 var_1561_to_fp16 = const()[name = string("op_1561_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_9_cast_fp16 = layer_norm(axes = out_9_axes_0, epsilon = var_1561_to_fp16, gamma = out_9_gamma_0_to_fp16, x = doubled_17_cast_fp16)[name = string("out_9_cast_fp16")];
            tensor<int32, [2]> var_1572_split_sizes_0 = const()[name = string("op_1572_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1572_axis_0 = const()[name = string("op_1572_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_1572_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_1572_cast_fp16_1 = split(axis = var_1572_axis_0, split_sizes = var_1572_split_sizes_0, x = out_9_cast_fp16)[name = string("op_1572_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_2_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_2_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1137351680)))];
            tensor<int32, [2]> query_states_13_strides_0 = const()[name = string("query_states_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_13_pad_type_0 = const()[name = string("query_states_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_13_pad_0 = const()[name = string("query_states_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_13_dilations_0 = const()[name = string("query_states_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_13_groups_0 = const()[name = string("query_states_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_13_cast_fp16 = conv(dilations = query_states_13_dilations_0, groups = query_states_13_groups_0, pad = query_states_13_pad_0, pad_type = query_states_13_pad_type_0, strides = query_states_13_strides_0, weight = layers_2_self_attn_q_proj_weight_to_fp16, x = var_1572_cast_fp16_0)[name = string("query_states_13_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_2_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_2_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1145740352)))];
            tensor<int32, [2]> key_states_21_strides_0 = const()[name = string("key_states_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_21_pad_type_0 = const()[name = string("key_states_21_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_21_pad_0 = const()[name = string("key_states_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_21_dilations_0 = const()[name = string("key_states_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_21_groups_0 = const()[name = string("key_states_21_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_21_cast_fp16 = conv(dilations = key_states_21_dilations_0, groups = key_states_21_groups_0, pad = key_states_21_pad_0, pad_type = key_states_21_pad_type_0, strides = key_states_21_strides_0, weight = layers_2_self_attn_k_proj_weight_to_fp16, x = var_1572_cast_fp16_0)[name = string("key_states_21_cast_fp16")];
            tensor<int32, [2]> value_states_13_strides_0 = const()[name = string("value_states_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_13_pad_type_0 = const()[name = string("value_states_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_13_pad_0 = const()[name = string("value_states_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_13_dilations_0 = const()[name = string("value_states_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_13_groups_0 = const()[name = string("value_states_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_13_cast_fp16 = conv(dilations = value_states_13_dilations_0, groups = value_states_13_groups_0, pad = value_states_13_pad_0, pad_type = value_states_13_pad_type_0, strides = value_states_13_strides_0, weight = layers_2_self_attn_v_proj_weight_cast_fp16, x = var_1572_cast_fp16_0)[name = string("value_states_13_cast_fp16")];
            tensor<int32, [4]> concat_24x = const()[name = string("concat_24x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_21_cast_fp16 = reshape(shape = concat_24x, x = query_states_13_cast_fp16)[name = string("x_21_cast_fp16")];
            tensor<int32, [4]> concat_25x = const()[name = string("concat_25x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_1629_cast_fp16 = reshape(shape = concat_25x, x = key_states_21_cast_fp16)[name = string("op_1629_cast_fp16")];
            tensor<int32, [4]> concat_26x = const()[name = string("concat_26x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_1636_cast_fp16 = reshape(shape = concat_26x, x = value_states_13_cast_fp16)[name = string("op_1636_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_1640_cast_fp16 = mul(x = x_21_cast_fp16, y = var_859_cast_fp16)[name = string("op_1640_cast_fp16")];
            tensor<int32, [2]> var_1641_split_sizes_0 = const()[name = string("op_1641_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1641_axis_0 = const()[name = string("op_1641_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_1641_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_1641_cast_fp16_1 = split(axis = var_1641_axis_0, split_sizes = var_1641_split_sizes_0, x = x_21_cast_fp16)[name = string("op_1641_cast_fp16")];
            fp16 const_24_promoted_to_fp16 = const()[name = string("const_24_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_1643_cast_fp16 = mul(x = var_1641_cast_fp16_1, y = const_24_promoted_to_fp16)[name = string("op_1643_cast_fp16")];
            int32 var_1645 = const()[name = string("op_1645"), val = int32(-2)];
            bool var_1646_interleave_0 = const()[name = string("op_1646_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_1646_cast_fp16 = concat(axis = var_1645, interleave = var_1646_interleave_0, values = (var_1643_cast_fp16, var_1641_cast_fp16_0))[name = string("op_1646_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_1647_cast_fp16 = mul(x = var_1646_cast_fp16, y = var_866_cast_fp16)[name = string("op_1647_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_15_cast_fp16 = add(x = var_1640_cast_fp16, y = var_1647_cast_fp16)[name = string("query_states_15_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_1653_cast_fp16 = mul(x = var_1629_cast_fp16, y = var_859_cast_fp16)[name = string("op_1653_cast_fp16")];
            tensor<int32, [2]> var_1654_split_sizes_0 = const()[name = string("op_1654_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1654_axis_0 = const()[name = string("op_1654_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_1654_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_1654_cast_fp16_1 = split(axis = var_1654_axis_0, split_sizes = var_1654_split_sizes_0, x = var_1629_cast_fp16)[name = string("op_1654_cast_fp16")];
            fp16 const_25_promoted_to_fp16 = const()[name = string("const_25_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_1656_cast_fp16 = mul(x = var_1654_cast_fp16_1, y = const_25_promoted_to_fp16)[name = string("op_1656_cast_fp16")];
            int32 var_1658 = const()[name = string("op_1658"), val = int32(-2)];
            bool var_1659_interleave_0 = const()[name = string("op_1659_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_1659_cast_fp16 = concat(axis = var_1658, interleave = var_1659_interleave_0, values = (var_1656_cast_fp16, var_1654_cast_fp16_0))[name = string("op_1659_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_1660_cast_fp16 = mul(x = var_1659_cast_fp16, y = var_866_cast_fp16)[name = string("op_1660_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_25_cast_fp16 = add(x = var_1653_cast_fp16, y = var_1660_cast_fp16)[name = string("key_states_25_cast_fp16")];
            tensor<int32, [1]> expand_dims_24 = const()[name = string("expand_dims_24"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> expand_dims_25 = const()[name = string("expand_dims_25"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_27 = const()[name = string("expand_dims_27"), val = tensor<int32, [1]>([0])];
            int32 concat_29_axis_0 = const()[name = string("concat_29_axis_0"), val = int32(0)];
            bool concat_29_interleave_0 = const()[name = string("concat_29_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_29 = concat(axis = concat_29_axis_0, interleave = concat_29_interleave_0, values = (expand_dims_24, expand_dims_25, position_id, expand_dims_27))[name = string("concat_29")];
            tensor<int32, [1]> expand_dims_28 = const()[name = string("expand_dims_28"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> concat_30_values1_0 = const()[name = string("concat_30_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_30_values3_0 = const()[name = string("concat_30_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_30_axis_0 = const()[name = string("concat_30_axis_0"), val = int32(0)];
            bool concat_30_interleave_0 = const()[name = string("concat_30_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_30 = concat(axis = concat_30_axis_0, interleave = concat_30_interleave_0, values = (expand_dims_28, concat_30_values1_0, cache_position_end, concat_30_values3_0))[name = string("concat_30")];
            tensor<int32, [4]> key_states_27_perm_0 = const()[name = string("key_states_27_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_3_stride_0 = const()[name = string("key_cache_internal_tensor_assign_3_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_3_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_3_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_3_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_3_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_3_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_3_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_27_cast_fp16 = transpose(perm = key_states_27_perm_0, x = key_states_25_cast_fp16)[name = string("transpose_512")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_3_cast_fp16 = slice_update(begin = concat_29, begin_mask = key_cache_internal_tensor_assign_3_begin_mask_0, end = concat_30, end_mask = key_cache_internal_tensor_assign_3_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_3_squeeze_mask_0, stride = key_cache_internal_tensor_assign_3_stride_0, update = key_states_27_cast_fp16, x = coreml_update_state_282)[name = string("key_cache_internal_tensor_assign_3_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_3_cast_fp16, input = key_cache)[name = string("coreml_update_state_284_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_284 = read_state(input = key_cache)[name = string("coreml_update_state_284")];
            tensor<int32, [4]> value_states_15_perm_0 = const()[name = string("value_states_15_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_3_stride_0 = const()[name = string("value_cache_internal_tensor_assign_3_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_3_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_3_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_3_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_3_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_3_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_3_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_15_cast_fp16 = transpose(perm = value_states_15_perm_0, x = var_1636_cast_fp16)[name = string("transpose_511")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_3_cast_fp16 = slice_update(begin = concat_29, begin_mask = value_cache_internal_tensor_assign_3_begin_mask_0, end = concat_30, end_mask = value_cache_internal_tensor_assign_3_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_3_squeeze_mask_0, stride = value_cache_internal_tensor_assign_3_stride_0, update = value_states_15_cast_fp16, x = coreml_update_state_283)[name = string("value_cache_internal_tensor_assign_3_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_3_cast_fp16, input = value_cache)[name = string("coreml_update_state_285_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_285 = read_state(input = value_cache)[name = string("coreml_update_state_285")];
            tensor<int32, [4]> var_1730_begin_0 = const()[name = string("op_1730_begin_0"), val = tensor<int32, [4]>([2, 0, 0, 0])];
            tensor<int32, [4]> var_1730_end_0 = const()[name = string("op_1730_end_0"), val = tensor<int32, [4]>([3, 2, 2048, 128])];
            tensor<bool, [4]> var_1730_end_mask_0 = const()[name = string("op_1730_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1730_cast_fp16 = slice_by_index(begin = var_1730_begin_0, end = var_1730_end_0, end_mask = var_1730_end_mask_0, x = coreml_update_state_284)[name = string("op_1730_cast_fp16")];
            tensor<int32, [2]> tile_4 = const()[name = string("tile_4"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1733_axis_0 = const()[name = string("op_1733_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1733_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1733_cast_fp16_1 = split(axis = var_1733_axis_0, split_sizes = tile_4, x = var_1730_cast_fp16)[name = string("op_1733_cast_fp16")];
            tensor<int32, [4]> var_1740_begin_0 = const()[name = string("op_1740_begin_0"), val = tensor<int32, [4]>([2, 0, 0, 0])];
            tensor<int32, [4]> var_1740_end_0 = const()[name = string("op_1740_end_0"), val = tensor<int32, [4]>([3, 2, 2048, 128])];
            tensor<bool, [4]> var_1740_end_mask_0 = const()[name = string("op_1740_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1740_cast_fp16 = slice_by_index(begin = var_1740_begin_0, end = var_1740_end_0, end_mask = var_1740_end_mask_0, x = coreml_update_state_285)[name = string("op_1740_cast_fp16")];
            tensor<int32, [2]> tile_5 = const()[name = string("tile_5"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1743_axis_0 = const()[name = string("op_1743_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1743_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1743_cast_fp16_1 = split(axis = var_1743_axis_0, split_sizes = tile_5, x = var_1740_cast_fp16)[name = string("op_1743_cast_fp16")];
            tensor<int32, [2]> var_1746_split_sizes_0 = const()[name = string("op_1746_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_1746_axis_0 = const()[name = string("op_1746_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_1746_0, tensor<fp16, [1, 8, 128, 64]> var_1746_1 = split(axis = var_1746_axis_0, split_sizes = var_1746_split_sizes_0, x = query_states_15_cast_fp16)[name = string("op_1746")];
            bool attn_weights_33_transpose_x_0 = const()[name = string("attn_weights_33_transpose_x_0"), val = bool(false)];
            bool attn_weights_33_transpose_y_0 = const()[name = string("attn_weights_33_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_33_cast_fp16 = matmul(transpose_x = attn_weights_33_transpose_x_0, transpose_y = attn_weights_33_transpose_y_0, x = var_1733_cast_fp16_0, y = var_1746_0)[name = string("attn_weights_33_cast_fp16")];
            fp16 var_1749_to_fp16 = const()[name = string("op_1749_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_35_cast_fp16 = mul(x = attn_weights_33_cast_fp16, y = var_1749_to_fp16)[name = string("attn_weights_35_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_37_cast_fp16 = add(x = attn_weights_35_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_37_cast_fp16")];
            int32 var_1753 = const()[name = string("op_1753"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_39_cast_fp16 = softmax(axis = var_1753, x = attn_weights_37_cast_fp16)[name = string("attn_weights_39_cast_fp16")];
            bool var_1759_transpose_x_1 = const()[name = string("op_1759_transpose_x_1"), val = bool(true)];
            bool var_1759_transpose_y_1 = const()[name = string("op_1759_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_1759_cast_fp16 = matmul(transpose_x = var_1759_transpose_x_1, transpose_y = var_1759_transpose_y_1, x = attn_weights_39_cast_fp16, y = var_1743_cast_fp16_0)[name = string("op_1759_cast_fp16")];
            bool attn_weights_41_transpose_x_0 = const()[name = string("attn_weights_41_transpose_x_0"), val = bool(false)];
            bool attn_weights_41_transpose_y_0 = const()[name = string("attn_weights_41_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_41_cast_fp16 = matmul(transpose_x = attn_weights_41_transpose_x_0, transpose_y = attn_weights_41_transpose_y_0, x = var_1733_cast_fp16_1, y = var_1746_1)[name = string("attn_weights_41_cast_fp16")];
            fp16 var_1761_to_fp16 = const()[name = string("op_1761_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_43_cast_fp16 = mul(x = attn_weights_41_cast_fp16, y = var_1761_to_fp16)[name = string("attn_weights_43_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_45_cast_fp16 = add(x = attn_weights_43_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_45_cast_fp16")];
            int32 var_1765 = const()[name = string("op_1765"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_47_cast_fp16 = softmax(axis = var_1765, x = attn_weights_45_cast_fp16)[name = string("attn_weights_47_cast_fp16")];
            bool attn_output_17_transpose_x_1 = const()[name = string("attn_output_17_transpose_x_1"), val = bool(true)];
            bool attn_output_17_transpose_y_1 = const()[name = string("attn_output_17_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_17_cast_fp16 = matmul(transpose_x = attn_output_17_transpose_x_1, transpose_y = attn_output_17_transpose_y_1, x = attn_weights_47_cast_fp16, y = var_1743_cast_fp16_1)[name = string("attn_output_17_cast_fp16")];
            int32 var_1773 = const()[name = string("op_1773"), val = int32(1)];
            bool attn_output_19_interleave_0 = const()[name = string("attn_output_19_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_19_cast_fp16 = concat(axis = var_1773, interleave = attn_output_19_interleave_0, values = (var_1759_cast_fp16, attn_output_17_cast_fp16))[name = string("attn_output_19_cast_fp16")];
            tensor<int32, [4]> var_1777_perm_0 = const()[name = string("op_1777_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_35x = const()[name = string("concat_35x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_1777_cast_fp16 = transpose(perm = var_1777_perm_0, x = attn_output_19_cast_fp16)[name = string("transpose_510")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_23_cast_fp16 = reshape(shape = concat_35x, x = var_1777_cast_fp16)[name = string("attn_output_23_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_2_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_2_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1146788992)))];
            tensor<int32, [2]> hidden_states_23_strides_0 = const()[name = string("hidden_states_23_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_23_pad_type_0 = const()[name = string("hidden_states_23_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_23_pad_0 = const()[name = string("hidden_states_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_23_dilations_0 = const()[name = string("hidden_states_23_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_23_groups_0 = const()[name = string("hidden_states_23_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_23_cast_fp16 = conv(dilations = hidden_states_23_dilations_0, groups = hidden_states_23_groups_0, pad = hidden_states_23_pad_0, pad_type = hidden_states_23_pad_type_0, strides = hidden_states_23_strides_0, weight = layers_2_self_attn_o_proj_weight_to_fp16, x = attn_output_23_cast_fp16)[name = string("hidden_states_23_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_25_cast_fp16 = add(x = hidden_states_19_cast_fp16, y = hidden_states_23_cast_fp16)[name = string("hidden_states_25_cast_fp16")];
            fp16 const_30_promoted_to_fp16 = const()[name = string("const_30_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_1810_cast_fp16 = mul(x = hidden_states_25_cast_fp16, y = const_30_promoted_to_fp16)[name = string("op_1810_cast_fp16")];
            int32 var_1808 = const()[name = string("op_1808"), val = int32(1)];
            bool doubled_21_interleave_0 = const()[name = string("doubled_21_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_21_cast_fp16 = concat(axis = var_1808, interleave = doubled_21_interleave_0, values = (hidden_states_25_cast_fp16, var_1810_cast_fp16))[name = string("doubled_21_cast_fp16")];
            tensor<int32, [1]> out_11_axes_0 = const()[name = string("out_11_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_11_gamma_0_to_fp16 = const()[name = string("out_11_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1155177664)))];
            fp16 var_1820_to_fp16 = const()[name = string("op_1820_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_11_cast_fp16 = layer_norm(axes = out_11_axes_0, epsilon = var_1820_to_fp16, gamma = out_11_gamma_0_to_fp16, x = doubled_21_cast_fp16)[name = string("out_11_cast_fp16")];
            tensor<int32, [2]> var_1831_split_sizes_0 = const()[name = string("op_1831_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1831_axis_0 = const()[name = string("op_1831_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_1831_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_1831_cast_fp16_1 = split(axis = var_1831_axis_0, split_sizes = var_1831_split_sizes_0, x = out_11_cast_fp16)[name = string("op_1831_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_2_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_2_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1155185920)))];
            tensor<int32, [2]> input_5_strides_0 = const()[name = string("input_5_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_5_pad_type_0 = const()[name = string("input_5_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_5_dilations_0 = const()[name = string("input_5_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_5_groups_0 = const()[name = string("input_5_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_5_cast_fp16 = conv(dilations = input_5_dilations_0, groups = input_5_groups_0, pad = input_5_pad_0, pad_type = input_5_pad_type_0, strides = input_5_strides_0, weight = layers_2_mlp_gate_proj_weight_to_fp16, x = var_1831_cast_fp16_0)[name = string("input_5_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_1848_cast_fp16 = silu(x = input_5_cast_fp16)[name = string("op_1848_cast_fp16")];
            tensor<int32, [2]> var_1854_strides_0 = const()[name = string("op_1854_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_1854_pad_type_0 = const()[name = string("op_1854_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_1854_pad_0 = const()[name = string("op_1854_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_1854_dilations_0 = const()[name = string("op_1854_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1854_groups_0 = const()[name = string("op_1854_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_1854_cast_fp16 = conv(dilations = var_1854_dilations_0, groups = var_1854_groups_0, pad = var_1854_pad_0, pad_type = var_1854_pad_type_0, strides = var_1854_strides_0, weight = layers_2_mlp_up_proj_weight_cast_fp16, x = var_1831_cast_fp16_0)[name = string("op_1854_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_29_cast_fp16 = mul(x = var_1848_cast_fp16, y = var_1854_cast_fp16)[name = string("x_29_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_2_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_2_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1180351808)))];
            tensor<int32, [2]> hidden_states_27_strides_0 = const()[name = string("hidden_states_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_27_pad_type_0 = const()[name = string("hidden_states_27_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_27_pad_0 = const()[name = string("hidden_states_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_27_dilations_0 = const()[name = string("hidden_states_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_27_groups_0 = const()[name = string("hidden_states_27_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_27_cast_fp16 = conv(dilations = hidden_states_27_dilations_0, groups = hidden_states_27_groups_0, pad = hidden_states_27_pad_0, pad_type = hidden_states_27_pad_type_0, strides = hidden_states_27_strides_0, weight = layers_2_mlp_down_proj_weight_to_fp16, x = x_29_cast_fp16)[name = string("hidden_states_27_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_29_cast_fp16 = add(x = hidden_states_25_cast_fp16, y = hidden_states_27_cast_fp16)[name = string("hidden_states_29_cast_fp16")];
            fp16 const_32_promoted_to_fp16 = const()[name = string("const_32_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_1872_cast_fp16 = mul(x = hidden_states_29_cast_fp16, y = const_32_promoted_to_fp16)[name = string("op_1872_cast_fp16")];
            int32 var_1870 = const()[name = string("op_1870"), val = int32(1)];
            bool doubled_25_interleave_0 = const()[name = string("doubled_25_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_25_cast_fp16 = concat(axis = var_1870, interleave = doubled_25_interleave_0, values = (hidden_states_29_cast_fp16, var_1872_cast_fp16))[name = string("doubled_25_cast_fp16")];
            tensor<int32, [1]> out_13_axes_0 = const()[name = string("out_13_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_13_gamma_0_to_fp16 = const()[name = string("out_13_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1205517696)))];
            fp16 var_1882_to_fp16 = const()[name = string("op_1882_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_13_cast_fp16 = layer_norm(axes = out_13_axes_0, epsilon = var_1882_to_fp16, gamma = out_13_gamma_0_to_fp16, x = doubled_25_cast_fp16)[name = string("out_13_cast_fp16")];
            tensor<int32, [2]> var_1893_split_sizes_0 = const()[name = string("op_1893_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1893_axis_0 = const()[name = string("op_1893_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_1893_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_1893_cast_fp16_1 = split(axis = var_1893_axis_0, split_sizes = var_1893_split_sizes_0, x = out_13_cast_fp16)[name = string("op_1893_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_3_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_3_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1205525952)))];
            tensor<int32, [2]> query_states_19_strides_0 = const()[name = string("query_states_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_19_pad_type_0 = const()[name = string("query_states_19_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_19_pad_0 = const()[name = string("query_states_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_19_dilations_0 = const()[name = string("query_states_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_19_groups_0 = const()[name = string("query_states_19_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_19_cast_fp16 = conv(dilations = query_states_19_dilations_0, groups = query_states_19_groups_0, pad = query_states_19_pad_0, pad_type = query_states_19_pad_type_0, strides = query_states_19_strides_0, weight = layers_3_self_attn_q_proj_weight_to_fp16, x = var_1893_cast_fp16_0)[name = string("query_states_19_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_3_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_3_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1213914624)))];
            tensor<int32, [2]> key_states_31_strides_0 = const()[name = string("key_states_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_31_pad_type_0 = const()[name = string("key_states_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_31_pad_0 = const()[name = string("key_states_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_31_dilations_0 = const()[name = string("key_states_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_31_groups_0 = const()[name = string("key_states_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_31_cast_fp16 = conv(dilations = key_states_31_dilations_0, groups = key_states_31_groups_0, pad = key_states_31_pad_0, pad_type = key_states_31_pad_type_0, strides = key_states_31_strides_0, weight = layers_3_self_attn_k_proj_weight_to_fp16, x = var_1893_cast_fp16_0)[name = string("key_states_31_cast_fp16")];
            tensor<int32, [2]> value_states_19_strides_0 = const()[name = string("value_states_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_19_pad_type_0 = const()[name = string("value_states_19_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_19_pad_0 = const()[name = string("value_states_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_19_dilations_0 = const()[name = string("value_states_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_19_groups_0 = const()[name = string("value_states_19_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_19_cast_fp16 = conv(dilations = value_states_19_dilations_0, groups = value_states_19_groups_0, pad = value_states_19_pad_0, pad_type = value_states_19_pad_type_0, strides = value_states_19_strides_0, weight = layers_3_self_attn_v_proj_weight_cast_fp16, x = var_1893_cast_fp16_0)[name = string("value_states_19_cast_fp16")];
            tensor<int32, [4]> concat_36x = const()[name = string("concat_36x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_31_cast_fp16 = reshape(shape = concat_36x, x = query_states_19_cast_fp16)[name = string("x_31_cast_fp16")];
            tensor<int32, [4]> concat_37x = const()[name = string("concat_37x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_1950_cast_fp16 = reshape(shape = concat_37x, x = key_states_31_cast_fp16)[name = string("op_1950_cast_fp16")];
            tensor<int32, [4]> concat_38x = const()[name = string("concat_38x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_1957_cast_fp16 = reshape(shape = concat_38x, x = value_states_19_cast_fp16)[name = string("op_1957_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_1961_cast_fp16 = mul(x = x_31_cast_fp16, y = var_859_cast_fp16)[name = string("op_1961_cast_fp16")];
            tensor<int32, [2]> var_1962_split_sizes_0 = const()[name = string("op_1962_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1962_axis_0 = const()[name = string("op_1962_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_1962_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_1962_cast_fp16_1 = split(axis = var_1962_axis_0, split_sizes = var_1962_split_sizes_0, x = x_31_cast_fp16)[name = string("op_1962_cast_fp16")];
            fp16 const_34_promoted_to_fp16 = const()[name = string("const_34_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_1964_cast_fp16 = mul(x = var_1962_cast_fp16_1, y = const_34_promoted_to_fp16)[name = string("op_1964_cast_fp16")];
            int32 var_1966 = const()[name = string("op_1966"), val = int32(-2)];
            bool var_1967_interleave_0 = const()[name = string("op_1967_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_1967_cast_fp16 = concat(axis = var_1966, interleave = var_1967_interleave_0, values = (var_1964_cast_fp16, var_1962_cast_fp16_0))[name = string("op_1967_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_1968_cast_fp16 = mul(x = var_1967_cast_fp16, y = var_866_cast_fp16)[name = string("op_1968_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_21_cast_fp16 = add(x = var_1961_cast_fp16, y = var_1968_cast_fp16)[name = string("query_states_21_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_1974_cast_fp16 = mul(x = var_1950_cast_fp16, y = var_859_cast_fp16)[name = string("op_1974_cast_fp16")];
            tensor<int32, [2]> var_1975_split_sizes_0 = const()[name = string("op_1975_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1975_axis_0 = const()[name = string("op_1975_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_1975_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_1975_cast_fp16_1 = split(axis = var_1975_axis_0, split_sizes = var_1975_split_sizes_0, x = var_1950_cast_fp16)[name = string("op_1975_cast_fp16")];
            fp16 const_35_promoted_to_fp16 = const()[name = string("const_35_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_1977_cast_fp16 = mul(x = var_1975_cast_fp16_1, y = const_35_promoted_to_fp16)[name = string("op_1977_cast_fp16")];
            int32 var_1979 = const()[name = string("op_1979"), val = int32(-2)];
            bool var_1980_interleave_0 = const()[name = string("op_1980_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_1980_cast_fp16 = concat(axis = var_1979, interleave = var_1980_interleave_0, values = (var_1977_cast_fp16, var_1975_cast_fp16_0))[name = string("op_1980_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_1981_cast_fp16 = mul(x = var_1980_cast_fp16, y = var_866_cast_fp16)[name = string("op_1981_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_35_cast_fp16 = add(x = var_1974_cast_fp16, y = var_1981_cast_fp16)[name = string("key_states_35_cast_fp16")];
            tensor<int32, [1]> expand_dims_36 = const()[name = string("expand_dims_36"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> expand_dims_37 = const()[name = string("expand_dims_37"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_39 = const()[name = string("expand_dims_39"), val = tensor<int32, [1]>([0])];
            int32 concat_41_axis_0 = const()[name = string("concat_41_axis_0"), val = int32(0)];
            bool concat_41_interleave_0 = const()[name = string("concat_41_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_41 = concat(axis = concat_41_axis_0, interleave = concat_41_interleave_0, values = (expand_dims_36, expand_dims_37, position_id, expand_dims_39))[name = string("concat_41")];
            tensor<int32, [1]> expand_dims_40 = const()[name = string("expand_dims_40"), val = tensor<int32, [1]>([4])];
            tensor<int32, [1]> concat_42_values1_0 = const()[name = string("concat_42_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_42_values3_0 = const()[name = string("concat_42_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_42_axis_0 = const()[name = string("concat_42_axis_0"), val = int32(0)];
            bool concat_42_interleave_0 = const()[name = string("concat_42_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_42 = concat(axis = concat_42_axis_0, interleave = concat_42_interleave_0, values = (expand_dims_40, concat_42_values1_0, cache_position_end, concat_42_values3_0))[name = string("concat_42")];
            tensor<int32, [4]> key_states_37_perm_0 = const()[name = string("key_states_37_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_4_stride_0 = const()[name = string("key_cache_internal_tensor_assign_4_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_4_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_4_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_4_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_4_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_4_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_4_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_37_cast_fp16 = transpose(perm = key_states_37_perm_0, x = key_states_35_cast_fp16)[name = string("transpose_509")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_4_cast_fp16 = slice_update(begin = concat_41, begin_mask = key_cache_internal_tensor_assign_4_begin_mask_0, end = concat_42, end_mask = key_cache_internal_tensor_assign_4_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_4_squeeze_mask_0, stride = key_cache_internal_tensor_assign_4_stride_0, update = key_states_37_cast_fp16, x = coreml_update_state_284)[name = string("key_cache_internal_tensor_assign_4_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_4_cast_fp16, input = key_cache)[name = string("coreml_update_state_286_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_286 = read_state(input = key_cache)[name = string("coreml_update_state_286")];
            tensor<int32, [4]> value_states_21_perm_0 = const()[name = string("value_states_21_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_4_stride_0 = const()[name = string("value_cache_internal_tensor_assign_4_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_4_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_4_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_4_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_4_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_4_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_4_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_21_cast_fp16 = transpose(perm = value_states_21_perm_0, x = var_1957_cast_fp16)[name = string("transpose_508")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_4_cast_fp16 = slice_update(begin = concat_41, begin_mask = value_cache_internal_tensor_assign_4_begin_mask_0, end = concat_42, end_mask = value_cache_internal_tensor_assign_4_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_4_squeeze_mask_0, stride = value_cache_internal_tensor_assign_4_stride_0, update = value_states_21_cast_fp16, x = coreml_update_state_285)[name = string("value_cache_internal_tensor_assign_4_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_4_cast_fp16, input = value_cache)[name = string("coreml_update_state_287_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_287 = read_state(input = value_cache)[name = string("coreml_update_state_287")];
            tensor<int32, [4]> var_2051_begin_0 = const()[name = string("op_2051_begin_0"), val = tensor<int32, [4]>([3, 0, 0, 0])];
            tensor<int32, [4]> var_2051_end_0 = const()[name = string("op_2051_end_0"), val = tensor<int32, [4]>([4, 2, 2048, 128])];
            tensor<bool, [4]> var_2051_end_mask_0 = const()[name = string("op_2051_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2051_cast_fp16 = slice_by_index(begin = var_2051_begin_0, end = var_2051_end_0, end_mask = var_2051_end_mask_0, x = coreml_update_state_286)[name = string("op_2051_cast_fp16")];
            tensor<int32, [2]> tile_6 = const()[name = string("tile_6"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2054_axis_0 = const()[name = string("op_2054_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2054_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2054_cast_fp16_1 = split(axis = var_2054_axis_0, split_sizes = tile_6, x = var_2051_cast_fp16)[name = string("op_2054_cast_fp16")];
            tensor<int32, [4]> var_2061_begin_0 = const()[name = string("op_2061_begin_0"), val = tensor<int32, [4]>([3, 0, 0, 0])];
            tensor<int32, [4]> var_2061_end_0 = const()[name = string("op_2061_end_0"), val = tensor<int32, [4]>([4, 2, 2048, 128])];
            tensor<bool, [4]> var_2061_end_mask_0 = const()[name = string("op_2061_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2061_cast_fp16 = slice_by_index(begin = var_2061_begin_0, end = var_2061_end_0, end_mask = var_2061_end_mask_0, x = coreml_update_state_287)[name = string("op_2061_cast_fp16")];
            tensor<int32, [2]> tile_7 = const()[name = string("tile_7"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2064_axis_0 = const()[name = string("op_2064_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2064_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2064_cast_fp16_1 = split(axis = var_2064_axis_0, split_sizes = tile_7, x = var_2061_cast_fp16)[name = string("op_2064_cast_fp16")];
            tensor<int32, [2]> var_2067_split_sizes_0 = const()[name = string("op_2067_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_2067_axis_0 = const()[name = string("op_2067_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_2067_0, tensor<fp16, [1, 8, 128, 64]> var_2067_1 = split(axis = var_2067_axis_0, split_sizes = var_2067_split_sizes_0, x = query_states_21_cast_fp16)[name = string("op_2067")];
            bool attn_weights_49_transpose_x_0 = const()[name = string("attn_weights_49_transpose_x_0"), val = bool(false)];
            bool attn_weights_49_transpose_y_0 = const()[name = string("attn_weights_49_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_49_cast_fp16 = matmul(transpose_x = attn_weights_49_transpose_x_0, transpose_y = attn_weights_49_transpose_y_0, x = var_2054_cast_fp16_0, y = var_2067_0)[name = string("attn_weights_49_cast_fp16")];
            fp16 var_2070_to_fp16 = const()[name = string("op_2070_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_51_cast_fp16 = mul(x = attn_weights_49_cast_fp16, y = var_2070_to_fp16)[name = string("attn_weights_51_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_53_cast_fp16 = add(x = attn_weights_51_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_53_cast_fp16")];
            int32 var_2074 = const()[name = string("op_2074"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_55_cast_fp16 = softmax(axis = var_2074, x = attn_weights_53_cast_fp16)[name = string("attn_weights_55_cast_fp16")];
            bool var_2080_transpose_x_1 = const()[name = string("op_2080_transpose_x_1"), val = bool(true)];
            bool var_2080_transpose_y_1 = const()[name = string("op_2080_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_2080_cast_fp16 = matmul(transpose_x = var_2080_transpose_x_1, transpose_y = var_2080_transpose_y_1, x = attn_weights_55_cast_fp16, y = var_2064_cast_fp16_0)[name = string("op_2080_cast_fp16")];
            bool attn_weights_57_transpose_x_0 = const()[name = string("attn_weights_57_transpose_x_0"), val = bool(false)];
            bool attn_weights_57_transpose_y_0 = const()[name = string("attn_weights_57_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_57_cast_fp16 = matmul(transpose_x = attn_weights_57_transpose_x_0, transpose_y = attn_weights_57_transpose_y_0, x = var_2054_cast_fp16_1, y = var_2067_1)[name = string("attn_weights_57_cast_fp16")];
            fp16 var_2082_to_fp16 = const()[name = string("op_2082_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_59_cast_fp16 = mul(x = attn_weights_57_cast_fp16, y = var_2082_to_fp16)[name = string("attn_weights_59_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_61_cast_fp16 = add(x = attn_weights_59_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_61_cast_fp16")];
            int32 var_2086 = const()[name = string("op_2086"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_63_cast_fp16 = softmax(axis = var_2086, x = attn_weights_61_cast_fp16)[name = string("attn_weights_63_cast_fp16")];
            bool attn_output_25_transpose_x_1 = const()[name = string("attn_output_25_transpose_x_1"), val = bool(true)];
            bool attn_output_25_transpose_y_1 = const()[name = string("attn_output_25_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_25_cast_fp16 = matmul(transpose_x = attn_output_25_transpose_x_1, transpose_y = attn_output_25_transpose_y_1, x = attn_weights_63_cast_fp16, y = var_2064_cast_fp16_1)[name = string("attn_output_25_cast_fp16")];
            int32 var_2094 = const()[name = string("op_2094"), val = int32(1)];
            bool attn_output_27_interleave_0 = const()[name = string("attn_output_27_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_27_cast_fp16 = concat(axis = var_2094, interleave = attn_output_27_interleave_0, values = (var_2080_cast_fp16, attn_output_25_cast_fp16))[name = string("attn_output_27_cast_fp16")];
            tensor<int32, [4]> var_2098_perm_0 = const()[name = string("op_2098_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_47x = const()[name = string("concat_47x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_2098_cast_fp16 = transpose(perm = var_2098_perm_0, x = attn_output_27_cast_fp16)[name = string("transpose_507")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_31_cast_fp16 = reshape(shape = concat_47x, x = var_2098_cast_fp16)[name = string("attn_output_31_cast_fp16")];
            tensor<int32, [2]> hidden_states_33_strides_0 = const()[name = string("hidden_states_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_33_pad_type_0 = const()[name = string("hidden_states_33_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_33_pad_0 = const()[name = string("hidden_states_33_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_33_dilations_0 = const()[name = string("hidden_states_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_33_groups_0 = const()[name = string("hidden_states_33_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_33_cast_fp16 = conv(dilations = hidden_states_33_dilations_0, groups = hidden_states_33_groups_0, pad = hidden_states_33_pad_0, pad_type = hidden_states_33_pad_type_0, strides = hidden_states_33_strides_0, weight = layers_3_self_attn_o_proj_weight_cast_fp16, x = attn_output_31_cast_fp16)[name = string("hidden_states_33_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_35_cast_fp16 = add(x = hidden_states_29_cast_fp16, y = hidden_states_33_cast_fp16)[name = string("hidden_states_35_cast_fp16")];
            fp16 const_40_promoted_to_fp16 = const()[name = string("const_40_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_2131_cast_fp16 = mul(x = hidden_states_35_cast_fp16, y = const_40_promoted_to_fp16)[name = string("op_2131_cast_fp16")];
            int32 var_2129 = const()[name = string("op_2129"), val = int32(1)];
            bool doubled_29_interleave_0 = const()[name = string("doubled_29_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_29_cast_fp16 = concat(axis = var_2129, interleave = doubled_29_interleave_0, values = (hidden_states_35_cast_fp16, var_2131_cast_fp16))[name = string("doubled_29_cast_fp16")];
            tensor<int32, [1]> out_15_axes_0 = const()[name = string("out_15_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_15_gamma_0_to_fp16 = const()[name = string("out_15_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1214963264)))];
            fp16 var_2141_to_fp16 = const()[name = string("op_2141_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_15_cast_fp16 = layer_norm(axes = out_15_axes_0, epsilon = var_2141_to_fp16, gamma = out_15_gamma_0_to_fp16, x = doubled_29_cast_fp16)[name = string("out_15_cast_fp16")];
            tensor<int32, [2]> var_2152_split_sizes_0 = const()[name = string("op_2152_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2152_axis_0 = const()[name = string("op_2152_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_2152_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_2152_cast_fp16_1 = split(axis = var_2152_axis_0, split_sizes = var_2152_split_sizes_0, x = out_15_cast_fp16)[name = string("op_2152_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_3_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_3_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1214971520)))];
            tensor<int32, [2]> input_7_strides_0 = const()[name = string("input_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_7_pad_type_0 = const()[name = string("input_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_7_pad_0 = const()[name = string("input_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_7_dilations_0 = const()[name = string("input_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_7_groups_0 = const()[name = string("input_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_7_cast_fp16 = conv(dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = layers_3_mlp_gate_proj_weight_to_fp16, x = var_2152_cast_fp16_0)[name = string("input_7_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_2169_cast_fp16 = silu(x = input_7_cast_fp16)[name = string("op_2169_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_3_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_3_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1240137408)))];
            tensor<int32, [2]> var_2175_strides_0 = const()[name = string("op_2175_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_2175_pad_type_0 = const()[name = string("op_2175_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_2175_pad_0 = const()[name = string("op_2175_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_2175_dilations_0 = const()[name = string("op_2175_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2175_groups_0 = const()[name = string("op_2175_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_2175_cast_fp16 = conv(dilations = var_2175_dilations_0, groups = var_2175_groups_0, pad = var_2175_pad_0, pad_type = var_2175_pad_type_0, strides = var_2175_strides_0, weight = layers_3_mlp_up_proj_weight_to_fp16, x = var_2152_cast_fp16_0)[name = string("op_2175_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_39_cast_fp16 = mul(x = var_2169_cast_fp16, y = var_2175_cast_fp16)[name = string("x_39_cast_fp16")];
            tensor<int32, [2]> hidden_states_37_strides_0 = const()[name = string("hidden_states_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_37_pad_type_0 = const()[name = string("hidden_states_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_37_pad_0 = const()[name = string("hidden_states_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_37_dilations_0 = const()[name = string("hidden_states_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_37_groups_0 = const()[name = string("hidden_states_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_37_cast_fp16 = conv(dilations = hidden_states_37_dilations_0, groups = hidden_states_37_groups_0, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = hidden_states_37_strides_0, weight = layers_3_mlp_down_proj_weight_cast_fp16, x = x_39_cast_fp16)[name = string("hidden_states_37_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_39_cast_fp16 = add(x = hidden_states_35_cast_fp16, y = hidden_states_37_cast_fp16)[name = string("hidden_states_39_cast_fp16")];
            fp16 const_42_promoted_to_fp16 = const()[name = string("const_42_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_2193_cast_fp16 = mul(x = hidden_states_39_cast_fp16, y = const_42_promoted_to_fp16)[name = string("op_2193_cast_fp16")];
            int32 var_2191 = const()[name = string("op_2191"), val = int32(1)];
            bool doubled_33_interleave_0 = const()[name = string("doubled_33_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_33_cast_fp16 = concat(axis = var_2191, interleave = doubled_33_interleave_0, values = (hidden_states_39_cast_fp16, var_2193_cast_fp16))[name = string("doubled_33_cast_fp16")];
            tensor<int32, [1]> out_17_axes_0 = const()[name = string("out_17_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_17_gamma_0_to_fp16 = const()[name = string("out_17_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1265303296)))];
            fp16 var_2203_to_fp16 = const()[name = string("op_2203_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_17_cast_fp16 = layer_norm(axes = out_17_axes_0, epsilon = var_2203_to_fp16, gamma = out_17_gamma_0_to_fp16, x = doubled_33_cast_fp16)[name = string("out_17_cast_fp16")];
            tensor<int32, [2]> var_2214_split_sizes_0 = const()[name = string("op_2214_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2214_axis_0 = const()[name = string("op_2214_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_2214_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_2214_cast_fp16_1 = split(axis = var_2214_axis_0, split_sizes = var_2214_split_sizes_0, x = out_17_cast_fp16)[name = string("op_2214_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_4_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_4_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1265311552)))];
            tensor<int32, [2]> query_states_25_strides_0 = const()[name = string("query_states_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_25_pad_type_0 = const()[name = string("query_states_25_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_25_pad_0 = const()[name = string("query_states_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_25_dilations_0 = const()[name = string("query_states_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_25_groups_0 = const()[name = string("query_states_25_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_25_cast_fp16 = conv(dilations = query_states_25_dilations_0, groups = query_states_25_groups_0, pad = query_states_25_pad_0, pad_type = query_states_25_pad_type_0, strides = query_states_25_strides_0, weight = layers_4_self_attn_q_proj_weight_to_fp16, x = var_2214_cast_fp16_0)[name = string("query_states_25_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_4_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_4_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1273700224)))];
            tensor<int32, [2]> key_states_41_strides_0 = const()[name = string("key_states_41_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_41_pad_type_0 = const()[name = string("key_states_41_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_41_pad_0 = const()[name = string("key_states_41_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_41_dilations_0 = const()[name = string("key_states_41_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_41_groups_0 = const()[name = string("key_states_41_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_41_cast_fp16 = conv(dilations = key_states_41_dilations_0, groups = key_states_41_groups_0, pad = key_states_41_pad_0, pad_type = key_states_41_pad_type_0, strides = key_states_41_strides_0, weight = layers_4_self_attn_k_proj_weight_to_fp16, x = var_2214_cast_fp16_0)[name = string("key_states_41_cast_fp16")];
            tensor<int32, [2]> value_states_25_strides_0 = const()[name = string("value_states_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_25_pad_type_0 = const()[name = string("value_states_25_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_25_pad_0 = const()[name = string("value_states_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_25_dilations_0 = const()[name = string("value_states_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_25_groups_0 = const()[name = string("value_states_25_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_25_cast_fp16 = conv(dilations = value_states_25_dilations_0, groups = value_states_25_groups_0, pad = value_states_25_pad_0, pad_type = value_states_25_pad_type_0, strides = value_states_25_strides_0, weight = layers_4_self_attn_v_proj_weight_cast_fp16, x = var_2214_cast_fp16_0)[name = string("value_states_25_cast_fp16")];
            tensor<int32, [4]> concat_48x = const()[name = string("concat_48x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_41_cast_fp16 = reshape(shape = concat_48x, x = query_states_25_cast_fp16)[name = string("x_41_cast_fp16")];
            tensor<int32, [4]> concat_49x = const()[name = string("concat_49x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_2271_cast_fp16 = reshape(shape = concat_49x, x = key_states_41_cast_fp16)[name = string("op_2271_cast_fp16")];
            tensor<int32, [4]> concat_50x = const()[name = string("concat_50x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_2278_cast_fp16 = reshape(shape = concat_50x, x = value_states_25_cast_fp16)[name = string("op_2278_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_2282_cast_fp16 = mul(x = x_41_cast_fp16, y = var_859_cast_fp16)[name = string("op_2282_cast_fp16")];
            tensor<int32, [2]> var_2283_split_sizes_0 = const()[name = string("op_2283_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2283_axis_0 = const()[name = string("op_2283_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_2283_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_2283_cast_fp16_1 = split(axis = var_2283_axis_0, split_sizes = var_2283_split_sizes_0, x = x_41_cast_fp16)[name = string("op_2283_cast_fp16")];
            fp16 const_44_promoted_to_fp16 = const()[name = string("const_44_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_2285_cast_fp16 = mul(x = var_2283_cast_fp16_1, y = const_44_promoted_to_fp16)[name = string("op_2285_cast_fp16")];
            int32 var_2287 = const()[name = string("op_2287"), val = int32(-2)];
            bool var_2288_interleave_0 = const()[name = string("op_2288_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_2288_cast_fp16 = concat(axis = var_2287, interleave = var_2288_interleave_0, values = (var_2285_cast_fp16, var_2283_cast_fp16_0))[name = string("op_2288_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_2289_cast_fp16 = mul(x = var_2288_cast_fp16, y = var_866_cast_fp16)[name = string("op_2289_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_27_cast_fp16 = add(x = var_2282_cast_fp16, y = var_2289_cast_fp16)[name = string("query_states_27_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_2295_cast_fp16 = mul(x = var_2271_cast_fp16, y = var_859_cast_fp16)[name = string("op_2295_cast_fp16")];
            tensor<int32, [2]> var_2296_split_sizes_0 = const()[name = string("op_2296_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2296_axis_0 = const()[name = string("op_2296_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_2296_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_2296_cast_fp16_1 = split(axis = var_2296_axis_0, split_sizes = var_2296_split_sizes_0, x = var_2271_cast_fp16)[name = string("op_2296_cast_fp16")];
            fp16 const_45_promoted_to_fp16 = const()[name = string("const_45_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_2298_cast_fp16 = mul(x = var_2296_cast_fp16_1, y = const_45_promoted_to_fp16)[name = string("op_2298_cast_fp16")];
            int32 var_2300 = const()[name = string("op_2300"), val = int32(-2)];
            bool var_2301_interleave_0 = const()[name = string("op_2301_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_2301_cast_fp16 = concat(axis = var_2300, interleave = var_2301_interleave_0, values = (var_2298_cast_fp16, var_2296_cast_fp16_0))[name = string("op_2301_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_2302_cast_fp16 = mul(x = var_2301_cast_fp16, y = var_866_cast_fp16)[name = string("op_2302_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_45_cast_fp16 = add(x = var_2295_cast_fp16, y = var_2302_cast_fp16)[name = string("key_states_45_cast_fp16")];
            tensor<int32, [1]> expand_dims_48 = const()[name = string("expand_dims_48"), val = tensor<int32, [1]>([4])];
            tensor<int32, [1]> expand_dims_49 = const()[name = string("expand_dims_49"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_51 = const()[name = string("expand_dims_51"), val = tensor<int32, [1]>([0])];
            int32 concat_53_axis_0 = const()[name = string("concat_53_axis_0"), val = int32(0)];
            bool concat_53_interleave_0 = const()[name = string("concat_53_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_53 = concat(axis = concat_53_axis_0, interleave = concat_53_interleave_0, values = (expand_dims_48, expand_dims_49, position_id, expand_dims_51))[name = string("concat_53")];
            tensor<int32, [1]> expand_dims_52 = const()[name = string("expand_dims_52"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> concat_54_values1_0 = const()[name = string("concat_54_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_54_values3_0 = const()[name = string("concat_54_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_54_axis_0 = const()[name = string("concat_54_axis_0"), val = int32(0)];
            bool concat_54_interleave_0 = const()[name = string("concat_54_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_54 = concat(axis = concat_54_axis_0, interleave = concat_54_interleave_0, values = (expand_dims_52, concat_54_values1_0, cache_position_end, concat_54_values3_0))[name = string("concat_54")];
            tensor<int32, [4]> key_states_47_perm_0 = const()[name = string("key_states_47_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_5_stride_0 = const()[name = string("key_cache_internal_tensor_assign_5_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_5_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_5_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_5_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_5_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_5_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_5_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_47_cast_fp16 = transpose(perm = key_states_47_perm_0, x = key_states_45_cast_fp16)[name = string("transpose_506")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_5_cast_fp16 = slice_update(begin = concat_53, begin_mask = key_cache_internal_tensor_assign_5_begin_mask_0, end = concat_54, end_mask = key_cache_internal_tensor_assign_5_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_5_squeeze_mask_0, stride = key_cache_internal_tensor_assign_5_stride_0, update = key_states_47_cast_fp16, x = coreml_update_state_286)[name = string("key_cache_internal_tensor_assign_5_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_5_cast_fp16, input = key_cache)[name = string("coreml_update_state_288_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_288 = read_state(input = key_cache)[name = string("coreml_update_state_288")];
            tensor<int32, [4]> value_states_27_perm_0 = const()[name = string("value_states_27_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_5_stride_0 = const()[name = string("value_cache_internal_tensor_assign_5_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_5_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_5_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_5_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_5_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_5_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_5_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_27_cast_fp16 = transpose(perm = value_states_27_perm_0, x = var_2278_cast_fp16)[name = string("transpose_505")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_5_cast_fp16 = slice_update(begin = concat_53, begin_mask = value_cache_internal_tensor_assign_5_begin_mask_0, end = concat_54, end_mask = value_cache_internal_tensor_assign_5_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_5_squeeze_mask_0, stride = value_cache_internal_tensor_assign_5_stride_0, update = value_states_27_cast_fp16, x = coreml_update_state_287)[name = string("value_cache_internal_tensor_assign_5_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_5_cast_fp16, input = value_cache)[name = string("coreml_update_state_289_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_289 = read_state(input = value_cache)[name = string("coreml_update_state_289")];
            tensor<int32, [4]> var_2372_begin_0 = const()[name = string("op_2372_begin_0"), val = tensor<int32, [4]>([4, 0, 0, 0])];
            tensor<int32, [4]> var_2372_end_0 = const()[name = string("op_2372_end_0"), val = tensor<int32, [4]>([5, 2, 2048, 128])];
            tensor<bool, [4]> var_2372_end_mask_0 = const()[name = string("op_2372_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2372_cast_fp16 = slice_by_index(begin = var_2372_begin_0, end = var_2372_end_0, end_mask = var_2372_end_mask_0, x = coreml_update_state_288)[name = string("op_2372_cast_fp16")];
            tensor<int32, [2]> tile_8 = const()[name = string("tile_8"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2375_axis_0 = const()[name = string("op_2375_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2375_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2375_cast_fp16_1 = split(axis = var_2375_axis_0, split_sizes = tile_8, x = var_2372_cast_fp16)[name = string("op_2375_cast_fp16")];
            tensor<int32, [4]> var_2382_begin_0 = const()[name = string("op_2382_begin_0"), val = tensor<int32, [4]>([4, 0, 0, 0])];
            tensor<int32, [4]> var_2382_end_0 = const()[name = string("op_2382_end_0"), val = tensor<int32, [4]>([5, 2, 2048, 128])];
            tensor<bool, [4]> var_2382_end_mask_0 = const()[name = string("op_2382_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2382_cast_fp16 = slice_by_index(begin = var_2382_begin_0, end = var_2382_end_0, end_mask = var_2382_end_mask_0, x = coreml_update_state_289)[name = string("op_2382_cast_fp16")];
            tensor<int32, [2]> tile_9 = const()[name = string("tile_9"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2385_axis_0 = const()[name = string("op_2385_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2385_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2385_cast_fp16_1 = split(axis = var_2385_axis_0, split_sizes = tile_9, x = var_2382_cast_fp16)[name = string("op_2385_cast_fp16")];
            tensor<int32, [2]> var_2388_split_sizes_0 = const()[name = string("op_2388_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_2388_axis_0 = const()[name = string("op_2388_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_2388_0, tensor<fp16, [1, 8, 128, 64]> var_2388_1 = split(axis = var_2388_axis_0, split_sizes = var_2388_split_sizes_0, x = query_states_27_cast_fp16)[name = string("op_2388")];
            bool attn_weights_65_transpose_x_0 = const()[name = string("attn_weights_65_transpose_x_0"), val = bool(false)];
            bool attn_weights_65_transpose_y_0 = const()[name = string("attn_weights_65_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_65_cast_fp16 = matmul(transpose_x = attn_weights_65_transpose_x_0, transpose_y = attn_weights_65_transpose_y_0, x = var_2375_cast_fp16_0, y = var_2388_0)[name = string("attn_weights_65_cast_fp16")];
            fp16 var_2391_to_fp16 = const()[name = string("op_2391_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_67_cast_fp16 = mul(x = attn_weights_65_cast_fp16, y = var_2391_to_fp16)[name = string("attn_weights_67_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_69_cast_fp16 = add(x = attn_weights_67_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_69_cast_fp16")];
            int32 var_2395 = const()[name = string("op_2395"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_71_cast_fp16 = softmax(axis = var_2395, x = attn_weights_69_cast_fp16)[name = string("attn_weights_71_cast_fp16")];
            bool var_2401_transpose_x_1 = const()[name = string("op_2401_transpose_x_1"), val = bool(true)];
            bool var_2401_transpose_y_1 = const()[name = string("op_2401_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_2401_cast_fp16 = matmul(transpose_x = var_2401_transpose_x_1, transpose_y = var_2401_transpose_y_1, x = attn_weights_71_cast_fp16, y = var_2385_cast_fp16_0)[name = string("op_2401_cast_fp16")];
            bool attn_weights_73_transpose_x_0 = const()[name = string("attn_weights_73_transpose_x_0"), val = bool(false)];
            bool attn_weights_73_transpose_y_0 = const()[name = string("attn_weights_73_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_73_cast_fp16 = matmul(transpose_x = attn_weights_73_transpose_x_0, transpose_y = attn_weights_73_transpose_y_0, x = var_2375_cast_fp16_1, y = var_2388_1)[name = string("attn_weights_73_cast_fp16")];
            fp16 var_2403_to_fp16 = const()[name = string("op_2403_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_75_cast_fp16 = mul(x = attn_weights_73_cast_fp16, y = var_2403_to_fp16)[name = string("attn_weights_75_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_77_cast_fp16 = add(x = attn_weights_75_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_77_cast_fp16")];
            int32 var_2407 = const()[name = string("op_2407"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_79_cast_fp16 = softmax(axis = var_2407, x = attn_weights_77_cast_fp16)[name = string("attn_weights_79_cast_fp16")];
            bool attn_output_33_transpose_x_1 = const()[name = string("attn_output_33_transpose_x_1"), val = bool(true)];
            bool attn_output_33_transpose_y_1 = const()[name = string("attn_output_33_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_33_cast_fp16 = matmul(transpose_x = attn_output_33_transpose_x_1, transpose_y = attn_output_33_transpose_y_1, x = attn_weights_79_cast_fp16, y = var_2385_cast_fp16_1)[name = string("attn_output_33_cast_fp16")];
            int32 var_2415 = const()[name = string("op_2415"), val = int32(1)];
            bool attn_output_35_interleave_0 = const()[name = string("attn_output_35_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_35_cast_fp16 = concat(axis = var_2415, interleave = attn_output_35_interleave_0, values = (var_2401_cast_fp16, attn_output_33_cast_fp16))[name = string("attn_output_35_cast_fp16")];
            tensor<int32, [4]> var_2419_perm_0 = const()[name = string("op_2419_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_59x = const()[name = string("concat_59x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_2419_cast_fp16 = transpose(perm = var_2419_perm_0, x = attn_output_35_cast_fp16)[name = string("transpose_504")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_39_cast_fp16 = reshape(shape = concat_59x, x = var_2419_cast_fp16)[name = string("attn_output_39_cast_fp16")];
            tensor<int32, [2]> hidden_states_43_strides_0 = const()[name = string("hidden_states_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_43_pad_type_0 = const()[name = string("hidden_states_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_43_pad_0 = const()[name = string("hidden_states_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_43_dilations_0 = const()[name = string("hidden_states_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_43_groups_0 = const()[name = string("hidden_states_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_43_cast_fp16 = conv(dilations = hidden_states_43_dilations_0, groups = hidden_states_43_groups_0, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = hidden_states_43_strides_0, weight = layers_4_self_attn_o_proj_weight_cast_fp16, x = attn_output_39_cast_fp16)[name = string("hidden_states_43_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_45_cast_fp16 = add(x = hidden_states_39_cast_fp16, y = hidden_states_43_cast_fp16)[name = string("hidden_states_45_cast_fp16")];
            fp16 const_50_promoted_to_fp16 = const()[name = string("const_50_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_2452_cast_fp16 = mul(x = hidden_states_45_cast_fp16, y = const_50_promoted_to_fp16)[name = string("op_2452_cast_fp16")];
            int32 var_2450 = const()[name = string("op_2450"), val = int32(1)];
            bool doubled_37_interleave_0 = const()[name = string("doubled_37_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_37_cast_fp16 = concat(axis = var_2450, interleave = doubled_37_interleave_0, values = (hidden_states_45_cast_fp16, var_2452_cast_fp16))[name = string("doubled_37_cast_fp16")];
            tensor<int32, [1]> out_19_axes_0 = const()[name = string("out_19_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_19_gamma_0_to_fp16 = const()[name = string("out_19_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1274748864)))];
            fp16 var_2462_to_fp16 = const()[name = string("op_2462_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_19_cast_fp16 = layer_norm(axes = out_19_axes_0, epsilon = var_2462_to_fp16, gamma = out_19_gamma_0_to_fp16, x = doubled_37_cast_fp16)[name = string("out_19_cast_fp16")];
            tensor<int32, [2]> var_2473_split_sizes_0 = const()[name = string("op_2473_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2473_axis_0 = const()[name = string("op_2473_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_2473_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_2473_cast_fp16_1 = split(axis = var_2473_axis_0, split_sizes = var_2473_split_sizes_0, x = out_19_cast_fp16)[name = string("op_2473_cast_fp16")];
            tensor<int32, [2]> input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_9_cast_fp16 = conv(dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = layers_4_mlp_gate_proj_weight_cast_fp16, x = var_2473_cast_fp16_0)[name = string("input_9_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_2490_cast_fp16 = silu(x = input_9_cast_fp16)[name = string("op_2490_cast_fp16")];
            tensor<int32, [2]> var_2496_strides_0 = const()[name = string("op_2496_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_2496_pad_type_0 = const()[name = string("op_2496_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_2496_pad_0 = const()[name = string("op_2496_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_2496_dilations_0 = const()[name = string("op_2496_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2496_groups_0 = const()[name = string("op_2496_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_2496_cast_fp16 = conv(dilations = var_2496_dilations_0, groups = var_2496_groups_0, pad = var_2496_pad_0, pad_type = var_2496_pad_type_0, strides = var_2496_strides_0, weight = layers_4_mlp_up_proj_weight_cast_fp16, x = var_2473_cast_fp16_0)[name = string("op_2496_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_49_cast_fp16 = mul(x = var_2490_cast_fp16, y = var_2496_cast_fp16)[name = string("x_49_cast_fp16")];
            tensor<int32, [2]> hidden_states_47_strides_0 = const()[name = string("hidden_states_47_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_47_pad_type_0 = const()[name = string("hidden_states_47_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_47_pad_0 = const()[name = string("hidden_states_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_47_dilations_0 = const()[name = string("hidden_states_47_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_47_groups_0 = const()[name = string("hidden_states_47_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_47_cast_fp16 = conv(dilations = hidden_states_47_dilations_0, groups = hidden_states_47_groups_0, pad = hidden_states_47_pad_0, pad_type = hidden_states_47_pad_type_0, strides = hidden_states_47_strides_0, weight = layers_4_mlp_down_proj_weight_cast_fp16, x = x_49_cast_fp16)[name = string("hidden_states_47_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_49_cast_fp16 = add(x = hidden_states_45_cast_fp16, y = hidden_states_47_cast_fp16)[name = string("hidden_states_49_cast_fp16")];
            fp16 const_52_promoted_to_fp16 = const()[name = string("const_52_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_2514_cast_fp16 = mul(x = hidden_states_49_cast_fp16, y = const_52_promoted_to_fp16)[name = string("op_2514_cast_fp16")];
            int32 var_2512 = const()[name = string("op_2512"), val = int32(1)];
            bool doubled_41_interleave_0 = const()[name = string("doubled_41_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_41_cast_fp16 = concat(axis = var_2512, interleave = doubled_41_interleave_0, values = (hidden_states_49_cast_fp16, var_2514_cast_fp16))[name = string("doubled_41_cast_fp16")];
            tensor<int32, [1]> out_21_axes_0 = const()[name = string("out_21_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_21_gamma_0_to_fp16 = const()[name = string("out_21_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1274757120)))];
            fp16 var_2524_to_fp16 = const()[name = string("op_2524_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_21_cast_fp16 = layer_norm(axes = out_21_axes_0, epsilon = var_2524_to_fp16, gamma = out_21_gamma_0_to_fp16, x = doubled_41_cast_fp16)[name = string("out_21_cast_fp16")];
            tensor<int32, [2]> var_2535_split_sizes_0 = const()[name = string("op_2535_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2535_axis_0 = const()[name = string("op_2535_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_2535_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_2535_cast_fp16_1 = split(axis = var_2535_axis_0, split_sizes = var_2535_split_sizes_0, x = out_21_cast_fp16)[name = string("op_2535_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_5_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_5_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1274765376)))];
            tensor<int32, [2]> query_states_31_strides_0 = const()[name = string("query_states_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_31_pad_type_0 = const()[name = string("query_states_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_31_pad_0 = const()[name = string("query_states_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_31_dilations_0 = const()[name = string("query_states_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_31_groups_0 = const()[name = string("query_states_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_31_cast_fp16 = conv(dilations = query_states_31_dilations_0, groups = query_states_31_groups_0, pad = query_states_31_pad_0, pad_type = query_states_31_pad_type_0, strides = query_states_31_strides_0, weight = layers_5_self_attn_q_proj_weight_to_fp16, x = var_2535_cast_fp16_0)[name = string("query_states_31_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_5_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_5_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1283154048)))];
            tensor<int32, [2]> key_states_51_strides_0 = const()[name = string("key_states_51_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_51_pad_type_0 = const()[name = string("key_states_51_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_51_pad_0 = const()[name = string("key_states_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_51_dilations_0 = const()[name = string("key_states_51_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_51_groups_0 = const()[name = string("key_states_51_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_51_cast_fp16 = conv(dilations = key_states_51_dilations_0, groups = key_states_51_groups_0, pad = key_states_51_pad_0, pad_type = key_states_51_pad_type_0, strides = key_states_51_strides_0, weight = layers_5_self_attn_k_proj_weight_to_fp16, x = var_2535_cast_fp16_0)[name = string("key_states_51_cast_fp16")];
            tensor<int32, [2]> value_states_31_strides_0 = const()[name = string("value_states_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_31_pad_type_0 = const()[name = string("value_states_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_31_pad_0 = const()[name = string("value_states_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_31_dilations_0 = const()[name = string("value_states_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_31_groups_0 = const()[name = string("value_states_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_31_cast_fp16 = conv(dilations = value_states_31_dilations_0, groups = value_states_31_groups_0, pad = value_states_31_pad_0, pad_type = value_states_31_pad_type_0, strides = value_states_31_strides_0, weight = layers_5_self_attn_v_proj_weight_cast_fp16, x = var_2535_cast_fp16_0)[name = string("value_states_31_cast_fp16")];
            tensor<int32, [4]> concat_60x = const()[name = string("concat_60x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_51_cast_fp16 = reshape(shape = concat_60x, x = query_states_31_cast_fp16)[name = string("x_51_cast_fp16")];
            tensor<int32, [4]> concat_61x = const()[name = string("concat_61x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_2592_cast_fp16 = reshape(shape = concat_61x, x = key_states_51_cast_fp16)[name = string("op_2592_cast_fp16")];
            tensor<int32, [4]> concat_62x = const()[name = string("concat_62x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_2599_cast_fp16 = reshape(shape = concat_62x, x = value_states_31_cast_fp16)[name = string("op_2599_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_2603_cast_fp16 = mul(x = x_51_cast_fp16, y = var_859_cast_fp16)[name = string("op_2603_cast_fp16")];
            tensor<int32, [2]> var_2604_split_sizes_0 = const()[name = string("op_2604_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2604_axis_0 = const()[name = string("op_2604_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_2604_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_2604_cast_fp16_1 = split(axis = var_2604_axis_0, split_sizes = var_2604_split_sizes_0, x = x_51_cast_fp16)[name = string("op_2604_cast_fp16")];
            fp16 const_54_promoted_to_fp16 = const()[name = string("const_54_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_2606_cast_fp16 = mul(x = var_2604_cast_fp16_1, y = const_54_promoted_to_fp16)[name = string("op_2606_cast_fp16")];
            int32 var_2608 = const()[name = string("op_2608"), val = int32(-2)];
            bool var_2609_interleave_0 = const()[name = string("op_2609_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_2609_cast_fp16 = concat(axis = var_2608, interleave = var_2609_interleave_0, values = (var_2606_cast_fp16, var_2604_cast_fp16_0))[name = string("op_2609_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_2610_cast_fp16 = mul(x = var_2609_cast_fp16, y = var_866_cast_fp16)[name = string("op_2610_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_33_cast_fp16 = add(x = var_2603_cast_fp16, y = var_2610_cast_fp16)[name = string("query_states_33_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_2616_cast_fp16 = mul(x = var_2592_cast_fp16, y = var_859_cast_fp16)[name = string("op_2616_cast_fp16")];
            tensor<int32, [2]> var_2617_split_sizes_0 = const()[name = string("op_2617_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2617_axis_0 = const()[name = string("op_2617_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_2617_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_2617_cast_fp16_1 = split(axis = var_2617_axis_0, split_sizes = var_2617_split_sizes_0, x = var_2592_cast_fp16)[name = string("op_2617_cast_fp16")];
            fp16 const_55_promoted_to_fp16 = const()[name = string("const_55_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_2619_cast_fp16 = mul(x = var_2617_cast_fp16_1, y = const_55_promoted_to_fp16)[name = string("op_2619_cast_fp16")];
            int32 var_2621 = const()[name = string("op_2621"), val = int32(-2)];
            bool var_2622_interleave_0 = const()[name = string("op_2622_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_2622_cast_fp16 = concat(axis = var_2621, interleave = var_2622_interleave_0, values = (var_2619_cast_fp16, var_2617_cast_fp16_0))[name = string("op_2622_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_2623_cast_fp16 = mul(x = var_2622_cast_fp16, y = var_866_cast_fp16)[name = string("op_2623_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_55_cast_fp16 = add(x = var_2616_cast_fp16, y = var_2623_cast_fp16)[name = string("key_states_55_cast_fp16")];
            tensor<int32, [1]> expand_dims_60 = const()[name = string("expand_dims_60"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> expand_dims_61 = const()[name = string("expand_dims_61"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_63 = const()[name = string("expand_dims_63"), val = tensor<int32, [1]>([0])];
            int32 concat_65_axis_0 = const()[name = string("concat_65_axis_0"), val = int32(0)];
            bool concat_65_interleave_0 = const()[name = string("concat_65_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_65 = concat(axis = concat_65_axis_0, interleave = concat_65_interleave_0, values = (expand_dims_60, expand_dims_61, position_id, expand_dims_63))[name = string("concat_65")];
            tensor<int32, [1]> expand_dims_64 = const()[name = string("expand_dims_64"), val = tensor<int32, [1]>([6])];
            tensor<int32, [1]> concat_66_values1_0 = const()[name = string("concat_66_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_66_values3_0 = const()[name = string("concat_66_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_66_axis_0 = const()[name = string("concat_66_axis_0"), val = int32(0)];
            bool concat_66_interleave_0 = const()[name = string("concat_66_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_66 = concat(axis = concat_66_axis_0, interleave = concat_66_interleave_0, values = (expand_dims_64, concat_66_values1_0, cache_position_end, concat_66_values3_0))[name = string("concat_66")];
            tensor<int32, [4]> key_states_57_perm_0 = const()[name = string("key_states_57_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_6_stride_0 = const()[name = string("key_cache_internal_tensor_assign_6_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_6_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_6_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_6_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_6_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_6_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_6_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_57_cast_fp16 = transpose(perm = key_states_57_perm_0, x = key_states_55_cast_fp16)[name = string("transpose_503")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_6_cast_fp16 = slice_update(begin = concat_65, begin_mask = key_cache_internal_tensor_assign_6_begin_mask_0, end = concat_66, end_mask = key_cache_internal_tensor_assign_6_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_6_squeeze_mask_0, stride = key_cache_internal_tensor_assign_6_stride_0, update = key_states_57_cast_fp16, x = coreml_update_state_288)[name = string("key_cache_internal_tensor_assign_6_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_6_cast_fp16, input = key_cache)[name = string("coreml_update_state_290_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_290 = read_state(input = key_cache)[name = string("coreml_update_state_290")];
            tensor<int32, [4]> value_states_33_perm_0 = const()[name = string("value_states_33_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_6_stride_0 = const()[name = string("value_cache_internal_tensor_assign_6_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_6_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_6_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_6_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_6_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_6_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_6_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_33_cast_fp16 = transpose(perm = value_states_33_perm_0, x = var_2599_cast_fp16)[name = string("transpose_502")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_6_cast_fp16 = slice_update(begin = concat_65, begin_mask = value_cache_internal_tensor_assign_6_begin_mask_0, end = concat_66, end_mask = value_cache_internal_tensor_assign_6_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_6_squeeze_mask_0, stride = value_cache_internal_tensor_assign_6_stride_0, update = value_states_33_cast_fp16, x = coreml_update_state_289)[name = string("value_cache_internal_tensor_assign_6_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_6_cast_fp16, input = value_cache)[name = string("coreml_update_state_291_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_291 = read_state(input = value_cache)[name = string("coreml_update_state_291")];
            tensor<int32, [4]> var_2693_begin_0 = const()[name = string("op_2693_begin_0"), val = tensor<int32, [4]>([5, 0, 0, 0])];
            tensor<int32, [4]> var_2693_end_0 = const()[name = string("op_2693_end_0"), val = tensor<int32, [4]>([6, 2, 2048, 128])];
            tensor<bool, [4]> var_2693_end_mask_0 = const()[name = string("op_2693_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2693_cast_fp16 = slice_by_index(begin = var_2693_begin_0, end = var_2693_end_0, end_mask = var_2693_end_mask_0, x = coreml_update_state_290)[name = string("op_2693_cast_fp16")];
            tensor<int32, [2]> tile_10 = const()[name = string("tile_10"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2696_axis_0 = const()[name = string("op_2696_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2696_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2696_cast_fp16_1 = split(axis = var_2696_axis_0, split_sizes = tile_10, x = var_2693_cast_fp16)[name = string("op_2696_cast_fp16")];
            tensor<int32, [4]> var_2703_begin_0 = const()[name = string("op_2703_begin_0"), val = tensor<int32, [4]>([5, 0, 0, 0])];
            tensor<int32, [4]> var_2703_end_0 = const()[name = string("op_2703_end_0"), val = tensor<int32, [4]>([6, 2, 2048, 128])];
            tensor<bool, [4]> var_2703_end_mask_0 = const()[name = string("op_2703_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2703_cast_fp16 = slice_by_index(begin = var_2703_begin_0, end = var_2703_end_0, end_mask = var_2703_end_mask_0, x = coreml_update_state_291)[name = string("op_2703_cast_fp16")];
            tensor<int32, [2]> tile_11 = const()[name = string("tile_11"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2706_axis_0 = const()[name = string("op_2706_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2706_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2706_cast_fp16_1 = split(axis = var_2706_axis_0, split_sizes = tile_11, x = var_2703_cast_fp16)[name = string("op_2706_cast_fp16")];
            tensor<int32, [2]> var_2709_split_sizes_0 = const()[name = string("op_2709_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_2709_axis_0 = const()[name = string("op_2709_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_2709_0, tensor<fp16, [1, 8, 128, 64]> var_2709_1 = split(axis = var_2709_axis_0, split_sizes = var_2709_split_sizes_0, x = query_states_33_cast_fp16)[name = string("op_2709")];
            bool attn_weights_81_transpose_x_0 = const()[name = string("attn_weights_81_transpose_x_0"), val = bool(false)];
            bool attn_weights_81_transpose_y_0 = const()[name = string("attn_weights_81_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_81_cast_fp16 = matmul(transpose_x = attn_weights_81_transpose_x_0, transpose_y = attn_weights_81_transpose_y_0, x = var_2696_cast_fp16_0, y = var_2709_0)[name = string("attn_weights_81_cast_fp16")];
            fp16 var_2712_to_fp16 = const()[name = string("op_2712_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_83_cast_fp16 = mul(x = attn_weights_81_cast_fp16, y = var_2712_to_fp16)[name = string("attn_weights_83_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_85_cast_fp16 = add(x = attn_weights_83_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_85_cast_fp16")];
            int32 var_2716 = const()[name = string("op_2716"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_87_cast_fp16 = softmax(axis = var_2716, x = attn_weights_85_cast_fp16)[name = string("attn_weights_87_cast_fp16")];
            bool var_2722_transpose_x_1 = const()[name = string("op_2722_transpose_x_1"), val = bool(true)];
            bool var_2722_transpose_y_1 = const()[name = string("op_2722_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_2722_cast_fp16 = matmul(transpose_x = var_2722_transpose_x_1, transpose_y = var_2722_transpose_y_1, x = attn_weights_87_cast_fp16, y = var_2706_cast_fp16_0)[name = string("op_2722_cast_fp16")];
            bool attn_weights_89_transpose_x_0 = const()[name = string("attn_weights_89_transpose_x_0"), val = bool(false)];
            bool attn_weights_89_transpose_y_0 = const()[name = string("attn_weights_89_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_89_cast_fp16 = matmul(transpose_x = attn_weights_89_transpose_x_0, transpose_y = attn_weights_89_transpose_y_0, x = var_2696_cast_fp16_1, y = var_2709_1)[name = string("attn_weights_89_cast_fp16")];
            fp16 var_2724_to_fp16 = const()[name = string("op_2724_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_91_cast_fp16 = mul(x = attn_weights_89_cast_fp16, y = var_2724_to_fp16)[name = string("attn_weights_91_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_93_cast_fp16 = add(x = attn_weights_91_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_93_cast_fp16")];
            int32 var_2728 = const()[name = string("op_2728"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_95_cast_fp16 = softmax(axis = var_2728, x = attn_weights_93_cast_fp16)[name = string("attn_weights_95_cast_fp16")];
            bool attn_output_41_transpose_x_1 = const()[name = string("attn_output_41_transpose_x_1"), val = bool(true)];
            bool attn_output_41_transpose_y_1 = const()[name = string("attn_output_41_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_41_cast_fp16 = matmul(transpose_x = attn_output_41_transpose_x_1, transpose_y = attn_output_41_transpose_y_1, x = attn_weights_95_cast_fp16, y = var_2706_cast_fp16_1)[name = string("attn_output_41_cast_fp16")];
            int32 var_2736 = const()[name = string("op_2736"), val = int32(1)];
            bool attn_output_43_interleave_0 = const()[name = string("attn_output_43_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_43_cast_fp16 = concat(axis = var_2736, interleave = attn_output_43_interleave_0, values = (var_2722_cast_fp16, attn_output_41_cast_fp16))[name = string("attn_output_43_cast_fp16")];
            tensor<int32, [4]> var_2740_perm_0 = const()[name = string("op_2740_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_71x = const()[name = string("concat_71x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_2740_cast_fp16 = transpose(perm = var_2740_perm_0, x = attn_output_43_cast_fp16)[name = string("transpose_501")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_47_cast_fp16 = reshape(shape = concat_71x, x = var_2740_cast_fp16)[name = string("attn_output_47_cast_fp16")];
            tensor<int32, [2]> hidden_states_53_strides_0 = const()[name = string("hidden_states_53_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_53_pad_type_0 = const()[name = string("hidden_states_53_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_53_pad_0 = const()[name = string("hidden_states_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_53_dilations_0 = const()[name = string("hidden_states_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_53_groups_0 = const()[name = string("hidden_states_53_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_53_cast_fp16 = conv(dilations = hidden_states_53_dilations_0, groups = hidden_states_53_groups_0, pad = hidden_states_53_pad_0, pad_type = hidden_states_53_pad_type_0, strides = hidden_states_53_strides_0, weight = layers_5_self_attn_o_proj_weight_cast_fp16, x = attn_output_47_cast_fp16)[name = string("hidden_states_53_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_55_cast_fp16 = add(x = hidden_states_49_cast_fp16, y = hidden_states_53_cast_fp16)[name = string("hidden_states_55_cast_fp16")];
            fp16 const_60_promoted_to_fp16 = const()[name = string("const_60_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_2773_cast_fp16 = mul(x = hidden_states_55_cast_fp16, y = const_60_promoted_to_fp16)[name = string("op_2773_cast_fp16")];
            int32 var_2771 = const()[name = string("op_2771"), val = int32(1)];
            bool doubled_45_interleave_0 = const()[name = string("doubled_45_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_45_cast_fp16 = concat(axis = var_2771, interleave = doubled_45_interleave_0, values = (hidden_states_55_cast_fp16, var_2773_cast_fp16))[name = string("doubled_45_cast_fp16")];
            tensor<int32, [1]> out_23_axes_0 = const()[name = string("out_23_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_23_gamma_0_to_fp16 = const()[name = string("out_23_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1284202688)))];
            fp16 var_2783_to_fp16 = const()[name = string("op_2783_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_23_cast_fp16 = layer_norm(axes = out_23_axes_0, epsilon = var_2783_to_fp16, gamma = out_23_gamma_0_to_fp16, x = doubled_45_cast_fp16)[name = string("out_23_cast_fp16")];
            tensor<int32, [2]> var_2794_split_sizes_0 = const()[name = string("op_2794_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2794_axis_0 = const()[name = string("op_2794_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_2794_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_2794_cast_fp16_1 = split(axis = var_2794_axis_0, split_sizes = var_2794_split_sizes_0, x = out_23_cast_fp16)[name = string("op_2794_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_5_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_5_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1284210944)))];
            tensor<int32, [2]> input_11_strides_0 = const()[name = string("input_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_11_pad_type_0 = const()[name = string("input_11_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_11_dilations_0 = const()[name = string("input_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_11_groups_0 = const()[name = string("input_11_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_11_cast_fp16 = conv(dilations = input_11_dilations_0, groups = input_11_groups_0, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = input_11_strides_0, weight = layers_5_mlp_gate_proj_weight_to_fp16, x = var_2794_cast_fp16_0)[name = string("input_11_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_2811_cast_fp16 = silu(x = input_11_cast_fp16)[name = string("op_2811_cast_fp16")];
            tensor<int32, [2]> var_2817_strides_0 = const()[name = string("op_2817_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_2817_pad_type_0 = const()[name = string("op_2817_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_2817_pad_0 = const()[name = string("op_2817_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_2817_dilations_0 = const()[name = string("op_2817_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2817_groups_0 = const()[name = string("op_2817_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_2817_cast_fp16 = conv(dilations = var_2817_dilations_0, groups = var_2817_groups_0, pad = var_2817_pad_0, pad_type = var_2817_pad_type_0, strides = var_2817_strides_0, weight = layers_5_mlp_up_proj_weight_cast_fp16, x = var_2794_cast_fp16_0)[name = string("op_2817_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_59_cast_fp16 = mul(x = var_2811_cast_fp16, y = var_2817_cast_fp16)[name = string("x_59_cast_fp16")];
            tensor<int32, [2]> hidden_states_57_strides_0 = const()[name = string("hidden_states_57_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_57_pad_type_0 = const()[name = string("hidden_states_57_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_57_pad_0 = const()[name = string("hidden_states_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_57_dilations_0 = const()[name = string("hidden_states_57_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_57_groups_0 = const()[name = string("hidden_states_57_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_57_cast_fp16 = conv(dilations = hidden_states_57_dilations_0, groups = hidden_states_57_groups_0, pad = hidden_states_57_pad_0, pad_type = hidden_states_57_pad_type_0, strides = hidden_states_57_strides_0, weight = layers_5_mlp_down_proj_weight_cast_fp16, x = x_59_cast_fp16)[name = string("hidden_states_57_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_59_cast_fp16 = add(x = hidden_states_55_cast_fp16, y = hidden_states_57_cast_fp16)[name = string("hidden_states_59_cast_fp16")];
            fp16 const_62_promoted_to_fp16 = const()[name = string("const_62_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_2835_cast_fp16 = mul(x = hidden_states_59_cast_fp16, y = const_62_promoted_to_fp16)[name = string("op_2835_cast_fp16")];
            int32 var_2833 = const()[name = string("op_2833"), val = int32(1)];
            bool doubled_49_interleave_0 = const()[name = string("doubled_49_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_49_cast_fp16 = concat(axis = var_2833, interleave = doubled_49_interleave_0, values = (hidden_states_59_cast_fp16, var_2835_cast_fp16))[name = string("doubled_49_cast_fp16")];
            tensor<int32, [1]> out_25_axes_0 = const()[name = string("out_25_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_25_gamma_0_to_fp16 = const()[name = string("out_25_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1309376832)))];
            fp16 var_2845_to_fp16 = const()[name = string("op_2845_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_25_cast_fp16 = layer_norm(axes = out_25_axes_0, epsilon = var_2845_to_fp16, gamma = out_25_gamma_0_to_fp16, x = doubled_49_cast_fp16)[name = string("out_25_cast_fp16")];
            tensor<int32, [2]> var_2856_split_sizes_0 = const()[name = string("op_2856_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2856_axis_0 = const()[name = string("op_2856_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_2856_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_2856_cast_fp16_1 = split(axis = var_2856_axis_0, split_sizes = var_2856_split_sizes_0, x = out_25_cast_fp16)[name = string("op_2856_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_6_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_6_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1309385088)))];
            tensor<int32, [2]> query_states_37_strides_0 = const()[name = string("query_states_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_37_pad_type_0 = const()[name = string("query_states_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_37_pad_0 = const()[name = string("query_states_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_37_dilations_0 = const()[name = string("query_states_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_37_groups_0 = const()[name = string("query_states_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_37_cast_fp16 = conv(dilations = query_states_37_dilations_0, groups = query_states_37_groups_0, pad = query_states_37_pad_0, pad_type = query_states_37_pad_type_0, strides = query_states_37_strides_0, weight = layers_6_self_attn_q_proj_weight_to_fp16, x = var_2856_cast_fp16_0)[name = string("query_states_37_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_6_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_6_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1317773760)))];
            tensor<int32, [2]> key_states_61_strides_0 = const()[name = string("key_states_61_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_61_pad_type_0 = const()[name = string("key_states_61_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_61_pad_0 = const()[name = string("key_states_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_61_dilations_0 = const()[name = string("key_states_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_61_groups_0 = const()[name = string("key_states_61_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_61_cast_fp16 = conv(dilations = key_states_61_dilations_0, groups = key_states_61_groups_0, pad = key_states_61_pad_0, pad_type = key_states_61_pad_type_0, strides = key_states_61_strides_0, weight = layers_6_self_attn_k_proj_weight_to_fp16, x = var_2856_cast_fp16_0)[name = string("key_states_61_cast_fp16")];
            tensor<int32, [2]> value_states_37_strides_0 = const()[name = string("value_states_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_37_pad_type_0 = const()[name = string("value_states_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_37_pad_0 = const()[name = string("value_states_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_37_dilations_0 = const()[name = string("value_states_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_37_groups_0 = const()[name = string("value_states_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_37_cast_fp16 = conv(dilations = value_states_37_dilations_0, groups = value_states_37_groups_0, pad = value_states_37_pad_0, pad_type = value_states_37_pad_type_0, strides = value_states_37_strides_0, weight = layers_6_self_attn_v_proj_weight_cast_fp16, x = var_2856_cast_fp16_0)[name = string("value_states_37_cast_fp16")];
            tensor<int32, [4]> concat_72x = const()[name = string("concat_72x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_61_cast_fp16 = reshape(shape = concat_72x, x = query_states_37_cast_fp16)[name = string("x_61_cast_fp16")];
            tensor<int32, [4]> concat_73x = const()[name = string("concat_73x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_2913_cast_fp16 = reshape(shape = concat_73x, x = key_states_61_cast_fp16)[name = string("op_2913_cast_fp16")];
            tensor<int32, [4]> concat_74x = const()[name = string("concat_74x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_2920_cast_fp16 = reshape(shape = concat_74x, x = value_states_37_cast_fp16)[name = string("op_2920_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_2924_cast_fp16 = mul(x = x_61_cast_fp16, y = var_859_cast_fp16)[name = string("op_2924_cast_fp16")];
            tensor<int32, [2]> var_2925_split_sizes_0 = const()[name = string("op_2925_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2925_axis_0 = const()[name = string("op_2925_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_2925_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_2925_cast_fp16_1 = split(axis = var_2925_axis_0, split_sizes = var_2925_split_sizes_0, x = x_61_cast_fp16)[name = string("op_2925_cast_fp16")];
            fp16 const_64_promoted_to_fp16 = const()[name = string("const_64_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_2927_cast_fp16 = mul(x = var_2925_cast_fp16_1, y = const_64_promoted_to_fp16)[name = string("op_2927_cast_fp16")];
            int32 var_2929 = const()[name = string("op_2929"), val = int32(-2)];
            bool var_2930_interleave_0 = const()[name = string("op_2930_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_2930_cast_fp16 = concat(axis = var_2929, interleave = var_2930_interleave_0, values = (var_2927_cast_fp16, var_2925_cast_fp16_0))[name = string("op_2930_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_2931_cast_fp16 = mul(x = var_2930_cast_fp16, y = var_866_cast_fp16)[name = string("op_2931_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_39_cast_fp16 = add(x = var_2924_cast_fp16, y = var_2931_cast_fp16)[name = string("query_states_39_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_2937_cast_fp16 = mul(x = var_2913_cast_fp16, y = var_859_cast_fp16)[name = string("op_2937_cast_fp16")];
            tensor<int32, [2]> var_2938_split_sizes_0 = const()[name = string("op_2938_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2938_axis_0 = const()[name = string("op_2938_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_2938_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_2938_cast_fp16_1 = split(axis = var_2938_axis_0, split_sizes = var_2938_split_sizes_0, x = var_2913_cast_fp16)[name = string("op_2938_cast_fp16")];
            fp16 const_65_promoted_to_fp16 = const()[name = string("const_65_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_2940_cast_fp16 = mul(x = var_2938_cast_fp16_1, y = const_65_promoted_to_fp16)[name = string("op_2940_cast_fp16")];
            int32 var_2942 = const()[name = string("op_2942"), val = int32(-2)];
            bool var_2943_interleave_0 = const()[name = string("op_2943_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_2943_cast_fp16 = concat(axis = var_2942, interleave = var_2943_interleave_0, values = (var_2940_cast_fp16, var_2938_cast_fp16_0))[name = string("op_2943_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_2944_cast_fp16 = mul(x = var_2943_cast_fp16, y = var_866_cast_fp16)[name = string("op_2944_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_65_cast_fp16 = add(x = var_2937_cast_fp16, y = var_2944_cast_fp16)[name = string("key_states_65_cast_fp16")];
            tensor<int32, [1]> expand_dims_72 = const()[name = string("expand_dims_72"), val = tensor<int32, [1]>([6])];
            tensor<int32, [1]> expand_dims_73 = const()[name = string("expand_dims_73"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_75 = const()[name = string("expand_dims_75"), val = tensor<int32, [1]>([0])];
            int32 concat_77_axis_0 = const()[name = string("concat_77_axis_0"), val = int32(0)];
            bool concat_77_interleave_0 = const()[name = string("concat_77_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_77 = concat(axis = concat_77_axis_0, interleave = concat_77_interleave_0, values = (expand_dims_72, expand_dims_73, position_id, expand_dims_75))[name = string("concat_77")];
            tensor<int32, [1]> expand_dims_76 = const()[name = string("expand_dims_76"), val = tensor<int32, [1]>([7])];
            tensor<int32, [1]> concat_78_values1_0 = const()[name = string("concat_78_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_78_values3_0 = const()[name = string("concat_78_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_78_axis_0 = const()[name = string("concat_78_axis_0"), val = int32(0)];
            bool concat_78_interleave_0 = const()[name = string("concat_78_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_78 = concat(axis = concat_78_axis_0, interleave = concat_78_interleave_0, values = (expand_dims_76, concat_78_values1_0, cache_position_end, concat_78_values3_0))[name = string("concat_78")];
            tensor<int32, [4]> key_states_67_perm_0 = const()[name = string("key_states_67_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_7_stride_0 = const()[name = string("key_cache_internal_tensor_assign_7_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_7_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_7_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_7_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_7_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_7_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_7_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_67_cast_fp16 = transpose(perm = key_states_67_perm_0, x = key_states_65_cast_fp16)[name = string("transpose_500")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_7_cast_fp16 = slice_update(begin = concat_77, begin_mask = key_cache_internal_tensor_assign_7_begin_mask_0, end = concat_78, end_mask = key_cache_internal_tensor_assign_7_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_7_squeeze_mask_0, stride = key_cache_internal_tensor_assign_7_stride_0, update = key_states_67_cast_fp16, x = coreml_update_state_290)[name = string("key_cache_internal_tensor_assign_7_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_7_cast_fp16, input = key_cache)[name = string("coreml_update_state_292_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_292 = read_state(input = key_cache)[name = string("coreml_update_state_292")];
            tensor<int32, [4]> value_states_39_perm_0 = const()[name = string("value_states_39_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_7_stride_0 = const()[name = string("value_cache_internal_tensor_assign_7_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_7_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_7_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_7_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_7_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_7_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_7_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_39_cast_fp16 = transpose(perm = value_states_39_perm_0, x = var_2920_cast_fp16)[name = string("transpose_499")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_7_cast_fp16 = slice_update(begin = concat_77, begin_mask = value_cache_internal_tensor_assign_7_begin_mask_0, end = concat_78, end_mask = value_cache_internal_tensor_assign_7_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_7_squeeze_mask_0, stride = value_cache_internal_tensor_assign_7_stride_0, update = value_states_39_cast_fp16, x = coreml_update_state_291)[name = string("value_cache_internal_tensor_assign_7_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_7_cast_fp16, input = value_cache)[name = string("coreml_update_state_293_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_293 = read_state(input = value_cache)[name = string("coreml_update_state_293")];
            tensor<int32, [4]> var_3014_begin_0 = const()[name = string("op_3014_begin_0"), val = tensor<int32, [4]>([6, 0, 0, 0])];
            tensor<int32, [4]> var_3014_end_0 = const()[name = string("op_3014_end_0"), val = tensor<int32, [4]>([7, 2, 2048, 128])];
            tensor<bool, [4]> var_3014_end_mask_0 = const()[name = string("op_3014_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3014_cast_fp16 = slice_by_index(begin = var_3014_begin_0, end = var_3014_end_0, end_mask = var_3014_end_mask_0, x = coreml_update_state_292)[name = string("op_3014_cast_fp16")];
            tensor<int32, [2]> tile_12 = const()[name = string("tile_12"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3017_axis_0 = const()[name = string("op_3017_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3017_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3017_cast_fp16_1 = split(axis = var_3017_axis_0, split_sizes = tile_12, x = var_3014_cast_fp16)[name = string("op_3017_cast_fp16")];
            tensor<int32, [4]> var_3024_begin_0 = const()[name = string("op_3024_begin_0"), val = tensor<int32, [4]>([6, 0, 0, 0])];
            tensor<int32, [4]> var_3024_end_0 = const()[name = string("op_3024_end_0"), val = tensor<int32, [4]>([7, 2, 2048, 128])];
            tensor<bool, [4]> var_3024_end_mask_0 = const()[name = string("op_3024_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3024_cast_fp16 = slice_by_index(begin = var_3024_begin_0, end = var_3024_end_0, end_mask = var_3024_end_mask_0, x = coreml_update_state_293)[name = string("op_3024_cast_fp16")];
            tensor<int32, [2]> tile_13 = const()[name = string("tile_13"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3027_axis_0 = const()[name = string("op_3027_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3027_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3027_cast_fp16_1 = split(axis = var_3027_axis_0, split_sizes = tile_13, x = var_3024_cast_fp16)[name = string("op_3027_cast_fp16")];
            tensor<int32, [2]> var_3030_split_sizes_0 = const()[name = string("op_3030_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3030_axis_0 = const()[name = string("op_3030_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_3030_0, tensor<fp16, [1, 8, 128, 64]> var_3030_1 = split(axis = var_3030_axis_0, split_sizes = var_3030_split_sizes_0, x = query_states_39_cast_fp16)[name = string("op_3030")];
            bool attn_weights_97_transpose_x_0 = const()[name = string("attn_weights_97_transpose_x_0"), val = bool(false)];
            bool attn_weights_97_transpose_y_0 = const()[name = string("attn_weights_97_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_97_cast_fp16 = matmul(transpose_x = attn_weights_97_transpose_x_0, transpose_y = attn_weights_97_transpose_y_0, x = var_3017_cast_fp16_0, y = var_3030_0)[name = string("attn_weights_97_cast_fp16")];
            fp16 var_3033_to_fp16 = const()[name = string("op_3033_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_99_cast_fp16 = mul(x = attn_weights_97_cast_fp16, y = var_3033_to_fp16)[name = string("attn_weights_99_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_101_cast_fp16 = add(x = attn_weights_99_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_101_cast_fp16")];
            int32 var_3037 = const()[name = string("op_3037"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_103_cast_fp16 = softmax(axis = var_3037, x = attn_weights_101_cast_fp16)[name = string("attn_weights_103_cast_fp16")];
            bool var_3043_transpose_x_1 = const()[name = string("op_3043_transpose_x_1"), val = bool(true)];
            bool var_3043_transpose_y_1 = const()[name = string("op_3043_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_3043_cast_fp16 = matmul(transpose_x = var_3043_transpose_x_1, transpose_y = var_3043_transpose_y_1, x = attn_weights_103_cast_fp16, y = var_3027_cast_fp16_0)[name = string("op_3043_cast_fp16")];
            bool attn_weights_105_transpose_x_0 = const()[name = string("attn_weights_105_transpose_x_0"), val = bool(false)];
            bool attn_weights_105_transpose_y_0 = const()[name = string("attn_weights_105_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_105_cast_fp16 = matmul(transpose_x = attn_weights_105_transpose_x_0, transpose_y = attn_weights_105_transpose_y_0, x = var_3017_cast_fp16_1, y = var_3030_1)[name = string("attn_weights_105_cast_fp16")];
            fp16 var_3045_to_fp16 = const()[name = string("op_3045_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_107_cast_fp16 = mul(x = attn_weights_105_cast_fp16, y = var_3045_to_fp16)[name = string("attn_weights_107_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_109_cast_fp16 = add(x = attn_weights_107_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_109_cast_fp16")];
            int32 var_3049 = const()[name = string("op_3049"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_111_cast_fp16 = softmax(axis = var_3049, x = attn_weights_109_cast_fp16)[name = string("attn_weights_111_cast_fp16")];
            bool attn_output_49_transpose_x_1 = const()[name = string("attn_output_49_transpose_x_1"), val = bool(true)];
            bool attn_output_49_transpose_y_1 = const()[name = string("attn_output_49_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_49_cast_fp16 = matmul(transpose_x = attn_output_49_transpose_x_1, transpose_y = attn_output_49_transpose_y_1, x = attn_weights_111_cast_fp16, y = var_3027_cast_fp16_1)[name = string("attn_output_49_cast_fp16")];
            int32 var_3057 = const()[name = string("op_3057"), val = int32(1)];
            bool attn_output_51_interleave_0 = const()[name = string("attn_output_51_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_51_cast_fp16 = concat(axis = var_3057, interleave = attn_output_51_interleave_0, values = (var_3043_cast_fp16, attn_output_49_cast_fp16))[name = string("attn_output_51_cast_fp16")];
            tensor<int32, [4]> var_3061_perm_0 = const()[name = string("op_3061_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_83x = const()[name = string("concat_83x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_3061_cast_fp16 = transpose(perm = var_3061_perm_0, x = attn_output_51_cast_fp16)[name = string("transpose_498")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_55_cast_fp16 = reshape(shape = concat_83x, x = var_3061_cast_fp16)[name = string("attn_output_55_cast_fp16")];
            tensor<int32, [2]> hidden_states_63_strides_0 = const()[name = string("hidden_states_63_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_63_pad_type_0 = const()[name = string("hidden_states_63_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_63_pad_0 = const()[name = string("hidden_states_63_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_63_dilations_0 = const()[name = string("hidden_states_63_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_63_groups_0 = const()[name = string("hidden_states_63_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_63_cast_fp16 = conv(dilations = hidden_states_63_dilations_0, groups = hidden_states_63_groups_0, pad = hidden_states_63_pad_0, pad_type = hidden_states_63_pad_type_0, strides = hidden_states_63_strides_0, weight = layers_6_self_attn_o_proj_weight_cast_fp16, x = attn_output_55_cast_fp16)[name = string("hidden_states_63_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_65_cast_fp16 = add(x = hidden_states_59_cast_fp16, y = hidden_states_63_cast_fp16)[name = string("hidden_states_65_cast_fp16")];
            fp16 const_70_promoted_to_fp16 = const()[name = string("const_70_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_3094_cast_fp16 = mul(x = hidden_states_65_cast_fp16, y = const_70_promoted_to_fp16)[name = string("op_3094_cast_fp16")];
            int32 var_3092 = const()[name = string("op_3092"), val = int32(1)];
            bool doubled_53_interleave_0 = const()[name = string("doubled_53_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_53_cast_fp16 = concat(axis = var_3092, interleave = doubled_53_interleave_0, values = (hidden_states_65_cast_fp16, var_3094_cast_fp16))[name = string("doubled_53_cast_fp16")];
            tensor<int32, [1]> out_27_axes_0 = const()[name = string("out_27_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_27_gamma_0_to_fp16 = const()[name = string("out_27_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1318822400)))];
            fp16 var_3104_to_fp16 = const()[name = string("op_3104_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_27_cast_fp16 = layer_norm(axes = out_27_axes_0, epsilon = var_3104_to_fp16, gamma = out_27_gamma_0_to_fp16, x = doubled_53_cast_fp16)[name = string("out_27_cast_fp16")];
            tensor<int32, [2]> var_3115_split_sizes_0 = const()[name = string("op_3115_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3115_axis_0 = const()[name = string("op_3115_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_3115_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_3115_cast_fp16_1 = split(axis = var_3115_axis_0, split_sizes = var_3115_split_sizes_0, x = out_27_cast_fp16)[name = string("op_3115_cast_fp16")];
            tensor<int32, [2]> input_13_strides_0 = const()[name = string("input_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_13_pad_type_0 = const()[name = string("input_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_13_pad_0 = const()[name = string("input_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_13_dilations_0 = const()[name = string("input_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_13_groups_0 = const()[name = string("input_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_13_cast_fp16 = conv(dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = layers_6_mlp_gate_proj_weight_cast_fp16, x = var_3115_cast_fp16_0)[name = string("input_13_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_3132_cast_fp16 = silu(x = input_13_cast_fp16)[name = string("op_3132_cast_fp16")];
            tensor<int32, [2]> var_3138_strides_0 = const()[name = string("op_3138_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_3138_pad_type_0 = const()[name = string("op_3138_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_3138_pad_0 = const()[name = string("op_3138_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_3138_dilations_0 = const()[name = string("op_3138_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3138_groups_0 = const()[name = string("op_3138_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_3138_cast_fp16 = conv(dilations = var_3138_dilations_0, groups = var_3138_groups_0, pad = var_3138_pad_0, pad_type = var_3138_pad_type_0, strides = var_3138_strides_0, weight = layers_6_mlp_up_proj_weight_cast_fp16, x = var_3115_cast_fp16_0)[name = string("op_3138_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_69_cast_fp16 = mul(x = var_3132_cast_fp16, y = var_3138_cast_fp16)[name = string("x_69_cast_fp16")];
            tensor<int32, [2]> hidden_states_67_strides_0 = const()[name = string("hidden_states_67_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_67_pad_type_0 = const()[name = string("hidden_states_67_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_67_pad_0 = const()[name = string("hidden_states_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_67_dilations_0 = const()[name = string("hidden_states_67_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_67_groups_0 = const()[name = string("hidden_states_67_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_67_cast_fp16 = conv(dilations = hidden_states_67_dilations_0, groups = hidden_states_67_groups_0, pad = hidden_states_67_pad_0, pad_type = hidden_states_67_pad_type_0, strides = hidden_states_67_strides_0, weight = layers_6_mlp_down_proj_weight_cast_fp16, x = x_69_cast_fp16)[name = string("hidden_states_67_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_69_cast_fp16 = add(x = hidden_states_65_cast_fp16, y = hidden_states_67_cast_fp16)[name = string("hidden_states_69_cast_fp16")];
            fp16 const_72_promoted_to_fp16 = const()[name = string("const_72_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_3156_cast_fp16 = mul(x = hidden_states_69_cast_fp16, y = const_72_promoted_to_fp16)[name = string("op_3156_cast_fp16")];
            int32 var_3154 = const()[name = string("op_3154"), val = int32(1)];
            bool doubled_57_interleave_0 = const()[name = string("doubled_57_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_57_cast_fp16 = concat(axis = var_3154, interleave = doubled_57_interleave_0, values = (hidden_states_69_cast_fp16, var_3156_cast_fp16))[name = string("doubled_57_cast_fp16")];
            tensor<int32, [1]> out_29_axes_0 = const()[name = string("out_29_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_29_gamma_0_to_fp16 = const()[name = string("out_29_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1318830656)))];
            fp16 var_3166_to_fp16 = const()[name = string("op_3166_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_29_cast_fp16 = layer_norm(axes = out_29_axes_0, epsilon = var_3166_to_fp16, gamma = out_29_gamma_0_to_fp16, x = doubled_57_cast_fp16)[name = string("out_29_cast_fp16")];
            tensor<int32, [2]> var_3177_split_sizes_0 = const()[name = string("op_3177_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3177_axis_0 = const()[name = string("op_3177_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_3177_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_3177_cast_fp16_1 = split(axis = var_3177_axis_0, split_sizes = var_3177_split_sizes_0, x = out_29_cast_fp16)[name = string("op_3177_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_7_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_7_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1318838912)))];
            tensor<int32, [2]> query_states_43_strides_0 = const()[name = string("query_states_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_43_pad_type_0 = const()[name = string("query_states_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_43_pad_0 = const()[name = string("query_states_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_43_dilations_0 = const()[name = string("query_states_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_43_groups_0 = const()[name = string("query_states_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_43_cast_fp16 = conv(dilations = query_states_43_dilations_0, groups = query_states_43_groups_0, pad = query_states_43_pad_0, pad_type = query_states_43_pad_type_0, strides = query_states_43_strides_0, weight = layers_7_self_attn_q_proj_weight_to_fp16, x = var_3177_cast_fp16_0)[name = string("query_states_43_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_7_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_7_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1327227584)))];
            tensor<int32, [2]> key_states_71_strides_0 = const()[name = string("key_states_71_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_71_pad_type_0 = const()[name = string("key_states_71_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_71_pad_0 = const()[name = string("key_states_71_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_71_dilations_0 = const()[name = string("key_states_71_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_71_groups_0 = const()[name = string("key_states_71_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_71_cast_fp16 = conv(dilations = key_states_71_dilations_0, groups = key_states_71_groups_0, pad = key_states_71_pad_0, pad_type = key_states_71_pad_type_0, strides = key_states_71_strides_0, weight = layers_7_self_attn_k_proj_weight_to_fp16, x = var_3177_cast_fp16_0)[name = string("key_states_71_cast_fp16")];
            tensor<int32, [2]> value_states_43_strides_0 = const()[name = string("value_states_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_43_pad_type_0 = const()[name = string("value_states_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_43_pad_0 = const()[name = string("value_states_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_43_dilations_0 = const()[name = string("value_states_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_43_groups_0 = const()[name = string("value_states_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_43_cast_fp16 = conv(dilations = value_states_43_dilations_0, groups = value_states_43_groups_0, pad = value_states_43_pad_0, pad_type = value_states_43_pad_type_0, strides = value_states_43_strides_0, weight = layers_7_self_attn_v_proj_weight_cast_fp16, x = var_3177_cast_fp16_0)[name = string("value_states_43_cast_fp16")];
            tensor<int32, [4]> concat_84x = const()[name = string("concat_84x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_71_cast_fp16 = reshape(shape = concat_84x, x = query_states_43_cast_fp16)[name = string("x_71_cast_fp16")];
            tensor<int32, [4]> concat_85x = const()[name = string("concat_85x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_3234_cast_fp16 = reshape(shape = concat_85x, x = key_states_71_cast_fp16)[name = string("op_3234_cast_fp16")];
            tensor<int32, [4]> concat_86x = const()[name = string("concat_86x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_3241_cast_fp16 = reshape(shape = concat_86x, x = value_states_43_cast_fp16)[name = string("op_3241_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_3245_cast_fp16 = mul(x = x_71_cast_fp16, y = var_859_cast_fp16)[name = string("op_3245_cast_fp16")];
            tensor<int32, [2]> var_3246_split_sizes_0 = const()[name = string("op_3246_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3246_axis_0 = const()[name = string("op_3246_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_3246_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_3246_cast_fp16_1 = split(axis = var_3246_axis_0, split_sizes = var_3246_split_sizes_0, x = x_71_cast_fp16)[name = string("op_3246_cast_fp16")];
            fp16 const_74_promoted_to_fp16 = const()[name = string("const_74_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_3248_cast_fp16 = mul(x = var_3246_cast_fp16_1, y = const_74_promoted_to_fp16)[name = string("op_3248_cast_fp16")];
            int32 var_3250 = const()[name = string("op_3250"), val = int32(-2)];
            bool var_3251_interleave_0 = const()[name = string("op_3251_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_3251_cast_fp16 = concat(axis = var_3250, interleave = var_3251_interleave_0, values = (var_3248_cast_fp16, var_3246_cast_fp16_0))[name = string("op_3251_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_3252_cast_fp16 = mul(x = var_3251_cast_fp16, y = var_866_cast_fp16)[name = string("op_3252_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_45_cast_fp16 = add(x = var_3245_cast_fp16, y = var_3252_cast_fp16)[name = string("query_states_45_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_3258_cast_fp16 = mul(x = var_3234_cast_fp16, y = var_859_cast_fp16)[name = string("op_3258_cast_fp16")];
            tensor<int32, [2]> var_3259_split_sizes_0 = const()[name = string("op_3259_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3259_axis_0 = const()[name = string("op_3259_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_3259_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_3259_cast_fp16_1 = split(axis = var_3259_axis_0, split_sizes = var_3259_split_sizes_0, x = var_3234_cast_fp16)[name = string("op_3259_cast_fp16")];
            fp16 const_75_promoted_to_fp16 = const()[name = string("const_75_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_3261_cast_fp16 = mul(x = var_3259_cast_fp16_1, y = const_75_promoted_to_fp16)[name = string("op_3261_cast_fp16")];
            int32 var_3263 = const()[name = string("op_3263"), val = int32(-2)];
            bool var_3264_interleave_0 = const()[name = string("op_3264_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_3264_cast_fp16 = concat(axis = var_3263, interleave = var_3264_interleave_0, values = (var_3261_cast_fp16, var_3259_cast_fp16_0))[name = string("op_3264_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_3265_cast_fp16 = mul(x = var_3264_cast_fp16, y = var_866_cast_fp16)[name = string("op_3265_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_75_cast_fp16 = add(x = var_3258_cast_fp16, y = var_3265_cast_fp16)[name = string("key_states_75_cast_fp16")];
            tensor<int32, [1]> expand_dims_84 = const()[name = string("expand_dims_84"), val = tensor<int32, [1]>([7])];
            tensor<int32, [1]> expand_dims_85 = const()[name = string("expand_dims_85"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_87 = const()[name = string("expand_dims_87"), val = tensor<int32, [1]>([0])];
            int32 concat_89_axis_0 = const()[name = string("concat_89_axis_0"), val = int32(0)];
            bool concat_89_interleave_0 = const()[name = string("concat_89_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_89 = concat(axis = concat_89_axis_0, interleave = concat_89_interleave_0, values = (expand_dims_84, expand_dims_85, position_id, expand_dims_87))[name = string("concat_89")];
            tensor<int32, [1]> expand_dims_88 = const()[name = string("expand_dims_88"), val = tensor<int32, [1]>([8])];
            tensor<int32, [1]> concat_90_values1_0 = const()[name = string("concat_90_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_90_values3_0 = const()[name = string("concat_90_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_90_axis_0 = const()[name = string("concat_90_axis_0"), val = int32(0)];
            bool concat_90_interleave_0 = const()[name = string("concat_90_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_90 = concat(axis = concat_90_axis_0, interleave = concat_90_interleave_0, values = (expand_dims_88, concat_90_values1_0, cache_position_end, concat_90_values3_0))[name = string("concat_90")];
            tensor<int32, [4]> key_states_77_perm_0 = const()[name = string("key_states_77_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_8_stride_0 = const()[name = string("key_cache_internal_tensor_assign_8_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_8_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_8_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_8_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_8_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_8_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_8_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_77_cast_fp16 = transpose(perm = key_states_77_perm_0, x = key_states_75_cast_fp16)[name = string("transpose_497")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_8_cast_fp16 = slice_update(begin = concat_89, begin_mask = key_cache_internal_tensor_assign_8_begin_mask_0, end = concat_90, end_mask = key_cache_internal_tensor_assign_8_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_8_squeeze_mask_0, stride = key_cache_internal_tensor_assign_8_stride_0, update = key_states_77_cast_fp16, x = coreml_update_state_292)[name = string("key_cache_internal_tensor_assign_8_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_8_cast_fp16, input = key_cache)[name = string("coreml_update_state_294_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_294 = read_state(input = key_cache)[name = string("coreml_update_state_294")];
            tensor<int32, [4]> value_states_45_perm_0 = const()[name = string("value_states_45_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_8_stride_0 = const()[name = string("value_cache_internal_tensor_assign_8_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_8_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_8_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_8_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_8_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_8_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_8_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_45_cast_fp16 = transpose(perm = value_states_45_perm_0, x = var_3241_cast_fp16)[name = string("transpose_496")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_8_cast_fp16 = slice_update(begin = concat_89, begin_mask = value_cache_internal_tensor_assign_8_begin_mask_0, end = concat_90, end_mask = value_cache_internal_tensor_assign_8_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_8_squeeze_mask_0, stride = value_cache_internal_tensor_assign_8_stride_0, update = value_states_45_cast_fp16, x = coreml_update_state_293)[name = string("value_cache_internal_tensor_assign_8_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_8_cast_fp16, input = value_cache)[name = string("coreml_update_state_295_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_295 = read_state(input = value_cache)[name = string("coreml_update_state_295")];
            tensor<int32, [4]> var_3335_begin_0 = const()[name = string("op_3335_begin_0"), val = tensor<int32, [4]>([7, 0, 0, 0])];
            tensor<int32, [4]> var_3335_end_0 = const()[name = string("op_3335_end_0"), val = tensor<int32, [4]>([8, 2, 2048, 128])];
            tensor<bool, [4]> var_3335_end_mask_0 = const()[name = string("op_3335_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3335_cast_fp16 = slice_by_index(begin = var_3335_begin_0, end = var_3335_end_0, end_mask = var_3335_end_mask_0, x = coreml_update_state_294)[name = string("op_3335_cast_fp16")];
            tensor<int32, [2]> tile_14 = const()[name = string("tile_14"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3338_axis_0 = const()[name = string("op_3338_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3338_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3338_cast_fp16_1 = split(axis = var_3338_axis_0, split_sizes = tile_14, x = var_3335_cast_fp16)[name = string("op_3338_cast_fp16")];
            tensor<int32, [4]> var_3345_begin_0 = const()[name = string("op_3345_begin_0"), val = tensor<int32, [4]>([7, 0, 0, 0])];
            tensor<int32, [4]> var_3345_end_0 = const()[name = string("op_3345_end_0"), val = tensor<int32, [4]>([8, 2, 2048, 128])];
            tensor<bool, [4]> var_3345_end_mask_0 = const()[name = string("op_3345_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3345_cast_fp16 = slice_by_index(begin = var_3345_begin_0, end = var_3345_end_0, end_mask = var_3345_end_mask_0, x = coreml_update_state_295)[name = string("op_3345_cast_fp16")];
            tensor<int32, [2]> tile_15 = const()[name = string("tile_15"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3348_axis_0 = const()[name = string("op_3348_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3348_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3348_cast_fp16_1 = split(axis = var_3348_axis_0, split_sizes = tile_15, x = var_3345_cast_fp16)[name = string("op_3348_cast_fp16")];
            tensor<int32, [2]> var_3351_split_sizes_0 = const()[name = string("op_3351_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3351_axis_0 = const()[name = string("op_3351_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_3351_0, tensor<fp16, [1, 8, 128, 64]> var_3351_1 = split(axis = var_3351_axis_0, split_sizes = var_3351_split_sizes_0, x = query_states_45_cast_fp16)[name = string("op_3351")];
            bool attn_weights_113_transpose_x_0 = const()[name = string("attn_weights_113_transpose_x_0"), val = bool(false)];
            bool attn_weights_113_transpose_y_0 = const()[name = string("attn_weights_113_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_113_cast_fp16 = matmul(transpose_x = attn_weights_113_transpose_x_0, transpose_y = attn_weights_113_transpose_y_0, x = var_3338_cast_fp16_0, y = var_3351_0)[name = string("attn_weights_113_cast_fp16")];
            fp16 var_3354_to_fp16 = const()[name = string("op_3354_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_115_cast_fp16 = mul(x = attn_weights_113_cast_fp16, y = var_3354_to_fp16)[name = string("attn_weights_115_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_117_cast_fp16 = add(x = attn_weights_115_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_117_cast_fp16")];
            int32 var_3358 = const()[name = string("op_3358"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_119_cast_fp16 = softmax(axis = var_3358, x = attn_weights_117_cast_fp16)[name = string("attn_weights_119_cast_fp16")];
            bool var_3364_transpose_x_1 = const()[name = string("op_3364_transpose_x_1"), val = bool(true)];
            bool var_3364_transpose_y_1 = const()[name = string("op_3364_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_3364_cast_fp16 = matmul(transpose_x = var_3364_transpose_x_1, transpose_y = var_3364_transpose_y_1, x = attn_weights_119_cast_fp16, y = var_3348_cast_fp16_0)[name = string("op_3364_cast_fp16")];
            bool attn_weights_121_transpose_x_0 = const()[name = string("attn_weights_121_transpose_x_0"), val = bool(false)];
            bool attn_weights_121_transpose_y_0 = const()[name = string("attn_weights_121_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_121_cast_fp16 = matmul(transpose_x = attn_weights_121_transpose_x_0, transpose_y = attn_weights_121_transpose_y_0, x = var_3338_cast_fp16_1, y = var_3351_1)[name = string("attn_weights_121_cast_fp16")];
            fp16 var_3366_to_fp16 = const()[name = string("op_3366_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_123_cast_fp16 = mul(x = attn_weights_121_cast_fp16, y = var_3366_to_fp16)[name = string("attn_weights_123_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_125_cast_fp16 = add(x = attn_weights_123_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_125_cast_fp16")];
            int32 var_3370 = const()[name = string("op_3370"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_127_cast_fp16 = softmax(axis = var_3370, x = attn_weights_125_cast_fp16)[name = string("attn_weights_127_cast_fp16")];
            bool attn_output_57_transpose_x_1 = const()[name = string("attn_output_57_transpose_x_1"), val = bool(true)];
            bool attn_output_57_transpose_y_1 = const()[name = string("attn_output_57_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_57_cast_fp16 = matmul(transpose_x = attn_output_57_transpose_x_1, transpose_y = attn_output_57_transpose_y_1, x = attn_weights_127_cast_fp16, y = var_3348_cast_fp16_1)[name = string("attn_output_57_cast_fp16")];
            int32 var_3378 = const()[name = string("op_3378"), val = int32(1)];
            bool attn_output_59_interleave_0 = const()[name = string("attn_output_59_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_59_cast_fp16 = concat(axis = var_3378, interleave = attn_output_59_interleave_0, values = (var_3364_cast_fp16, attn_output_57_cast_fp16))[name = string("attn_output_59_cast_fp16")];
            tensor<int32, [4]> var_3382_perm_0 = const()[name = string("op_3382_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_95x = const()[name = string("concat_95x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_3382_cast_fp16 = transpose(perm = var_3382_perm_0, x = attn_output_59_cast_fp16)[name = string("transpose_495")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_63_cast_fp16 = reshape(shape = concat_95x, x = var_3382_cast_fp16)[name = string("attn_output_63_cast_fp16")];
            tensor<int32, [2]> hidden_states_73_strides_0 = const()[name = string("hidden_states_73_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_73_pad_type_0 = const()[name = string("hidden_states_73_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_73_pad_0 = const()[name = string("hidden_states_73_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_73_dilations_0 = const()[name = string("hidden_states_73_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_73_groups_0 = const()[name = string("hidden_states_73_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_73_cast_fp16 = conv(dilations = hidden_states_73_dilations_0, groups = hidden_states_73_groups_0, pad = hidden_states_73_pad_0, pad_type = hidden_states_73_pad_type_0, strides = hidden_states_73_strides_0, weight = layers_7_self_attn_o_proj_weight_cast_fp16, x = attn_output_63_cast_fp16)[name = string("hidden_states_73_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_75_cast_fp16 = add(x = hidden_states_69_cast_fp16, y = hidden_states_73_cast_fp16)[name = string("hidden_states_75_cast_fp16")];
            fp16 const_80_promoted_to_fp16 = const()[name = string("const_80_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_3415_cast_fp16 = mul(x = hidden_states_75_cast_fp16, y = const_80_promoted_to_fp16)[name = string("op_3415_cast_fp16")];
            int32 var_3413 = const()[name = string("op_3413"), val = int32(1)];
            bool doubled_61_interleave_0 = const()[name = string("doubled_61_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_61_cast_fp16 = concat(axis = var_3413, interleave = doubled_61_interleave_0, values = (hidden_states_75_cast_fp16, var_3415_cast_fp16))[name = string("doubled_61_cast_fp16")];
            tensor<int32, [1]> out_31_axes_0 = const()[name = string("out_31_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_31_gamma_0_to_fp16 = const()[name = string("out_31_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1328276224)))];
            fp16 var_3425_to_fp16 = const()[name = string("op_3425_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_31_cast_fp16 = layer_norm(axes = out_31_axes_0, epsilon = var_3425_to_fp16, gamma = out_31_gamma_0_to_fp16, x = doubled_61_cast_fp16)[name = string("out_31_cast_fp16")];
            tensor<int32, [2]> var_3436_split_sizes_0 = const()[name = string("op_3436_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3436_axis_0 = const()[name = string("op_3436_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_3436_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_3436_cast_fp16_1 = split(axis = var_3436_axis_0, split_sizes = var_3436_split_sizes_0, x = out_31_cast_fp16)[name = string("op_3436_cast_fp16")];
            tensor<int32, [2]> input_15_strides_0 = const()[name = string("input_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_15_pad_type_0 = const()[name = string("input_15_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_15_dilations_0 = const()[name = string("input_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_15_groups_0 = const()[name = string("input_15_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_15_cast_fp16 = conv(dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = layers_7_mlp_gate_proj_weight_cast_fp16, x = var_3436_cast_fp16_0)[name = string("input_15_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_3453_cast_fp16 = silu(x = input_15_cast_fp16)[name = string("op_3453_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_7_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_7_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1328284480)))];
            tensor<int32, [2]> var_3459_strides_0 = const()[name = string("op_3459_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_3459_pad_type_0 = const()[name = string("op_3459_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_3459_pad_0 = const()[name = string("op_3459_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_3459_dilations_0 = const()[name = string("op_3459_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3459_groups_0 = const()[name = string("op_3459_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_3459_cast_fp16 = conv(dilations = var_3459_dilations_0, groups = var_3459_groups_0, pad = var_3459_pad_0, pad_type = var_3459_pad_type_0, strides = var_3459_strides_0, weight = layers_7_mlp_up_proj_weight_to_fp16, x = var_3436_cast_fp16_0)[name = string("op_3459_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_79_cast_fp16 = mul(x = var_3453_cast_fp16, y = var_3459_cast_fp16)[name = string("x_79_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_7_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_7_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353450368)))];
            tensor<int32, [2]> hidden_states_77_strides_0 = const()[name = string("hidden_states_77_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_77_pad_type_0 = const()[name = string("hidden_states_77_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_77_pad_0 = const()[name = string("hidden_states_77_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_77_dilations_0 = const()[name = string("hidden_states_77_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_77_groups_0 = const()[name = string("hidden_states_77_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_77_cast_fp16 = conv(dilations = hidden_states_77_dilations_0, groups = hidden_states_77_groups_0, pad = hidden_states_77_pad_0, pad_type = hidden_states_77_pad_type_0, strides = hidden_states_77_strides_0, weight = layers_7_mlp_down_proj_weight_to_fp16, x = x_79_cast_fp16)[name = string("hidden_states_77_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_79_cast_fp16 = add(x = hidden_states_75_cast_fp16, y = hidden_states_77_cast_fp16)[name = string("hidden_states_79_cast_fp16")];
            fp16 const_82_promoted_to_fp16 = const()[name = string("const_82_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_3477_cast_fp16 = mul(x = hidden_states_79_cast_fp16, y = const_82_promoted_to_fp16)[name = string("op_3477_cast_fp16")];
            int32 var_3475 = const()[name = string("op_3475"), val = int32(1)];
            bool doubled_65_interleave_0 = const()[name = string("doubled_65_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_65_cast_fp16 = concat(axis = var_3475, interleave = doubled_65_interleave_0, values = (hidden_states_79_cast_fp16, var_3477_cast_fp16))[name = string("doubled_65_cast_fp16")];
            tensor<int32, [1]> out_33_axes_0 = const()[name = string("out_33_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_33_gamma_0_to_fp16 = const()[name = string("out_33_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1378616256)))];
            fp16 var_3487_to_fp16 = const()[name = string("op_3487_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_33_cast_fp16 = layer_norm(axes = out_33_axes_0, epsilon = var_3487_to_fp16, gamma = out_33_gamma_0_to_fp16, x = doubled_65_cast_fp16)[name = string("out_33_cast_fp16")];
            tensor<int32, [2]> var_3498_split_sizes_0 = const()[name = string("op_3498_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3498_axis_0 = const()[name = string("op_3498_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_3498_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_3498_cast_fp16_1 = split(axis = var_3498_axis_0, split_sizes = var_3498_split_sizes_0, x = out_33_cast_fp16)[name = string("op_3498_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_8_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_8_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1378624512)))];
            tensor<int32, [2]> query_states_49_strides_0 = const()[name = string("query_states_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_49_pad_type_0 = const()[name = string("query_states_49_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_49_pad_0 = const()[name = string("query_states_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_49_dilations_0 = const()[name = string("query_states_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_49_groups_0 = const()[name = string("query_states_49_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_49_cast_fp16 = conv(dilations = query_states_49_dilations_0, groups = query_states_49_groups_0, pad = query_states_49_pad_0, pad_type = query_states_49_pad_type_0, strides = query_states_49_strides_0, weight = layers_8_self_attn_q_proj_weight_to_fp16, x = var_3498_cast_fp16_0)[name = string("query_states_49_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_8_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_8_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1387013184)))];
            tensor<int32, [2]> key_states_81_strides_0 = const()[name = string("key_states_81_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_81_pad_type_0 = const()[name = string("key_states_81_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_81_pad_0 = const()[name = string("key_states_81_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_81_dilations_0 = const()[name = string("key_states_81_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_81_groups_0 = const()[name = string("key_states_81_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_81_cast_fp16 = conv(dilations = key_states_81_dilations_0, groups = key_states_81_groups_0, pad = key_states_81_pad_0, pad_type = key_states_81_pad_type_0, strides = key_states_81_strides_0, weight = layers_8_self_attn_k_proj_weight_to_fp16, x = var_3498_cast_fp16_0)[name = string("key_states_81_cast_fp16")];
            tensor<int32, [2]> value_states_49_strides_0 = const()[name = string("value_states_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_49_pad_type_0 = const()[name = string("value_states_49_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_49_pad_0 = const()[name = string("value_states_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_49_dilations_0 = const()[name = string("value_states_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_49_groups_0 = const()[name = string("value_states_49_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_49_cast_fp16 = conv(dilations = value_states_49_dilations_0, groups = value_states_49_groups_0, pad = value_states_49_pad_0, pad_type = value_states_49_pad_type_0, strides = value_states_49_strides_0, weight = layers_8_self_attn_v_proj_weight_cast_fp16, x = var_3498_cast_fp16_0)[name = string("value_states_49_cast_fp16")];
            tensor<int32, [4]> concat_96x = const()[name = string("concat_96x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_81_cast_fp16 = reshape(shape = concat_96x, x = query_states_49_cast_fp16)[name = string("x_81_cast_fp16")];
            tensor<int32, [4]> concat_97x = const()[name = string("concat_97x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_3555_cast_fp16 = reshape(shape = concat_97x, x = key_states_81_cast_fp16)[name = string("op_3555_cast_fp16")];
            tensor<int32, [4]> concat_98x = const()[name = string("concat_98x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_3562_cast_fp16 = reshape(shape = concat_98x, x = value_states_49_cast_fp16)[name = string("op_3562_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_3566_cast_fp16 = mul(x = x_81_cast_fp16, y = var_859_cast_fp16)[name = string("op_3566_cast_fp16")];
            tensor<int32, [2]> var_3567_split_sizes_0 = const()[name = string("op_3567_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3567_axis_0 = const()[name = string("op_3567_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_3567_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_3567_cast_fp16_1 = split(axis = var_3567_axis_0, split_sizes = var_3567_split_sizes_0, x = x_81_cast_fp16)[name = string("op_3567_cast_fp16")];
            fp16 const_84_promoted_to_fp16 = const()[name = string("const_84_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_3569_cast_fp16 = mul(x = var_3567_cast_fp16_1, y = const_84_promoted_to_fp16)[name = string("op_3569_cast_fp16")];
            int32 var_3571 = const()[name = string("op_3571"), val = int32(-2)];
            bool var_3572_interleave_0 = const()[name = string("op_3572_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_3572_cast_fp16 = concat(axis = var_3571, interleave = var_3572_interleave_0, values = (var_3569_cast_fp16, var_3567_cast_fp16_0))[name = string("op_3572_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_3573_cast_fp16 = mul(x = var_3572_cast_fp16, y = var_866_cast_fp16)[name = string("op_3573_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_51_cast_fp16 = add(x = var_3566_cast_fp16, y = var_3573_cast_fp16)[name = string("query_states_51_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_3579_cast_fp16 = mul(x = var_3555_cast_fp16, y = var_859_cast_fp16)[name = string("op_3579_cast_fp16")];
            tensor<int32, [2]> var_3580_split_sizes_0 = const()[name = string("op_3580_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3580_axis_0 = const()[name = string("op_3580_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_3580_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_3580_cast_fp16_1 = split(axis = var_3580_axis_0, split_sizes = var_3580_split_sizes_0, x = var_3555_cast_fp16)[name = string("op_3580_cast_fp16")];
            fp16 const_85_promoted_to_fp16 = const()[name = string("const_85_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_3582_cast_fp16 = mul(x = var_3580_cast_fp16_1, y = const_85_promoted_to_fp16)[name = string("op_3582_cast_fp16")];
            int32 var_3584 = const()[name = string("op_3584"), val = int32(-2)];
            bool var_3585_interleave_0 = const()[name = string("op_3585_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_3585_cast_fp16 = concat(axis = var_3584, interleave = var_3585_interleave_0, values = (var_3582_cast_fp16, var_3580_cast_fp16_0))[name = string("op_3585_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_3586_cast_fp16 = mul(x = var_3585_cast_fp16, y = var_866_cast_fp16)[name = string("op_3586_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_85_cast_fp16 = add(x = var_3579_cast_fp16, y = var_3586_cast_fp16)[name = string("key_states_85_cast_fp16")];
            tensor<int32, [1]> expand_dims_96 = const()[name = string("expand_dims_96"), val = tensor<int32, [1]>([8])];
            tensor<int32, [1]> expand_dims_97 = const()[name = string("expand_dims_97"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_99 = const()[name = string("expand_dims_99"), val = tensor<int32, [1]>([0])];
            int32 concat_101_axis_0 = const()[name = string("concat_101_axis_0"), val = int32(0)];
            bool concat_101_interleave_0 = const()[name = string("concat_101_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_101 = concat(axis = concat_101_axis_0, interleave = concat_101_interleave_0, values = (expand_dims_96, expand_dims_97, position_id, expand_dims_99))[name = string("concat_101")];
            tensor<int32, [1]> expand_dims_100 = const()[name = string("expand_dims_100"), val = tensor<int32, [1]>([9])];
            tensor<int32, [1]> concat_102_values1_0 = const()[name = string("concat_102_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_102_values3_0 = const()[name = string("concat_102_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_102_axis_0 = const()[name = string("concat_102_axis_0"), val = int32(0)];
            bool concat_102_interleave_0 = const()[name = string("concat_102_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_102 = concat(axis = concat_102_axis_0, interleave = concat_102_interleave_0, values = (expand_dims_100, concat_102_values1_0, cache_position_end, concat_102_values3_0))[name = string("concat_102")];
            tensor<int32, [4]> key_states_87_perm_0 = const()[name = string("key_states_87_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_9_stride_0 = const()[name = string("key_cache_internal_tensor_assign_9_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_9_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_9_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_9_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_9_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_9_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_9_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_87_cast_fp16 = transpose(perm = key_states_87_perm_0, x = key_states_85_cast_fp16)[name = string("transpose_494")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_9_cast_fp16 = slice_update(begin = concat_101, begin_mask = key_cache_internal_tensor_assign_9_begin_mask_0, end = concat_102, end_mask = key_cache_internal_tensor_assign_9_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_9_squeeze_mask_0, stride = key_cache_internal_tensor_assign_9_stride_0, update = key_states_87_cast_fp16, x = coreml_update_state_294)[name = string("key_cache_internal_tensor_assign_9_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_9_cast_fp16, input = key_cache)[name = string("coreml_update_state_296_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_296 = read_state(input = key_cache)[name = string("coreml_update_state_296")];
            tensor<int32, [4]> value_states_51_perm_0 = const()[name = string("value_states_51_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_9_stride_0 = const()[name = string("value_cache_internal_tensor_assign_9_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_9_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_9_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_9_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_9_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_9_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_9_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_51_cast_fp16 = transpose(perm = value_states_51_perm_0, x = var_3562_cast_fp16)[name = string("transpose_493")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_9_cast_fp16 = slice_update(begin = concat_101, begin_mask = value_cache_internal_tensor_assign_9_begin_mask_0, end = concat_102, end_mask = value_cache_internal_tensor_assign_9_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_9_squeeze_mask_0, stride = value_cache_internal_tensor_assign_9_stride_0, update = value_states_51_cast_fp16, x = coreml_update_state_295)[name = string("value_cache_internal_tensor_assign_9_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_9_cast_fp16, input = value_cache)[name = string("coreml_update_state_297_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_297 = read_state(input = value_cache)[name = string("coreml_update_state_297")];
            tensor<int32, [4]> var_3656_begin_0 = const()[name = string("op_3656_begin_0"), val = tensor<int32, [4]>([8, 0, 0, 0])];
            tensor<int32, [4]> var_3656_end_0 = const()[name = string("op_3656_end_0"), val = tensor<int32, [4]>([9, 2, 2048, 128])];
            tensor<bool, [4]> var_3656_end_mask_0 = const()[name = string("op_3656_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3656_cast_fp16 = slice_by_index(begin = var_3656_begin_0, end = var_3656_end_0, end_mask = var_3656_end_mask_0, x = coreml_update_state_296)[name = string("op_3656_cast_fp16")];
            tensor<int32, [2]> tile_16 = const()[name = string("tile_16"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3659_axis_0 = const()[name = string("op_3659_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3659_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3659_cast_fp16_1 = split(axis = var_3659_axis_0, split_sizes = tile_16, x = var_3656_cast_fp16)[name = string("op_3659_cast_fp16")];
            tensor<int32, [4]> var_3666_begin_0 = const()[name = string("op_3666_begin_0"), val = tensor<int32, [4]>([8, 0, 0, 0])];
            tensor<int32, [4]> var_3666_end_0 = const()[name = string("op_3666_end_0"), val = tensor<int32, [4]>([9, 2, 2048, 128])];
            tensor<bool, [4]> var_3666_end_mask_0 = const()[name = string("op_3666_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3666_cast_fp16 = slice_by_index(begin = var_3666_begin_0, end = var_3666_end_0, end_mask = var_3666_end_mask_0, x = coreml_update_state_297)[name = string("op_3666_cast_fp16")];
            tensor<int32, [2]> tile_17 = const()[name = string("tile_17"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3669_axis_0 = const()[name = string("op_3669_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3669_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3669_cast_fp16_1 = split(axis = var_3669_axis_0, split_sizes = tile_17, x = var_3666_cast_fp16)[name = string("op_3669_cast_fp16")];
            tensor<int32, [2]> var_3672_split_sizes_0 = const()[name = string("op_3672_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3672_axis_0 = const()[name = string("op_3672_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_3672_0, tensor<fp16, [1, 8, 128, 64]> var_3672_1 = split(axis = var_3672_axis_0, split_sizes = var_3672_split_sizes_0, x = query_states_51_cast_fp16)[name = string("op_3672")];
            bool attn_weights_129_transpose_x_0 = const()[name = string("attn_weights_129_transpose_x_0"), val = bool(false)];
            bool attn_weights_129_transpose_y_0 = const()[name = string("attn_weights_129_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_129_cast_fp16 = matmul(transpose_x = attn_weights_129_transpose_x_0, transpose_y = attn_weights_129_transpose_y_0, x = var_3659_cast_fp16_0, y = var_3672_0)[name = string("attn_weights_129_cast_fp16")];
            fp16 var_3675_to_fp16 = const()[name = string("op_3675_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_131_cast_fp16 = mul(x = attn_weights_129_cast_fp16, y = var_3675_to_fp16)[name = string("attn_weights_131_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_133_cast_fp16 = add(x = attn_weights_131_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_133_cast_fp16")];
            int32 var_3679 = const()[name = string("op_3679"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_135_cast_fp16 = softmax(axis = var_3679, x = attn_weights_133_cast_fp16)[name = string("attn_weights_135_cast_fp16")];
            bool var_3685_transpose_x_1 = const()[name = string("op_3685_transpose_x_1"), val = bool(true)];
            bool var_3685_transpose_y_1 = const()[name = string("op_3685_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_3685_cast_fp16 = matmul(transpose_x = var_3685_transpose_x_1, transpose_y = var_3685_transpose_y_1, x = attn_weights_135_cast_fp16, y = var_3669_cast_fp16_0)[name = string("op_3685_cast_fp16")];
            bool attn_weights_137_transpose_x_0 = const()[name = string("attn_weights_137_transpose_x_0"), val = bool(false)];
            bool attn_weights_137_transpose_y_0 = const()[name = string("attn_weights_137_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_137_cast_fp16 = matmul(transpose_x = attn_weights_137_transpose_x_0, transpose_y = attn_weights_137_transpose_y_0, x = var_3659_cast_fp16_1, y = var_3672_1)[name = string("attn_weights_137_cast_fp16")];
            fp16 var_3687_to_fp16 = const()[name = string("op_3687_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_139_cast_fp16 = mul(x = attn_weights_137_cast_fp16, y = var_3687_to_fp16)[name = string("attn_weights_139_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_141_cast_fp16 = add(x = attn_weights_139_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_141_cast_fp16")];
            int32 var_3691 = const()[name = string("op_3691"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_143_cast_fp16 = softmax(axis = var_3691, x = attn_weights_141_cast_fp16)[name = string("attn_weights_143_cast_fp16")];
            bool attn_output_65_transpose_x_1 = const()[name = string("attn_output_65_transpose_x_1"), val = bool(true)];
            bool attn_output_65_transpose_y_1 = const()[name = string("attn_output_65_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_65_cast_fp16 = matmul(transpose_x = attn_output_65_transpose_x_1, transpose_y = attn_output_65_transpose_y_1, x = attn_weights_143_cast_fp16, y = var_3669_cast_fp16_1)[name = string("attn_output_65_cast_fp16")];
            int32 var_3699 = const()[name = string("op_3699"), val = int32(1)];
            bool attn_output_67_interleave_0 = const()[name = string("attn_output_67_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_67_cast_fp16 = concat(axis = var_3699, interleave = attn_output_67_interleave_0, values = (var_3685_cast_fp16, attn_output_65_cast_fp16))[name = string("attn_output_67_cast_fp16")];
            tensor<int32, [4]> var_3703_perm_0 = const()[name = string("op_3703_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_107x = const()[name = string("concat_107x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_3703_cast_fp16 = transpose(perm = var_3703_perm_0, x = attn_output_67_cast_fp16)[name = string("transpose_492")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_71_cast_fp16 = reshape(shape = concat_107x, x = var_3703_cast_fp16)[name = string("attn_output_71_cast_fp16")];
            tensor<int32, [2]> hidden_states_83_strides_0 = const()[name = string("hidden_states_83_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_83_pad_type_0 = const()[name = string("hidden_states_83_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_83_pad_0 = const()[name = string("hidden_states_83_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_83_dilations_0 = const()[name = string("hidden_states_83_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_83_groups_0 = const()[name = string("hidden_states_83_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_83_cast_fp16 = conv(dilations = hidden_states_83_dilations_0, groups = hidden_states_83_groups_0, pad = hidden_states_83_pad_0, pad_type = hidden_states_83_pad_type_0, strides = hidden_states_83_strides_0, weight = layers_8_self_attn_o_proj_weight_cast_fp16, x = attn_output_71_cast_fp16)[name = string("hidden_states_83_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_85_cast_fp16 = add(x = hidden_states_79_cast_fp16, y = hidden_states_83_cast_fp16)[name = string("hidden_states_85_cast_fp16")];
            fp16 const_90_promoted_to_fp16 = const()[name = string("const_90_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_3736_cast_fp16 = mul(x = hidden_states_85_cast_fp16, y = const_90_promoted_to_fp16)[name = string("op_3736_cast_fp16")];
            int32 var_3734 = const()[name = string("op_3734"), val = int32(1)];
            bool doubled_69_interleave_0 = const()[name = string("doubled_69_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_69_cast_fp16 = concat(axis = var_3734, interleave = doubled_69_interleave_0, values = (hidden_states_85_cast_fp16, var_3736_cast_fp16))[name = string("doubled_69_cast_fp16")];
            tensor<int32, [1]> out_35_axes_0 = const()[name = string("out_35_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_35_gamma_0_to_fp16 = const()[name = string("out_35_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1388061824)))];
            fp16 var_3746_to_fp16 = const()[name = string("op_3746_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_35_cast_fp16 = layer_norm(axes = out_35_axes_0, epsilon = var_3746_to_fp16, gamma = out_35_gamma_0_to_fp16, x = doubled_69_cast_fp16)[name = string("out_35_cast_fp16")];
            tensor<int32, [2]> var_3757_split_sizes_0 = const()[name = string("op_3757_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3757_axis_0 = const()[name = string("op_3757_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_3757_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_3757_cast_fp16_1 = split(axis = var_3757_axis_0, split_sizes = var_3757_split_sizes_0, x = out_35_cast_fp16)[name = string("op_3757_cast_fp16")];
            tensor<int32, [2]> input_17_strides_0 = const()[name = string("input_17_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_17_pad_type_0 = const()[name = string("input_17_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_17_pad_0 = const()[name = string("input_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_17_dilations_0 = const()[name = string("input_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_17_groups_0 = const()[name = string("input_17_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_17_cast_fp16 = conv(dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = layers_8_mlp_gate_proj_weight_cast_fp16, x = var_3757_cast_fp16_0)[name = string("input_17_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_3774_cast_fp16 = silu(x = input_17_cast_fp16)[name = string("op_3774_cast_fp16")];
            tensor<int32, [2]> var_3780_strides_0 = const()[name = string("op_3780_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_3780_pad_type_0 = const()[name = string("op_3780_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_3780_pad_0 = const()[name = string("op_3780_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_3780_dilations_0 = const()[name = string("op_3780_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3780_groups_0 = const()[name = string("op_3780_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_3780_cast_fp16 = conv(dilations = var_3780_dilations_0, groups = var_3780_groups_0, pad = var_3780_pad_0, pad_type = var_3780_pad_type_0, strides = var_3780_strides_0, weight = layers_8_mlp_up_proj_weight_cast_fp16, x = var_3757_cast_fp16_0)[name = string("op_3780_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_89_cast_fp16 = mul(x = var_3774_cast_fp16, y = var_3780_cast_fp16)[name = string("x_89_cast_fp16")];
            tensor<int32, [2]> hidden_states_87_strides_0 = const()[name = string("hidden_states_87_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_87_pad_type_0 = const()[name = string("hidden_states_87_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_87_pad_0 = const()[name = string("hidden_states_87_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_87_dilations_0 = const()[name = string("hidden_states_87_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_87_groups_0 = const()[name = string("hidden_states_87_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_87_cast_fp16 = conv(dilations = hidden_states_87_dilations_0, groups = hidden_states_87_groups_0, pad = hidden_states_87_pad_0, pad_type = hidden_states_87_pad_type_0, strides = hidden_states_87_strides_0, weight = layers_8_mlp_down_proj_weight_cast_fp16, x = x_89_cast_fp16)[name = string("hidden_states_87_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_89_cast_fp16 = add(x = hidden_states_85_cast_fp16, y = hidden_states_87_cast_fp16)[name = string("hidden_states_89_cast_fp16")];
            fp16 const_92_promoted_to_fp16 = const()[name = string("const_92_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_3798_cast_fp16 = mul(x = hidden_states_89_cast_fp16, y = const_92_promoted_to_fp16)[name = string("op_3798_cast_fp16")];
            int32 var_3796 = const()[name = string("op_3796"), val = int32(1)];
            bool doubled_73_interleave_0 = const()[name = string("doubled_73_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_73_cast_fp16 = concat(axis = var_3796, interleave = doubled_73_interleave_0, values = (hidden_states_89_cast_fp16, var_3798_cast_fp16))[name = string("doubled_73_cast_fp16")];
            tensor<int32, [1]> out_37_axes_0 = const()[name = string("out_37_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_37_gamma_0_to_fp16 = const()[name = string("out_37_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1388070080)))];
            fp16 var_3808_to_fp16 = const()[name = string("op_3808_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_37_cast_fp16 = layer_norm(axes = out_37_axes_0, epsilon = var_3808_to_fp16, gamma = out_37_gamma_0_to_fp16, x = doubled_73_cast_fp16)[name = string("out_37_cast_fp16")];
            tensor<int32, [2]> var_3819_split_sizes_0 = const()[name = string("op_3819_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3819_axis_0 = const()[name = string("op_3819_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_3819_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_3819_cast_fp16_1 = split(axis = var_3819_axis_0, split_sizes = var_3819_split_sizes_0, x = out_37_cast_fp16)[name = string("op_3819_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_9_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_9_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1388078336)))];
            tensor<int32, [2]> query_states_55_strides_0 = const()[name = string("query_states_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_55_pad_type_0 = const()[name = string("query_states_55_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_55_pad_0 = const()[name = string("query_states_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_55_dilations_0 = const()[name = string("query_states_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_55_groups_0 = const()[name = string("query_states_55_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_55_cast_fp16 = conv(dilations = query_states_55_dilations_0, groups = query_states_55_groups_0, pad = query_states_55_pad_0, pad_type = query_states_55_pad_type_0, strides = query_states_55_strides_0, weight = layers_9_self_attn_q_proj_weight_to_fp16, x = var_3819_cast_fp16_0)[name = string("query_states_55_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_9_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_9_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1396467008)))];
            tensor<int32, [2]> key_states_91_strides_0 = const()[name = string("key_states_91_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_91_pad_type_0 = const()[name = string("key_states_91_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_91_pad_0 = const()[name = string("key_states_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_91_dilations_0 = const()[name = string("key_states_91_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_91_groups_0 = const()[name = string("key_states_91_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_91_cast_fp16 = conv(dilations = key_states_91_dilations_0, groups = key_states_91_groups_0, pad = key_states_91_pad_0, pad_type = key_states_91_pad_type_0, strides = key_states_91_strides_0, weight = layers_9_self_attn_k_proj_weight_to_fp16, x = var_3819_cast_fp16_0)[name = string("key_states_91_cast_fp16")];
            tensor<int32, [2]> value_states_55_strides_0 = const()[name = string("value_states_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_55_pad_type_0 = const()[name = string("value_states_55_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_55_pad_0 = const()[name = string("value_states_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_55_dilations_0 = const()[name = string("value_states_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_55_groups_0 = const()[name = string("value_states_55_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_55_cast_fp16 = conv(dilations = value_states_55_dilations_0, groups = value_states_55_groups_0, pad = value_states_55_pad_0, pad_type = value_states_55_pad_type_0, strides = value_states_55_strides_0, weight = layers_9_self_attn_v_proj_weight_cast_fp16, x = var_3819_cast_fp16_0)[name = string("value_states_55_cast_fp16")];
            tensor<int32, [4]> concat_108x = const()[name = string("concat_108x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_91_cast_fp16 = reshape(shape = concat_108x, x = query_states_55_cast_fp16)[name = string("x_91_cast_fp16")];
            tensor<int32, [4]> concat_109x = const()[name = string("concat_109x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_3876_cast_fp16 = reshape(shape = concat_109x, x = key_states_91_cast_fp16)[name = string("op_3876_cast_fp16")];
            tensor<int32, [4]> concat_110x = const()[name = string("concat_110x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_3883_cast_fp16 = reshape(shape = concat_110x, x = value_states_55_cast_fp16)[name = string("op_3883_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_3887_cast_fp16 = mul(x = x_91_cast_fp16, y = var_859_cast_fp16)[name = string("op_3887_cast_fp16")];
            tensor<int32, [2]> var_3888_split_sizes_0 = const()[name = string("op_3888_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3888_axis_0 = const()[name = string("op_3888_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_3888_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_3888_cast_fp16_1 = split(axis = var_3888_axis_0, split_sizes = var_3888_split_sizes_0, x = x_91_cast_fp16)[name = string("op_3888_cast_fp16")];
            fp16 const_94_promoted_to_fp16 = const()[name = string("const_94_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_3890_cast_fp16 = mul(x = var_3888_cast_fp16_1, y = const_94_promoted_to_fp16)[name = string("op_3890_cast_fp16")];
            int32 var_3892 = const()[name = string("op_3892"), val = int32(-2)];
            bool var_3893_interleave_0 = const()[name = string("op_3893_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_3893_cast_fp16 = concat(axis = var_3892, interleave = var_3893_interleave_0, values = (var_3890_cast_fp16, var_3888_cast_fp16_0))[name = string("op_3893_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_3894_cast_fp16 = mul(x = var_3893_cast_fp16, y = var_866_cast_fp16)[name = string("op_3894_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_57_cast_fp16 = add(x = var_3887_cast_fp16, y = var_3894_cast_fp16)[name = string("query_states_57_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_3900_cast_fp16 = mul(x = var_3876_cast_fp16, y = var_859_cast_fp16)[name = string("op_3900_cast_fp16")];
            tensor<int32, [2]> var_3901_split_sizes_0 = const()[name = string("op_3901_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3901_axis_0 = const()[name = string("op_3901_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_3901_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_3901_cast_fp16_1 = split(axis = var_3901_axis_0, split_sizes = var_3901_split_sizes_0, x = var_3876_cast_fp16)[name = string("op_3901_cast_fp16")];
            fp16 const_95_promoted_to_fp16 = const()[name = string("const_95_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_3903_cast_fp16 = mul(x = var_3901_cast_fp16_1, y = const_95_promoted_to_fp16)[name = string("op_3903_cast_fp16")];
            int32 var_3905 = const()[name = string("op_3905"), val = int32(-2)];
            bool var_3906_interleave_0 = const()[name = string("op_3906_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_3906_cast_fp16 = concat(axis = var_3905, interleave = var_3906_interleave_0, values = (var_3903_cast_fp16, var_3901_cast_fp16_0))[name = string("op_3906_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_3907_cast_fp16 = mul(x = var_3906_cast_fp16, y = var_866_cast_fp16)[name = string("op_3907_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_95_cast_fp16 = add(x = var_3900_cast_fp16, y = var_3907_cast_fp16)[name = string("key_states_95_cast_fp16")];
            tensor<int32, [1]> expand_dims_108 = const()[name = string("expand_dims_108"), val = tensor<int32, [1]>([9])];
            tensor<int32, [1]> expand_dims_109 = const()[name = string("expand_dims_109"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_111 = const()[name = string("expand_dims_111"), val = tensor<int32, [1]>([0])];
            int32 concat_113_axis_0 = const()[name = string("concat_113_axis_0"), val = int32(0)];
            bool concat_113_interleave_0 = const()[name = string("concat_113_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_113 = concat(axis = concat_113_axis_0, interleave = concat_113_interleave_0, values = (expand_dims_108, expand_dims_109, position_id, expand_dims_111))[name = string("concat_113")];
            tensor<int32, [1]> expand_dims_112 = const()[name = string("expand_dims_112"), val = tensor<int32, [1]>([10])];
            tensor<int32, [1]> concat_114_values1_0 = const()[name = string("concat_114_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_114_values3_0 = const()[name = string("concat_114_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_114_axis_0 = const()[name = string("concat_114_axis_0"), val = int32(0)];
            bool concat_114_interleave_0 = const()[name = string("concat_114_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_114 = concat(axis = concat_114_axis_0, interleave = concat_114_interleave_0, values = (expand_dims_112, concat_114_values1_0, cache_position_end, concat_114_values3_0))[name = string("concat_114")];
            tensor<int32, [4]> key_states_97_perm_0 = const()[name = string("key_states_97_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_10_stride_0 = const()[name = string("key_cache_internal_tensor_assign_10_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_10_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_10_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_10_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_10_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_10_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_10_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_97_cast_fp16 = transpose(perm = key_states_97_perm_0, x = key_states_95_cast_fp16)[name = string("transpose_491")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_10_cast_fp16 = slice_update(begin = concat_113, begin_mask = key_cache_internal_tensor_assign_10_begin_mask_0, end = concat_114, end_mask = key_cache_internal_tensor_assign_10_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_10_squeeze_mask_0, stride = key_cache_internal_tensor_assign_10_stride_0, update = key_states_97_cast_fp16, x = coreml_update_state_296)[name = string("key_cache_internal_tensor_assign_10_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_10_cast_fp16, input = key_cache)[name = string("coreml_update_state_298_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_298 = read_state(input = key_cache)[name = string("coreml_update_state_298")];
            tensor<int32, [4]> value_states_57_perm_0 = const()[name = string("value_states_57_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_10_stride_0 = const()[name = string("value_cache_internal_tensor_assign_10_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_10_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_10_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_10_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_10_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_10_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_10_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_57_cast_fp16 = transpose(perm = value_states_57_perm_0, x = var_3883_cast_fp16)[name = string("transpose_490")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_10_cast_fp16 = slice_update(begin = concat_113, begin_mask = value_cache_internal_tensor_assign_10_begin_mask_0, end = concat_114, end_mask = value_cache_internal_tensor_assign_10_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_10_squeeze_mask_0, stride = value_cache_internal_tensor_assign_10_stride_0, update = value_states_57_cast_fp16, x = coreml_update_state_297)[name = string("value_cache_internal_tensor_assign_10_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_10_cast_fp16, input = value_cache)[name = string("coreml_update_state_299_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_299 = read_state(input = value_cache)[name = string("coreml_update_state_299")];
            tensor<int32, [4]> var_3977_begin_0 = const()[name = string("op_3977_begin_0"), val = tensor<int32, [4]>([9, 0, 0, 0])];
            tensor<int32, [4]> var_3977_end_0 = const()[name = string("op_3977_end_0"), val = tensor<int32, [4]>([10, 2, 2048, 128])];
            tensor<bool, [4]> var_3977_end_mask_0 = const()[name = string("op_3977_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3977_cast_fp16 = slice_by_index(begin = var_3977_begin_0, end = var_3977_end_0, end_mask = var_3977_end_mask_0, x = coreml_update_state_298)[name = string("op_3977_cast_fp16")];
            tensor<int32, [2]> tile_18 = const()[name = string("tile_18"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3980_axis_0 = const()[name = string("op_3980_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3980_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3980_cast_fp16_1 = split(axis = var_3980_axis_0, split_sizes = tile_18, x = var_3977_cast_fp16)[name = string("op_3980_cast_fp16")];
            tensor<int32, [4]> var_3987_begin_0 = const()[name = string("op_3987_begin_0"), val = tensor<int32, [4]>([9, 0, 0, 0])];
            tensor<int32, [4]> var_3987_end_0 = const()[name = string("op_3987_end_0"), val = tensor<int32, [4]>([10, 2, 2048, 128])];
            tensor<bool, [4]> var_3987_end_mask_0 = const()[name = string("op_3987_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3987_cast_fp16 = slice_by_index(begin = var_3987_begin_0, end = var_3987_end_0, end_mask = var_3987_end_mask_0, x = coreml_update_state_299)[name = string("op_3987_cast_fp16")];
            tensor<int32, [2]> tile_19 = const()[name = string("tile_19"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3990_axis_0 = const()[name = string("op_3990_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3990_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3990_cast_fp16_1 = split(axis = var_3990_axis_0, split_sizes = tile_19, x = var_3987_cast_fp16)[name = string("op_3990_cast_fp16")];
            tensor<int32, [2]> var_3993_split_sizes_0 = const()[name = string("op_3993_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3993_axis_0 = const()[name = string("op_3993_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_3993_0, tensor<fp16, [1, 8, 128, 64]> var_3993_1 = split(axis = var_3993_axis_0, split_sizes = var_3993_split_sizes_0, x = query_states_57_cast_fp16)[name = string("op_3993")];
            bool attn_weights_145_transpose_x_0 = const()[name = string("attn_weights_145_transpose_x_0"), val = bool(false)];
            bool attn_weights_145_transpose_y_0 = const()[name = string("attn_weights_145_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_145_cast_fp16 = matmul(transpose_x = attn_weights_145_transpose_x_0, transpose_y = attn_weights_145_transpose_y_0, x = var_3980_cast_fp16_0, y = var_3993_0)[name = string("attn_weights_145_cast_fp16")];
            fp16 var_3996_to_fp16 = const()[name = string("op_3996_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_147_cast_fp16 = mul(x = attn_weights_145_cast_fp16, y = var_3996_to_fp16)[name = string("attn_weights_147_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_149_cast_fp16 = add(x = attn_weights_147_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_149_cast_fp16")];
            int32 var_4000 = const()[name = string("op_4000"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_151_cast_fp16 = softmax(axis = var_4000, x = attn_weights_149_cast_fp16)[name = string("attn_weights_151_cast_fp16")];
            bool var_4006_transpose_x_1 = const()[name = string("op_4006_transpose_x_1"), val = bool(true)];
            bool var_4006_transpose_y_1 = const()[name = string("op_4006_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_4006_cast_fp16 = matmul(transpose_x = var_4006_transpose_x_1, transpose_y = var_4006_transpose_y_1, x = attn_weights_151_cast_fp16, y = var_3990_cast_fp16_0)[name = string("op_4006_cast_fp16")];
            bool attn_weights_153_transpose_x_0 = const()[name = string("attn_weights_153_transpose_x_0"), val = bool(false)];
            bool attn_weights_153_transpose_y_0 = const()[name = string("attn_weights_153_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_153_cast_fp16 = matmul(transpose_x = attn_weights_153_transpose_x_0, transpose_y = attn_weights_153_transpose_y_0, x = var_3980_cast_fp16_1, y = var_3993_1)[name = string("attn_weights_153_cast_fp16")];
            fp16 var_4008_to_fp16 = const()[name = string("op_4008_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_155_cast_fp16 = mul(x = attn_weights_153_cast_fp16, y = var_4008_to_fp16)[name = string("attn_weights_155_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_157_cast_fp16 = add(x = attn_weights_155_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_157_cast_fp16")];
            int32 var_4012 = const()[name = string("op_4012"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_159_cast_fp16 = softmax(axis = var_4012, x = attn_weights_157_cast_fp16)[name = string("attn_weights_159_cast_fp16")];
            bool attn_output_73_transpose_x_1 = const()[name = string("attn_output_73_transpose_x_1"), val = bool(true)];
            bool attn_output_73_transpose_y_1 = const()[name = string("attn_output_73_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_73_cast_fp16 = matmul(transpose_x = attn_output_73_transpose_x_1, transpose_y = attn_output_73_transpose_y_1, x = attn_weights_159_cast_fp16, y = var_3990_cast_fp16_1)[name = string("attn_output_73_cast_fp16")];
            int32 var_4020 = const()[name = string("op_4020"), val = int32(1)];
            bool attn_output_75_interleave_0 = const()[name = string("attn_output_75_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_75_cast_fp16 = concat(axis = var_4020, interleave = attn_output_75_interleave_0, values = (var_4006_cast_fp16, attn_output_73_cast_fp16))[name = string("attn_output_75_cast_fp16")];
            tensor<int32, [4]> var_4024_perm_0 = const()[name = string("op_4024_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_119x = const()[name = string("concat_119x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_4024_cast_fp16 = transpose(perm = var_4024_perm_0, x = attn_output_75_cast_fp16)[name = string("transpose_489")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_79_cast_fp16 = reshape(shape = concat_119x, x = var_4024_cast_fp16)[name = string("attn_output_79_cast_fp16")];
            tensor<int32, [2]> hidden_states_93_strides_0 = const()[name = string("hidden_states_93_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_93_pad_type_0 = const()[name = string("hidden_states_93_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_93_pad_0 = const()[name = string("hidden_states_93_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_93_dilations_0 = const()[name = string("hidden_states_93_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_93_groups_0 = const()[name = string("hidden_states_93_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_93_cast_fp16 = conv(dilations = hidden_states_93_dilations_0, groups = hidden_states_93_groups_0, pad = hidden_states_93_pad_0, pad_type = hidden_states_93_pad_type_0, strides = hidden_states_93_strides_0, weight = layers_9_self_attn_o_proj_weight_cast_fp16, x = attn_output_79_cast_fp16)[name = string("hidden_states_93_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_95_cast_fp16 = add(x = hidden_states_89_cast_fp16, y = hidden_states_93_cast_fp16)[name = string("hidden_states_95_cast_fp16")];
            fp16 const_100_promoted_to_fp16 = const()[name = string("const_100_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_4057_cast_fp16 = mul(x = hidden_states_95_cast_fp16, y = const_100_promoted_to_fp16)[name = string("op_4057_cast_fp16")];
            int32 var_4055 = const()[name = string("op_4055"), val = int32(1)];
            bool doubled_77_interleave_0 = const()[name = string("doubled_77_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_77_cast_fp16 = concat(axis = var_4055, interleave = doubled_77_interleave_0, values = (hidden_states_95_cast_fp16, var_4057_cast_fp16))[name = string("doubled_77_cast_fp16")];
            tensor<int32, [1]> out_39_axes_0 = const()[name = string("out_39_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_39_gamma_0_to_fp16 = const()[name = string("out_39_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1397515648)))];
            fp16 var_4067_to_fp16 = const()[name = string("op_4067_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_39_cast_fp16 = layer_norm(axes = out_39_axes_0, epsilon = var_4067_to_fp16, gamma = out_39_gamma_0_to_fp16, x = doubled_77_cast_fp16)[name = string("out_39_cast_fp16")];
            tensor<int32, [2]> var_4078_split_sizes_0 = const()[name = string("op_4078_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4078_axis_0 = const()[name = string("op_4078_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_4078_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_4078_cast_fp16_1 = split(axis = var_4078_axis_0, split_sizes = var_4078_split_sizes_0, x = out_39_cast_fp16)[name = string("op_4078_cast_fp16")];
            tensor<int32, [2]> input_19_strides_0 = const()[name = string("input_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_19_pad_type_0 = const()[name = string("input_19_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_19_pad_0 = const()[name = string("input_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_19_dilations_0 = const()[name = string("input_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_19_groups_0 = const()[name = string("input_19_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_19_cast_fp16 = conv(dilations = input_19_dilations_0, groups = input_19_groups_0, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = input_19_strides_0, weight = layers_9_mlp_gate_proj_weight_cast_fp16, x = var_4078_cast_fp16_0)[name = string("input_19_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_4095_cast_fp16 = silu(x = input_19_cast_fp16)[name = string("op_4095_cast_fp16")];
            tensor<int32, [2]> var_4101_strides_0 = const()[name = string("op_4101_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_4101_pad_type_0 = const()[name = string("op_4101_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_4101_pad_0 = const()[name = string("op_4101_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_4101_dilations_0 = const()[name = string("op_4101_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4101_groups_0 = const()[name = string("op_4101_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_4101_cast_fp16 = conv(dilations = var_4101_dilations_0, groups = var_4101_groups_0, pad = var_4101_pad_0, pad_type = var_4101_pad_type_0, strides = var_4101_strides_0, weight = layers_9_mlp_up_proj_weight_cast_fp16, x = var_4078_cast_fp16_0)[name = string("op_4101_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_99_cast_fp16 = mul(x = var_4095_cast_fp16, y = var_4101_cast_fp16)[name = string("x_99_cast_fp16")];
            tensor<int32, [2]> hidden_states_97_strides_0 = const()[name = string("hidden_states_97_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_97_pad_type_0 = const()[name = string("hidden_states_97_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_97_pad_0 = const()[name = string("hidden_states_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_97_dilations_0 = const()[name = string("hidden_states_97_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_97_groups_0 = const()[name = string("hidden_states_97_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_97_cast_fp16 = conv(dilations = hidden_states_97_dilations_0, groups = hidden_states_97_groups_0, pad = hidden_states_97_pad_0, pad_type = hidden_states_97_pad_type_0, strides = hidden_states_97_strides_0, weight = layers_9_mlp_down_proj_weight_cast_fp16, x = x_99_cast_fp16)[name = string("hidden_states_97_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_99_cast_fp16 = add(x = hidden_states_95_cast_fp16, y = hidden_states_97_cast_fp16)[name = string("hidden_states_99_cast_fp16")];
            fp16 const_102_promoted_to_fp16 = const()[name = string("const_102_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_4119_cast_fp16 = mul(x = hidden_states_99_cast_fp16, y = const_102_promoted_to_fp16)[name = string("op_4119_cast_fp16")];
            int32 var_4117 = const()[name = string("op_4117"), val = int32(1)];
            bool doubled_81_interleave_0 = const()[name = string("doubled_81_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_81_cast_fp16 = concat(axis = var_4117, interleave = doubled_81_interleave_0, values = (hidden_states_99_cast_fp16, var_4119_cast_fp16))[name = string("doubled_81_cast_fp16")];
            tensor<int32, [1]> out_41_axes_0 = const()[name = string("out_41_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_41_gamma_0_to_fp16 = const()[name = string("out_41_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1397523904)))];
            fp16 var_4129_to_fp16 = const()[name = string("op_4129_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_41_cast_fp16 = layer_norm(axes = out_41_axes_0, epsilon = var_4129_to_fp16, gamma = out_41_gamma_0_to_fp16, x = doubled_81_cast_fp16)[name = string("out_41_cast_fp16")];
            tensor<int32, [2]> var_4140_split_sizes_0 = const()[name = string("op_4140_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4140_axis_0 = const()[name = string("op_4140_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_4140_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_4140_cast_fp16_1 = split(axis = var_4140_axis_0, split_sizes = var_4140_split_sizes_0, x = out_41_cast_fp16)[name = string("op_4140_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_10_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_10_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1397532160)))];
            tensor<int32, [2]> query_states_61_strides_0 = const()[name = string("query_states_61_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_61_pad_type_0 = const()[name = string("query_states_61_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_61_pad_0 = const()[name = string("query_states_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_61_dilations_0 = const()[name = string("query_states_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_61_groups_0 = const()[name = string("query_states_61_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_61_cast_fp16 = conv(dilations = query_states_61_dilations_0, groups = query_states_61_groups_0, pad = query_states_61_pad_0, pad_type = query_states_61_pad_type_0, strides = query_states_61_strides_0, weight = layers_10_self_attn_q_proj_weight_to_fp16, x = var_4140_cast_fp16_0)[name = string("query_states_61_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_10_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_10_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1405920832)))];
            tensor<int32, [2]> key_states_101_strides_0 = const()[name = string("key_states_101_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_101_pad_type_0 = const()[name = string("key_states_101_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_101_pad_0 = const()[name = string("key_states_101_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_101_dilations_0 = const()[name = string("key_states_101_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_101_groups_0 = const()[name = string("key_states_101_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_101_cast_fp16 = conv(dilations = key_states_101_dilations_0, groups = key_states_101_groups_0, pad = key_states_101_pad_0, pad_type = key_states_101_pad_type_0, strides = key_states_101_strides_0, weight = layers_10_self_attn_k_proj_weight_to_fp16, x = var_4140_cast_fp16_0)[name = string("key_states_101_cast_fp16")];
            tensor<int32, [2]> value_states_61_strides_0 = const()[name = string("value_states_61_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_61_pad_type_0 = const()[name = string("value_states_61_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_61_pad_0 = const()[name = string("value_states_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_61_dilations_0 = const()[name = string("value_states_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_61_groups_0 = const()[name = string("value_states_61_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_61_cast_fp16 = conv(dilations = value_states_61_dilations_0, groups = value_states_61_groups_0, pad = value_states_61_pad_0, pad_type = value_states_61_pad_type_0, strides = value_states_61_strides_0, weight = layers_10_self_attn_v_proj_weight_cast_fp16, x = var_4140_cast_fp16_0)[name = string("value_states_61_cast_fp16")];
            tensor<int32, [4]> concat_120x = const()[name = string("concat_120x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_101_cast_fp16 = reshape(shape = concat_120x, x = query_states_61_cast_fp16)[name = string("x_101_cast_fp16")];
            tensor<int32, [4]> concat_121x = const()[name = string("concat_121x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_4197_cast_fp16 = reshape(shape = concat_121x, x = key_states_101_cast_fp16)[name = string("op_4197_cast_fp16")];
            tensor<int32, [4]> concat_122x = const()[name = string("concat_122x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_4204_cast_fp16 = reshape(shape = concat_122x, x = value_states_61_cast_fp16)[name = string("op_4204_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_4208_cast_fp16 = mul(x = x_101_cast_fp16, y = var_859_cast_fp16)[name = string("op_4208_cast_fp16")];
            tensor<int32, [2]> var_4209_split_sizes_0 = const()[name = string("op_4209_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4209_axis_0 = const()[name = string("op_4209_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_4209_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_4209_cast_fp16_1 = split(axis = var_4209_axis_0, split_sizes = var_4209_split_sizes_0, x = x_101_cast_fp16)[name = string("op_4209_cast_fp16")];
            fp16 const_104_promoted_to_fp16 = const()[name = string("const_104_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_4211_cast_fp16 = mul(x = var_4209_cast_fp16_1, y = const_104_promoted_to_fp16)[name = string("op_4211_cast_fp16")];
            int32 var_4213 = const()[name = string("op_4213"), val = int32(-2)];
            bool var_4214_interleave_0 = const()[name = string("op_4214_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_4214_cast_fp16 = concat(axis = var_4213, interleave = var_4214_interleave_0, values = (var_4211_cast_fp16, var_4209_cast_fp16_0))[name = string("op_4214_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_4215_cast_fp16 = mul(x = var_4214_cast_fp16, y = var_866_cast_fp16)[name = string("op_4215_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_63_cast_fp16 = add(x = var_4208_cast_fp16, y = var_4215_cast_fp16)[name = string("query_states_63_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_4221_cast_fp16 = mul(x = var_4197_cast_fp16, y = var_859_cast_fp16)[name = string("op_4221_cast_fp16")];
            tensor<int32, [2]> var_4222_split_sizes_0 = const()[name = string("op_4222_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4222_axis_0 = const()[name = string("op_4222_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_4222_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_4222_cast_fp16_1 = split(axis = var_4222_axis_0, split_sizes = var_4222_split_sizes_0, x = var_4197_cast_fp16)[name = string("op_4222_cast_fp16")];
            fp16 const_105_promoted_to_fp16 = const()[name = string("const_105_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_4224_cast_fp16 = mul(x = var_4222_cast_fp16_1, y = const_105_promoted_to_fp16)[name = string("op_4224_cast_fp16")];
            int32 var_4226 = const()[name = string("op_4226"), val = int32(-2)];
            bool var_4227_interleave_0 = const()[name = string("op_4227_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_4227_cast_fp16 = concat(axis = var_4226, interleave = var_4227_interleave_0, values = (var_4224_cast_fp16, var_4222_cast_fp16_0))[name = string("op_4227_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_4228_cast_fp16 = mul(x = var_4227_cast_fp16, y = var_866_cast_fp16)[name = string("op_4228_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_105_cast_fp16 = add(x = var_4221_cast_fp16, y = var_4228_cast_fp16)[name = string("key_states_105_cast_fp16")];
            tensor<int32, [1]> expand_dims_120 = const()[name = string("expand_dims_120"), val = tensor<int32, [1]>([10])];
            tensor<int32, [1]> expand_dims_121 = const()[name = string("expand_dims_121"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_123 = const()[name = string("expand_dims_123"), val = tensor<int32, [1]>([0])];
            int32 concat_125_axis_0 = const()[name = string("concat_125_axis_0"), val = int32(0)];
            bool concat_125_interleave_0 = const()[name = string("concat_125_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_125 = concat(axis = concat_125_axis_0, interleave = concat_125_interleave_0, values = (expand_dims_120, expand_dims_121, position_id, expand_dims_123))[name = string("concat_125")];
            tensor<int32, [1]> expand_dims_124 = const()[name = string("expand_dims_124"), val = tensor<int32, [1]>([11])];
            tensor<int32, [1]> concat_126_values1_0 = const()[name = string("concat_126_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_126_values3_0 = const()[name = string("concat_126_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_126_axis_0 = const()[name = string("concat_126_axis_0"), val = int32(0)];
            bool concat_126_interleave_0 = const()[name = string("concat_126_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_126 = concat(axis = concat_126_axis_0, interleave = concat_126_interleave_0, values = (expand_dims_124, concat_126_values1_0, cache_position_end, concat_126_values3_0))[name = string("concat_126")];
            tensor<int32, [4]> key_states_107_perm_0 = const()[name = string("key_states_107_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_11_stride_0 = const()[name = string("key_cache_internal_tensor_assign_11_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_11_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_11_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_11_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_11_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_11_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_11_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_107_cast_fp16 = transpose(perm = key_states_107_perm_0, x = key_states_105_cast_fp16)[name = string("transpose_488")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_11_cast_fp16 = slice_update(begin = concat_125, begin_mask = key_cache_internal_tensor_assign_11_begin_mask_0, end = concat_126, end_mask = key_cache_internal_tensor_assign_11_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_11_squeeze_mask_0, stride = key_cache_internal_tensor_assign_11_stride_0, update = key_states_107_cast_fp16, x = coreml_update_state_298)[name = string("key_cache_internal_tensor_assign_11_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_11_cast_fp16, input = key_cache)[name = string("coreml_update_state_300_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_300 = read_state(input = key_cache)[name = string("coreml_update_state_300")];
            tensor<int32, [4]> value_states_63_perm_0 = const()[name = string("value_states_63_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_11_stride_0 = const()[name = string("value_cache_internal_tensor_assign_11_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_11_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_11_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_11_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_11_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_11_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_11_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_63_cast_fp16 = transpose(perm = value_states_63_perm_0, x = var_4204_cast_fp16)[name = string("transpose_487")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_11_cast_fp16 = slice_update(begin = concat_125, begin_mask = value_cache_internal_tensor_assign_11_begin_mask_0, end = concat_126, end_mask = value_cache_internal_tensor_assign_11_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_11_squeeze_mask_0, stride = value_cache_internal_tensor_assign_11_stride_0, update = value_states_63_cast_fp16, x = coreml_update_state_299)[name = string("value_cache_internal_tensor_assign_11_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_11_cast_fp16, input = value_cache)[name = string("coreml_update_state_301_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_301 = read_state(input = value_cache)[name = string("coreml_update_state_301")];
            tensor<int32, [4]> var_4298_begin_0 = const()[name = string("op_4298_begin_0"), val = tensor<int32, [4]>([10, 0, 0, 0])];
            tensor<int32, [4]> var_4298_end_0 = const()[name = string("op_4298_end_0"), val = tensor<int32, [4]>([11, 2, 2048, 128])];
            tensor<bool, [4]> var_4298_end_mask_0 = const()[name = string("op_4298_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4298_cast_fp16 = slice_by_index(begin = var_4298_begin_0, end = var_4298_end_0, end_mask = var_4298_end_mask_0, x = coreml_update_state_300)[name = string("op_4298_cast_fp16")];
            tensor<int32, [2]> tile_20 = const()[name = string("tile_20"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4301_axis_0 = const()[name = string("op_4301_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4301_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4301_cast_fp16_1 = split(axis = var_4301_axis_0, split_sizes = tile_20, x = var_4298_cast_fp16)[name = string("op_4301_cast_fp16")];
            tensor<int32, [4]> var_4308_begin_0 = const()[name = string("op_4308_begin_0"), val = tensor<int32, [4]>([10, 0, 0, 0])];
            tensor<int32, [4]> var_4308_end_0 = const()[name = string("op_4308_end_0"), val = tensor<int32, [4]>([11, 2, 2048, 128])];
            tensor<bool, [4]> var_4308_end_mask_0 = const()[name = string("op_4308_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4308_cast_fp16 = slice_by_index(begin = var_4308_begin_0, end = var_4308_end_0, end_mask = var_4308_end_mask_0, x = coreml_update_state_301)[name = string("op_4308_cast_fp16")];
            tensor<int32, [2]> tile_21 = const()[name = string("tile_21"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4311_axis_0 = const()[name = string("op_4311_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4311_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4311_cast_fp16_1 = split(axis = var_4311_axis_0, split_sizes = tile_21, x = var_4308_cast_fp16)[name = string("op_4311_cast_fp16")];
            tensor<int32, [2]> var_4314_split_sizes_0 = const()[name = string("op_4314_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_4314_axis_0 = const()[name = string("op_4314_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_4314_0, tensor<fp16, [1, 8, 128, 64]> var_4314_1 = split(axis = var_4314_axis_0, split_sizes = var_4314_split_sizes_0, x = query_states_63_cast_fp16)[name = string("op_4314")];
            bool attn_weights_161_transpose_x_0 = const()[name = string("attn_weights_161_transpose_x_0"), val = bool(false)];
            bool attn_weights_161_transpose_y_0 = const()[name = string("attn_weights_161_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_161_cast_fp16 = matmul(transpose_x = attn_weights_161_transpose_x_0, transpose_y = attn_weights_161_transpose_y_0, x = var_4301_cast_fp16_0, y = var_4314_0)[name = string("attn_weights_161_cast_fp16")];
            fp16 var_4317_to_fp16 = const()[name = string("op_4317_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_163_cast_fp16 = mul(x = attn_weights_161_cast_fp16, y = var_4317_to_fp16)[name = string("attn_weights_163_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_165_cast_fp16 = add(x = attn_weights_163_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_165_cast_fp16")];
            int32 var_4321 = const()[name = string("op_4321"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_167_cast_fp16 = softmax(axis = var_4321, x = attn_weights_165_cast_fp16)[name = string("attn_weights_167_cast_fp16")];
            bool var_4327_transpose_x_1 = const()[name = string("op_4327_transpose_x_1"), val = bool(true)];
            bool var_4327_transpose_y_1 = const()[name = string("op_4327_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_4327_cast_fp16 = matmul(transpose_x = var_4327_transpose_x_1, transpose_y = var_4327_transpose_y_1, x = attn_weights_167_cast_fp16, y = var_4311_cast_fp16_0)[name = string("op_4327_cast_fp16")];
            bool attn_weights_169_transpose_x_0 = const()[name = string("attn_weights_169_transpose_x_0"), val = bool(false)];
            bool attn_weights_169_transpose_y_0 = const()[name = string("attn_weights_169_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_169_cast_fp16 = matmul(transpose_x = attn_weights_169_transpose_x_0, transpose_y = attn_weights_169_transpose_y_0, x = var_4301_cast_fp16_1, y = var_4314_1)[name = string("attn_weights_169_cast_fp16")];
            fp16 var_4329_to_fp16 = const()[name = string("op_4329_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_171_cast_fp16 = mul(x = attn_weights_169_cast_fp16, y = var_4329_to_fp16)[name = string("attn_weights_171_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_173_cast_fp16 = add(x = attn_weights_171_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_173_cast_fp16")];
            int32 var_4333 = const()[name = string("op_4333"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_175_cast_fp16 = softmax(axis = var_4333, x = attn_weights_173_cast_fp16)[name = string("attn_weights_175_cast_fp16")];
            bool attn_output_81_transpose_x_1 = const()[name = string("attn_output_81_transpose_x_1"), val = bool(true)];
            bool attn_output_81_transpose_y_1 = const()[name = string("attn_output_81_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_81_cast_fp16 = matmul(transpose_x = attn_output_81_transpose_x_1, transpose_y = attn_output_81_transpose_y_1, x = attn_weights_175_cast_fp16, y = var_4311_cast_fp16_1)[name = string("attn_output_81_cast_fp16")];
            int32 var_4341 = const()[name = string("op_4341"), val = int32(1)];
            bool attn_output_83_interleave_0 = const()[name = string("attn_output_83_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_83_cast_fp16 = concat(axis = var_4341, interleave = attn_output_83_interleave_0, values = (var_4327_cast_fp16, attn_output_81_cast_fp16))[name = string("attn_output_83_cast_fp16")];
            tensor<int32, [4]> var_4345_perm_0 = const()[name = string("op_4345_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_131x = const()[name = string("concat_131x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_4345_cast_fp16 = transpose(perm = var_4345_perm_0, x = attn_output_83_cast_fp16)[name = string("transpose_486")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_87_cast_fp16 = reshape(shape = concat_131x, x = var_4345_cast_fp16)[name = string("attn_output_87_cast_fp16")];
            tensor<int32, [2]> hidden_states_103_strides_0 = const()[name = string("hidden_states_103_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_103_pad_type_0 = const()[name = string("hidden_states_103_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_103_pad_0 = const()[name = string("hidden_states_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_103_dilations_0 = const()[name = string("hidden_states_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_103_groups_0 = const()[name = string("hidden_states_103_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_103_cast_fp16 = conv(dilations = hidden_states_103_dilations_0, groups = hidden_states_103_groups_0, pad = hidden_states_103_pad_0, pad_type = hidden_states_103_pad_type_0, strides = hidden_states_103_strides_0, weight = layers_10_self_attn_o_proj_weight_cast_fp16, x = attn_output_87_cast_fp16)[name = string("hidden_states_103_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_105_cast_fp16 = add(x = hidden_states_99_cast_fp16, y = hidden_states_103_cast_fp16)[name = string("hidden_states_105_cast_fp16")];
            fp16 const_110_promoted_to_fp16 = const()[name = string("const_110_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_4378_cast_fp16 = mul(x = hidden_states_105_cast_fp16, y = const_110_promoted_to_fp16)[name = string("op_4378_cast_fp16")];
            int32 var_4376 = const()[name = string("op_4376"), val = int32(1)];
            bool doubled_85_interleave_0 = const()[name = string("doubled_85_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_85_cast_fp16 = concat(axis = var_4376, interleave = doubled_85_interleave_0, values = (hidden_states_105_cast_fp16, var_4378_cast_fp16))[name = string("doubled_85_cast_fp16")];
            tensor<int32, [1]> out_43_axes_0 = const()[name = string("out_43_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_43_gamma_0_to_fp16 = const()[name = string("out_43_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406969472)))];
            fp16 var_4388_to_fp16 = const()[name = string("op_4388_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_43_cast_fp16 = layer_norm(axes = out_43_axes_0, epsilon = var_4388_to_fp16, gamma = out_43_gamma_0_to_fp16, x = doubled_85_cast_fp16)[name = string("out_43_cast_fp16")];
            tensor<int32, [2]> var_4399_split_sizes_0 = const()[name = string("op_4399_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4399_axis_0 = const()[name = string("op_4399_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_4399_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_4399_cast_fp16_1 = split(axis = var_4399_axis_0, split_sizes = var_4399_split_sizes_0, x = out_43_cast_fp16)[name = string("op_4399_cast_fp16")];
            tensor<int32, [2]> input_21_strides_0 = const()[name = string("input_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_21_pad_type_0 = const()[name = string("input_21_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_21_pad_0 = const()[name = string("input_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_21_dilations_0 = const()[name = string("input_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_21_groups_0 = const()[name = string("input_21_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_21_cast_fp16 = conv(dilations = input_21_dilations_0, groups = input_21_groups_0, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = input_21_strides_0, weight = layers_10_mlp_gate_proj_weight_cast_fp16, x = var_4399_cast_fp16_0)[name = string("input_21_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_4416_cast_fp16 = silu(x = input_21_cast_fp16)[name = string("op_4416_cast_fp16")];
            tensor<int32, [2]> var_4422_strides_0 = const()[name = string("op_4422_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_4422_pad_type_0 = const()[name = string("op_4422_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_4422_pad_0 = const()[name = string("op_4422_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_4422_dilations_0 = const()[name = string("op_4422_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4422_groups_0 = const()[name = string("op_4422_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_4422_cast_fp16 = conv(dilations = var_4422_dilations_0, groups = var_4422_groups_0, pad = var_4422_pad_0, pad_type = var_4422_pad_type_0, strides = var_4422_strides_0, weight = layers_10_mlp_up_proj_weight_cast_fp16, x = var_4399_cast_fp16_0)[name = string("op_4422_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_109_cast_fp16 = mul(x = var_4416_cast_fp16, y = var_4422_cast_fp16)[name = string("x_109_cast_fp16")];
            tensor<int32, [2]> hidden_states_107_strides_0 = const()[name = string("hidden_states_107_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_107_pad_type_0 = const()[name = string("hidden_states_107_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_107_pad_0 = const()[name = string("hidden_states_107_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_107_dilations_0 = const()[name = string("hidden_states_107_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_107_groups_0 = const()[name = string("hidden_states_107_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_107_cast_fp16 = conv(dilations = hidden_states_107_dilations_0, groups = hidden_states_107_groups_0, pad = hidden_states_107_pad_0, pad_type = hidden_states_107_pad_type_0, strides = hidden_states_107_strides_0, weight = layers_10_mlp_down_proj_weight_cast_fp16, x = x_109_cast_fp16)[name = string("hidden_states_107_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_109_cast_fp16 = add(x = hidden_states_105_cast_fp16, y = hidden_states_107_cast_fp16)[name = string("hidden_states_109_cast_fp16")];
            fp16 const_112_promoted_to_fp16 = const()[name = string("const_112_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_4440_cast_fp16 = mul(x = hidden_states_109_cast_fp16, y = const_112_promoted_to_fp16)[name = string("op_4440_cast_fp16")];
            int32 var_4438 = const()[name = string("op_4438"), val = int32(1)];
            bool doubled_89_interleave_0 = const()[name = string("doubled_89_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_89_cast_fp16 = concat(axis = var_4438, interleave = doubled_89_interleave_0, values = (hidden_states_109_cast_fp16, var_4440_cast_fp16))[name = string("doubled_89_cast_fp16")];
            tensor<int32, [1]> out_45_axes_0 = const()[name = string("out_45_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_45_gamma_0_to_fp16 = const()[name = string("out_45_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406977728)))];
            fp16 var_4450_to_fp16 = const()[name = string("op_4450_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_45_cast_fp16 = layer_norm(axes = out_45_axes_0, epsilon = var_4450_to_fp16, gamma = out_45_gamma_0_to_fp16, x = doubled_89_cast_fp16)[name = string("out_45_cast_fp16")];
            tensor<int32, [2]> var_4461_split_sizes_0 = const()[name = string("op_4461_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4461_axis_0 = const()[name = string("op_4461_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_4461_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_4461_cast_fp16_1 = split(axis = var_4461_axis_0, split_sizes = var_4461_split_sizes_0, x = out_45_cast_fp16)[name = string("op_4461_cast_fp16")];
            tensor<int32, [2]> query_states_67_strides_0 = const()[name = string("query_states_67_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_67_pad_type_0 = const()[name = string("query_states_67_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_67_pad_0 = const()[name = string("query_states_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_67_dilations_0 = const()[name = string("query_states_67_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_67_groups_0 = const()[name = string("query_states_67_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_67_cast_fp16 = conv(dilations = query_states_67_dilations_0, groups = query_states_67_groups_0, pad = query_states_67_pad_0, pad_type = query_states_67_pad_type_0, strides = query_states_67_strides_0, weight = layers_11_self_attn_q_proj_weight_cast_fp16, x = var_4461_cast_fp16_0)[name = string("query_states_67_cast_fp16")];
            tensor<int32, [2]> key_states_111_strides_0 = const()[name = string("key_states_111_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_111_pad_type_0 = const()[name = string("key_states_111_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_111_pad_0 = const()[name = string("key_states_111_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_111_dilations_0 = const()[name = string("key_states_111_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_111_groups_0 = const()[name = string("key_states_111_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_111_cast_fp16 = conv(dilations = key_states_111_dilations_0, groups = key_states_111_groups_0, pad = key_states_111_pad_0, pad_type = key_states_111_pad_type_0, strides = key_states_111_strides_0, weight = layers_11_self_attn_k_proj_weight_cast_fp16, x = var_4461_cast_fp16_0)[name = string("key_states_111_cast_fp16")];
            tensor<int32, [2]> value_states_67_strides_0 = const()[name = string("value_states_67_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_67_pad_type_0 = const()[name = string("value_states_67_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_67_pad_0 = const()[name = string("value_states_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_67_dilations_0 = const()[name = string("value_states_67_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_67_groups_0 = const()[name = string("value_states_67_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_67_cast_fp16 = conv(dilations = value_states_67_dilations_0, groups = value_states_67_groups_0, pad = value_states_67_pad_0, pad_type = value_states_67_pad_type_0, strides = value_states_67_strides_0, weight = layers_11_self_attn_v_proj_weight_cast_fp16, x = var_4461_cast_fp16_0)[name = string("value_states_67_cast_fp16")];
            tensor<int32, [4]> concat_132x = const()[name = string("concat_132x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_111_cast_fp16 = reshape(shape = concat_132x, x = query_states_67_cast_fp16)[name = string("x_111_cast_fp16")];
            tensor<int32, [4]> concat_133x = const()[name = string("concat_133x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_4518_cast_fp16 = reshape(shape = concat_133x, x = key_states_111_cast_fp16)[name = string("op_4518_cast_fp16")];
            tensor<int32, [4]> concat_134x = const()[name = string("concat_134x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_4525_cast_fp16 = reshape(shape = concat_134x, x = value_states_67_cast_fp16)[name = string("op_4525_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_4529_cast_fp16 = mul(x = x_111_cast_fp16, y = var_859_cast_fp16)[name = string("op_4529_cast_fp16")];
            tensor<int32, [2]> var_4530_split_sizes_0 = const()[name = string("op_4530_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4530_axis_0 = const()[name = string("op_4530_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_4530_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_4530_cast_fp16_1 = split(axis = var_4530_axis_0, split_sizes = var_4530_split_sizes_0, x = x_111_cast_fp16)[name = string("op_4530_cast_fp16")];
            fp16 const_114_promoted_to_fp16 = const()[name = string("const_114_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_4532_cast_fp16 = mul(x = var_4530_cast_fp16_1, y = const_114_promoted_to_fp16)[name = string("op_4532_cast_fp16")];
            int32 var_4534 = const()[name = string("op_4534"), val = int32(-2)];
            bool var_4535_interleave_0 = const()[name = string("op_4535_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_4535_cast_fp16 = concat(axis = var_4534, interleave = var_4535_interleave_0, values = (var_4532_cast_fp16, var_4530_cast_fp16_0))[name = string("op_4535_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_4536_cast_fp16 = mul(x = var_4535_cast_fp16, y = var_866_cast_fp16)[name = string("op_4536_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_69_cast_fp16 = add(x = var_4529_cast_fp16, y = var_4536_cast_fp16)[name = string("query_states_69_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_4542_cast_fp16 = mul(x = var_4518_cast_fp16, y = var_859_cast_fp16)[name = string("op_4542_cast_fp16")];
            tensor<int32, [2]> var_4543_split_sizes_0 = const()[name = string("op_4543_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4543_axis_0 = const()[name = string("op_4543_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_4543_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_4543_cast_fp16_1 = split(axis = var_4543_axis_0, split_sizes = var_4543_split_sizes_0, x = var_4518_cast_fp16)[name = string("op_4543_cast_fp16")];
            fp16 const_115_promoted_to_fp16 = const()[name = string("const_115_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_4545_cast_fp16 = mul(x = var_4543_cast_fp16_1, y = const_115_promoted_to_fp16)[name = string("op_4545_cast_fp16")];
            int32 var_4547 = const()[name = string("op_4547"), val = int32(-2)];
            bool var_4548_interleave_0 = const()[name = string("op_4548_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_4548_cast_fp16 = concat(axis = var_4547, interleave = var_4548_interleave_0, values = (var_4545_cast_fp16, var_4543_cast_fp16_0))[name = string("op_4548_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_4549_cast_fp16 = mul(x = var_4548_cast_fp16, y = var_866_cast_fp16)[name = string("op_4549_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_115_cast_fp16 = add(x = var_4542_cast_fp16, y = var_4549_cast_fp16)[name = string("key_states_115_cast_fp16")];
            tensor<int32, [1]> expand_dims_132 = const()[name = string("expand_dims_132"), val = tensor<int32, [1]>([11])];
            tensor<int32, [1]> expand_dims_133 = const()[name = string("expand_dims_133"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_135 = const()[name = string("expand_dims_135"), val = tensor<int32, [1]>([0])];
            int32 concat_137_axis_0 = const()[name = string("concat_137_axis_0"), val = int32(0)];
            bool concat_137_interleave_0 = const()[name = string("concat_137_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_137 = concat(axis = concat_137_axis_0, interleave = concat_137_interleave_0, values = (expand_dims_132, expand_dims_133, position_id, expand_dims_135))[name = string("concat_137")];
            tensor<int32, [1]> expand_dims_136 = const()[name = string("expand_dims_136"), val = tensor<int32, [1]>([12])];
            tensor<int32, [1]> concat_138_values1_0 = const()[name = string("concat_138_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_138_values3_0 = const()[name = string("concat_138_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_138_axis_0 = const()[name = string("concat_138_axis_0"), val = int32(0)];
            bool concat_138_interleave_0 = const()[name = string("concat_138_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_138 = concat(axis = concat_138_axis_0, interleave = concat_138_interleave_0, values = (expand_dims_136, concat_138_values1_0, cache_position_end, concat_138_values3_0))[name = string("concat_138")];
            tensor<int32, [4]> key_states_117_perm_0 = const()[name = string("key_states_117_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_12_stride_0 = const()[name = string("key_cache_internal_tensor_assign_12_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_12_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_12_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_12_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_12_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_12_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_12_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_117_cast_fp16 = transpose(perm = key_states_117_perm_0, x = key_states_115_cast_fp16)[name = string("transpose_485")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_12_cast_fp16 = slice_update(begin = concat_137, begin_mask = key_cache_internal_tensor_assign_12_begin_mask_0, end = concat_138, end_mask = key_cache_internal_tensor_assign_12_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_12_squeeze_mask_0, stride = key_cache_internal_tensor_assign_12_stride_0, update = key_states_117_cast_fp16, x = coreml_update_state_300)[name = string("key_cache_internal_tensor_assign_12_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_12_cast_fp16, input = key_cache)[name = string("coreml_update_state_302_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_302 = read_state(input = key_cache)[name = string("coreml_update_state_302")];
            tensor<int32, [4]> value_states_69_perm_0 = const()[name = string("value_states_69_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_12_stride_0 = const()[name = string("value_cache_internal_tensor_assign_12_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_12_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_12_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_12_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_12_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_12_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_12_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_69_cast_fp16 = transpose(perm = value_states_69_perm_0, x = var_4525_cast_fp16)[name = string("transpose_484")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_12_cast_fp16 = slice_update(begin = concat_137, begin_mask = value_cache_internal_tensor_assign_12_begin_mask_0, end = concat_138, end_mask = value_cache_internal_tensor_assign_12_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_12_squeeze_mask_0, stride = value_cache_internal_tensor_assign_12_stride_0, update = value_states_69_cast_fp16, x = coreml_update_state_301)[name = string("value_cache_internal_tensor_assign_12_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_12_cast_fp16, input = value_cache)[name = string("coreml_update_state_303_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_303 = read_state(input = value_cache)[name = string("coreml_update_state_303")];
            tensor<int32, [4]> var_4619_begin_0 = const()[name = string("op_4619_begin_0"), val = tensor<int32, [4]>([11, 0, 0, 0])];
            tensor<int32, [4]> var_4619_end_0 = const()[name = string("op_4619_end_0"), val = tensor<int32, [4]>([12, 2, 2048, 128])];
            tensor<bool, [4]> var_4619_end_mask_0 = const()[name = string("op_4619_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4619_cast_fp16 = slice_by_index(begin = var_4619_begin_0, end = var_4619_end_0, end_mask = var_4619_end_mask_0, x = coreml_update_state_302)[name = string("op_4619_cast_fp16")];
            tensor<int32, [2]> tile_22 = const()[name = string("tile_22"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4622_axis_0 = const()[name = string("op_4622_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4622_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4622_cast_fp16_1 = split(axis = var_4622_axis_0, split_sizes = tile_22, x = var_4619_cast_fp16)[name = string("op_4622_cast_fp16")];
            tensor<int32, [4]> var_4629_begin_0 = const()[name = string("op_4629_begin_0"), val = tensor<int32, [4]>([11, 0, 0, 0])];
            tensor<int32, [4]> var_4629_end_0 = const()[name = string("op_4629_end_0"), val = tensor<int32, [4]>([12, 2, 2048, 128])];
            tensor<bool, [4]> var_4629_end_mask_0 = const()[name = string("op_4629_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4629_cast_fp16 = slice_by_index(begin = var_4629_begin_0, end = var_4629_end_0, end_mask = var_4629_end_mask_0, x = coreml_update_state_303)[name = string("op_4629_cast_fp16")];
            tensor<int32, [2]> tile_23 = const()[name = string("tile_23"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4632_axis_0 = const()[name = string("op_4632_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4632_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4632_cast_fp16_1 = split(axis = var_4632_axis_0, split_sizes = tile_23, x = var_4629_cast_fp16)[name = string("op_4632_cast_fp16")];
            tensor<int32, [2]> var_4635_split_sizes_0 = const()[name = string("op_4635_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_4635_axis_0 = const()[name = string("op_4635_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_4635_0, tensor<fp16, [1, 8, 128, 64]> var_4635_1 = split(axis = var_4635_axis_0, split_sizes = var_4635_split_sizes_0, x = query_states_69_cast_fp16)[name = string("op_4635")];
            bool attn_weights_177_transpose_x_0 = const()[name = string("attn_weights_177_transpose_x_0"), val = bool(false)];
            bool attn_weights_177_transpose_y_0 = const()[name = string("attn_weights_177_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_177_cast_fp16 = matmul(transpose_x = attn_weights_177_transpose_x_0, transpose_y = attn_weights_177_transpose_y_0, x = var_4622_cast_fp16_0, y = var_4635_0)[name = string("attn_weights_177_cast_fp16")];
            fp16 var_4638_to_fp16 = const()[name = string("op_4638_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_179_cast_fp16 = mul(x = attn_weights_177_cast_fp16, y = var_4638_to_fp16)[name = string("attn_weights_179_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_181_cast_fp16 = add(x = attn_weights_179_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_181_cast_fp16")];
            int32 var_4642 = const()[name = string("op_4642"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_183_cast_fp16 = softmax(axis = var_4642, x = attn_weights_181_cast_fp16)[name = string("attn_weights_183_cast_fp16")];
            bool var_4648_transpose_x_1 = const()[name = string("op_4648_transpose_x_1"), val = bool(true)];
            bool var_4648_transpose_y_1 = const()[name = string("op_4648_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_4648_cast_fp16 = matmul(transpose_x = var_4648_transpose_x_1, transpose_y = var_4648_transpose_y_1, x = attn_weights_183_cast_fp16, y = var_4632_cast_fp16_0)[name = string("op_4648_cast_fp16")];
            bool attn_weights_185_transpose_x_0 = const()[name = string("attn_weights_185_transpose_x_0"), val = bool(false)];
            bool attn_weights_185_transpose_y_0 = const()[name = string("attn_weights_185_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_185_cast_fp16 = matmul(transpose_x = attn_weights_185_transpose_x_0, transpose_y = attn_weights_185_transpose_y_0, x = var_4622_cast_fp16_1, y = var_4635_1)[name = string("attn_weights_185_cast_fp16")];
            fp16 var_4650_to_fp16 = const()[name = string("op_4650_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_187_cast_fp16 = mul(x = attn_weights_185_cast_fp16, y = var_4650_to_fp16)[name = string("attn_weights_187_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_189_cast_fp16 = add(x = attn_weights_187_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_189_cast_fp16")];
            int32 var_4654 = const()[name = string("op_4654"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_191_cast_fp16 = softmax(axis = var_4654, x = attn_weights_189_cast_fp16)[name = string("attn_weights_191_cast_fp16")];
            bool attn_output_89_transpose_x_1 = const()[name = string("attn_output_89_transpose_x_1"), val = bool(true)];
            bool attn_output_89_transpose_y_1 = const()[name = string("attn_output_89_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_89_cast_fp16 = matmul(transpose_x = attn_output_89_transpose_x_1, transpose_y = attn_output_89_transpose_y_1, x = attn_weights_191_cast_fp16, y = var_4632_cast_fp16_1)[name = string("attn_output_89_cast_fp16")];
            int32 var_4662 = const()[name = string("op_4662"), val = int32(1)];
            bool attn_output_91_interleave_0 = const()[name = string("attn_output_91_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_91_cast_fp16 = concat(axis = var_4662, interleave = attn_output_91_interleave_0, values = (var_4648_cast_fp16, attn_output_89_cast_fp16))[name = string("attn_output_91_cast_fp16")];
            tensor<int32, [4]> var_4666_perm_0 = const()[name = string("op_4666_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_143x = const()[name = string("concat_143x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_4666_cast_fp16 = transpose(perm = var_4666_perm_0, x = attn_output_91_cast_fp16)[name = string("transpose_483")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_95_cast_fp16 = reshape(shape = concat_143x, x = var_4666_cast_fp16)[name = string("attn_output_95_cast_fp16")];
            tensor<int32, [2]> hidden_states_113_strides_0 = const()[name = string("hidden_states_113_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_113_pad_type_0 = const()[name = string("hidden_states_113_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_113_pad_0 = const()[name = string("hidden_states_113_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_113_dilations_0 = const()[name = string("hidden_states_113_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_113_groups_0 = const()[name = string("hidden_states_113_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_113_cast_fp16 = conv(dilations = hidden_states_113_dilations_0, groups = hidden_states_113_groups_0, pad = hidden_states_113_pad_0, pad_type = hidden_states_113_pad_type_0, strides = hidden_states_113_strides_0, weight = layers_11_self_attn_o_proj_weight_cast_fp16, x = attn_output_95_cast_fp16)[name = string("hidden_states_113_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_115_cast_fp16 = add(x = hidden_states_109_cast_fp16, y = hidden_states_113_cast_fp16)[name = string("hidden_states_115_cast_fp16")];
            fp16 const_120_promoted_to_fp16 = const()[name = string("const_120_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_4699_cast_fp16 = mul(x = hidden_states_115_cast_fp16, y = const_120_promoted_to_fp16)[name = string("op_4699_cast_fp16")];
            int32 var_4697 = const()[name = string("op_4697"), val = int32(1)];
            bool doubled_93_interleave_0 = const()[name = string("doubled_93_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_93_cast_fp16 = concat(axis = var_4697, interleave = doubled_93_interleave_0, values = (hidden_states_115_cast_fp16, var_4699_cast_fp16))[name = string("doubled_93_cast_fp16")];
            tensor<int32, [1]> out_47_axes_0 = const()[name = string("out_47_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_47_gamma_0_to_fp16 = const()[name = string("out_47_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406985984)))];
            fp16 var_4709_to_fp16 = const()[name = string("op_4709_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_47_cast_fp16 = layer_norm(axes = out_47_axes_0, epsilon = var_4709_to_fp16, gamma = out_47_gamma_0_to_fp16, x = doubled_93_cast_fp16)[name = string("out_47_cast_fp16")];
            tensor<int32, [2]> var_4720_split_sizes_0 = const()[name = string("op_4720_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4720_axis_0 = const()[name = string("op_4720_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_4720_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_4720_cast_fp16_1 = split(axis = var_4720_axis_0, split_sizes = var_4720_split_sizes_0, x = out_47_cast_fp16)[name = string("op_4720_cast_fp16")];
            tensor<int32, [2]> input_23_strides_0 = const()[name = string("input_23_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_23_pad_type_0 = const()[name = string("input_23_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_23_pad_0 = const()[name = string("input_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_23_dilations_0 = const()[name = string("input_23_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_23_groups_0 = const()[name = string("input_23_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_23_cast_fp16 = conv(dilations = input_23_dilations_0, groups = input_23_groups_0, pad = input_23_pad_0, pad_type = input_23_pad_type_0, strides = input_23_strides_0, weight = layers_11_mlp_gate_proj_weight_cast_fp16, x = var_4720_cast_fp16_0)[name = string("input_23_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_4737_cast_fp16 = silu(x = input_23_cast_fp16)[name = string("op_4737_cast_fp16")];
            tensor<int32, [2]> var_4743_strides_0 = const()[name = string("op_4743_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_4743_pad_type_0 = const()[name = string("op_4743_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_4743_pad_0 = const()[name = string("op_4743_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_4743_dilations_0 = const()[name = string("op_4743_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4743_groups_0 = const()[name = string("op_4743_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_4743_cast_fp16 = conv(dilations = var_4743_dilations_0, groups = var_4743_groups_0, pad = var_4743_pad_0, pad_type = var_4743_pad_type_0, strides = var_4743_strides_0, weight = layers_11_mlp_up_proj_weight_cast_fp16, x = var_4720_cast_fp16_0)[name = string("op_4743_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_119_cast_fp16 = mul(x = var_4737_cast_fp16, y = var_4743_cast_fp16)[name = string("x_119_cast_fp16")];
            tensor<int32, [2]> hidden_states_117_strides_0 = const()[name = string("hidden_states_117_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_117_pad_type_0 = const()[name = string("hidden_states_117_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_117_pad_0 = const()[name = string("hidden_states_117_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_117_dilations_0 = const()[name = string("hidden_states_117_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_117_groups_0 = const()[name = string("hidden_states_117_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_117_cast_fp16 = conv(dilations = hidden_states_117_dilations_0, groups = hidden_states_117_groups_0, pad = hidden_states_117_pad_0, pad_type = hidden_states_117_pad_type_0, strides = hidden_states_117_strides_0, weight = layers_11_mlp_down_proj_weight_cast_fp16, x = x_119_cast_fp16)[name = string("hidden_states_117_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_119_cast_fp16 = add(x = hidden_states_115_cast_fp16, y = hidden_states_117_cast_fp16)[name = string("hidden_states_119_cast_fp16")];
            fp16 const_122_promoted_to_fp16 = const()[name = string("const_122_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_4761_cast_fp16 = mul(x = hidden_states_119_cast_fp16, y = const_122_promoted_to_fp16)[name = string("op_4761_cast_fp16")];
            int32 var_4759 = const()[name = string("op_4759"), val = int32(1)];
            bool doubled_97_interleave_0 = const()[name = string("doubled_97_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_97_cast_fp16 = concat(axis = var_4759, interleave = doubled_97_interleave_0, values = (hidden_states_119_cast_fp16, var_4761_cast_fp16))[name = string("doubled_97_cast_fp16")];
            tensor<int32, [1]> out_49_axes_0 = const()[name = string("out_49_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_49_gamma_0_to_fp16 = const()[name = string("out_49_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406994240)))];
            fp16 var_4771_to_fp16 = const()[name = string("op_4771_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_49_cast_fp16 = layer_norm(axes = out_49_axes_0, epsilon = var_4771_to_fp16, gamma = out_49_gamma_0_to_fp16, x = doubled_97_cast_fp16)[name = string("out_49_cast_fp16")];
            tensor<int32, [2]> var_4782_split_sizes_0 = const()[name = string("op_4782_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4782_axis_0 = const()[name = string("op_4782_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_4782_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_4782_cast_fp16_1 = split(axis = var_4782_axis_0, split_sizes = var_4782_split_sizes_0, x = out_49_cast_fp16)[name = string("op_4782_cast_fp16")];
            tensor<int32, [2]> query_states_73_strides_0 = const()[name = string("query_states_73_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_73_pad_type_0 = const()[name = string("query_states_73_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_73_pad_0 = const()[name = string("query_states_73_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_73_dilations_0 = const()[name = string("query_states_73_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_73_groups_0 = const()[name = string("query_states_73_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_73_cast_fp16 = conv(dilations = query_states_73_dilations_0, groups = query_states_73_groups_0, pad = query_states_73_pad_0, pad_type = query_states_73_pad_type_0, strides = query_states_73_strides_0, weight = layers_12_self_attn_q_proj_weight_cast_fp16, x = var_4782_cast_fp16_0)[name = string("query_states_73_cast_fp16")];
            tensor<int32, [2]> key_states_121_strides_0 = const()[name = string("key_states_121_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_121_pad_type_0 = const()[name = string("key_states_121_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_121_pad_0 = const()[name = string("key_states_121_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_121_dilations_0 = const()[name = string("key_states_121_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_121_groups_0 = const()[name = string("key_states_121_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_121_cast_fp16 = conv(dilations = key_states_121_dilations_0, groups = key_states_121_groups_0, pad = key_states_121_pad_0, pad_type = key_states_121_pad_type_0, strides = key_states_121_strides_0, weight = layers_12_self_attn_k_proj_weight_cast_fp16, x = var_4782_cast_fp16_0)[name = string("key_states_121_cast_fp16")];
            tensor<int32, [2]> value_states_73_strides_0 = const()[name = string("value_states_73_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_73_pad_type_0 = const()[name = string("value_states_73_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_73_pad_0 = const()[name = string("value_states_73_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_73_dilations_0 = const()[name = string("value_states_73_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_73_groups_0 = const()[name = string("value_states_73_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_73_cast_fp16 = conv(dilations = value_states_73_dilations_0, groups = value_states_73_groups_0, pad = value_states_73_pad_0, pad_type = value_states_73_pad_type_0, strides = value_states_73_strides_0, weight = layers_12_self_attn_v_proj_weight_cast_fp16, x = var_4782_cast_fp16_0)[name = string("value_states_73_cast_fp16")];
            tensor<int32, [4]> concat_144x = const()[name = string("concat_144x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_121_cast_fp16 = reshape(shape = concat_144x, x = query_states_73_cast_fp16)[name = string("x_121_cast_fp16")];
            tensor<int32, [4]> concat_145x = const()[name = string("concat_145x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_4839_cast_fp16 = reshape(shape = concat_145x, x = key_states_121_cast_fp16)[name = string("op_4839_cast_fp16")];
            tensor<int32, [4]> concat_146x = const()[name = string("concat_146x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_4846_cast_fp16 = reshape(shape = concat_146x, x = value_states_73_cast_fp16)[name = string("op_4846_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_4850_cast_fp16 = mul(x = x_121_cast_fp16, y = var_859_cast_fp16)[name = string("op_4850_cast_fp16")];
            tensor<int32, [2]> var_4851_split_sizes_0 = const()[name = string("op_4851_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4851_axis_0 = const()[name = string("op_4851_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_4851_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_4851_cast_fp16_1 = split(axis = var_4851_axis_0, split_sizes = var_4851_split_sizes_0, x = x_121_cast_fp16)[name = string("op_4851_cast_fp16")];
            fp16 const_124_promoted_to_fp16 = const()[name = string("const_124_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_4853_cast_fp16 = mul(x = var_4851_cast_fp16_1, y = const_124_promoted_to_fp16)[name = string("op_4853_cast_fp16")];
            int32 var_4855 = const()[name = string("op_4855"), val = int32(-2)];
            bool var_4856_interleave_0 = const()[name = string("op_4856_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_4856_cast_fp16 = concat(axis = var_4855, interleave = var_4856_interleave_0, values = (var_4853_cast_fp16, var_4851_cast_fp16_0))[name = string("op_4856_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_4857_cast_fp16 = mul(x = var_4856_cast_fp16, y = var_866_cast_fp16)[name = string("op_4857_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_75_cast_fp16 = add(x = var_4850_cast_fp16, y = var_4857_cast_fp16)[name = string("query_states_75_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_4863_cast_fp16 = mul(x = var_4839_cast_fp16, y = var_859_cast_fp16)[name = string("op_4863_cast_fp16")];
            tensor<int32, [2]> var_4864_split_sizes_0 = const()[name = string("op_4864_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4864_axis_0 = const()[name = string("op_4864_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_4864_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_4864_cast_fp16_1 = split(axis = var_4864_axis_0, split_sizes = var_4864_split_sizes_0, x = var_4839_cast_fp16)[name = string("op_4864_cast_fp16")];
            fp16 const_125_promoted_to_fp16 = const()[name = string("const_125_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_4866_cast_fp16 = mul(x = var_4864_cast_fp16_1, y = const_125_promoted_to_fp16)[name = string("op_4866_cast_fp16")];
            int32 var_4868 = const()[name = string("op_4868"), val = int32(-2)];
            bool var_4869_interleave_0 = const()[name = string("op_4869_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_4869_cast_fp16 = concat(axis = var_4868, interleave = var_4869_interleave_0, values = (var_4866_cast_fp16, var_4864_cast_fp16_0))[name = string("op_4869_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_4870_cast_fp16 = mul(x = var_4869_cast_fp16, y = var_866_cast_fp16)[name = string("op_4870_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_125_cast_fp16 = add(x = var_4863_cast_fp16, y = var_4870_cast_fp16)[name = string("key_states_125_cast_fp16")];
            tensor<int32, [1]> expand_dims_144 = const()[name = string("expand_dims_144"), val = tensor<int32, [1]>([12])];
            tensor<int32, [1]> expand_dims_145 = const()[name = string("expand_dims_145"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_147 = const()[name = string("expand_dims_147"), val = tensor<int32, [1]>([0])];
            int32 concat_149_axis_0 = const()[name = string("concat_149_axis_0"), val = int32(0)];
            bool concat_149_interleave_0 = const()[name = string("concat_149_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_149 = concat(axis = concat_149_axis_0, interleave = concat_149_interleave_0, values = (expand_dims_144, expand_dims_145, position_id, expand_dims_147))[name = string("concat_149")];
            tensor<int32, [1]> expand_dims_148 = const()[name = string("expand_dims_148"), val = tensor<int32, [1]>([13])];
            tensor<int32, [1]> concat_150_values1_0 = const()[name = string("concat_150_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_150_values3_0 = const()[name = string("concat_150_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_150_axis_0 = const()[name = string("concat_150_axis_0"), val = int32(0)];
            bool concat_150_interleave_0 = const()[name = string("concat_150_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_150 = concat(axis = concat_150_axis_0, interleave = concat_150_interleave_0, values = (expand_dims_148, concat_150_values1_0, cache_position_end, concat_150_values3_0))[name = string("concat_150")];
            tensor<int32, [4]> key_states_127_perm_0 = const()[name = string("key_states_127_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_13_stride_0 = const()[name = string("key_cache_internal_tensor_assign_13_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_13_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_13_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_13_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_13_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_13_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_13_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_127_cast_fp16 = transpose(perm = key_states_127_perm_0, x = key_states_125_cast_fp16)[name = string("transpose_482")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_13_cast_fp16 = slice_update(begin = concat_149, begin_mask = key_cache_internal_tensor_assign_13_begin_mask_0, end = concat_150, end_mask = key_cache_internal_tensor_assign_13_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_13_squeeze_mask_0, stride = key_cache_internal_tensor_assign_13_stride_0, update = key_states_127_cast_fp16, x = coreml_update_state_302)[name = string("key_cache_internal_tensor_assign_13_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_13_cast_fp16, input = key_cache)[name = string("coreml_update_state_304_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_304 = read_state(input = key_cache)[name = string("coreml_update_state_304")];
            tensor<int32, [4]> value_states_75_perm_0 = const()[name = string("value_states_75_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_13_stride_0 = const()[name = string("value_cache_internal_tensor_assign_13_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_13_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_13_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_13_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_13_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_13_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_13_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_75_cast_fp16 = transpose(perm = value_states_75_perm_0, x = var_4846_cast_fp16)[name = string("transpose_481")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_13_cast_fp16 = slice_update(begin = concat_149, begin_mask = value_cache_internal_tensor_assign_13_begin_mask_0, end = concat_150, end_mask = value_cache_internal_tensor_assign_13_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_13_squeeze_mask_0, stride = value_cache_internal_tensor_assign_13_stride_0, update = value_states_75_cast_fp16, x = coreml_update_state_303)[name = string("value_cache_internal_tensor_assign_13_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_13_cast_fp16, input = value_cache)[name = string("coreml_update_state_305_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_305 = read_state(input = value_cache)[name = string("coreml_update_state_305")];
            tensor<int32, [4]> var_4940_begin_0 = const()[name = string("op_4940_begin_0"), val = tensor<int32, [4]>([12, 0, 0, 0])];
            tensor<int32, [4]> var_4940_end_0 = const()[name = string("op_4940_end_0"), val = tensor<int32, [4]>([13, 2, 2048, 128])];
            tensor<bool, [4]> var_4940_end_mask_0 = const()[name = string("op_4940_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4940_cast_fp16 = slice_by_index(begin = var_4940_begin_0, end = var_4940_end_0, end_mask = var_4940_end_mask_0, x = coreml_update_state_304)[name = string("op_4940_cast_fp16")];
            tensor<int32, [2]> tile_24 = const()[name = string("tile_24"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4943_axis_0 = const()[name = string("op_4943_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4943_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4943_cast_fp16_1 = split(axis = var_4943_axis_0, split_sizes = tile_24, x = var_4940_cast_fp16)[name = string("op_4943_cast_fp16")];
            tensor<int32, [4]> var_4950_begin_0 = const()[name = string("op_4950_begin_0"), val = tensor<int32, [4]>([12, 0, 0, 0])];
            tensor<int32, [4]> var_4950_end_0 = const()[name = string("op_4950_end_0"), val = tensor<int32, [4]>([13, 2, 2048, 128])];
            tensor<bool, [4]> var_4950_end_mask_0 = const()[name = string("op_4950_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4950_cast_fp16 = slice_by_index(begin = var_4950_begin_0, end = var_4950_end_0, end_mask = var_4950_end_mask_0, x = coreml_update_state_305)[name = string("op_4950_cast_fp16")];
            tensor<int32, [2]> tile_25 = const()[name = string("tile_25"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4953_axis_0 = const()[name = string("op_4953_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4953_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4953_cast_fp16_1 = split(axis = var_4953_axis_0, split_sizes = tile_25, x = var_4950_cast_fp16)[name = string("op_4953_cast_fp16")];
            tensor<int32, [2]> var_4956_split_sizes_0 = const()[name = string("op_4956_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_4956_axis_0 = const()[name = string("op_4956_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_4956_0, tensor<fp16, [1, 8, 128, 64]> var_4956_1 = split(axis = var_4956_axis_0, split_sizes = var_4956_split_sizes_0, x = query_states_75_cast_fp16)[name = string("op_4956")];
            bool attn_weights_193_transpose_x_0 = const()[name = string("attn_weights_193_transpose_x_0"), val = bool(false)];
            bool attn_weights_193_transpose_y_0 = const()[name = string("attn_weights_193_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_193_cast_fp16 = matmul(transpose_x = attn_weights_193_transpose_x_0, transpose_y = attn_weights_193_transpose_y_0, x = var_4943_cast_fp16_0, y = var_4956_0)[name = string("attn_weights_193_cast_fp16")];
            fp16 var_4959_to_fp16 = const()[name = string("op_4959_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_195_cast_fp16 = mul(x = attn_weights_193_cast_fp16, y = var_4959_to_fp16)[name = string("attn_weights_195_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_197_cast_fp16 = add(x = attn_weights_195_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_197_cast_fp16")];
            int32 var_4963 = const()[name = string("op_4963"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_199_cast_fp16 = softmax(axis = var_4963, x = attn_weights_197_cast_fp16)[name = string("attn_weights_199_cast_fp16")];
            bool var_4969_transpose_x_1 = const()[name = string("op_4969_transpose_x_1"), val = bool(true)];
            bool var_4969_transpose_y_1 = const()[name = string("op_4969_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_4969_cast_fp16 = matmul(transpose_x = var_4969_transpose_x_1, transpose_y = var_4969_transpose_y_1, x = attn_weights_199_cast_fp16, y = var_4953_cast_fp16_0)[name = string("op_4969_cast_fp16")];
            bool attn_weights_201_transpose_x_0 = const()[name = string("attn_weights_201_transpose_x_0"), val = bool(false)];
            bool attn_weights_201_transpose_y_0 = const()[name = string("attn_weights_201_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_201_cast_fp16 = matmul(transpose_x = attn_weights_201_transpose_x_0, transpose_y = attn_weights_201_transpose_y_0, x = var_4943_cast_fp16_1, y = var_4956_1)[name = string("attn_weights_201_cast_fp16")];
            fp16 var_4971_to_fp16 = const()[name = string("op_4971_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_203_cast_fp16 = mul(x = attn_weights_201_cast_fp16, y = var_4971_to_fp16)[name = string("attn_weights_203_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_205_cast_fp16 = add(x = attn_weights_203_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_205_cast_fp16")];
            int32 var_4975 = const()[name = string("op_4975"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_207_cast_fp16 = softmax(axis = var_4975, x = attn_weights_205_cast_fp16)[name = string("attn_weights_207_cast_fp16")];
            bool attn_output_97_transpose_x_1 = const()[name = string("attn_output_97_transpose_x_1"), val = bool(true)];
            bool attn_output_97_transpose_y_1 = const()[name = string("attn_output_97_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_97_cast_fp16 = matmul(transpose_x = attn_output_97_transpose_x_1, transpose_y = attn_output_97_transpose_y_1, x = attn_weights_207_cast_fp16, y = var_4953_cast_fp16_1)[name = string("attn_output_97_cast_fp16")];
            int32 var_4983 = const()[name = string("op_4983"), val = int32(1)];
            bool attn_output_99_interleave_0 = const()[name = string("attn_output_99_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_99_cast_fp16 = concat(axis = var_4983, interleave = attn_output_99_interleave_0, values = (var_4969_cast_fp16, attn_output_97_cast_fp16))[name = string("attn_output_99_cast_fp16")];
            tensor<int32, [4]> var_4987_perm_0 = const()[name = string("op_4987_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_155x = const()[name = string("concat_155x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_4987_cast_fp16 = transpose(perm = var_4987_perm_0, x = attn_output_99_cast_fp16)[name = string("transpose_480")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_103_cast_fp16 = reshape(shape = concat_155x, x = var_4987_cast_fp16)[name = string("attn_output_103_cast_fp16")];
            tensor<int32, [2]> hidden_states_123_strides_0 = const()[name = string("hidden_states_123_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_123_pad_type_0 = const()[name = string("hidden_states_123_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_123_pad_0 = const()[name = string("hidden_states_123_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_123_dilations_0 = const()[name = string("hidden_states_123_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_123_groups_0 = const()[name = string("hidden_states_123_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_123_cast_fp16 = conv(dilations = hidden_states_123_dilations_0, groups = hidden_states_123_groups_0, pad = hidden_states_123_pad_0, pad_type = hidden_states_123_pad_type_0, strides = hidden_states_123_strides_0, weight = layers_12_self_attn_o_proj_weight_cast_fp16, x = attn_output_103_cast_fp16)[name = string("hidden_states_123_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_125_cast_fp16 = add(x = hidden_states_119_cast_fp16, y = hidden_states_123_cast_fp16)[name = string("hidden_states_125_cast_fp16")];
            fp16 const_130_promoted_to_fp16 = const()[name = string("const_130_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_5020_cast_fp16 = mul(x = hidden_states_125_cast_fp16, y = const_130_promoted_to_fp16)[name = string("op_5020_cast_fp16")];
            int32 var_5018 = const()[name = string("op_5018"), val = int32(1)];
            bool doubled_101_interleave_0 = const()[name = string("doubled_101_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_101_cast_fp16 = concat(axis = var_5018, interleave = doubled_101_interleave_0, values = (hidden_states_125_cast_fp16, var_5020_cast_fp16))[name = string("doubled_101_cast_fp16")];
            tensor<int32, [1]> out_51_axes_0 = const()[name = string("out_51_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_51_gamma_0_to_fp16 = const()[name = string("out_51_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407002496)))];
            fp16 var_5030_to_fp16 = const()[name = string("op_5030_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_51_cast_fp16 = layer_norm(axes = out_51_axes_0, epsilon = var_5030_to_fp16, gamma = out_51_gamma_0_to_fp16, x = doubled_101_cast_fp16)[name = string("out_51_cast_fp16")];
            tensor<int32, [2]> var_5041_split_sizes_0 = const()[name = string("op_5041_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5041_axis_0 = const()[name = string("op_5041_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_5041_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_5041_cast_fp16_1 = split(axis = var_5041_axis_0, split_sizes = var_5041_split_sizes_0, x = out_51_cast_fp16)[name = string("op_5041_cast_fp16")];
            tensor<int32, [2]> input_25_strides_0 = const()[name = string("input_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_25_pad_type_0 = const()[name = string("input_25_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_25_pad_0 = const()[name = string("input_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_25_dilations_0 = const()[name = string("input_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_25_groups_0 = const()[name = string("input_25_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_25_cast_fp16 = conv(dilations = input_25_dilations_0, groups = input_25_groups_0, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = input_25_strides_0, weight = layers_12_mlp_gate_proj_weight_cast_fp16, x = var_5041_cast_fp16_0)[name = string("input_25_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_5058_cast_fp16 = silu(x = input_25_cast_fp16)[name = string("op_5058_cast_fp16")];
            tensor<int32, [2]> var_5064_strides_0 = const()[name = string("op_5064_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_5064_pad_type_0 = const()[name = string("op_5064_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_5064_pad_0 = const()[name = string("op_5064_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_5064_dilations_0 = const()[name = string("op_5064_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5064_groups_0 = const()[name = string("op_5064_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_5064_cast_fp16 = conv(dilations = var_5064_dilations_0, groups = var_5064_groups_0, pad = var_5064_pad_0, pad_type = var_5064_pad_type_0, strides = var_5064_strides_0, weight = layers_12_mlp_up_proj_weight_cast_fp16, x = var_5041_cast_fp16_0)[name = string("op_5064_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_129_cast_fp16 = mul(x = var_5058_cast_fp16, y = var_5064_cast_fp16)[name = string("x_129_cast_fp16")];
            tensor<int32, [2]> hidden_states_127_strides_0 = const()[name = string("hidden_states_127_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_127_pad_type_0 = const()[name = string("hidden_states_127_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_127_pad_0 = const()[name = string("hidden_states_127_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_127_dilations_0 = const()[name = string("hidden_states_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_127_groups_0 = const()[name = string("hidden_states_127_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_127_cast_fp16 = conv(dilations = hidden_states_127_dilations_0, groups = hidden_states_127_groups_0, pad = hidden_states_127_pad_0, pad_type = hidden_states_127_pad_type_0, strides = hidden_states_127_strides_0, weight = layers_12_mlp_down_proj_weight_cast_fp16, x = x_129_cast_fp16)[name = string("hidden_states_127_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_129_cast_fp16 = add(x = hidden_states_125_cast_fp16, y = hidden_states_127_cast_fp16)[name = string("hidden_states_129_cast_fp16")];
            fp16 const_132_promoted_to_fp16 = const()[name = string("const_132_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_5082_cast_fp16 = mul(x = hidden_states_129_cast_fp16, y = const_132_promoted_to_fp16)[name = string("op_5082_cast_fp16")];
            int32 var_5080 = const()[name = string("op_5080"), val = int32(1)];
            bool doubled_105_interleave_0 = const()[name = string("doubled_105_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_105_cast_fp16 = concat(axis = var_5080, interleave = doubled_105_interleave_0, values = (hidden_states_129_cast_fp16, var_5082_cast_fp16))[name = string("doubled_105_cast_fp16")];
            tensor<int32, [1]> out_53_axes_0 = const()[name = string("out_53_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_53_gamma_0_to_fp16 = const()[name = string("out_53_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407010752)))];
            fp16 var_5092_to_fp16 = const()[name = string("op_5092_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_53_cast_fp16 = layer_norm(axes = out_53_axes_0, epsilon = var_5092_to_fp16, gamma = out_53_gamma_0_to_fp16, x = doubled_105_cast_fp16)[name = string("out_53_cast_fp16")];
            tensor<int32, [2]> var_5103_split_sizes_0 = const()[name = string("op_5103_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5103_axis_0 = const()[name = string("op_5103_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_5103_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_5103_cast_fp16_1 = split(axis = var_5103_axis_0, split_sizes = var_5103_split_sizes_0, x = out_53_cast_fp16)[name = string("op_5103_cast_fp16")];
            tensor<int32, [2]> query_states_79_strides_0 = const()[name = string("query_states_79_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_79_pad_type_0 = const()[name = string("query_states_79_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_79_pad_0 = const()[name = string("query_states_79_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_79_dilations_0 = const()[name = string("query_states_79_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_79_groups_0 = const()[name = string("query_states_79_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_79_cast_fp16 = conv(dilations = query_states_79_dilations_0, groups = query_states_79_groups_0, pad = query_states_79_pad_0, pad_type = query_states_79_pad_type_0, strides = query_states_79_strides_0, weight = layers_13_self_attn_q_proj_weight_cast_fp16, x = var_5103_cast_fp16_0)[name = string("query_states_79_cast_fp16")];
            tensor<int32, [2]> key_states_131_strides_0 = const()[name = string("key_states_131_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_131_pad_type_0 = const()[name = string("key_states_131_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_131_pad_0 = const()[name = string("key_states_131_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_131_dilations_0 = const()[name = string("key_states_131_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_131_groups_0 = const()[name = string("key_states_131_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_131_cast_fp16 = conv(dilations = key_states_131_dilations_0, groups = key_states_131_groups_0, pad = key_states_131_pad_0, pad_type = key_states_131_pad_type_0, strides = key_states_131_strides_0, weight = layers_13_self_attn_k_proj_weight_cast_fp16, x = var_5103_cast_fp16_0)[name = string("key_states_131_cast_fp16")];
            tensor<int32, [2]> value_states_79_strides_0 = const()[name = string("value_states_79_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_79_pad_type_0 = const()[name = string("value_states_79_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_79_pad_0 = const()[name = string("value_states_79_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_79_dilations_0 = const()[name = string("value_states_79_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_79_groups_0 = const()[name = string("value_states_79_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_79_cast_fp16 = conv(dilations = value_states_79_dilations_0, groups = value_states_79_groups_0, pad = value_states_79_pad_0, pad_type = value_states_79_pad_type_0, strides = value_states_79_strides_0, weight = layers_13_self_attn_v_proj_weight_cast_fp16, x = var_5103_cast_fp16_0)[name = string("value_states_79_cast_fp16")];
            tensor<int32, [4]> concat_156x = const()[name = string("concat_156x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_131_cast_fp16 = reshape(shape = concat_156x, x = query_states_79_cast_fp16)[name = string("x_131_cast_fp16")];
            tensor<int32, [4]> concat_157x = const()[name = string("concat_157x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_5160_cast_fp16 = reshape(shape = concat_157x, x = key_states_131_cast_fp16)[name = string("op_5160_cast_fp16")];
            tensor<int32, [4]> concat_158x = const()[name = string("concat_158x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_5167_cast_fp16 = reshape(shape = concat_158x, x = value_states_79_cast_fp16)[name = string("op_5167_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_5171_cast_fp16 = mul(x = x_131_cast_fp16, y = var_859_cast_fp16)[name = string("op_5171_cast_fp16")];
            tensor<int32, [2]> var_5172_split_sizes_0 = const()[name = string("op_5172_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5172_axis_0 = const()[name = string("op_5172_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_5172_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_5172_cast_fp16_1 = split(axis = var_5172_axis_0, split_sizes = var_5172_split_sizes_0, x = x_131_cast_fp16)[name = string("op_5172_cast_fp16")];
            fp16 const_134_promoted_to_fp16 = const()[name = string("const_134_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_5174_cast_fp16 = mul(x = var_5172_cast_fp16_1, y = const_134_promoted_to_fp16)[name = string("op_5174_cast_fp16")];
            int32 var_5176 = const()[name = string("op_5176"), val = int32(-2)];
            bool var_5177_interleave_0 = const()[name = string("op_5177_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_5177_cast_fp16 = concat(axis = var_5176, interleave = var_5177_interleave_0, values = (var_5174_cast_fp16, var_5172_cast_fp16_0))[name = string("op_5177_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_5178_cast_fp16 = mul(x = var_5177_cast_fp16, y = var_866_cast_fp16)[name = string("op_5178_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_81_cast_fp16 = add(x = var_5171_cast_fp16, y = var_5178_cast_fp16)[name = string("query_states_81_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_5184_cast_fp16 = mul(x = var_5160_cast_fp16, y = var_859_cast_fp16)[name = string("op_5184_cast_fp16")];
            tensor<int32, [2]> var_5185_split_sizes_0 = const()[name = string("op_5185_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5185_axis_0 = const()[name = string("op_5185_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_5185_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_5185_cast_fp16_1 = split(axis = var_5185_axis_0, split_sizes = var_5185_split_sizes_0, x = var_5160_cast_fp16)[name = string("op_5185_cast_fp16")];
            fp16 const_135_promoted_to_fp16 = const()[name = string("const_135_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_5187_cast_fp16 = mul(x = var_5185_cast_fp16_1, y = const_135_promoted_to_fp16)[name = string("op_5187_cast_fp16")];
            int32 var_5189 = const()[name = string("op_5189"), val = int32(-2)];
            bool var_5190_interleave_0 = const()[name = string("op_5190_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_5190_cast_fp16 = concat(axis = var_5189, interleave = var_5190_interleave_0, values = (var_5187_cast_fp16, var_5185_cast_fp16_0))[name = string("op_5190_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_5191_cast_fp16 = mul(x = var_5190_cast_fp16, y = var_866_cast_fp16)[name = string("op_5191_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_135_cast_fp16 = add(x = var_5184_cast_fp16, y = var_5191_cast_fp16)[name = string("key_states_135_cast_fp16")];
            tensor<int32, [1]> expand_dims_156 = const()[name = string("expand_dims_156"), val = tensor<int32, [1]>([13])];
            tensor<int32, [1]> expand_dims_157 = const()[name = string("expand_dims_157"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_159 = const()[name = string("expand_dims_159"), val = tensor<int32, [1]>([0])];
            int32 concat_161_axis_0 = const()[name = string("concat_161_axis_0"), val = int32(0)];
            bool concat_161_interleave_0 = const()[name = string("concat_161_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_161 = concat(axis = concat_161_axis_0, interleave = concat_161_interleave_0, values = (expand_dims_156, expand_dims_157, position_id, expand_dims_159))[name = string("concat_161")];
            tensor<int32, [1]> expand_dims_160 = const()[name = string("expand_dims_160"), val = tensor<int32, [1]>([14])];
            tensor<int32, [1]> concat_162_values1_0 = const()[name = string("concat_162_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_162_values3_0 = const()[name = string("concat_162_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_162_axis_0 = const()[name = string("concat_162_axis_0"), val = int32(0)];
            bool concat_162_interleave_0 = const()[name = string("concat_162_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_162 = concat(axis = concat_162_axis_0, interleave = concat_162_interleave_0, values = (expand_dims_160, concat_162_values1_0, cache_position_end, concat_162_values3_0))[name = string("concat_162")];
            tensor<int32, [4]> key_states_137_perm_0 = const()[name = string("key_states_137_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_14_stride_0 = const()[name = string("key_cache_internal_tensor_assign_14_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_14_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_14_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_14_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_14_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_14_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_14_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_137_cast_fp16 = transpose(perm = key_states_137_perm_0, x = key_states_135_cast_fp16)[name = string("transpose_479")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_14_cast_fp16 = slice_update(begin = concat_161, begin_mask = key_cache_internal_tensor_assign_14_begin_mask_0, end = concat_162, end_mask = key_cache_internal_tensor_assign_14_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_14_squeeze_mask_0, stride = key_cache_internal_tensor_assign_14_stride_0, update = key_states_137_cast_fp16, x = coreml_update_state_304)[name = string("key_cache_internal_tensor_assign_14_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_14_cast_fp16, input = key_cache)[name = string("coreml_update_state_306_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_306 = read_state(input = key_cache)[name = string("coreml_update_state_306")];
            tensor<int32, [4]> value_states_81_perm_0 = const()[name = string("value_states_81_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_14_stride_0 = const()[name = string("value_cache_internal_tensor_assign_14_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_14_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_14_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_14_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_14_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_14_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_14_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_81_cast_fp16 = transpose(perm = value_states_81_perm_0, x = var_5167_cast_fp16)[name = string("transpose_478")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_14_cast_fp16 = slice_update(begin = concat_161, begin_mask = value_cache_internal_tensor_assign_14_begin_mask_0, end = concat_162, end_mask = value_cache_internal_tensor_assign_14_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_14_squeeze_mask_0, stride = value_cache_internal_tensor_assign_14_stride_0, update = value_states_81_cast_fp16, x = coreml_update_state_305)[name = string("value_cache_internal_tensor_assign_14_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_14_cast_fp16, input = value_cache)[name = string("coreml_update_state_307_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_307 = read_state(input = value_cache)[name = string("coreml_update_state_307")];
            tensor<int32, [4]> var_5261_begin_0 = const()[name = string("op_5261_begin_0"), val = tensor<int32, [4]>([13, 0, 0, 0])];
            tensor<int32, [4]> var_5261_end_0 = const()[name = string("op_5261_end_0"), val = tensor<int32, [4]>([14, 2, 2048, 128])];
            tensor<bool, [4]> var_5261_end_mask_0 = const()[name = string("op_5261_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5261_cast_fp16 = slice_by_index(begin = var_5261_begin_0, end = var_5261_end_0, end_mask = var_5261_end_mask_0, x = coreml_update_state_306)[name = string("op_5261_cast_fp16")];
            tensor<int32, [2]> tile_26 = const()[name = string("tile_26"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5264_axis_0 = const()[name = string("op_5264_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5264_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5264_cast_fp16_1 = split(axis = var_5264_axis_0, split_sizes = tile_26, x = var_5261_cast_fp16)[name = string("op_5264_cast_fp16")];
            tensor<int32, [4]> var_5271_begin_0 = const()[name = string("op_5271_begin_0"), val = tensor<int32, [4]>([13, 0, 0, 0])];
            tensor<int32, [4]> var_5271_end_0 = const()[name = string("op_5271_end_0"), val = tensor<int32, [4]>([14, 2, 2048, 128])];
            tensor<bool, [4]> var_5271_end_mask_0 = const()[name = string("op_5271_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5271_cast_fp16 = slice_by_index(begin = var_5271_begin_0, end = var_5271_end_0, end_mask = var_5271_end_mask_0, x = coreml_update_state_307)[name = string("op_5271_cast_fp16")];
            tensor<int32, [2]> tile_27 = const()[name = string("tile_27"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5274_axis_0 = const()[name = string("op_5274_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5274_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5274_cast_fp16_1 = split(axis = var_5274_axis_0, split_sizes = tile_27, x = var_5271_cast_fp16)[name = string("op_5274_cast_fp16")];
            tensor<int32, [2]> var_5277_split_sizes_0 = const()[name = string("op_5277_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_5277_axis_0 = const()[name = string("op_5277_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_5277_0, tensor<fp16, [1, 8, 128, 64]> var_5277_1 = split(axis = var_5277_axis_0, split_sizes = var_5277_split_sizes_0, x = query_states_81_cast_fp16)[name = string("op_5277")];
            bool attn_weights_209_transpose_x_0 = const()[name = string("attn_weights_209_transpose_x_0"), val = bool(false)];
            bool attn_weights_209_transpose_y_0 = const()[name = string("attn_weights_209_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_209_cast_fp16 = matmul(transpose_x = attn_weights_209_transpose_x_0, transpose_y = attn_weights_209_transpose_y_0, x = var_5264_cast_fp16_0, y = var_5277_0)[name = string("attn_weights_209_cast_fp16")];
            fp16 var_5280_to_fp16 = const()[name = string("op_5280_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_211_cast_fp16 = mul(x = attn_weights_209_cast_fp16, y = var_5280_to_fp16)[name = string("attn_weights_211_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_213_cast_fp16 = add(x = attn_weights_211_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_213_cast_fp16")];
            int32 var_5284 = const()[name = string("op_5284"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_215_cast_fp16 = softmax(axis = var_5284, x = attn_weights_213_cast_fp16)[name = string("attn_weights_215_cast_fp16")];
            bool var_5290_transpose_x_1 = const()[name = string("op_5290_transpose_x_1"), val = bool(true)];
            bool var_5290_transpose_y_1 = const()[name = string("op_5290_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_5290_cast_fp16 = matmul(transpose_x = var_5290_transpose_x_1, transpose_y = var_5290_transpose_y_1, x = attn_weights_215_cast_fp16, y = var_5274_cast_fp16_0)[name = string("op_5290_cast_fp16")];
            bool attn_weights_217_transpose_x_0 = const()[name = string("attn_weights_217_transpose_x_0"), val = bool(false)];
            bool attn_weights_217_transpose_y_0 = const()[name = string("attn_weights_217_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_217_cast_fp16 = matmul(transpose_x = attn_weights_217_transpose_x_0, transpose_y = attn_weights_217_transpose_y_0, x = var_5264_cast_fp16_1, y = var_5277_1)[name = string("attn_weights_217_cast_fp16")];
            fp16 var_5292_to_fp16 = const()[name = string("op_5292_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_219_cast_fp16 = mul(x = attn_weights_217_cast_fp16, y = var_5292_to_fp16)[name = string("attn_weights_219_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_221_cast_fp16 = add(x = attn_weights_219_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_221_cast_fp16")];
            int32 var_5296 = const()[name = string("op_5296"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_223_cast_fp16 = softmax(axis = var_5296, x = attn_weights_221_cast_fp16)[name = string("attn_weights_223_cast_fp16")];
            bool attn_output_105_transpose_x_1 = const()[name = string("attn_output_105_transpose_x_1"), val = bool(true)];
            bool attn_output_105_transpose_y_1 = const()[name = string("attn_output_105_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_105_cast_fp16 = matmul(transpose_x = attn_output_105_transpose_x_1, transpose_y = attn_output_105_transpose_y_1, x = attn_weights_223_cast_fp16, y = var_5274_cast_fp16_1)[name = string("attn_output_105_cast_fp16")];
            int32 var_5304 = const()[name = string("op_5304"), val = int32(1)];
            bool attn_output_107_interleave_0 = const()[name = string("attn_output_107_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_107_cast_fp16 = concat(axis = var_5304, interleave = attn_output_107_interleave_0, values = (var_5290_cast_fp16, attn_output_105_cast_fp16))[name = string("attn_output_107_cast_fp16")];
            tensor<int32, [4]> var_5308_perm_0 = const()[name = string("op_5308_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_167x = const()[name = string("concat_167x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_5308_cast_fp16 = transpose(perm = var_5308_perm_0, x = attn_output_107_cast_fp16)[name = string("transpose_477")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_111_cast_fp16 = reshape(shape = concat_167x, x = var_5308_cast_fp16)[name = string("attn_output_111_cast_fp16")];
            tensor<int32, [2]> hidden_states_133_strides_0 = const()[name = string("hidden_states_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_133_pad_type_0 = const()[name = string("hidden_states_133_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_133_pad_0 = const()[name = string("hidden_states_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_133_dilations_0 = const()[name = string("hidden_states_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_133_groups_0 = const()[name = string("hidden_states_133_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_133_cast_fp16 = conv(dilations = hidden_states_133_dilations_0, groups = hidden_states_133_groups_0, pad = hidden_states_133_pad_0, pad_type = hidden_states_133_pad_type_0, strides = hidden_states_133_strides_0, weight = layers_13_self_attn_o_proj_weight_cast_fp16, x = attn_output_111_cast_fp16)[name = string("hidden_states_133_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_135_cast_fp16 = add(x = hidden_states_129_cast_fp16, y = hidden_states_133_cast_fp16)[name = string("hidden_states_135_cast_fp16")];
            fp16 const_140_promoted_to_fp16 = const()[name = string("const_140_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_5341_cast_fp16 = mul(x = hidden_states_135_cast_fp16, y = const_140_promoted_to_fp16)[name = string("op_5341_cast_fp16")];
            int32 var_5339 = const()[name = string("op_5339"), val = int32(1)];
            bool doubled_109_interleave_0 = const()[name = string("doubled_109_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_109_cast_fp16 = concat(axis = var_5339, interleave = doubled_109_interleave_0, values = (hidden_states_135_cast_fp16, var_5341_cast_fp16))[name = string("doubled_109_cast_fp16")];
            tensor<int32, [1]> out_55_axes_0 = const()[name = string("out_55_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_55_gamma_0_to_fp16 = const()[name = string("out_55_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407019008)))];
            fp16 var_5351_to_fp16 = const()[name = string("op_5351_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_55_cast_fp16 = layer_norm(axes = out_55_axes_0, epsilon = var_5351_to_fp16, gamma = out_55_gamma_0_to_fp16, x = doubled_109_cast_fp16)[name = string("out_55_cast_fp16")];
            tensor<int32, [2]> var_5362_split_sizes_0 = const()[name = string("op_5362_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5362_axis_0 = const()[name = string("op_5362_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_5362_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_5362_cast_fp16_1 = split(axis = var_5362_axis_0, split_sizes = var_5362_split_sizes_0, x = out_55_cast_fp16)[name = string("op_5362_cast_fp16")];
            tensor<int32, [2]> input_27_strides_0 = const()[name = string("input_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_27_pad_type_0 = const()[name = string("input_27_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_27_pad_0 = const()[name = string("input_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_27_dilations_0 = const()[name = string("input_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_27_groups_0 = const()[name = string("input_27_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_27_cast_fp16 = conv(dilations = input_27_dilations_0, groups = input_27_groups_0, pad = input_27_pad_0, pad_type = input_27_pad_type_0, strides = input_27_strides_0, weight = layers_13_mlp_gate_proj_weight_cast_fp16, x = var_5362_cast_fp16_0)[name = string("input_27_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_5379_cast_fp16 = silu(x = input_27_cast_fp16)[name = string("op_5379_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_13_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_13_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407027264)))];
            tensor<int32, [2]> var_5385_strides_0 = const()[name = string("op_5385_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_5385_pad_type_0 = const()[name = string("op_5385_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_5385_pad_0 = const()[name = string("op_5385_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_5385_dilations_0 = const()[name = string("op_5385_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5385_groups_0 = const()[name = string("op_5385_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_5385_cast_fp16 = conv(dilations = var_5385_dilations_0, groups = var_5385_groups_0, pad = var_5385_pad_0, pad_type = var_5385_pad_type_0, strides = var_5385_strides_0, weight = layers_13_mlp_up_proj_weight_to_fp16, x = var_5362_cast_fp16_0)[name = string("op_5385_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_139_cast_fp16 = mul(x = var_5379_cast_fp16, y = var_5385_cast_fp16)[name = string("x_139_cast_fp16")];
            tensor<int32, [2]> hidden_states_137_strides_0 = const()[name = string("hidden_states_137_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_137_pad_type_0 = const()[name = string("hidden_states_137_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_137_pad_0 = const()[name = string("hidden_states_137_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_137_dilations_0 = const()[name = string("hidden_states_137_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_137_groups_0 = const()[name = string("hidden_states_137_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_137_cast_fp16 = conv(dilations = hidden_states_137_dilations_0, groups = hidden_states_137_groups_0, pad = hidden_states_137_pad_0, pad_type = hidden_states_137_pad_type_0, strides = hidden_states_137_strides_0, weight = layers_13_mlp_down_proj_weight_cast_fp16, x = x_139_cast_fp16)[name = string("hidden_states_137_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_139_cast_fp16 = add(x = hidden_states_135_cast_fp16, y = hidden_states_137_cast_fp16)[name = string("hidden_states_139_cast_fp16")];
            fp16 const_142_promoted_to_fp16 = const()[name = string("const_142_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_5403_cast_fp16 = mul(x = hidden_states_139_cast_fp16, y = const_142_promoted_to_fp16)[name = string("op_5403_cast_fp16")];
            int32 var_5401 = const()[name = string("op_5401"), val = int32(1)];
            bool doubled_113_interleave_0 = const()[name = string("doubled_113_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_113_cast_fp16 = concat(axis = var_5401, interleave = doubled_113_interleave_0, values = (hidden_states_139_cast_fp16, var_5403_cast_fp16))[name = string("doubled_113_cast_fp16")];
            tensor<int32, [1]> out_57_axes_0 = const()[name = string("out_57_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_57_gamma_0_to_fp16 = const()[name = string("out_57_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1432193152)))];
            fp16 var_5413_to_fp16 = const()[name = string("op_5413_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_57_cast_fp16 = layer_norm(axes = out_57_axes_0, epsilon = var_5413_to_fp16, gamma = out_57_gamma_0_to_fp16, x = doubled_113_cast_fp16)[name = string("out_57_cast_fp16")];
            tensor<int32, [2]> var_5424_split_sizes_0 = const()[name = string("op_5424_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5424_axis_0 = const()[name = string("op_5424_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_5424_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_5424_cast_fp16_1 = split(axis = var_5424_axis_0, split_sizes = var_5424_split_sizes_0, x = out_57_cast_fp16)[name = string("op_5424_cast_fp16")];
            tensor<int32, [2]> query_states_85_strides_0 = const()[name = string("query_states_85_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_85_pad_type_0 = const()[name = string("query_states_85_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_85_pad_0 = const()[name = string("query_states_85_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_85_dilations_0 = const()[name = string("query_states_85_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_85_groups_0 = const()[name = string("query_states_85_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_85_cast_fp16 = conv(dilations = query_states_85_dilations_0, groups = query_states_85_groups_0, pad = query_states_85_pad_0, pad_type = query_states_85_pad_type_0, strides = query_states_85_strides_0, weight = layers_14_self_attn_q_proj_weight_cast_fp16, x = var_5424_cast_fp16_0)[name = string("query_states_85_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_14_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_14_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1432201408)))];
            tensor<int32, [2]> key_states_141_strides_0 = const()[name = string("key_states_141_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_141_pad_type_0 = const()[name = string("key_states_141_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_141_pad_0 = const()[name = string("key_states_141_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_141_dilations_0 = const()[name = string("key_states_141_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_141_groups_0 = const()[name = string("key_states_141_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_141_cast_fp16 = conv(dilations = key_states_141_dilations_0, groups = key_states_141_groups_0, pad = key_states_141_pad_0, pad_type = key_states_141_pad_type_0, strides = key_states_141_strides_0, weight = layers_14_self_attn_k_proj_weight_to_fp16, x = var_5424_cast_fp16_0)[name = string("key_states_141_cast_fp16")];
            tensor<int32, [2]> value_states_85_strides_0 = const()[name = string("value_states_85_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_85_pad_type_0 = const()[name = string("value_states_85_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_85_pad_0 = const()[name = string("value_states_85_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_85_dilations_0 = const()[name = string("value_states_85_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_85_groups_0 = const()[name = string("value_states_85_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_85_cast_fp16 = conv(dilations = value_states_85_dilations_0, groups = value_states_85_groups_0, pad = value_states_85_pad_0, pad_type = value_states_85_pad_type_0, strides = value_states_85_strides_0, weight = layers_14_self_attn_v_proj_weight_cast_fp16, x = var_5424_cast_fp16_0)[name = string("value_states_85_cast_fp16")];
            tensor<int32, [4]> concat_168x = const()[name = string("concat_168x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_141_cast_fp16 = reshape(shape = concat_168x, x = query_states_85_cast_fp16)[name = string("x_141_cast_fp16")];
            tensor<int32, [4]> concat_169x = const()[name = string("concat_169x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_5481_cast_fp16 = reshape(shape = concat_169x, x = key_states_141_cast_fp16)[name = string("op_5481_cast_fp16")];
            tensor<int32, [4]> concat_170x = const()[name = string("concat_170x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_5488_cast_fp16 = reshape(shape = concat_170x, x = value_states_85_cast_fp16)[name = string("op_5488_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_5492_cast_fp16 = mul(x = x_141_cast_fp16, y = var_859_cast_fp16)[name = string("op_5492_cast_fp16")];
            tensor<int32, [2]> var_5493_split_sizes_0 = const()[name = string("op_5493_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5493_axis_0 = const()[name = string("op_5493_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_5493_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_5493_cast_fp16_1 = split(axis = var_5493_axis_0, split_sizes = var_5493_split_sizes_0, x = x_141_cast_fp16)[name = string("op_5493_cast_fp16")];
            fp16 const_144_promoted_to_fp16 = const()[name = string("const_144_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_5495_cast_fp16 = mul(x = var_5493_cast_fp16_1, y = const_144_promoted_to_fp16)[name = string("op_5495_cast_fp16")];
            int32 var_5497 = const()[name = string("op_5497"), val = int32(-2)];
            bool var_5498_interleave_0 = const()[name = string("op_5498_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_5498_cast_fp16 = concat(axis = var_5497, interleave = var_5498_interleave_0, values = (var_5495_cast_fp16, var_5493_cast_fp16_0))[name = string("op_5498_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_5499_cast_fp16 = mul(x = var_5498_cast_fp16, y = var_866_cast_fp16)[name = string("op_5499_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_87_cast_fp16 = add(x = var_5492_cast_fp16, y = var_5499_cast_fp16)[name = string("query_states_87_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_5505_cast_fp16 = mul(x = var_5481_cast_fp16, y = var_859_cast_fp16)[name = string("op_5505_cast_fp16")];
            tensor<int32, [2]> var_5506_split_sizes_0 = const()[name = string("op_5506_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5506_axis_0 = const()[name = string("op_5506_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_5506_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_5506_cast_fp16_1 = split(axis = var_5506_axis_0, split_sizes = var_5506_split_sizes_0, x = var_5481_cast_fp16)[name = string("op_5506_cast_fp16")];
            fp16 const_145_promoted_to_fp16 = const()[name = string("const_145_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_5508_cast_fp16 = mul(x = var_5506_cast_fp16_1, y = const_145_promoted_to_fp16)[name = string("op_5508_cast_fp16")];
            int32 var_5510 = const()[name = string("op_5510"), val = int32(-2)];
            bool var_5511_interleave_0 = const()[name = string("op_5511_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_5511_cast_fp16 = concat(axis = var_5510, interleave = var_5511_interleave_0, values = (var_5508_cast_fp16, var_5506_cast_fp16_0))[name = string("op_5511_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_5512_cast_fp16 = mul(x = var_5511_cast_fp16, y = var_866_cast_fp16)[name = string("op_5512_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_145_cast_fp16 = add(x = var_5505_cast_fp16, y = var_5512_cast_fp16)[name = string("key_states_145_cast_fp16")];
            tensor<int32, [1]> expand_dims_168 = const()[name = string("expand_dims_168"), val = tensor<int32, [1]>([14])];
            tensor<int32, [1]> expand_dims_169 = const()[name = string("expand_dims_169"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_171 = const()[name = string("expand_dims_171"), val = tensor<int32, [1]>([0])];
            int32 concat_173_axis_0 = const()[name = string("concat_173_axis_0"), val = int32(0)];
            bool concat_173_interleave_0 = const()[name = string("concat_173_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_173 = concat(axis = concat_173_axis_0, interleave = concat_173_interleave_0, values = (expand_dims_168, expand_dims_169, position_id, expand_dims_171))[name = string("concat_173")];
            tensor<int32, [1]> expand_dims_172 = const()[name = string("expand_dims_172"), val = tensor<int32, [1]>([15])];
            tensor<int32, [1]> concat_174_values1_0 = const()[name = string("concat_174_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_174_values3_0 = const()[name = string("concat_174_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_174_axis_0 = const()[name = string("concat_174_axis_0"), val = int32(0)];
            bool concat_174_interleave_0 = const()[name = string("concat_174_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_174 = concat(axis = concat_174_axis_0, interleave = concat_174_interleave_0, values = (expand_dims_172, concat_174_values1_0, cache_position_end, concat_174_values3_0))[name = string("concat_174")];
            tensor<int32, [4]> key_states_147_perm_0 = const()[name = string("key_states_147_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_15_stride_0 = const()[name = string("key_cache_internal_tensor_assign_15_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_15_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_15_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_15_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_15_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_15_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_15_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_147_cast_fp16 = transpose(perm = key_states_147_perm_0, x = key_states_145_cast_fp16)[name = string("transpose_476")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_15_cast_fp16 = slice_update(begin = concat_173, begin_mask = key_cache_internal_tensor_assign_15_begin_mask_0, end = concat_174, end_mask = key_cache_internal_tensor_assign_15_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_15_squeeze_mask_0, stride = key_cache_internal_tensor_assign_15_stride_0, update = key_states_147_cast_fp16, x = coreml_update_state_306)[name = string("key_cache_internal_tensor_assign_15_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_15_cast_fp16, input = key_cache)[name = string("coreml_update_state_308_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_308 = read_state(input = key_cache)[name = string("coreml_update_state_308")];
            tensor<int32, [4]> value_states_87_perm_0 = const()[name = string("value_states_87_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_15_stride_0 = const()[name = string("value_cache_internal_tensor_assign_15_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_15_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_15_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_15_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_15_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_15_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_15_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_87_cast_fp16 = transpose(perm = value_states_87_perm_0, x = var_5488_cast_fp16)[name = string("transpose_475")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_15_cast_fp16 = slice_update(begin = concat_173, begin_mask = value_cache_internal_tensor_assign_15_begin_mask_0, end = concat_174, end_mask = value_cache_internal_tensor_assign_15_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_15_squeeze_mask_0, stride = value_cache_internal_tensor_assign_15_stride_0, update = value_states_87_cast_fp16, x = coreml_update_state_307)[name = string("value_cache_internal_tensor_assign_15_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_15_cast_fp16, input = value_cache)[name = string("coreml_update_state_309_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_309 = read_state(input = value_cache)[name = string("coreml_update_state_309")];
            tensor<int32, [4]> var_5582_begin_0 = const()[name = string("op_5582_begin_0"), val = tensor<int32, [4]>([14, 0, 0, 0])];
            tensor<int32, [4]> var_5582_end_0 = const()[name = string("op_5582_end_0"), val = tensor<int32, [4]>([15, 2, 2048, 128])];
            tensor<bool, [4]> var_5582_end_mask_0 = const()[name = string("op_5582_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5582_cast_fp16 = slice_by_index(begin = var_5582_begin_0, end = var_5582_end_0, end_mask = var_5582_end_mask_0, x = coreml_update_state_308)[name = string("op_5582_cast_fp16")];
            tensor<int32, [2]> tile_28 = const()[name = string("tile_28"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5585_axis_0 = const()[name = string("op_5585_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5585_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5585_cast_fp16_1 = split(axis = var_5585_axis_0, split_sizes = tile_28, x = var_5582_cast_fp16)[name = string("op_5585_cast_fp16")];
            tensor<int32, [4]> var_5592_begin_0 = const()[name = string("op_5592_begin_0"), val = tensor<int32, [4]>([14, 0, 0, 0])];
            tensor<int32, [4]> var_5592_end_0 = const()[name = string("op_5592_end_0"), val = tensor<int32, [4]>([15, 2, 2048, 128])];
            tensor<bool, [4]> var_5592_end_mask_0 = const()[name = string("op_5592_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5592_cast_fp16 = slice_by_index(begin = var_5592_begin_0, end = var_5592_end_0, end_mask = var_5592_end_mask_0, x = coreml_update_state_309)[name = string("op_5592_cast_fp16")];
            tensor<int32, [2]> tile_29 = const()[name = string("tile_29"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5595_axis_0 = const()[name = string("op_5595_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5595_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5595_cast_fp16_1 = split(axis = var_5595_axis_0, split_sizes = tile_29, x = var_5592_cast_fp16)[name = string("op_5595_cast_fp16")];
            tensor<int32, [2]> var_5598_split_sizes_0 = const()[name = string("op_5598_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_5598_axis_0 = const()[name = string("op_5598_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_5598_0, tensor<fp16, [1, 8, 128, 64]> var_5598_1 = split(axis = var_5598_axis_0, split_sizes = var_5598_split_sizes_0, x = query_states_87_cast_fp16)[name = string("op_5598")];
            bool attn_weights_225_transpose_x_0 = const()[name = string("attn_weights_225_transpose_x_0"), val = bool(false)];
            bool attn_weights_225_transpose_y_0 = const()[name = string("attn_weights_225_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_225_cast_fp16 = matmul(transpose_x = attn_weights_225_transpose_x_0, transpose_y = attn_weights_225_transpose_y_0, x = var_5585_cast_fp16_0, y = var_5598_0)[name = string("attn_weights_225_cast_fp16")];
            fp16 var_5601_to_fp16 = const()[name = string("op_5601_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_227_cast_fp16 = mul(x = attn_weights_225_cast_fp16, y = var_5601_to_fp16)[name = string("attn_weights_227_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_229_cast_fp16 = add(x = attn_weights_227_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_229_cast_fp16")];
            int32 var_5605 = const()[name = string("op_5605"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_231_cast_fp16 = softmax(axis = var_5605, x = attn_weights_229_cast_fp16)[name = string("attn_weights_231_cast_fp16")];
            bool var_5611_transpose_x_1 = const()[name = string("op_5611_transpose_x_1"), val = bool(true)];
            bool var_5611_transpose_y_1 = const()[name = string("op_5611_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_5611_cast_fp16 = matmul(transpose_x = var_5611_transpose_x_1, transpose_y = var_5611_transpose_y_1, x = attn_weights_231_cast_fp16, y = var_5595_cast_fp16_0)[name = string("op_5611_cast_fp16")];
            bool attn_weights_233_transpose_x_0 = const()[name = string("attn_weights_233_transpose_x_0"), val = bool(false)];
            bool attn_weights_233_transpose_y_0 = const()[name = string("attn_weights_233_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_233_cast_fp16 = matmul(transpose_x = attn_weights_233_transpose_x_0, transpose_y = attn_weights_233_transpose_y_0, x = var_5585_cast_fp16_1, y = var_5598_1)[name = string("attn_weights_233_cast_fp16")];
            fp16 var_5613_to_fp16 = const()[name = string("op_5613_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_235_cast_fp16 = mul(x = attn_weights_233_cast_fp16, y = var_5613_to_fp16)[name = string("attn_weights_235_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_237_cast_fp16 = add(x = attn_weights_235_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_237_cast_fp16")];
            int32 var_5617 = const()[name = string("op_5617"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_239_cast_fp16 = softmax(axis = var_5617, x = attn_weights_237_cast_fp16)[name = string("attn_weights_239_cast_fp16")];
            bool attn_output_113_transpose_x_1 = const()[name = string("attn_output_113_transpose_x_1"), val = bool(true)];
            bool attn_output_113_transpose_y_1 = const()[name = string("attn_output_113_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_113_cast_fp16 = matmul(transpose_x = attn_output_113_transpose_x_1, transpose_y = attn_output_113_transpose_y_1, x = attn_weights_239_cast_fp16, y = var_5595_cast_fp16_1)[name = string("attn_output_113_cast_fp16")];
            int32 var_5625 = const()[name = string("op_5625"), val = int32(1)];
            bool attn_output_115_interleave_0 = const()[name = string("attn_output_115_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_115_cast_fp16 = concat(axis = var_5625, interleave = attn_output_115_interleave_0, values = (var_5611_cast_fp16, attn_output_113_cast_fp16))[name = string("attn_output_115_cast_fp16")];
            tensor<int32, [4]> var_5629_perm_0 = const()[name = string("op_5629_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_179x = const()[name = string("concat_179x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_5629_cast_fp16 = transpose(perm = var_5629_perm_0, x = attn_output_115_cast_fp16)[name = string("transpose_474")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_119_cast_fp16 = reshape(shape = concat_179x, x = var_5629_cast_fp16)[name = string("attn_output_119_cast_fp16")];
            tensor<int32, [2]> hidden_states_143_strides_0 = const()[name = string("hidden_states_143_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_143_pad_type_0 = const()[name = string("hidden_states_143_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_143_pad_0 = const()[name = string("hidden_states_143_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_143_dilations_0 = const()[name = string("hidden_states_143_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_143_groups_0 = const()[name = string("hidden_states_143_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_143_cast_fp16 = conv(dilations = hidden_states_143_dilations_0, groups = hidden_states_143_groups_0, pad = hidden_states_143_pad_0, pad_type = hidden_states_143_pad_type_0, strides = hidden_states_143_strides_0, weight = layers_14_self_attn_o_proj_weight_cast_fp16, x = attn_output_119_cast_fp16)[name = string("hidden_states_143_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_145_cast_fp16 = add(x = hidden_states_139_cast_fp16, y = hidden_states_143_cast_fp16)[name = string("hidden_states_145_cast_fp16")];
            fp16 const_150_promoted_to_fp16 = const()[name = string("const_150_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_5662_cast_fp16 = mul(x = hidden_states_145_cast_fp16, y = const_150_promoted_to_fp16)[name = string("op_5662_cast_fp16")];
            int32 var_5660 = const()[name = string("op_5660"), val = int32(1)];
            bool doubled_117_interleave_0 = const()[name = string("doubled_117_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_117_cast_fp16 = concat(axis = var_5660, interleave = doubled_117_interleave_0, values = (hidden_states_145_cast_fp16, var_5662_cast_fp16))[name = string("doubled_117_cast_fp16")];
            tensor<int32, [1]> out_59_axes_0 = const()[name = string("out_59_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_59_gamma_0_to_fp16 = const()[name = string("out_59_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433250048)))];
            fp16 var_5672_to_fp16 = const()[name = string("op_5672_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_59_cast_fp16 = layer_norm(axes = out_59_axes_0, epsilon = var_5672_to_fp16, gamma = out_59_gamma_0_to_fp16, x = doubled_117_cast_fp16)[name = string("out_59_cast_fp16")];
            tensor<int32, [2]> var_5683_split_sizes_0 = const()[name = string("op_5683_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5683_axis_0 = const()[name = string("op_5683_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_5683_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_5683_cast_fp16_1 = split(axis = var_5683_axis_0, split_sizes = var_5683_split_sizes_0, x = out_59_cast_fp16)[name = string("op_5683_cast_fp16")];
            tensor<int32, [2]> input_29_strides_0 = const()[name = string("input_29_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_29_pad_type_0 = const()[name = string("input_29_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_29_pad_0 = const()[name = string("input_29_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_29_dilations_0 = const()[name = string("input_29_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_29_groups_0 = const()[name = string("input_29_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_29_cast_fp16 = conv(dilations = input_29_dilations_0, groups = input_29_groups_0, pad = input_29_pad_0, pad_type = input_29_pad_type_0, strides = input_29_strides_0, weight = layers_14_mlp_gate_proj_weight_cast_fp16, x = var_5683_cast_fp16_0)[name = string("input_29_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_5700_cast_fp16 = silu(x = input_29_cast_fp16)[name = string("op_5700_cast_fp16")];
            tensor<int32, [2]> var_5706_strides_0 = const()[name = string("op_5706_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_5706_pad_type_0 = const()[name = string("op_5706_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_5706_pad_0 = const()[name = string("op_5706_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_5706_dilations_0 = const()[name = string("op_5706_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5706_groups_0 = const()[name = string("op_5706_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_5706_cast_fp16 = conv(dilations = var_5706_dilations_0, groups = var_5706_groups_0, pad = var_5706_pad_0, pad_type = var_5706_pad_type_0, strides = var_5706_strides_0, weight = layers_14_mlp_up_proj_weight_cast_fp16, x = var_5683_cast_fp16_0)[name = string("op_5706_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_149_cast_fp16 = mul(x = var_5700_cast_fp16, y = var_5706_cast_fp16)[name = string("x_149_cast_fp16")];
            tensor<int32, [2]> hidden_states_147_strides_0 = const()[name = string("hidden_states_147_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_147_pad_type_0 = const()[name = string("hidden_states_147_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_147_pad_0 = const()[name = string("hidden_states_147_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_147_dilations_0 = const()[name = string("hidden_states_147_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_147_groups_0 = const()[name = string("hidden_states_147_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_147_cast_fp16 = conv(dilations = hidden_states_147_dilations_0, groups = hidden_states_147_groups_0, pad = hidden_states_147_pad_0, pad_type = hidden_states_147_pad_type_0, strides = hidden_states_147_strides_0, weight = layers_14_mlp_down_proj_weight_cast_fp16, x = x_149_cast_fp16)[name = string("hidden_states_147_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_149_cast_fp16 = add(x = hidden_states_145_cast_fp16, y = hidden_states_147_cast_fp16)[name = string("hidden_states_149_cast_fp16")];
            fp16 const_152_promoted_to_fp16 = const()[name = string("const_152_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_5724_cast_fp16 = mul(x = hidden_states_149_cast_fp16, y = const_152_promoted_to_fp16)[name = string("op_5724_cast_fp16")];
            int32 var_5722 = const()[name = string("op_5722"), val = int32(1)];
            bool doubled_121_interleave_0 = const()[name = string("doubled_121_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_121_cast_fp16 = concat(axis = var_5722, interleave = doubled_121_interleave_0, values = (hidden_states_149_cast_fp16, var_5724_cast_fp16))[name = string("doubled_121_cast_fp16")];
            tensor<int32, [1]> out_61_axes_0 = const()[name = string("out_61_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_61_gamma_0_to_fp16 = const()[name = string("out_61_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433258304)))];
            fp16 var_5734_to_fp16 = const()[name = string("op_5734_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_61_cast_fp16 = layer_norm(axes = out_61_axes_0, epsilon = var_5734_to_fp16, gamma = out_61_gamma_0_to_fp16, x = doubled_121_cast_fp16)[name = string("out_61_cast_fp16")];
            tensor<int32, [2]> var_5745_split_sizes_0 = const()[name = string("op_5745_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5745_axis_0 = const()[name = string("op_5745_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_5745_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_5745_cast_fp16_1 = split(axis = var_5745_axis_0, split_sizes = var_5745_split_sizes_0, x = out_61_cast_fp16)[name = string("op_5745_cast_fp16")];
            tensor<int32, [2]> query_states_91_strides_0 = const()[name = string("query_states_91_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_91_pad_type_0 = const()[name = string("query_states_91_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_91_pad_0 = const()[name = string("query_states_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_91_dilations_0 = const()[name = string("query_states_91_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_91_groups_0 = const()[name = string("query_states_91_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_91_cast_fp16 = conv(dilations = query_states_91_dilations_0, groups = query_states_91_groups_0, pad = query_states_91_pad_0, pad_type = query_states_91_pad_type_0, strides = query_states_91_strides_0, weight = layers_15_self_attn_q_proj_weight_cast_fp16, x = var_5745_cast_fp16_0)[name = string("query_states_91_cast_fp16")];
            tensor<int32, [2]> key_states_151_strides_0 = const()[name = string("key_states_151_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_151_pad_type_0 = const()[name = string("key_states_151_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_151_pad_0 = const()[name = string("key_states_151_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_151_dilations_0 = const()[name = string("key_states_151_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_151_groups_0 = const()[name = string("key_states_151_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_151_cast_fp16 = conv(dilations = key_states_151_dilations_0, groups = key_states_151_groups_0, pad = key_states_151_pad_0, pad_type = key_states_151_pad_type_0, strides = key_states_151_strides_0, weight = layers_15_self_attn_k_proj_weight_cast_fp16, x = var_5745_cast_fp16_0)[name = string("key_states_151_cast_fp16")];
            tensor<int32, [2]> value_states_91_strides_0 = const()[name = string("value_states_91_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_91_pad_type_0 = const()[name = string("value_states_91_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_91_pad_0 = const()[name = string("value_states_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_91_dilations_0 = const()[name = string("value_states_91_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_91_groups_0 = const()[name = string("value_states_91_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_91_cast_fp16 = conv(dilations = value_states_91_dilations_0, groups = value_states_91_groups_0, pad = value_states_91_pad_0, pad_type = value_states_91_pad_type_0, strides = value_states_91_strides_0, weight = layers_15_self_attn_v_proj_weight_cast_fp16, x = var_5745_cast_fp16_0)[name = string("value_states_91_cast_fp16")];
            tensor<int32, [4]> concat_180x = const()[name = string("concat_180x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_151_cast_fp16 = reshape(shape = concat_180x, x = query_states_91_cast_fp16)[name = string("x_151_cast_fp16")];
            tensor<int32, [4]> concat_181x = const()[name = string("concat_181x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_5802_cast_fp16 = reshape(shape = concat_181x, x = key_states_151_cast_fp16)[name = string("op_5802_cast_fp16")];
            tensor<int32, [4]> concat_182x = const()[name = string("concat_182x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_5809_cast_fp16 = reshape(shape = concat_182x, x = value_states_91_cast_fp16)[name = string("op_5809_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_5813_cast_fp16 = mul(x = x_151_cast_fp16, y = var_859_cast_fp16)[name = string("op_5813_cast_fp16")];
            tensor<int32, [2]> var_5814_split_sizes_0 = const()[name = string("op_5814_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5814_axis_0 = const()[name = string("op_5814_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_5814_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_5814_cast_fp16_1 = split(axis = var_5814_axis_0, split_sizes = var_5814_split_sizes_0, x = x_151_cast_fp16)[name = string("op_5814_cast_fp16")];
            fp16 const_154_promoted_to_fp16 = const()[name = string("const_154_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_5816_cast_fp16 = mul(x = var_5814_cast_fp16_1, y = const_154_promoted_to_fp16)[name = string("op_5816_cast_fp16")];
            int32 var_5818 = const()[name = string("op_5818"), val = int32(-2)];
            bool var_5819_interleave_0 = const()[name = string("op_5819_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_5819_cast_fp16 = concat(axis = var_5818, interleave = var_5819_interleave_0, values = (var_5816_cast_fp16, var_5814_cast_fp16_0))[name = string("op_5819_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_5820_cast_fp16 = mul(x = var_5819_cast_fp16, y = var_866_cast_fp16)[name = string("op_5820_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_93_cast_fp16 = add(x = var_5813_cast_fp16, y = var_5820_cast_fp16)[name = string("query_states_93_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_5826_cast_fp16 = mul(x = var_5802_cast_fp16, y = var_859_cast_fp16)[name = string("op_5826_cast_fp16")];
            tensor<int32, [2]> var_5827_split_sizes_0 = const()[name = string("op_5827_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5827_axis_0 = const()[name = string("op_5827_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_5827_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_5827_cast_fp16_1 = split(axis = var_5827_axis_0, split_sizes = var_5827_split_sizes_0, x = var_5802_cast_fp16)[name = string("op_5827_cast_fp16")];
            fp16 const_155_promoted_to_fp16 = const()[name = string("const_155_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_5829_cast_fp16 = mul(x = var_5827_cast_fp16_1, y = const_155_promoted_to_fp16)[name = string("op_5829_cast_fp16")];
            int32 var_5831 = const()[name = string("op_5831"), val = int32(-2)];
            bool var_5832_interleave_0 = const()[name = string("op_5832_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_5832_cast_fp16 = concat(axis = var_5831, interleave = var_5832_interleave_0, values = (var_5829_cast_fp16, var_5827_cast_fp16_0))[name = string("op_5832_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_5833_cast_fp16 = mul(x = var_5832_cast_fp16, y = var_866_cast_fp16)[name = string("op_5833_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_155_cast_fp16 = add(x = var_5826_cast_fp16, y = var_5833_cast_fp16)[name = string("key_states_155_cast_fp16")];
            tensor<int32, [1]> expand_dims_180 = const()[name = string("expand_dims_180"), val = tensor<int32, [1]>([15])];
            tensor<int32, [1]> expand_dims_181 = const()[name = string("expand_dims_181"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_183 = const()[name = string("expand_dims_183"), val = tensor<int32, [1]>([0])];
            int32 concat_185_axis_0 = const()[name = string("concat_185_axis_0"), val = int32(0)];
            bool concat_185_interleave_0 = const()[name = string("concat_185_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_185 = concat(axis = concat_185_axis_0, interleave = concat_185_interleave_0, values = (expand_dims_180, expand_dims_181, position_id, expand_dims_183))[name = string("concat_185")];
            tensor<int32, [1]> expand_dims_184 = const()[name = string("expand_dims_184"), val = tensor<int32, [1]>([16])];
            tensor<int32, [1]> concat_186_values1_0 = const()[name = string("concat_186_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_186_values3_0 = const()[name = string("concat_186_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_186_axis_0 = const()[name = string("concat_186_axis_0"), val = int32(0)];
            bool concat_186_interleave_0 = const()[name = string("concat_186_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_186 = concat(axis = concat_186_axis_0, interleave = concat_186_interleave_0, values = (expand_dims_184, concat_186_values1_0, cache_position_end, concat_186_values3_0))[name = string("concat_186")];
            tensor<int32, [4]> key_states_157_perm_0 = const()[name = string("key_states_157_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_16_stride_0 = const()[name = string("key_cache_internal_tensor_assign_16_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_16_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_16_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_16_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_16_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_16_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_16_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_157_cast_fp16 = transpose(perm = key_states_157_perm_0, x = key_states_155_cast_fp16)[name = string("transpose_473")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_16_cast_fp16 = slice_update(begin = concat_185, begin_mask = key_cache_internal_tensor_assign_16_begin_mask_0, end = concat_186, end_mask = key_cache_internal_tensor_assign_16_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_16_squeeze_mask_0, stride = key_cache_internal_tensor_assign_16_stride_0, update = key_states_157_cast_fp16, x = coreml_update_state_308)[name = string("key_cache_internal_tensor_assign_16_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_16_cast_fp16, input = key_cache)[name = string("coreml_update_state_310_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_310 = read_state(input = key_cache)[name = string("coreml_update_state_310")];
            tensor<int32, [4]> value_states_93_perm_0 = const()[name = string("value_states_93_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_16_stride_0 = const()[name = string("value_cache_internal_tensor_assign_16_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_16_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_16_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_16_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_16_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_16_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_16_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_93_cast_fp16 = transpose(perm = value_states_93_perm_0, x = var_5809_cast_fp16)[name = string("transpose_472")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_16_cast_fp16 = slice_update(begin = concat_185, begin_mask = value_cache_internal_tensor_assign_16_begin_mask_0, end = concat_186, end_mask = value_cache_internal_tensor_assign_16_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_16_squeeze_mask_0, stride = value_cache_internal_tensor_assign_16_stride_0, update = value_states_93_cast_fp16, x = coreml_update_state_309)[name = string("value_cache_internal_tensor_assign_16_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_16_cast_fp16, input = value_cache)[name = string("coreml_update_state_311_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_311 = read_state(input = value_cache)[name = string("coreml_update_state_311")];
            tensor<int32, [4]> var_5903_begin_0 = const()[name = string("op_5903_begin_0"), val = tensor<int32, [4]>([15, 0, 0, 0])];
            tensor<int32, [4]> var_5903_end_0 = const()[name = string("op_5903_end_0"), val = tensor<int32, [4]>([16, 2, 2048, 128])];
            tensor<bool, [4]> var_5903_end_mask_0 = const()[name = string("op_5903_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5903_cast_fp16 = slice_by_index(begin = var_5903_begin_0, end = var_5903_end_0, end_mask = var_5903_end_mask_0, x = coreml_update_state_310)[name = string("op_5903_cast_fp16")];
            tensor<int32, [2]> tile_30 = const()[name = string("tile_30"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5906_axis_0 = const()[name = string("op_5906_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5906_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5906_cast_fp16_1 = split(axis = var_5906_axis_0, split_sizes = tile_30, x = var_5903_cast_fp16)[name = string("op_5906_cast_fp16")];
            tensor<int32, [4]> var_5913_begin_0 = const()[name = string("op_5913_begin_0"), val = tensor<int32, [4]>([15, 0, 0, 0])];
            tensor<int32, [4]> var_5913_end_0 = const()[name = string("op_5913_end_0"), val = tensor<int32, [4]>([16, 2, 2048, 128])];
            tensor<bool, [4]> var_5913_end_mask_0 = const()[name = string("op_5913_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5913_cast_fp16 = slice_by_index(begin = var_5913_begin_0, end = var_5913_end_0, end_mask = var_5913_end_mask_0, x = coreml_update_state_311)[name = string("op_5913_cast_fp16")];
            tensor<int32, [2]> tile_31 = const()[name = string("tile_31"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5916_axis_0 = const()[name = string("op_5916_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5916_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5916_cast_fp16_1 = split(axis = var_5916_axis_0, split_sizes = tile_31, x = var_5913_cast_fp16)[name = string("op_5916_cast_fp16")];
            tensor<int32, [2]> var_5919_split_sizes_0 = const()[name = string("op_5919_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_5919_axis_0 = const()[name = string("op_5919_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_5919_0, tensor<fp16, [1, 8, 128, 64]> var_5919_1 = split(axis = var_5919_axis_0, split_sizes = var_5919_split_sizes_0, x = query_states_93_cast_fp16)[name = string("op_5919")];
            bool attn_weights_241_transpose_x_0 = const()[name = string("attn_weights_241_transpose_x_0"), val = bool(false)];
            bool attn_weights_241_transpose_y_0 = const()[name = string("attn_weights_241_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_241_cast_fp16 = matmul(transpose_x = attn_weights_241_transpose_x_0, transpose_y = attn_weights_241_transpose_y_0, x = var_5906_cast_fp16_0, y = var_5919_0)[name = string("attn_weights_241_cast_fp16")];
            fp16 var_5922_to_fp16 = const()[name = string("op_5922_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_243_cast_fp16 = mul(x = attn_weights_241_cast_fp16, y = var_5922_to_fp16)[name = string("attn_weights_243_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_245_cast_fp16 = add(x = attn_weights_243_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_245_cast_fp16")];
            int32 var_5926 = const()[name = string("op_5926"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_247_cast_fp16 = softmax(axis = var_5926, x = attn_weights_245_cast_fp16)[name = string("attn_weights_247_cast_fp16")];
            bool var_5932_transpose_x_1 = const()[name = string("op_5932_transpose_x_1"), val = bool(true)];
            bool var_5932_transpose_y_1 = const()[name = string("op_5932_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_5932_cast_fp16 = matmul(transpose_x = var_5932_transpose_x_1, transpose_y = var_5932_transpose_y_1, x = attn_weights_247_cast_fp16, y = var_5916_cast_fp16_0)[name = string("op_5932_cast_fp16")];
            bool attn_weights_249_transpose_x_0 = const()[name = string("attn_weights_249_transpose_x_0"), val = bool(false)];
            bool attn_weights_249_transpose_y_0 = const()[name = string("attn_weights_249_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_249_cast_fp16 = matmul(transpose_x = attn_weights_249_transpose_x_0, transpose_y = attn_weights_249_transpose_y_0, x = var_5906_cast_fp16_1, y = var_5919_1)[name = string("attn_weights_249_cast_fp16")];
            fp16 var_5934_to_fp16 = const()[name = string("op_5934_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_251_cast_fp16 = mul(x = attn_weights_249_cast_fp16, y = var_5934_to_fp16)[name = string("attn_weights_251_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_253_cast_fp16 = add(x = attn_weights_251_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_253_cast_fp16")];
            int32 var_5938 = const()[name = string("op_5938"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_255_cast_fp16 = softmax(axis = var_5938, x = attn_weights_253_cast_fp16)[name = string("attn_weights_255_cast_fp16")];
            bool attn_output_121_transpose_x_1 = const()[name = string("attn_output_121_transpose_x_1"), val = bool(true)];
            bool attn_output_121_transpose_y_1 = const()[name = string("attn_output_121_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_121_cast_fp16 = matmul(transpose_x = attn_output_121_transpose_x_1, transpose_y = attn_output_121_transpose_y_1, x = attn_weights_255_cast_fp16, y = var_5916_cast_fp16_1)[name = string("attn_output_121_cast_fp16")];
            int32 var_5946 = const()[name = string("op_5946"), val = int32(1)];
            bool attn_output_123_interleave_0 = const()[name = string("attn_output_123_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_123_cast_fp16 = concat(axis = var_5946, interleave = attn_output_123_interleave_0, values = (var_5932_cast_fp16, attn_output_121_cast_fp16))[name = string("attn_output_123_cast_fp16")];
            tensor<int32, [4]> var_5950_perm_0 = const()[name = string("op_5950_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_191x = const()[name = string("concat_191x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_5950_cast_fp16 = transpose(perm = var_5950_perm_0, x = attn_output_123_cast_fp16)[name = string("transpose_471")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_127_cast_fp16 = reshape(shape = concat_191x, x = var_5950_cast_fp16)[name = string("attn_output_127_cast_fp16")];
            tensor<int32, [2]> hidden_states_153_strides_0 = const()[name = string("hidden_states_153_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_153_pad_type_0 = const()[name = string("hidden_states_153_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_153_pad_0 = const()[name = string("hidden_states_153_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_153_dilations_0 = const()[name = string("hidden_states_153_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_153_groups_0 = const()[name = string("hidden_states_153_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_153_cast_fp16 = conv(dilations = hidden_states_153_dilations_0, groups = hidden_states_153_groups_0, pad = hidden_states_153_pad_0, pad_type = hidden_states_153_pad_type_0, strides = hidden_states_153_strides_0, weight = layers_15_self_attn_o_proj_weight_cast_fp16, x = attn_output_127_cast_fp16)[name = string("hidden_states_153_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_155_cast_fp16 = add(x = hidden_states_149_cast_fp16, y = hidden_states_153_cast_fp16)[name = string("hidden_states_155_cast_fp16")];
            fp16 const_160_promoted_to_fp16 = const()[name = string("const_160_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_5983_cast_fp16 = mul(x = hidden_states_155_cast_fp16, y = const_160_promoted_to_fp16)[name = string("op_5983_cast_fp16")];
            int32 var_5981 = const()[name = string("op_5981"), val = int32(1)];
            bool doubled_125_interleave_0 = const()[name = string("doubled_125_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_125_cast_fp16 = concat(axis = var_5981, interleave = doubled_125_interleave_0, values = (hidden_states_155_cast_fp16, var_5983_cast_fp16))[name = string("doubled_125_cast_fp16")];
            tensor<int32, [1]> out_63_axes_0 = const()[name = string("out_63_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_63_gamma_0_to_fp16 = const()[name = string("out_63_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433266560)))];
            fp16 var_5993_to_fp16 = const()[name = string("op_5993_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_63_cast_fp16 = layer_norm(axes = out_63_axes_0, epsilon = var_5993_to_fp16, gamma = out_63_gamma_0_to_fp16, x = doubled_125_cast_fp16)[name = string("out_63_cast_fp16")];
            tensor<int32, [2]> var_6004_split_sizes_0 = const()[name = string("op_6004_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6004_axis_0 = const()[name = string("op_6004_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_6004_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_6004_cast_fp16_1 = split(axis = var_6004_axis_0, split_sizes = var_6004_split_sizes_0, x = out_63_cast_fp16)[name = string("op_6004_cast_fp16")];
            tensor<int32, [2]> input_31_strides_0 = const()[name = string("input_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_31_pad_type_0 = const()[name = string("input_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_31_pad_0 = const()[name = string("input_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_31_dilations_0 = const()[name = string("input_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_31_groups_0 = const()[name = string("input_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_31_cast_fp16 = conv(dilations = input_31_dilations_0, groups = input_31_groups_0, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = input_31_strides_0, weight = layers_15_mlp_gate_proj_weight_cast_fp16, x = var_6004_cast_fp16_0)[name = string("input_31_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_6021_cast_fp16 = silu(x = input_31_cast_fp16)[name = string("op_6021_cast_fp16")];
            tensor<int32, [2]> var_6027_strides_0 = const()[name = string("op_6027_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6027_pad_type_0 = const()[name = string("op_6027_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6027_pad_0 = const()[name = string("op_6027_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6027_dilations_0 = const()[name = string("op_6027_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6027_groups_0 = const()[name = string("op_6027_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_6027_cast_fp16 = conv(dilations = var_6027_dilations_0, groups = var_6027_groups_0, pad = var_6027_pad_0, pad_type = var_6027_pad_type_0, strides = var_6027_strides_0, weight = layers_15_mlp_up_proj_weight_cast_fp16, x = var_6004_cast_fp16_0)[name = string("op_6027_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_159_cast_fp16 = mul(x = var_6021_cast_fp16, y = var_6027_cast_fp16)[name = string("x_159_cast_fp16")];
            tensor<int32, [2]> hidden_states_157_strides_0 = const()[name = string("hidden_states_157_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_157_pad_type_0 = const()[name = string("hidden_states_157_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_157_pad_0 = const()[name = string("hidden_states_157_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_157_dilations_0 = const()[name = string("hidden_states_157_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_157_groups_0 = const()[name = string("hidden_states_157_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_157_cast_fp16 = conv(dilations = hidden_states_157_dilations_0, groups = hidden_states_157_groups_0, pad = hidden_states_157_pad_0, pad_type = hidden_states_157_pad_type_0, strides = hidden_states_157_strides_0, weight = layers_15_mlp_down_proj_weight_cast_fp16, x = x_159_cast_fp16)[name = string("hidden_states_157_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_159_cast_fp16 = add(x = hidden_states_155_cast_fp16, y = hidden_states_157_cast_fp16)[name = string("hidden_states_159_cast_fp16")];
            fp16 const_162_promoted_to_fp16 = const()[name = string("const_162_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_6045_cast_fp16 = mul(x = hidden_states_159_cast_fp16, y = const_162_promoted_to_fp16)[name = string("op_6045_cast_fp16")];
            int32 var_6043 = const()[name = string("op_6043"), val = int32(1)];
            bool doubled_129_interleave_0 = const()[name = string("doubled_129_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_129_cast_fp16 = concat(axis = var_6043, interleave = doubled_129_interleave_0, values = (hidden_states_159_cast_fp16, var_6045_cast_fp16))[name = string("doubled_129_cast_fp16")];
            tensor<int32, [1]> out_65_axes_0 = const()[name = string("out_65_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_65_gamma_0_to_fp16 = const()[name = string("out_65_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433274816)))];
            fp16 var_6055_to_fp16 = const()[name = string("op_6055_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_65_cast_fp16 = layer_norm(axes = out_65_axes_0, epsilon = var_6055_to_fp16, gamma = out_65_gamma_0_to_fp16, x = doubled_129_cast_fp16)[name = string("out_65_cast_fp16")];
            tensor<int32, [2]> var_6066_split_sizes_0 = const()[name = string("op_6066_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6066_axis_0 = const()[name = string("op_6066_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_6066_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_6066_cast_fp16_1 = split(axis = var_6066_axis_0, split_sizes = var_6066_split_sizes_0, x = out_65_cast_fp16)[name = string("op_6066_cast_fp16")];
            tensor<int32, [2]> query_states_97_strides_0 = const()[name = string("query_states_97_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_97_pad_type_0 = const()[name = string("query_states_97_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_97_pad_0 = const()[name = string("query_states_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_97_dilations_0 = const()[name = string("query_states_97_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_97_groups_0 = const()[name = string("query_states_97_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_97_cast_fp16 = conv(dilations = query_states_97_dilations_0, groups = query_states_97_groups_0, pad = query_states_97_pad_0, pad_type = query_states_97_pad_type_0, strides = query_states_97_strides_0, weight = layers_16_self_attn_q_proj_weight_cast_fp16, x = var_6066_cast_fp16_0)[name = string("query_states_97_cast_fp16")];
            tensor<int32, [2]> key_states_161_strides_0 = const()[name = string("key_states_161_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_161_pad_type_0 = const()[name = string("key_states_161_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_161_pad_0 = const()[name = string("key_states_161_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_161_dilations_0 = const()[name = string("key_states_161_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_161_groups_0 = const()[name = string("key_states_161_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_161_cast_fp16 = conv(dilations = key_states_161_dilations_0, groups = key_states_161_groups_0, pad = key_states_161_pad_0, pad_type = key_states_161_pad_type_0, strides = key_states_161_strides_0, weight = layers_16_self_attn_k_proj_weight_cast_fp16, x = var_6066_cast_fp16_0)[name = string("key_states_161_cast_fp16")];
            tensor<int32, [2]> value_states_97_strides_0 = const()[name = string("value_states_97_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_97_pad_type_0 = const()[name = string("value_states_97_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_97_pad_0 = const()[name = string("value_states_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_97_dilations_0 = const()[name = string("value_states_97_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_97_groups_0 = const()[name = string("value_states_97_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_97_cast_fp16 = conv(dilations = value_states_97_dilations_0, groups = value_states_97_groups_0, pad = value_states_97_pad_0, pad_type = value_states_97_pad_type_0, strides = value_states_97_strides_0, weight = layers_16_self_attn_v_proj_weight_cast_fp16, x = var_6066_cast_fp16_0)[name = string("value_states_97_cast_fp16")];
            tensor<int32, [4]> concat_192x = const()[name = string("concat_192x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_161_cast_fp16 = reshape(shape = concat_192x, x = query_states_97_cast_fp16)[name = string("x_161_cast_fp16")];
            tensor<int32, [4]> concat_193x = const()[name = string("concat_193x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_6123_cast_fp16 = reshape(shape = concat_193x, x = key_states_161_cast_fp16)[name = string("op_6123_cast_fp16")];
            tensor<int32, [4]> concat_194x = const()[name = string("concat_194x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_6130_cast_fp16 = reshape(shape = concat_194x, x = value_states_97_cast_fp16)[name = string("op_6130_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_6134_cast_fp16 = mul(x = x_161_cast_fp16, y = var_859_cast_fp16)[name = string("op_6134_cast_fp16")];
            tensor<int32, [2]> var_6135_split_sizes_0 = const()[name = string("op_6135_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6135_axis_0 = const()[name = string("op_6135_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_6135_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_6135_cast_fp16_1 = split(axis = var_6135_axis_0, split_sizes = var_6135_split_sizes_0, x = x_161_cast_fp16)[name = string("op_6135_cast_fp16")];
            fp16 const_164_promoted_to_fp16 = const()[name = string("const_164_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_6137_cast_fp16 = mul(x = var_6135_cast_fp16_1, y = const_164_promoted_to_fp16)[name = string("op_6137_cast_fp16")];
            int32 var_6139 = const()[name = string("op_6139"), val = int32(-2)];
            bool var_6140_interleave_0 = const()[name = string("op_6140_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_6140_cast_fp16 = concat(axis = var_6139, interleave = var_6140_interleave_0, values = (var_6137_cast_fp16, var_6135_cast_fp16_0))[name = string("op_6140_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_6141_cast_fp16 = mul(x = var_6140_cast_fp16, y = var_866_cast_fp16)[name = string("op_6141_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_99_cast_fp16 = add(x = var_6134_cast_fp16, y = var_6141_cast_fp16)[name = string("query_states_99_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_6147_cast_fp16 = mul(x = var_6123_cast_fp16, y = var_859_cast_fp16)[name = string("op_6147_cast_fp16")];
            tensor<int32, [2]> var_6148_split_sizes_0 = const()[name = string("op_6148_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6148_axis_0 = const()[name = string("op_6148_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_6148_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_6148_cast_fp16_1 = split(axis = var_6148_axis_0, split_sizes = var_6148_split_sizes_0, x = var_6123_cast_fp16)[name = string("op_6148_cast_fp16")];
            fp16 const_165_promoted_to_fp16 = const()[name = string("const_165_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_6150_cast_fp16 = mul(x = var_6148_cast_fp16_1, y = const_165_promoted_to_fp16)[name = string("op_6150_cast_fp16")];
            int32 var_6152 = const()[name = string("op_6152"), val = int32(-2)];
            bool var_6153_interleave_0 = const()[name = string("op_6153_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_6153_cast_fp16 = concat(axis = var_6152, interleave = var_6153_interleave_0, values = (var_6150_cast_fp16, var_6148_cast_fp16_0))[name = string("op_6153_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_6154_cast_fp16 = mul(x = var_6153_cast_fp16, y = var_866_cast_fp16)[name = string("op_6154_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_165_cast_fp16 = add(x = var_6147_cast_fp16, y = var_6154_cast_fp16)[name = string("key_states_165_cast_fp16")];
            tensor<int32, [1]> expand_dims_192 = const()[name = string("expand_dims_192"), val = tensor<int32, [1]>([16])];
            tensor<int32, [1]> expand_dims_193 = const()[name = string("expand_dims_193"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_195 = const()[name = string("expand_dims_195"), val = tensor<int32, [1]>([0])];
            int32 concat_197_axis_0 = const()[name = string("concat_197_axis_0"), val = int32(0)];
            bool concat_197_interleave_0 = const()[name = string("concat_197_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_197 = concat(axis = concat_197_axis_0, interleave = concat_197_interleave_0, values = (expand_dims_192, expand_dims_193, position_id, expand_dims_195))[name = string("concat_197")];
            tensor<int32, [1]> expand_dims_196 = const()[name = string("expand_dims_196"), val = tensor<int32, [1]>([17])];
            tensor<int32, [1]> concat_198_values1_0 = const()[name = string("concat_198_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_198_values3_0 = const()[name = string("concat_198_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_198_axis_0 = const()[name = string("concat_198_axis_0"), val = int32(0)];
            bool concat_198_interleave_0 = const()[name = string("concat_198_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_198 = concat(axis = concat_198_axis_0, interleave = concat_198_interleave_0, values = (expand_dims_196, concat_198_values1_0, cache_position_end, concat_198_values3_0))[name = string("concat_198")];
            tensor<int32, [4]> key_states_167_perm_0 = const()[name = string("key_states_167_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_17_stride_0 = const()[name = string("key_cache_internal_tensor_assign_17_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_17_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_17_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_17_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_17_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_17_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_17_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_167_cast_fp16 = transpose(perm = key_states_167_perm_0, x = key_states_165_cast_fp16)[name = string("transpose_470")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_17_cast_fp16 = slice_update(begin = concat_197, begin_mask = key_cache_internal_tensor_assign_17_begin_mask_0, end = concat_198, end_mask = key_cache_internal_tensor_assign_17_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_17_squeeze_mask_0, stride = key_cache_internal_tensor_assign_17_stride_0, update = key_states_167_cast_fp16, x = coreml_update_state_310)[name = string("key_cache_internal_tensor_assign_17_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_17_cast_fp16, input = key_cache)[name = string("coreml_update_state_312_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_312 = read_state(input = key_cache)[name = string("coreml_update_state_312")];
            tensor<int32, [4]> value_states_99_perm_0 = const()[name = string("value_states_99_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_17_stride_0 = const()[name = string("value_cache_internal_tensor_assign_17_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_17_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_17_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_17_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_17_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_17_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_17_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_99_cast_fp16 = transpose(perm = value_states_99_perm_0, x = var_6130_cast_fp16)[name = string("transpose_469")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_17_cast_fp16 = slice_update(begin = concat_197, begin_mask = value_cache_internal_tensor_assign_17_begin_mask_0, end = concat_198, end_mask = value_cache_internal_tensor_assign_17_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_17_squeeze_mask_0, stride = value_cache_internal_tensor_assign_17_stride_0, update = value_states_99_cast_fp16, x = coreml_update_state_311)[name = string("value_cache_internal_tensor_assign_17_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_17_cast_fp16, input = value_cache)[name = string("coreml_update_state_313_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_313 = read_state(input = value_cache)[name = string("coreml_update_state_313")];
            tensor<int32, [4]> var_6224_begin_0 = const()[name = string("op_6224_begin_0"), val = tensor<int32, [4]>([16, 0, 0, 0])];
            tensor<int32, [4]> var_6224_end_0 = const()[name = string("op_6224_end_0"), val = tensor<int32, [4]>([17, 2, 2048, 128])];
            tensor<bool, [4]> var_6224_end_mask_0 = const()[name = string("op_6224_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6224_cast_fp16 = slice_by_index(begin = var_6224_begin_0, end = var_6224_end_0, end_mask = var_6224_end_mask_0, x = coreml_update_state_312)[name = string("op_6224_cast_fp16")];
            tensor<int32, [2]> tile_32 = const()[name = string("tile_32"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6227_axis_0 = const()[name = string("op_6227_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6227_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6227_cast_fp16_1 = split(axis = var_6227_axis_0, split_sizes = tile_32, x = var_6224_cast_fp16)[name = string("op_6227_cast_fp16")];
            tensor<int32, [4]> var_6234_begin_0 = const()[name = string("op_6234_begin_0"), val = tensor<int32, [4]>([16, 0, 0, 0])];
            tensor<int32, [4]> var_6234_end_0 = const()[name = string("op_6234_end_0"), val = tensor<int32, [4]>([17, 2, 2048, 128])];
            tensor<bool, [4]> var_6234_end_mask_0 = const()[name = string("op_6234_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6234_cast_fp16 = slice_by_index(begin = var_6234_begin_0, end = var_6234_end_0, end_mask = var_6234_end_mask_0, x = coreml_update_state_313)[name = string("op_6234_cast_fp16")];
            tensor<int32, [2]> tile_33 = const()[name = string("tile_33"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6237_axis_0 = const()[name = string("op_6237_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6237_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6237_cast_fp16_1 = split(axis = var_6237_axis_0, split_sizes = tile_33, x = var_6234_cast_fp16)[name = string("op_6237_cast_fp16")];
            tensor<int32, [2]> var_6240_split_sizes_0 = const()[name = string("op_6240_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_6240_axis_0 = const()[name = string("op_6240_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_6240_0, tensor<fp16, [1, 8, 128, 64]> var_6240_1 = split(axis = var_6240_axis_0, split_sizes = var_6240_split_sizes_0, x = query_states_99_cast_fp16)[name = string("op_6240")];
            bool attn_weights_257_transpose_x_0 = const()[name = string("attn_weights_257_transpose_x_0"), val = bool(false)];
            bool attn_weights_257_transpose_y_0 = const()[name = string("attn_weights_257_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_257_cast_fp16 = matmul(transpose_x = attn_weights_257_transpose_x_0, transpose_y = attn_weights_257_transpose_y_0, x = var_6227_cast_fp16_0, y = var_6240_0)[name = string("attn_weights_257_cast_fp16")];
            fp16 var_6243_to_fp16 = const()[name = string("op_6243_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_259_cast_fp16 = mul(x = attn_weights_257_cast_fp16, y = var_6243_to_fp16)[name = string("attn_weights_259_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_261_cast_fp16 = add(x = attn_weights_259_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_261_cast_fp16")];
            int32 var_6247 = const()[name = string("op_6247"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_263_cast_fp16 = softmax(axis = var_6247, x = attn_weights_261_cast_fp16)[name = string("attn_weights_263_cast_fp16")];
            bool var_6253_transpose_x_1 = const()[name = string("op_6253_transpose_x_1"), val = bool(true)];
            bool var_6253_transpose_y_1 = const()[name = string("op_6253_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_6253_cast_fp16 = matmul(transpose_x = var_6253_transpose_x_1, transpose_y = var_6253_transpose_y_1, x = attn_weights_263_cast_fp16, y = var_6237_cast_fp16_0)[name = string("op_6253_cast_fp16")];
            bool attn_weights_265_transpose_x_0 = const()[name = string("attn_weights_265_transpose_x_0"), val = bool(false)];
            bool attn_weights_265_transpose_y_0 = const()[name = string("attn_weights_265_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_265_cast_fp16 = matmul(transpose_x = attn_weights_265_transpose_x_0, transpose_y = attn_weights_265_transpose_y_0, x = var_6227_cast_fp16_1, y = var_6240_1)[name = string("attn_weights_265_cast_fp16")];
            fp16 var_6255_to_fp16 = const()[name = string("op_6255_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_267_cast_fp16 = mul(x = attn_weights_265_cast_fp16, y = var_6255_to_fp16)[name = string("attn_weights_267_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_269_cast_fp16 = add(x = attn_weights_267_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_269_cast_fp16")];
            int32 var_6259 = const()[name = string("op_6259"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_271_cast_fp16 = softmax(axis = var_6259, x = attn_weights_269_cast_fp16)[name = string("attn_weights_271_cast_fp16")];
            bool attn_output_129_transpose_x_1 = const()[name = string("attn_output_129_transpose_x_1"), val = bool(true)];
            bool attn_output_129_transpose_y_1 = const()[name = string("attn_output_129_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_129_cast_fp16 = matmul(transpose_x = attn_output_129_transpose_x_1, transpose_y = attn_output_129_transpose_y_1, x = attn_weights_271_cast_fp16, y = var_6237_cast_fp16_1)[name = string("attn_output_129_cast_fp16")];
            int32 var_6267 = const()[name = string("op_6267"), val = int32(1)];
            bool attn_output_131_interleave_0 = const()[name = string("attn_output_131_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_131_cast_fp16 = concat(axis = var_6267, interleave = attn_output_131_interleave_0, values = (var_6253_cast_fp16, attn_output_129_cast_fp16))[name = string("attn_output_131_cast_fp16")];
            tensor<int32, [4]> var_6271_perm_0 = const()[name = string("op_6271_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_203x = const()[name = string("concat_203x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_6271_cast_fp16 = transpose(perm = var_6271_perm_0, x = attn_output_131_cast_fp16)[name = string("transpose_468")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_135_cast_fp16 = reshape(shape = concat_203x, x = var_6271_cast_fp16)[name = string("attn_output_135_cast_fp16")];
            tensor<int32, [2]> hidden_states_163_strides_0 = const()[name = string("hidden_states_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_163_pad_type_0 = const()[name = string("hidden_states_163_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_163_pad_0 = const()[name = string("hidden_states_163_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_163_dilations_0 = const()[name = string("hidden_states_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_163_groups_0 = const()[name = string("hidden_states_163_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_163_cast_fp16 = conv(dilations = hidden_states_163_dilations_0, groups = hidden_states_163_groups_0, pad = hidden_states_163_pad_0, pad_type = hidden_states_163_pad_type_0, strides = hidden_states_163_strides_0, weight = layers_16_self_attn_o_proj_weight_cast_fp16, x = attn_output_135_cast_fp16)[name = string("hidden_states_163_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_165_cast_fp16 = add(x = hidden_states_159_cast_fp16, y = hidden_states_163_cast_fp16)[name = string("hidden_states_165_cast_fp16")];
            fp16 const_170_promoted_to_fp16 = const()[name = string("const_170_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_6304_cast_fp16 = mul(x = hidden_states_165_cast_fp16, y = const_170_promoted_to_fp16)[name = string("op_6304_cast_fp16")];
            int32 var_6302 = const()[name = string("op_6302"), val = int32(1)];
            bool doubled_133_interleave_0 = const()[name = string("doubled_133_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_133_cast_fp16 = concat(axis = var_6302, interleave = doubled_133_interleave_0, values = (hidden_states_165_cast_fp16, var_6304_cast_fp16))[name = string("doubled_133_cast_fp16")];
            tensor<int32, [1]> out_67_axes_0 = const()[name = string("out_67_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_67_gamma_0_to_fp16 = const()[name = string("out_67_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433283072)))];
            fp16 var_6314_to_fp16 = const()[name = string("op_6314_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_67_cast_fp16 = layer_norm(axes = out_67_axes_0, epsilon = var_6314_to_fp16, gamma = out_67_gamma_0_to_fp16, x = doubled_133_cast_fp16)[name = string("out_67_cast_fp16")];
            tensor<int32, [2]> var_6325_split_sizes_0 = const()[name = string("op_6325_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6325_axis_0 = const()[name = string("op_6325_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_6325_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_6325_cast_fp16_1 = split(axis = var_6325_axis_0, split_sizes = var_6325_split_sizes_0, x = out_67_cast_fp16)[name = string("op_6325_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_16_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_16_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433291328)))];
            tensor<int32, [2]> input_33_strides_0 = const()[name = string("input_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_33_pad_type_0 = const()[name = string("input_33_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_33_pad_0 = const()[name = string("input_33_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_33_dilations_0 = const()[name = string("input_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_33_groups_0 = const()[name = string("input_33_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_33_cast_fp16 = conv(dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = layers_16_mlp_gate_proj_weight_to_fp16, x = var_6325_cast_fp16_0)[name = string("input_33_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_6342_cast_fp16 = silu(x = input_33_cast_fp16)[name = string("op_6342_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_16_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_16_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1458457216)))];
            tensor<int32, [2]> var_6348_strides_0 = const()[name = string("op_6348_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6348_pad_type_0 = const()[name = string("op_6348_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6348_pad_0 = const()[name = string("op_6348_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6348_dilations_0 = const()[name = string("op_6348_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6348_groups_0 = const()[name = string("op_6348_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_6348_cast_fp16 = conv(dilations = var_6348_dilations_0, groups = var_6348_groups_0, pad = var_6348_pad_0, pad_type = var_6348_pad_type_0, strides = var_6348_strides_0, weight = layers_16_mlp_up_proj_weight_to_fp16, x = var_6325_cast_fp16_0)[name = string("op_6348_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_169_cast_fp16 = mul(x = var_6342_cast_fp16, y = var_6348_cast_fp16)[name = string("x_169_cast_fp16")];
            tensor<int32, [2]> hidden_states_167_strides_0 = const()[name = string("hidden_states_167_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_167_pad_type_0 = const()[name = string("hidden_states_167_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_167_pad_0 = const()[name = string("hidden_states_167_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_167_dilations_0 = const()[name = string("hidden_states_167_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_167_groups_0 = const()[name = string("hidden_states_167_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_167_cast_fp16 = conv(dilations = hidden_states_167_dilations_0, groups = hidden_states_167_groups_0, pad = hidden_states_167_pad_0, pad_type = hidden_states_167_pad_type_0, strides = hidden_states_167_strides_0, weight = layers_16_mlp_down_proj_weight_cast_fp16, x = x_169_cast_fp16)[name = string("hidden_states_167_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_169_cast_fp16 = add(x = hidden_states_165_cast_fp16, y = hidden_states_167_cast_fp16)[name = string("hidden_states_169_cast_fp16")];
            fp16 const_172_promoted_to_fp16 = const()[name = string("const_172_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_6366_cast_fp16 = mul(x = hidden_states_169_cast_fp16, y = const_172_promoted_to_fp16)[name = string("op_6366_cast_fp16")];
            int32 var_6364 = const()[name = string("op_6364"), val = int32(1)];
            bool doubled_137_interleave_0 = const()[name = string("doubled_137_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_137_cast_fp16 = concat(axis = var_6364, interleave = doubled_137_interleave_0, values = (hidden_states_169_cast_fp16, var_6366_cast_fp16))[name = string("doubled_137_cast_fp16")];
            tensor<int32, [1]> out_69_axes_0 = const()[name = string("out_69_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_69_gamma_0_to_fp16 = const()[name = string("out_69_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483623104)))];
            fp16 var_6376_to_fp16 = const()[name = string("op_6376_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_69_cast_fp16 = layer_norm(axes = out_69_axes_0, epsilon = var_6376_to_fp16, gamma = out_69_gamma_0_to_fp16, x = doubled_137_cast_fp16)[name = string("out_69_cast_fp16")];
            tensor<int32, [2]> var_6387_split_sizes_0 = const()[name = string("op_6387_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6387_axis_0 = const()[name = string("op_6387_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_6387_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_6387_cast_fp16_1 = split(axis = var_6387_axis_0, split_sizes = var_6387_split_sizes_0, x = out_69_cast_fp16)[name = string("op_6387_cast_fp16")];
            tensor<int32, [2]> query_states_103_strides_0 = const()[name = string("query_states_103_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_103_pad_type_0 = const()[name = string("query_states_103_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_103_pad_0 = const()[name = string("query_states_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_103_dilations_0 = const()[name = string("query_states_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_103_groups_0 = const()[name = string("query_states_103_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_103_cast_fp16 = conv(dilations = query_states_103_dilations_0, groups = query_states_103_groups_0, pad = query_states_103_pad_0, pad_type = query_states_103_pad_type_0, strides = query_states_103_strides_0, weight = layers_17_self_attn_q_proj_weight_cast_fp16, x = var_6387_cast_fp16_0)[name = string("query_states_103_cast_fp16")];
            tensor<int32, [2]> key_states_171_strides_0 = const()[name = string("key_states_171_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_171_pad_type_0 = const()[name = string("key_states_171_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_171_pad_0 = const()[name = string("key_states_171_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_171_dilations_0 = const()[name = string("key_states_171_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_171_groups_0 = const()[name = string("key_states_171_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_171_cast_fp16 = conv(dilations = key_states_171_dilations_0, groups = key_states_171_groups_0, pad = key_states_171_pad_0, pad_type = key_states_171_pad_type_0, strides = key_states_171_strides_0, weight = layers_17_self_attn_k_proj_weight_cast_fp16, x = var_6387_cast_fp16_0)[name = string("key_states_171_cast_fp16")];
            tensor<int32, [2]> value_states_103_strides_0 = const()[name = string("value_states_103_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_103_pad_type_0 = const()[name = string("value_states_103_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_103_pad_0 = const()[name = string("value_states_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_103_dilations_0 = const()[name = string("value_states_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_103_groups_0 = const()[name = string("value_states_103_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_103_cast_fp16 = conv(dilations = value_states_103_dilations_0, groups = value_states_103_groups_0, pad = value_states_103_pad_0, pad_type = value_states_103_pad_type_0, strides = value_states_103_strides_0, weight = layers_17_self_attn_v_proj_weight_cast_fp16, x = var_6387_cast_fp16_0)[name = string("value_states_103_cast_fp16")];
            tensor<int32, [4]> concat_204x = const()[name = string("concat_204x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_171_cast_fp16 = reshape(shape = concat_204x, x = query_states_103_cast_fp16)[name = string("x_171_cast_fp16")];
            tensor<int32, [4]> concat_205x = const()[name = string("concat_205x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_6444_cast_fp16 = reshape(shape = concat_205x, x = key_states_171_cast_fp16)[name = string("op_6444_cast_fp16")];
            tensor<int32, [4]> concat_206x = const()[name = string("concat_206x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_6451_cast_fp16 = reshape(shape = concat_206x, x = value_states_103_cast_fp16)[name = string("op_6451_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_6455_cast_fp16 = mul(x = x_171_cast_fp16, y = var_859_cast_fp16)[name = string("op_6455_cast_fp16")];
            tensor<int32, [2]> var_6456_split_sizes_0 = const()[name = string("op_6456_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6456_axis_0 = const()[name = string("op_6456_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_6456_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_6456_cast_fp16_1 = split(axis = var_6456_axis_0, split_sizes = var_6456_split_sizes_0, x = x_171_cast_fp16)[name = string("op_6456_cast_fp16")];
            fp16 const_174_promoted_to_fp16 = const()[name = string("const_174_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_6458_cast_fp16 = mul(x = var_6456_cast_fp16_1, y = const_174_promoted_to_fp16)[name = string("op_6458_cast_fp16")];
            int32 var_6460 = const()[name = string("op_6460"), val = int32(-2)];
            bool var_6461_interleave_0 = const()[name = string("op_6461_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_6461_cast_fp16 = concat(axis = var_6460, interleave = var_6461_interleave_0, values = (var_6458_cast_fp16, var_6456_cast_fp16_0))[name = string("op_6461_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_6462_cast_fp16 = mul(x = var_6461_cast_fp16, y = var_866_cast_fp16)[name = string("op_6462_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_105_cast_fp16 = add(x = var_6455_cast_fp16, y = var_6462_cast_fp16)[name = string("query_states_105_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_6468_cast_fp16 = mul(x = var_6444_cast_fp16, y = var_859_cast_fp16)[name = string("op_6468_cast_fp16")];
            tensor<int32, [2]> var_6469_split_sizes_0 = const()[name = string("op_6469_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6469_axis_0 = const()[name = string("op_6469_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_6469_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_6469_cast_fp16_1 = split(axis = var_6469_axis_0, split_sizes = var_6469_split_sizes_0, x = var_6444_cast_fp16)[name = string("op_6469_cast_fp16")];
            fp16 const_175_promoted_to_fp16 = const()[name = string("const_175_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_6471_cast_fp16 = mul(x = var_6469_cast_fp16_1, y = const_175_promoted_to_fp16)[name = string("op_6471_cast_fp16")];
            int32 var_6473 = const()[name = string("op_6473"), val = int32(-2)];
            bool var_6474_interleave_0 = const()[name = string("op_6474_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_6474_cast_fp16 = concat(axis = var_6473, interleave = var_6474_interleave_0, values = (var_6471_cast_fp16, var_6469_cast_fp16_0))[name = string("op_6474_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_6475_cast_fp16 = mul(x = var_6474_cast_fp16, y = var_866_cast_fp16)[name = string("op_6475_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_175_cast_fp16 = add(x = var_6468_cast_fp16, y = var_6475_cast_fp16)[name = string("key_states_175_cast_fp16")];
            tensor<int32, [1]> expand_dims_204 = const()[name = string("expand_dims_204"), val = tensor<int32, [1]>([17])];
            tensor<int32, [1]> expand_dims_205 = const()[name = string("expand_dims_205"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_207 = const()[name = string("expand_dims_207"), val = tensor<int32, [1]>([0])];
            int32 concat_209_axis_0 = const()[name = string("concat_209_axis_0"), val = int32(0)];
            bool concat_209_interleave_0 = const()[name = string("concat_209_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_209 = concat(axis = concat_209_axis_0, interleave = concat_209_interleave_0, values = (expand_dims_204, expand_dims_205, position_id, expand_dims_207))[name = string("concat_209")];
            tensor<int32, [1]> expand_dims_208 = const()[name = string("expand_dims_208"), val = tensor<int32, [1]>([18])];
            tensor<int32, [1]> concat_210_values1_0 = const()[name = string("concat_210_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_210_values3_0 = const()[name = string("concat_210_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_210_axis_0 = const()[name = string("concat_210_axis_0"), val = int32(0)];
            bool concat_210_interleave_0 = const()[name = string("concat_210_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_210 = concat(axis = concat_210_axis_0, interleave = concat_210_interleave_0, values = (expand_dims_208, concat_210_values1_0, cache_position_end, concat_210_values3_0))[name = string("concat_210")];
            tensor<int32, [4]> key_states_177_perm_0 = const()[name = string("key_states_177_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_18_stride_0 = const()[name = string("key_cache_internal_tensor_assign_18_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_18_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_18_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_18_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_18_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_18_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_18_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_177_cast_fp16 = transpose(perm = key_states_177_perm_0, x = key_states_175_cast_fp16)[name = string("transpose_467")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_18_cast_fp16 = slice_update(begin = concat_209, begin_mask = key_cache_internal_tensor_assign_18_begin_mask_0, end = concat_210, end_mask = key_cache_internal_tensor_assign_18_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_18_squeeze_mask_0, stride = key_cache_internal_tensor_assign_18_stride_0, update = key_states_177_cast_fp16, x = coreml_update_state_312)[name = string("key_cache_internal_tensor_assign_18_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_18_cast_fp16, input = key_cache)[name = string("coreml_update_state_314_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_314 = read_state(input = key_cache)[name = string("coreml_update_state_314")];
            tensor<int32, [4]> value_states_105_perm_0 = const()[name = string("value_states_105_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_18_stride_0 = const()[name = string("value_cache_internal_tensor_assign_18_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_18_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_18_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_18_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_18_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_18_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_18_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_105_cast_fp16 = transpose(perm = value_states_105_perm_0, x = var_6451_cast_fp16)[name = string("transpose_466")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_18_cast_fp16 = slice_update(begin = concat_209, begin_mask = value_cache_internal_tensor_assign_18_begin_mask_0, end = concat_210, end_mask = value_cache_internal_tensor_assign_18_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_18_squeeze_mask_0, stride = value_cache_internal_tensor_assign_18_stride_0, update = value_states_105_cast_fp16, x = coreml_update_state_313)[name = string("value_cache_internal_tensor_assign_18_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_18_cast_fp16, input = value_cache)[name = string("coreml_update_state_315_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_315 = read_state(input = value_cache)[name = string("coreml_update_state_315")];
            tensor<int32, [4]> var_6545_begin_0 = const()[name = string("op_6545_begin_0"), val = tensor<int32, [4]>([17, 0, 0, 0])];
            tensor<int32, [4]> var_6545_end_0 = const()[name = string("op_6545_end_0"), val = tensor<int32, [4]>([18, 2, 2048, 128])];
            tensor<bool, [4]> var_6545_end_mask_0 = const()[name = string("op_6545_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6545_cast_fp16 = slice_by_index(begin = var_6545_begin_0, end = var_6545_end_0, end_mask = var_6545_end_mask_0, x = coreml_update_state_314)[name = string("op_6545_cast_fp16")];
            tensor<int32, [2]> tile_34 = const()[name = string("tile_34"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6548_axis_0 = const()[name = string("op_6548_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6548_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6548_cast_fp16_1 = split(axis = var_6548_axis_0, split_sizes = tile_34, x = var_6545_cast_fp16)[name = string("op_6548_cast_fp16")];
            tensor<int32, [4]> var_6555_begin_0 = const()[name = string("op_6555_begin_0"), val = tensor<int32, [4]>([17, 0, 0, 0])];
            tensor<int32, [4]> var_6555_end_0 = const()[name = string("op_6555_end_0"), val = tensor<int32, [4]>([18, 2, 2048, 128])];
            tensor<bool, [4]> var_6555_end_mask_0 = const()[name = string("op_6555_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6555_cast_fp16 = slice_by_index(begin = var_6555_begin_0, end = var_6555_end_0, end_mask = var_6555_end_mask_0, x = coreml_update_state_315)[name = string("op_6555_cast_fp16")];
            tensor<int32, [2]> tile_35 = const()[name = string("tile_35"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6558_axis_0 = const()[name = string("op_6558_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6558_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6558_cast_fp16_1 = split(axis = var_6558_axis_0, split_sizes = tile_35, x = var_6555_cast_fp16)[name = string("op_6558_cast_fp16")];
            tensor<int32, [2]> var_6561_split_sizes_0 = const()[name = string("op_6561_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_6561_axis_0 = const()[name = string("op_6561_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_6561_0, tensor<fp16, [1, 8, 128, 64]> var_6561_1 = split(axis = var_6561_axis_0, split_sizes = var_6561_split_sizes_0, x = query_states_105_cast_fp16)[name = string("op_6561")];
            bool attn_weights_273_transpose_x_0 = const()[name = string("attn_weights_273_transpose_x_0"), val = bool(false)];
            bool attn_weights_273_transpose_y_0 = const()[name = string("attn_weights_273_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_273_cast_fp16 = matmul(transpose_x = attn_weights_273_transpose_x_0, transpose_y = attn_weights_273_transpose_y_0, x = var_6548_cast_fp16_0, y = var_6561_0)[name = string("attn_weights_273_cast_fp16")];
            fp16 var_6564_to_fp16 = const()[name = string("op_6564_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_275_cast_fp16 = mul(x = attn_weights_273_cast_fp16, y = var_6564_to_fp16)[name = string("attn_weights_275_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_277_cast_fp16 = add(x = attn_weights_275_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_277_cast_fp16")];
            int32 var_6568 = const()[name = string("op_6568"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_279_cast_fp16 = softmax(axis = var_6568, x = attn_weights_277_cast_fp16)[name = string("attn_weights_279_cast_fp16")];
            bool var_6574_transpose_x_1 = const()[name = string("op_6574_transpose_x_1"), val = bool(true)];
            bool var_6574_transpose_y_1 = const()[name = string("op_6574_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_6574_cast_fp16 = matmul(transpose_x = var_6574_transpose_x_1, transpose_y = var_6574_transpose_y_1, x = attn_weights_279_cast_fp16, y = var_6558_cast_fp16_0)[name = string("op_6574_cast_fp16")];
            bool attn_weights_281_transpose_x_0 = const()[name = string("attn_weights_281_transpose_x_0"), val = bool(false)];
            bool attn_weights_281_transpose_y_0 = const()[name = string("attn_weights_281_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_281_cast_fp16 = matmul(transpose_x = attn_weights_281_transpose_x_0, transpose_y = attn_weights_281_transpose_y_0, x = var_6548_cast_fp16_1, y = var_6561_1)[name = string("attn_weights_281_cast_fp16")];
            fp16 var_6576_to_fp16 = const()[name = string("op_6576_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_283_cast_fp16 = mul(x = attn_weights_281_cast_fp16, y = var_6576_to_fp16)[name = string("attn_weights_283_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_285_cast_fp16 = add(x = attn_weights_283_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_285_cast_fp16")];
            int32 var_6580 = const()[name = string("op_6580"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_287_cast_fp16 = softmax(axis = var_6580, x = attn_weights_285_cast_fp16)[name = string("attn_weights_287_cast_fp16")];
            bool attn_output_137_transpose_x_1 = const()[name = string("attn_output_137_transpose_x_1"), val = bool(true)];
            bool attn_output_137_transpose_y_1 = const()[name = string("attn_output_137_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_137_cast_fp16 = matmul(transpose_x = attn_output_137_transpose_x_1, transpose_y = attn_output_137_transpose_y_1, x = attn_weights_287_cast_fp16, y = var_6558_cast_fp16_1)[name = string("attn_output_137_cast_fp16")];
            int32 var_6588 = const()[name = string("op_6588"), val = int32(1)];
            bool attn_output_139_interleave_0 = const()[name = string("attn_output_139_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_139_cast_fp16 = concat(axis = var_6588, interleave = attn_output_139_interleave_0, values = (var_6574_cast_fp16, attn_output_137_cast_fp16))[name = string("attn_output_139_cast_fp16")];
            tensor<int32, [4]> var_6592_perm_0 = const()[name = string("op_6592_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_215x = const()[name = string("concat_215x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_6592_cast_fp16 = transpose(perm = var_6592_perm_0, x = attn_output_139_cast_fp16)[name = string("transpose_465")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_143_cast_fp16 = reshape(shape = concat_215x, x = var_6592_cast_fp16)[name = string("attn_output_143_cast_fp16")];
            tensor<int32, [2]> hidden_states_173_strides_0 = const()[name = string("hidden_states_173_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_173_pad_type_0 = const()[name = string("hidden_states_173_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_173_pad_0 = const()[name = string("hidden_states_173_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_173_dilations_0 = const()[name = string("hidden_states_173_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_173_groups_0 = const()[name = string("hidden_states_173_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_173_cast_fp16 = conv(dilations = hidden_states_173_dilations_0, groups = hidden_states_173_groups_0, pad = hidden_states_173_pad_0, pad_type = hidden_states_173_pad_type_0, strides = hidden_states_173_strides_0, weight = layers_17_self_attn_o_proj_weight_cast_fp16, x = attn_output_143_cast_fp16)[name = string("hidden_states_173_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_175_cast_fp16 = add(x = hidden_states_169_cast_fp16, y = hidden_states_173_cast_fp16)[name = string("hidden_states_175_cast_fp16")];
            fp16 const_180_promoted_to_fp16 = const()[name = string("const_180_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_6625_cast_fp16 = mul(x = hidden_states_175_cast_fp16, y = const_180_promoted_to_fp16)[name = string("op_6625_cast_fp16")];
            int32 var_6623 = const()[name = string("op_6623"), val = int32(1)];
            bool doubled_141_interleave_0 = const()[name = string("doubled_141_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_141_cast_fp16 = concat(axis = var_6623, interleave = doubled_141_interleave_0, values = (hidden_states_175_cast_fp16, var_6625_cast_fp16))[name = string("doubled_141_cast_fp16")];
            tensor<int32, [1]> out_71_axes_0 = const()[name = string("out_71_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_71_gamma_0_to_fp16 = const()[name = string("out_71_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483631360)))];
            fp16 var_6635_to_fp16 = const()[name = string("op_6635_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_71_cast_fp16 = layer_norm(axes = out_71_axes_0, epsilon = var_6635_to_fp16, gamma = out_71_gamma_0_to_fp16, x = doubled_141_cast_fp16)[name = string("out_71_cast_fp16")];
            tensor<int32, [2]> var_6646_split_sizes_0 = const()[name = string("op_6646_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6646_axis_0 = const()[name = string("op_6646_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_6646_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_6646_cast_fp16_1 = split(axis = var_6646_axis_0, split_sizes = var_6646_split_sizes_0, x = out_71_cast_fp16)[name = string("op_6646_cast_fp16")];
            tensor<int32, [2]> input_35_strides_0 = const()[name = string("input_35_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_35_pad_type_0 = const()[name = string("input_35_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_35_pad_0 = const()[name = string("input_35_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_35_dilations_0 = const()[name = string("input_35_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_35_groups_0 = const()[name = string("input_35_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_35_cast_fp16 = conv(dilations = input_35_dilations_0, groups = input_35_groups_0, pad = input_35_pad_0, pad_type = input_35_pad_type_0, strides = input_35_strides_0, weight = layers_17_mlp_gate_proj_weight_cast_fp16, x = var_6646_cast_fp16_0)[name = string("input_35_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_6663_cast_fp16 = silu(x = input_35_cast_fp16)[name = string("op_6663_cast_fp16")];
            tensor<int32, [2]> var_6669_strides_0 = const()[name = string("op_6669_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6669_pad_type_0 = const()[name = string("op_6669_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6669_pad_0 = const()[name = string("op_6669_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6669_dilations_0 = const()[name = string("op_6669_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6669_groups_0 = const()[name = string("op_6669_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_6669_cast_fp16 = conv(dilations = var_6669_dilations_0, groups = var_6669_groups_0, pad = var_6669_pad_0, pad_type = var_6669_pad_type_0, strides = var_6669_strides_0, weight = layers_17_mlp_up_proj_weight_cast_fp16, x = var_6646_cast_fp16_0)[name = string("op_6669_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_179_cast_fp16 = mul(x = var_6663_cast_fp16, y = var_6669_cast_fp16)[name = string("x_179_cast_fp16")];
            tensor<int32, [2]> hidden_states_177_strides_0 = const()[name = string("hidden_states_177_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_177_pad_type_0 = const()[name = string("hidden_states_177_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_177_pad_0 = const()[name = string("hidden_states_177_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_177_dilations_0 = const()[name = string("hidden_states_177_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_177_groups_0 = const()[name = string("hidden_states_177_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_177_cast_fp16 = conv(dilations = hidden_states_177_dilations_0, groups = hidden_states_177_groups_0, pad = hidden_states_177_pad_0, pad_type = hidden_states_177_pad_type_0, strides = hidden_states_177_strides_0, weight = layers_17_mlp_down_proj_weight_cast_fp16, x = x_179_cast_fp16)[name = string("hidden_states_177_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_179_cast_fp16 = add(x = hidden_states_175_cast_fp16, y = hidden_states_177_cast_fp16)[name = string("hidden_states_179_cast_fp16")];
            fp16 const_182_promoted_to_fp16 = const()[name = string("const_182_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_6687_cast_fp16 = mul(x = hidden_states_179_cast_fp16, y = const_182_promoted_to_fp16)[name = string("op_6687_cast_fp16")];
            int32 var_6685 = const()[name = string("op_6685"), val = int32(1)];
            bool doubled_145_interleave_0 = const()[name = string("doubled_145_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_145_cast_fp16 = concat(axis = var_6685, interleave = doubled_145_interleave_0, values = (hidden_states_179_cast_fp16, var_6687_cast_fp16))[name = string("doubled_145_cast_fp16")];
            tensor<int32, [1]> out_73_axes_0 = const()[name = string("out_73_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_73_gamma_0_to_fp16 = const()[name = string("out_73_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483639616)))];
            fp16 var_6697_to_fp16 = const()[name = string("op_6697_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_73_cast_fp16 = layer_norm(axes = out_73_axes_0, epsilon = var_6697_to_fp16, gamma = out_73_gamma_0_to_fp16, x = doubled_145_cast_fp16)[name = string("out_73_cast_fp16")];
            tensor<int32, [2]> var_6708_split_sizes_0 = const()[name = string("op_6708_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6708_axis_0 = const()[name = string("op_6708_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_6708_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_6708_cast_fp16_1 = split(axis = var_6708_axis_0, split_sizes = var_6708_split_sizes_0, x = out_73_cast_fp16)[name = string("op_6708_cast_fp16")];
            tensor<int32, [2]> query_states_109_strides_0 = const()[name = string("query_states_109_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_109_pad_type_0 = const()[name = string("query_states_109_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_109_pad_0 = const()[name = string("query_states_109_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_109_dilations_0 = const()[name = string("query_states_109_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_109_groups_0 = const()[name = string("query_states_109_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_109_cast_fp16 = conv(dilations = query_states_109_dilations_0, groups = query_states_109_groups_0, pad = query_states_109_pad_0, pad_type = query_states_109_pad_type_0, strides = query_states_109_strides_0, weight = layers_18_self_attn_q_proj_weight_cast_fp16, x = var_6708_cast_fp16_0)[name = string("query_states_109_cast_fp16")];
            tensor<int32, [2]> key_states_181_strides_0 = const()[name = string("key_states_181_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_181_pad_type_0 = const()[name = string("key_states_181_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_181_pad_0 = const()[name = string("key_states_181_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_181_dilations_0 = const()[name = string("key_states_181_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_181_groups_0 = const()[name = string("key_states_181_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_181_cast_fp16 = conv(dilations = key_states_181_dilations_0, groups = key_states_181_groups_0, pad = key_states_181_pad_0, pad_type = key_states_181_pad_type_0, strides = key_states_181_strides_0, weight = layers_18_self_attn_k_proj_weight_cast_fp16, x = var_6708_cast_fp16_0)[name = string("key_states_181_cast_fp16")];
            tensor<int32, [2]> value_states_109_strides_0 = const()[name = string("value_states_109_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_109_pad_type_0 = const()[name = string("value_states_109_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_109_pad_0 = const()[name = string("value_states_109_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_109_dilations_0 = const()[name = string("value_states_109_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_109_groups_0 = const()[name = string("value_states_109_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_109_cast_fp16 = conv(dilations = value_states_109_dilations_0, groups = value_states_109_groups_0, pad = value_states_109_pad_0, pad_type = value_states_109_pad_type_0, strides = value_states_109_strides_0, weight = layers_18_self_attn_v_proj_weight_cast_fp16, x = var_6708_cast_fp16_0)[name = string("value_states_109_cast_fp16")];
            tensor<int32, [4]> concat_216x = const()[name = string("concat_216x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_181_cast_fp16 = reshape(shape = concat_216x, x = query_states_109_cast_fp16)[name = string("x_181_cast_fp16")];
            tensor<int32, [4]> concat_217x = const()[name = string("concat_217x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_6765_cast_fp16 = reshape(shape = concat_217x, x = key_states_181_cast_fp16)[name = string("op_6765_cast_fp16")];
            tensor<int32, [4]> concat_218x = const()[name = string("concat_218x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_6772_cast_fp16 = reshape(shape = concat_218x, x = value_states_109_cast_fp16)[name = string("op_6772_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_6776_cast_fp16 = mul(x = x_181_cast_fp16, y = var_859_cast_fp16)[name = string("op_6776_cast_fp16")];
            tensor<int32, [2]> var_6777_split_sizes_0 = const()[name = string("op_6777_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6777_axis_0 = const()[name = string("op_6777_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_6777_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_6777_cast_fp16_1 = split(axis = var_6777_axis_0, split_sizes = var_6777_split_sizes_0, x = x_181_cast_fp16)[name = string("op_6777_cast_fp16")];
            fp16 const_184_promoted_to_fp16 = const()[name = string("const_184_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_6779_cast_fp16 = mul(x = var_6777_cast_fp16_1, y = const_184_promoted_to_fp16)[name = string("op_6779_cast_fp16")];
            int32 var_6781 = const()[name = string("op_6781"), val = int32(-2)];
            bool var_6782_interleave_0 = const()[name = string("op_6782_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_6782_cast_fp16 = concat(axis = var_6781, interleave = var_6782_interleave_0, values = (var_6779_cast_fp16, var_6777_cast_fp16_0))[name = string("op_6782_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_6783_cast_fp16 = mul(x = var_6782_cast_fp16, y = var_866_cast_fp16)[name = string("op_6783_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_111_cast_fp16 = add(x = var_6776_cast_fp16, y = var_6783_cast_fp16)[name = string("query_states_111_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_6789_cast_fp16 = mul(x = var_6765_cast_fp16, y = var_859_cast_fp16)[name = string("op_6789_cast_fp16")];
            tensor<int32, [2]> var_6790_split_sizes_0 = const()[name = string("op_6790_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6790_axis_0 = const()[name = string("op_6790_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_6790_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_6790_cast_fp16_1 = split(axis = var_6790_axis_0, split_sizes = var_6790_split_sizes_0, x = var_6765_cast_fp16)[name = string("op_6790_cast_fp16")];
            fp16 const_185_promoted_to_fp16 = const()[name = string("const_185_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_6792_cast_fp16 = mul(x = var_6790_cast_fp16_1, y = const_185_promoted_to_fp16)[name = string("op_6792_cast_fp16")];
            int32 var_6794 = const()[name = string("op_6794"), val = int32(-2)];
            bool var_6795_interleave_0 = const()[name = string("op_6795_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_6795_cast_fp16 = concat(axis = var_6794, interleave = var_6795_interleave_0, values = (var_6792_cast_fp16, var_6790_cast_fp16_0))[name = string("op_6795_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_6796_cast_fp16 = mul(x = var_6795_cast_fp16, y = var_866_cast_fp16)[name = string("op_6796_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_185_cast_fp16 = add(x = var_6789_cast_fp16, y = var_6796_cast_fp16)[name = string("key_states_185_cast_fp16")];
            tensor<int32, [1]> expand_dims_216 = const()[name = string("expand_dims_216"), val = tensor<int32, [1]>([18])];
            tensor<int32, [1]> expand_dims_217 = const()[name = string("expand_dims_217"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_219 = const()[name = string("expand_dims_219"), val = tensor<int32, [1]>([0])];
            int32 concat_221_axis_0 = const()[name = string("concat_221_axis_0"), val = int32(0)];
            bool concat_221_interleave_0 = const()[name = string("concat_221_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_221 = concat(axis = concat_221_axis_0, interleave = concat_221_interleave_0, values = (expand_dims_216, expand_dims_217, position_id, expand_dims_219))[name = string("concat_221")];
            tensor<int32, [1]> expand_dims_220 = const()[name = string("expand_dims_220"), val = tensor<int32, [1]>([19])];
            tensor<int32, [1]> concat_222_values1_0 = const()[name = string("concat_222_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_222_values3_0 = const()[name = string("concat_222_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_222_axis_0 = const()[name = string("concat_222_axis_0"), val = int32(0)];
            bool concat_222_interleave_0 = const()[name = string("concat_222_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_222 = concat(axis = concat_222_axis_0, interleave = concat_222_interleave_0, values = (expand_dims_220, concat_222_values1_0, cache_position_end, concat_222_values3_0))[name = string("concat_222")];
            tensor<int32, [4]> key_states_187_perm_0 = const()[name = string("key_states_187_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_19_stride_0 = const()[name = string("key_cache_internal_tensor_assign_19_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_19_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_19_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_19_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_19_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_19_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_19_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_187_cast_fp16 = transpose(perm = key_states_187_perm_0, x = key_states_185_cast_fp16)[name = string("transpose_464")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_19_cast_fp16 = slice_update(begin = concat_221, begin_mask = key_cache_internal_tensor_assign_19_begin_mask_0, end = concat_222, end_mask = key_cache_internal_tensor_assign_19_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_19_squeeze_mask_0, stride = key_cache_internal_tensor_assign_19_stride_0, update = key_states_187_cast_fp16, x = coreml_update_state_314)[name = string("key_cache_internal_tensor_assign_19_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_19_cast_fp16, input = key_cache)[name = string("coreml_update_state_316_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_316 = read_state(input = key_cache)[name = string("coreml_update_state_316")];
            tensor<int32, [4]> value_states_111_perm_0 = const()[name = string("value_states_111_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_19_stride_0 = const()[name = string("value_cache_internal_tensor_assign_19_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_19_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_19_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_19_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_19_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_19_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_19_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_111_cast_fp16 = transpose(perm = value_states_111_perm_0, x = var_6772_cast_fp16)[name = string("transpose_463")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_19_cast_fp16 = slice_update(begin = concat_221, begin_mask = value_cache_internal_tensor_assign_19_begin_mask_0, end = concat_222, end_mask = value_cache_internal_tensor_assign_19_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_19_squeeze_mask_0, stride = value_cache_internal_tensor_assign_19_stride_0, update = value_states_111_cast_fp16, x = coreml_update_state_315)[name = string("value_cache_internal_tensor_assign_19_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_19_cast_fp16, input = value_cache)[name = string("coreml_update_state_317_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_317 = read_state(input = value_cache)[name = string("coreml_update_state_317")];
            tensor<int32, [4]> var_6866_begin_0 = const()[name = string("op_6866_begin_0"), val = tensor<int32, [4]>([18, 0, 0, 0])];
            tensor<int32, [4]> var_6866_end_0 = const()[name = string("op_6866_end_0"), val = tensor<int32, [4]>([19, 2, 2048, 128])];
            tensor<bool, [4]> var_6866_end_mask_0 = const()[name = string("op_6866_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6866_cast_fp16 = slice_by_index(begin = var_6866_begin_0, end = var_6866_end_0, end_mask = var_6866_end_mask_0, x = coreml_update_state_316)[name = string("op_6866_cast_fp16")];
            tensor<int32, [2]> tile_36 = const()[name = string("tile_36"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6869_axis_0 = const()[name = string("op_6869_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6869_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6869_cast_fp16_1 = split(axis = var_6869_axis_0, split_sizes = tile_36, x = var_6866_cast_fp16)[name = string("op_6869_cast_fp16")];
            tensor<int32, [4]> var_6876_begin_0 = const()[name = string("op_6876_begin_0"), val = tensor<int32, [4]>([18, 0, 0, 0])];
            tensor<int32, [4]> var_6876_end_0 = const()[name = string("op_6876_end_0"), val = tensor<int32, [4]>([19, 2, 2048, 128])];
            tensor<bool, [4]> var_6876_end_mask_0 = const()[name = string("op_6876_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6876_cast_fp16 = slice_by_index(begin = var_6876_begin_0, end = var_6876_end_0, end_mask = var_6876_end_mask_0, x = coreml_update_state_317)[name = string("op_6876_cast_fp16")];
            tensor<int32, [2]> tile_37 = const()[name = string("tile_37"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6879_axis_0 = const()[name = string("op_6879_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6879_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6879_cast_fp16_1 = split(axis = var_6879_axis_0, split_sizes = tile_37, x = var_6876_cast_fp16)[name = string("op_6879_cast_fp16")];
            tensor<int32, [2]> var_6882_split_sizes_0 = const()[name = string("op_6882_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_6882_axis_0 = const()[name = string("op_6882_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_6882_0, tensor<fp16, [1, 8, 128, 64]> var_6882_1 = split(axis = var_6882_axis_0, split_sizes = var_6882_split_sizes_0, x = query_states_111_cast_fp16)[name = string("op_6882")];
            bool attn_weights_289_transpose_x_0 = const()[name = string("attn_weights_289_transpose_x_0"), val = bool(false)];
            bool attn_weights_289_transpose_y_0 = const()[name = string("attn_weights_289_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_289_cast_fp16 = matmul(transpose_x = attn_weights_289_transpose_x_0, transpose_y = attn_weights_289_transpose_y_0, x = var_6869_cast_fp16_0, y = var_6882_0)[name = string("attn_weights_289_cast_fp16")];
            fp16 var_6885_to_fp16 = const()[name = string("op_6885_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_291_cast_fp16 = mul(x = attn_weights_289_cast_fp16, y = var_6885_to_fp16)[name = string("attn_weights_291_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_293_cast_fp16 = add(x = attn_weights_291_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_293_cast_fp16")];
            int32 var_6889 = const()[name = string("op_6889"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_295_cast_fp16 = softmax(axis = var_6889, x = attn_weights_293_cast_fp16)[name = string("attn_weights_295_cast_fp16")];
            bool var_6895_transpose_x_1 = const()[name = string("op_6895_transpose_x_1"), val = bool(true)];
            bool var_6895_transpose_y_1 = const()[name = string("op_6895_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_6895_cast_fp16 = matmul(transpose_x = var_6895_transpose_x_1, transpose_y = var_6895_transpose_y_1, x = attn_weights_295_cast_fp16, y = var_6879_cast_fp16_0)[name = string("op_6895_cast_fp16")];
            bool attn_weights_297_transpose_x_0 = const()[name = string("attn_weights_297_transpose_x_0"), val = bool(false)];
            bool attn_weights_297_transpose_y_0 = const()[name = string("attn_weights_297_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_297_cast_fp16 = matmul(transpose_x = attn_weights_297_transpose_x_0, transpose_y = attn_weights_297_transpose_y_0, x = var_6869_cast_fp16_1, y = var_6882_1)[name = string("attn_weights_297_cast_fp16")];
            fp16 var_6897_to_fp16 = const()[name = string("op_6897_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_299_cast_fp16 = mul(x = attn_weights_297_cast_fp16, y = var_6897_to_fp16)[name = string("attn_weights_299_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_301_cast_fp16 = add(x = attn_weights_299_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_301_cast_fp16")];
            int32 var_6901 = const()[name = string("op_6901"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_303_cast_fp16 = softmax(axis = var_6901, x = attn_weights_301_cast_fp16)[name = string("attn_weights_303_cast_fp16")];
            bool attn_output_145_transpose_x_1 = const()[name = string("attn_output_145_transpose_x_1"), val = bool(true)];
            bool attn_output_145_transpose_y_1 = const()[name = string("attn_output_145_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_145_cast_fp16 = matmul(transpose_x = attn_output_145_transpose_x_1, transpose_y = attn_output_145_transpose_y_1, x = attn_weights_303_cast_fp16, y = var_6879_cast_fp16_1)[name = string("attn_output_145_cast_fp16")];
            int32 var_6909 = const()[name = string("op_6909"), val = int32(1)];
            bool attn_output_147_interleave_0 = const()[name = string("attn_output_147_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_147_cast_fp16 = concat(axis = var_6909, interleave = attn_output_147_interleave_0, values = (var_6895_cast_fp16, attn_output_145_cast_fp16))[name = string("attn_output_147_cast_fp16")];
            tensor<int32, [4]> var_6913_perm_0 = const()[name = string("op_6913_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_227x = const()[name = string("concat_227x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_6913_cast_fp16 = transpose(perm = var_6913_perm_0, x = attn_output_147_cast_fp16)[name = string("transpose_462")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_151_cast_fp16 = reshape(shape = concat_227x, x = var_6913_cast_fp16)[name = string("attn_output_151_cast_fp16")];
            tensor<int32, [2]> hidden_states_183_strides_0 = const()[name = string("hidden_states_183_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_183_pad_type_0 = const()[name = string("hidden_states_183_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_183_pad_0 = const()[name = string("hidden_states_183_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_183_dilations_0 = const()[name = string("hidden_states_183_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_183_groups_0 = const()[name = string("hidden_states_183_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_183_cast_fp16 = conv(dilations = hidden_states_183_dilations_0, groups = hidden_states_183_groups_0, pad = hidden_states_183_pad_0, pad_type = hidden_states_183_pad_type_0, strides = hidden_states_183_strides_0, weight = layers_18_self_attn_o_proj_weight_cast_fp16, x = attn_output_151_cast_fp16)[name = string("hidden_states_183_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_185_cast_fp16 = add(x = hidden_states_179_cast_fp16, y = hidden_states_183_cast_fp16)[name = string("hidden_states_185_cast_fp16")];
            fp16 const_190_promoted_to_fp16 = const()[name = string("const_190_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_6946_cast_fp16 = mul(x = hidden_states_185_cast_fp16, y = const_190_promoted_to_fp16)[name = string("op_6946_cast_fp16")];
            int32 var_6944 = const()[name = string("op_6944"), val = int32(1)];
            bool doubled_149_interleave_0 = const()[name = string("doubled_149_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_149_cast_fp16 = concat(axis = var_6944, interleave = doubled_149_interleave_0, values = (hidden_states_185_cast_fp16, var_6946_cast_fp16))[name = string("doubled_149_cast_fp16")];
            tensor<int32, [1]> out_75_axes_0 = const()[name = string("out_75_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_75_gamma_0_to_fp16 = const()[name = string("out_75_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483647872)))];
            fp16 var_6956_to_fp16 = const()[name = string("op_6956_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_75_cast_fp16 = layer_norm(axes = out_75_axes_0, epsilon = var_6956_to_fp16, gamma = out_75_gamma_0_to_fp16, x = doubled_149_cast_fp16)[name = string("out_75_cast_fp16")];
            tensor<int32, [2]> var_6967_split_sizes_0 = const()[name = string("op_6967_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6967_axis_0 = const()[name = string("op_6967_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_6967_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_6967_cast_fp16_1 = split(axis = var_6967_axis_0, split_sizes = var_6967_split_sizes_0, x = out_75_cast_fp16)[name = string("op_6967_cast_fp16")];
            tensor<int32, [2]> input_37_strides_0 = const()[name = string("input_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_37_pad_type_0 = const()[name = string("input_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_37_pad_0 = const()[name = string("input_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_37_dilations_0 = const()[name = string("input_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_37_groups_0 = const()[name = string("input_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_37_cast_fp16 = conv(dilations = input_37_dilations_0, groups = input_37_groups_0, pad = input_37_pad_0, pad_type = input_37_pad_type_0, strides = input_37_strides_0, weight = layers_18_mlp_gate_proj_weight_cast_fp16, x = var_6967_cast_fp16_0)[name = string("input_37_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_6984_cast_fp16 = silu(x = input_37_cast_fp16)[name = string("op_6984_cast_fp16")];
            tensor<int32, [2]> var_6990_strides_0 = const()[name = string("op_6990_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6990_pad_type_0 = const()[name = string("op_6990_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6990_pad_0 = const()[name = string("op_6990_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6990_dilations_0 = const()[name = string("op_6990_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6990_groups_0 = const()[name = string("op_6990_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_6990_cast_fp16 = conv(dilations = var_6990_dilations_0, groups = var_6990_groups_0, pad = var_6990_pad_0, pad_type = var_6990_pad_type_0, strides = var_6990_strides_0, weight = layers_18_mlp_up_proj_weight_cast_fp16, x = var_6967_cast_fp16_0)[name = string("op_6990_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_189_cast_fp16 = mul(x = var_6984_cast_fp16, y = var_6990_cast_fp16)[name = string("x_189_cast_fp16")];
            tensor<int32, [2]> hidden_states_187_strides_0 = const()[name = string("hidden_states_187_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_187_pad_type_0 = const()[name = string("hidden_states_187_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_187_pad_0 = const()[name = string("hidden_states_187_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_187_dilations_0 = const()[name = string("hidden_states_187_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_187_groups_0 = const()[name = string("hidden_states_187_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_187_cast_fp16 = conv(dilations = hidden_states_187_dilations_0, groups = hidden_states_187_groups_0, pad = hidden_states_187_pad_0, pad_type = hidden_states_187_pad_type_0, strides = hidden_states_187_strides_0, weight = layers_18_mlp_down_proj_weight_cast_fp16, x = x_189_cast_fp16)[name = string("hidden_states_187_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_189_cast_fp16 = add(x = hidden_states_185_cast_fp16, y = hidden_states_187_cast_fp16)[name = string("hidden_states_189_cast_fp16")];
            fp16 const_192_promoted_to_fp16 = const()[name = string("const_192_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_7008_cast_fp16 = mul(x = hidden_states_189_cast_fp16, y = const_192_promoted_to_fp16)[name = string("op_7008_cast_fp16")];
            int32 var_7006 = const()[name = string("op_7006"), val = int32(1)];
            bool doubled_153_interleave_0 = const()[name = string("doubled_153_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_153_cast_fp16 = concat(axis = var_7006, interleave = doubled_153_interleave_0, values = (hidden_states_189_cast_fp16, var_7008_cast_fp16))[name = string("doubled_153_cast_fp16")];
            tensor<int32, [1]> out_77_axes_0 = const()[name = string("out_77_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_77_gamma_0_to_fp16 = const()[name = string("out_77_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483656128)))];
            fp16 var_7018_to_fp16 = const()[name = string("op_7018_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_77_cast_fp16 = layer_norm(axes = out_77_axes_0, epsilon = var_7018_to_fp16, gamma = out_77_gamma_0_to_fp16, x = doubled_153_cast_fp16)[name = string("out_77_cast_fp16")];
            tensor<int32, [2]> var_7029_split_sizes_0 = const()[name = string("op_7029_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7029_axis_0 = const()[name = string("op_7029_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_7029_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_7029_cast_fp16_1 = split(axis = var_7029_axis_0, split_sizes = var_7029_split_sizes_0, x = out_77_cast_fp16)[name = string("op_7029_cast_fp16")];
            tensor<int32, [2]> query_states_115_strides_0 = const()[name = string("query_states_115_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_115_pad_type_0 = const()[name = string("query_states_115_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_115_pad_0 = const()[name = string("query_states_115_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_115_dilations_0 = const()[name = string("query_states_115_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_115_groups_0 = const()[name = string("query_states_115_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_115_cast_fp16 = conv(dilations = query_states_115_dilations_0, groups = query_states_115_groups_0, pad = query_states_115_pad_0, pad_type = query_states_115_pad_type_0, strides = query_states_115_strides_0, weight = layers_19_self_attn_q_proj_weight_cast_fp16, x = var_7029_cast_fp16_0)[name = string("query_states_115_cast_fp16")];
            tensor<int32, [2]> key_states_191_strides_0 = const()[name = string("key_states_191_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_191_pad_type_0 = const()[name = string("key_states_191_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_191_pad_0 = const()[name = string("key_states_191_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_191_dilations_0 = const()[name = string("key_states_191_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_191_groups_0 = const()[name = string("key_states_191_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_191_cast_fp16 = conv(dilations = key_states_191_dilations_0, groups = key_states_191_groups_0, pad = key_states_191_pad_0, pad_type = key_states_191_pad_type_0, strides = key_states_191_strides_0, weight = layers_19_self_attn_k_proj_weight_cast_fp16, x = var_7029_cast_fp16_0)[name = string("key_states_191_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_19_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_19_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483664384)))];
            tensor<int32, [2]> value_states_115_strides_0 = const()[name = string("value_states_115_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_115_pad_type_0 = const()[name = string("value_states_115_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_115_pad_0 = const()[name = string("value_states_115_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_115_dilations_0 = const()[name = string("value_states_115_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_115_groups_0 = const()[name = string("value_states_115_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_115_cast_fp16 = conv(dilations = value_states_115_dilations_0, groups = value_states_115_groups_0, pad = value_states_115_pad_0, pad_type = value_states_115_pad_type_0, strides = value_states_115_strides_0, weight = layers_19_self_attn_v_proj_weight_to_fp16, x = var_7029_cast_fp16_0)[name = string("value_states_115_cast_fp16")];
            tensor<int32, [4]> concat_228x = const()[name = string("concat_228x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_191_cast_fp16 = reshape(shape = concat_228x, x = query_states_115_cast_fp16)[name = string("x_191_cast_fp16")];
            tensor<int32, [4]> concat_229x = const()[name = string("concat_229x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_7086_cast_fp16 = reshape(shape = concat_229x, x = key_states_191_cast_fp16)[name = string("op_7086_cast_fp16")];
            tensor<int32, [4]> concat_230x = const()[name = string("concat_230x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_7093_cast_fp16 = reshape(shape = concat_230x, x = value_states_115_cast_fp16)[name = string("op_7093_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_7097_cast_fp16 = mul(x = x_191_cast_fp16, y = var_859_cast_fp16)[name = string("op_7097_cast_fp16")];
            tensor<int32, [2]> var_7098_split_sizes_0 = const()[name = string("op_7098_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7098_axis_0 = const()[name = string("op_7098_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_7098_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_7098_cast_fp16_1 = split(axis = var_7098_axis_0, split_sizes = var_7098_split_sizes_0, x = x_191_cast_fp16)[name = string("op_7098_cast_fp16")];
            fp16 const_194_promoted_to_fp16 = const()[name = string("const_194_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_7100_cast_fp16 = mul(x = var_7098_cast_fp16_1, y = const_194_promoted_to_fp16)[name = string("op_7100_cast_fp16")];
            int32 var_7102 = const()[name = string("op_7102"), val = int32(-2)];
            bool var_7103_interleave_0 = const()[name = string("op_7103_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_7103_cast_fp16 = concat(axis = var_7102, interleave = var_7103_interleave_0, values = (var_7100_cast_fp16, var_7098_cast_fp16_0))[name = string("op_7103_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_7104_cast_fp16 = mul(x = var_7103_cast_fp16, y = var_866_cast_fp16)[name = string("op_7104_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_117_cast_fp16 = add(x = var_7097_cast_fp16, y = var_7104_cast_fp16)[name = string("query_states_117_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_7110_cast_fp16 = mul(x = var_7086_cast_fp16, y = var_859_cast_fp16)[name = string("op_7110_cast_fp16")];
            tensor<int32, [2]> var_7111_split_sizes_0 = const()[name = string("op_7111_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7111_axis_0 = const()[name = string("op_7111_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_7111_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_7111_cast_fp16_1 = split(axis = var_7111_axis_0, split_sizes = var_7111_split_sizes_0, x = var_7086_cast_fp16)[name = string("op_7111_cast_fp16")];
            fp16 const_195_promoted_to_fp16 = const()[name = string("const_195_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_7113_cast_fp16 = mul(x = var_7111_cast_fp16_1, y = const_195_promoted_to_fp16)[name = string("op_7113_cast_fp16")];
            int32 var_7115 = const()[name = string("op_7115"), val = int32(-2)];
            bool var_7116_interleave_0 = const()[name = string("op_7116_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_7116_cast_fp16 = concat(axis = var_7115, interleave = var_7116_interleave_0, values = (var_7113_cast_fp16, var_7111_cast_fp16_0))[name = string("op_7116_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_7117_cast_fp16 = mul(x = var_7116_cast_fp16, y = var_866_cast_fp16)[name = string("op_7117_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_195_cast_fp16 = add(x = var_7110_cast_fp16, y = var_7117_cast_fp16)[name = string("key_states_195_cast_fp16")];
            tensor<int32, [1]> expand_dims_228 = const()[name = string("expand_dims_228"), val = tensor<int32, [1]>([19])];
            tensor<int32, [1]> expand_dims_229 = const()[name = string("expand_dims_229"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_231 = const()[name = string("expand_dims_231"), val = tensor<int32, [1]>([0])];
            int32 concat_233_axis_0 = const()[name = string("concat_233_axis_0"), val = int32(0)];
            bool concat_233_interleave_0 = const()[name = string("concat_233_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_233 = concat(axis = concat_233_axis_0, interleave = concat_233_interleave_0, values = (expand_dims_228, expand_dims_229, position_id, expand_dims_231))[name = string("concat_233")];
            tensor<int32, [1]> expand_dims_232 = const()[name = string("expand_dims_232"), val = tensor<int32, [1]>([20])];
            tensor<int32, [1]> concat_234_values1_0 = const()[name = string("concat_234_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_234_values3_0 = const()[name = string("concat_234_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_234_axis_0 = const()[name = string("concat_234_axis_0"), val = int32(0)];
            bool concat_234_interleave_0 = const()[name = string("concat_234_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_234 = concat(axis = concat_234_axis_0, interleave = concat_234_interleave_0, values = (expand_dims_232, concat_234_values1_0, cache_position_end, concat_234_values3_0))[name = string("concat_234")];
            tensor<int32, [4]> key_states_197_perm_0 = const()[name = string("key_states_197_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_20_stride_0 = const()[name = string("key_cache_internal_tensor_assign_20_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_20_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_20_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_20_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_20_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_20_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_20_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_197_cast_fp16 = transpose(perm = key_states_197_perm_0, x = key_states_195_cast_fp16)[name = string("transpose_461")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_20_cast_fp16 = slice_update(begin = concat_233, begin_mask = key_cache_internal_tensor_assign_20_begin_mask_0, end = concat_234, end_mask = key_cache_internal_tensor_assign_20_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_20_squeeze_mask_0, stride = key_cache_internal_tensor_assign_20_stride_0, update = key_states_197_cast_fp16, x = coreml_update_state_316)[name = string("key_cache_internal_tensor_assign_20_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_20_cast_fp16, input = key_cache)[name = string("coreml_update_state_318_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_318 = read_state(input = key_cache)[name = string("coreml_update_state_318")];
            tensor<int32, [4]> value_states_117_perm_0 = const()[name = string("value_states_117_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_20_stride_0 = const()[name = string("value_cache_internal_tensor_assign_20_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_20_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_20_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_20_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_20_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_20_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_20_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_117_cast_fp16 = transpose(perm = value_states_117_perm_0, x = var_7093_cast_fp16)[name = string("transpose_460")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_20_cast_fp16 = slice_update(begin = concat_233, begin_mask = value_cache_internal_tensor_assign_20_begin_mask_0, end = concat_234, end_mask = value_cache_internal_tensor_assign_20_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_20_squeeze_mask_0, stride = value_cache_internal_tensor_assign_20_stride_0, update = value_states_117_cast_fp16, x = coreml_update_state_317)[name = string("value_cache_internal_tensor_assign_20_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_20_cast_fp16, input = value_cache)[name = string("coreml_update_state_319_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_319 = read_state(input = value_cache)[name = string("coreml_update_state_319")];
            tensor<int32, [4]> var_7187_begin_0 = const()[name = string("op_7187_begin_0"), val = tensor<int32, [4]>([19, 0, 0, 0])];
            tensor<int32, [4]> var_7187_end_0 = const()[name = string("op_7187_end_0"), val = tensor<int32, [4]>([20, 2, 2048, 128])];
            tensor<bool, [4]> var_7187_end_mask_0 = const()[name = string("op_7187_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7187_cast_fp16 = slice_by_index(begin = var_7187_begin_0, end = var_7187_end_0, end_mask = var_7187_end_mask_0, x = coreml_update_state_318)[name = string("op_7187_cast_fp16")];
            tensor<int32, [2]> tile_38 = const()[name = string("tile_38"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7190_axis_0 = const()[name = string("op_7190_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7190_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7190_cast_fp16_1 = split(axis = var_7190_axis_0, split_sizes = tile_38, x = var_7187_cast_fp16)[name = string("op_7190_cast_fp16")];
            tensor<int32, [4]> var_7197_begin_0 = const()[name = string("op_7197_begin_0"), val = tensor<int32, [4]>([19, 0, 0, 0])];
            tensor<int32, [4]> var_7197_end_0 = const()[name = string("op_7197_end_0"), val = tensor<int32, [4]>([20, 2, 2048, 128])];
            tensor<bool, [4]> var_7197_end_mask_0 = const()[name = string("op_7197_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7197_cast_fp16 = slice_by_index(begin = var_7197_begin_0, end = var_7197_end_0, end_mask = var_7197_end_mask_0, x = coreml_update_state_319)[name = string("op_7197_cast_fp16")];
            tensor<int32, [2]> tile_39 = const()[name = string("tile_39"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7200_axis_0 = const()[name = string("op_7200_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7200_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7200_cast_fp16_1 = split(axis = var_7200_axis_0, split_sizes = tile_39, x = var_7197_cast_fp16)[name = string("op_7200_cast_fp16")];
            tensor<int32, [2]> var_7203_split_sizes_0 = const()[name = string("op_7203_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_7203_axis_0 = const()[name = string("op_7203_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_7203_0, tensor<fp16, [1, 8, 128, 64]> var_7203_1 = split(axis = var_7203_axis_0, split_sizes = var_7203_split_sizes_0, x = query_states_117_cast_fp16)[name = string("op_7203")];
            bool attn_weights_305_transpose_x_0 = const()[name = string("attn_weights_305_transpose_x_0"), val = bool(false)];
            bool attn_weights_305_transpose_y_0 = const()[name = string("attn_weights_305_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_305_cast_fp16 = matmul(transpose_x = attn_weights_305_transpose_x_0, transpose_y = attn_weights_305_transpose_y_0, x = var_7190_cast_fp16_0, y = var_7203_0)[name = string("attn_weights_305_cast_fp16")];
            fp16 var_7206_to_fp16 = const()[name = string("op_7206_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_307_cast_fp16 = mul(x = attn_weights_305_cast_fp16, y = var_7206_to_fp16)[name = string("attn_weights_307_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_309_cast_fp16 = add(x = attn_weights_307_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_309_cast_fp16")];
            int32 var_7210 = const()[name = string("op_7210"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_311_cast_fp16 = softmax(axis = var_7210, x = attn_weights_309_cast_fp16)[name = string("attn_weights_311_cast_fp16")];
            bool var_7216_transpose_x_1 = const()[name = string("op_7216_transpose_x_1"), val = bool(true)];
            bool var_7216_transpose_y_1 = const()[name = string("op_7216_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_7216_cast_fp16 = matmul(transpose_x = var_7216_transpose_x_1, transpose_y = var_7216_transpose_y_1, x = attn_weights_311_cast_fp16, y = var_7200_cast_fp16_0)[name = string("op_7216_cast_fp16")];
            bool attn_weights_313_transpose_x_0 = const()[name = string("attn_weights_313_transpose_x_0"), val = bool(false)];
            bool attn_weights_313_transpose_y_0 = const()[name = string("attn_weights_313_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_313_cast_fp16 = matmul(transpose_x = attn_weights_313_transpose_x_0, transpose_y = attn_weights_313_transpose_y_0, x = var_7190_cast_fp16_1, y = var_7203_1)[name = string("attn_weights_313_cast_fp16")];
            fp16 var_7218_to_fp16 = const()[name = string("op_7218_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_315_cast_fp16 = mul(x = attn_weights_313_cast_fp16, y = var_7218_to_fp16)[name = string("attn_weights_315_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_317_cast_fp16 = add(x = attn_weights_315_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_317_cast_fp16")];
            int32 var_7222 = const()[name = string("op_7222"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_319_cast_fp16 = softmax(axis = var_7222, x = attn_weights_317_cast_fp16)[name = string("attn_weights_319_cast_fp16")];
            bool attn_output_153_transpose_x_1 = const()[name = string("attn_output_153_transpose_x_1"), val = bool(true)];
            bool attn_output_153_transpose_y_1 = const()[name = string("attn_output_153_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_153_cast_fp16 = matmul(transpose_x = attn_output_153_transpose_x_1, transpose_y = attn_output_153_transpose_y_1, x = attn_weights_319_cast_fp16, y = var_7200_cast_fp16_1)[name = string("attn_output_153_cast_fp16")];
            int32 var_7230 = const()[name = string("op_7230"), val = int32(1)];
            bool attn_output_155_interleave_0 = const()[name = string("attn_output_155_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_155_cast_fp16 = concat(axis = var_7230, interleave = attn_output_155_interleave_0, values = (var_7216_cast_fp16, attn_output_153_cast_fp16))[name = string("attn_output_155_cast_fp16")];
            tensor<int32, [4]> var_7234_perm_0 = const()[name = string("op_7234_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_239x = const()[name = string("concat_239x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_7234_cast_fp16 = transpose(perm = var_7234_perm_0, x = attn_output_155_cast_fp16)[name = string("transpose_459")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_159_cast_fp16 = reshape(shape = concat_239x, x = var_7234_cast_fp16)[name = string("attn_output_159_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_19_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_19_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1484713024)))];
            tensor<int32, [2]> hidden_states_193_strides_0 = const()[name = string("hidden_states_193_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_193_pad_type_0 = const()[name = string("hidden_states_193_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_193_pad_0 = const()[name = string("hidden_states_193_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_193_dilations_0 = const()[name = string("hidden_states_193_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_193_groups_0 = const()[name = string("hidden_states_193_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_193_cast_fp16 = conv(dilations = hidden_states_193_dilations_0, groups = hidden_states_193_groups_0, pad = hidden_states_193_pad_0, pad_type = hidden_states_193_pad_type_0, strides = hidden_states_193_strides_0, weight = layers_19_self_attn_o_proj_weight_to_fp16, x = attn_output_159_cast_fp16)[name = string("hidden_states_193_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_195_cast_fp16 = add(x = hidden_states_189_cast_fp16, y = hidden_states_193_cast_fp16)[name = string("hidden_states_195_cast_fp16")];
            fp16 const_200_promoted_to_fp16 = const()[name = string("const_200_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_7267_cast_fp16 = mul(x = hidden_states_195_cast_fp16, y = const_200_promoted_to_fp16)[name = string("op_7267_cast_fp16")];
            int32 var_7265 = const()[name = string("op_7265"), val = int32(1)];
            bool doubled_157_interleave_0 = const()[name = string("doubled_157_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_157_cast_fp16 = concat(axis = var_7265, interleave = doubled_157_interleave_0, values = (hidden_states_195_cast_fp16, var_7267_cast_fp16))[name = string("doubled_157_cast_fp16")];
            tensor<int32, [1]> out_79_axes_0 = const()[name = string("out_79_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_79_gamma_0_to_fp16 = const()[name = string("out_79_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1493101696)))];
            fp16 var_7277_to_fp16 = const()[name = string("op_7277_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_79_cast_fp16 = layer_norm(axes = out_79_axes_0, epsilon = var_7277_to_fp16, gamma = out_79_gamma_0_to_fp16, x = doubled_157_cast_fp16)[name = string("out_79_cast_fp16")];
            tensor<int32, [2]> var_7288_split_sizes_0 = const()[name = string("op_7288_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7288_axis_0 = const()[name = string("op_7288_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_7288_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_7288_cast_fp16_1 = split(axis = var_7288_axis_0, split_sizes = var_7288_split_sizes_0, x = out_79_cast_fp16)[name = string("op_7288_cast_fp16")];
            tensor<int32, [2]> input_39_strides_0 = const()[name = string("input_39_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_39_pad_type_0 = const()[name = string("input_39_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_39_pad_0 = const()[name = string("input_39_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_39_dilations_0 = const()[name = string("input_39_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_39_groups_0 = const()[name = string("input_39_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_39_cast_fp16 = conv(dilations = input_39_dilations_0, groups = input_39_groups_0, pad = input_39_pad_0, pad_type = input_39_pad_type_0, strides = input_39_strides_0, weight = layers_19_mlp_gate_proj_weight_cast_fp16, x = var_7288_cast_fp16_0)[name = string("input_39_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_7305_cast_fp16 = silu(x = input_39_cast_fp16)[name = string("op_7305_cast_fp16")];
            tensor<int32, [2]> var_7311_strides_0 = const()[name = string("op_7311_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_7311_pad_type_0 = const()[name = string("op_7311_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_7311_pad_0 = const()[name = string("op_7311_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_7311_dilations_0 = const()[name = string("op_7311_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7311_groups_0 = const()[name = string("op_7311_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_7311_cast_fp16 = conv(dilations = var_7311_dilations_0, groups = var_7311_groups_0, pad = var_7311_pad_0, pad_type = var_7311_pad_type_0, strides = var_7311_strides_0, weight = layers_19_mlp_up_proj_weight_cast_fp16, x = var_7288_cast_fp16_0)[name = string("op_7311_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_199_cast_fp16 = mul(x = var_7305_cast_fp16, y = var_7311_cast_fp16)[name = string("x_199_cast_fp16")];
            tensor<int32, [2]> hidden_states_197_strides_0 = const()[name = string("hidden_states_197_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_197_pad_type_0 = const()[name = string("hidden_states_197_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_197_pad_0 = const()[name = string("hidden_states_197_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_197_dilations_0 = const()[name = string("hidden_states_197_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_197_groups_0 = const()[name = string("hidden_states_197_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_197_cast_fp16 = conv(dilations = hidden_states_197_dilations_0, groups = hidden_states_197_groups_0, pad = hidden_states_197_pad_0, pad_type = hidden_states_197_pad_type_0, strides = hidden_states_197_strides_0, weight = layers_19_mlp_down_proj_weight_cast_fp16, x = x_199_cast_fp16)[name = string("hidden_states_197_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_199_cast_fp16 = add(x = hidden_states_195_cast_fp16, y = hidden_states_197_cast_fp16)[name = string("hidden_states_199_cast_fp16")];
            fp16 const_202_promoted_to_fp16 = const()[name = string("const_202_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_7329_cast_fp16 = mul(x = hidden_states_199_cast_fp16, y = const_202_promoted_to_fp16)[name = string("op_7329_cast_fp16")];
            int32 var_7327 = const()[name = string("op_7327"), val = int32(1)];
            bool doubled_161_interleave_0 = const()[name = string("doubled_161_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_161_cast_fp16 = concat(axis = var_7327, interleave = doubled_161_interleave_0, values = (hidden_states_199_cast_fp16, var_7329_cast_fp16))[name = string("doubled_161_cast_fp16")];
            tensor<int32, [1]> out_81_axes_0 = const()[name = string("out_81_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_81_gamma_0_to_fp16 = const()[name = string("out_81_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1493109952)))];
            fp16 var_7339_to_fp16 = const()[name = string("op_7339_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_81_cast_fp16 = layer_norm(axes = out_81_axes_0, epsilon = var_7339_to_fp16, gamma = out_81_gamma_0_to_fp16, x = doubled_161_cast_fp16)[name = string("out_81_cast_fp16")];
            tensor<int32, [2]> var_7350_split_sizes_0 = const()[name = string("op_7350_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7350_axis_0 = const()[name = string("op_7350_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_7350_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_7350_cast_fp16_1 = split(axis = var_7350_axis_0, split_sizes = var_7350_split_sizes_0, x = out_81_cast_fp16)[name = string("op_7350_cast_fp16")];
            tensor<int32, [2]> query_states_121_strides_0 = const()[name = string("query_states_121_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_121_pad_type_0 = const()[name = string("query_states_121_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_121_pad_0 = const()[name = string("query_states_121_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_121_dilations_0 = const()[name = string("query_states_121_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_121_groups_0 = const()[name = string("query_states_121_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_121_cast_fp16 = conv(dilations = query_states_121_dilations_0, groups = query_states_121_groups_0, pad = query_states_121_pad_0, pad_type = query_states_121_pad_type_0, strides = query_states_121_strides_0, weight = layers_20_self_attn_q_proj_weight_cast_fp16, x = var_7350_cast_fp16_0)[name = string("query_states_121_cast_fp16")];
            tensor<int32, [2]> key_states_201_strides_0 = const()[name = string("key_states_201_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_201_pad_type_0 = const()[name = string("key_states_201_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_201_pad_0 = const()[name = string("key_states_201_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_201_dilations_0 = const()[name = string("key_states_201_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_201_groups_0 = const()[name = string("key_states_201_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_201_cast_fp16 = conv(dilations = key_states_201_dilations_0, groups = key_states_201_groups_0, pad = key_states_201_pad_0, pad_type = key_states_201_pad_type_0, strides = key_states_201_strides_0, weight = layers_20_self_attn_k_proj_weight_cast_fp16, x = var_7350_cast_fp16_0)[name = string("key_states_201_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_20_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_20_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1493118208)))];
            tensor<int32, [2]> value_states_121_strides_0 = const()[name = string("value_states_121_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_121_pad_type_0 = const()[name = string("value_states_121_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_121_pad_0 = const()[name = string("value_states_121_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_121_dilations_0 = const()[name = string("value_states_121_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_121_groups_0 = const()[name = string("value_states_121_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_121_cast_fp16 = conv(dilations = value_states_121_dilations_0, groups = value_states_121_groups_0, pad = value_states_121_pad_0, pad_type = value_states_121_pad_type_0, strides = value_states_121_strides_0, weight = layers_20_self_attn_v_proj_weight_to_fp16, x = var_7350_cast_fp16_0)[name = string("value_states_121_cast_fp16")];
            tensor<int32, [4]> concat_240x = const()[name = string("concat_240x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_201_cast_fp16 = reshape(shape = concat_240x, x = query_states_121_cast_fp16)[name = string("x_201_cast_fp16")];
            tensor<int32, [4]> concat_241x = const()[name = string("concat_241x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_7407_cast_fp16 = reshape(shape = concat_241x, x = key_states_201_cast_fp16)[name = string("op_7407_cast_fp16")];
            tensor<int32, [4]> concat_242x = const()[name = string("concat_242x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_7414_cast_fp16 = reshape(shape = concat_242x, x = value_states_121_cast_fp16)[name = string("op_7414_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_7418_cast_fp16 = mul(x = x_201_cast_fp16, y = var_859_cast_fp16)[name = string("op_7418_cast_fp16")];
            tensor<int32, [2]> var_7419_split_sizes_0 = const()[name = string("op_7419_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7419_axis_0 = const()[name = string("op_7419_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_7419_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_7419_cast_fp16_1 = split(axis = var_7419_axis_0, split_sizes = var_7419_split_sizes_0, x = x_201_cast_fp16)[name = string("op_7419_cast_fp16")];
            fp16 const_204_promoted_to_fp16 = const()[name = string("const_204_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_7421_cast_fp16 = mul(x = var_7419_cast_fp16_1, y = const_204_promoted_to_fp16)[name = string("op_7421_cast_fp16")];
            int32 var_7423 = const()[name = string("op_7423"), val = int32(-2)];
            bool var_7424_interleave_0 = const()[name = string("op_7424_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_7424_cast_fp16 = concat(axis = var_7423, interleave = var_7424_interleave_0, values = (var_7421_cast_fp16, var_7419_cast_fp16_0))[name = string("op_7424_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_7425_cast_fp16 = mul(x = var_7424_cast_fp16, y = var_866_cast_fp16)[name = string("op_7425_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_123_cast_fp16 = add(x = var_7418_cast_fp16, y = var_7425_cast_fp16)[name = string("query_states_123_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_7431_cast_fp16 = mul(x = var_7407_cast_fp16, y = var_859_cast_fp16)[name = string("op_7431_cast_fp16")];
            tensor<int32, [2]> var_7432_split_sizes_0 = const()[name = string("op_7432_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7432_axis_0 = const()[name = string("op_7432_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_7432_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_7432_cast_fp16_1 = split(axis = var_7432_axis_0, split_sizes = var_7432_split_sizes_0, x = var_7407_cast_fp16)[name = string("op_7432_cast_fp16")];
            fp16 const_205_promoted_to_fp16 = const()[name = string("const_205_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_7434_cast_fp16 = mul(x = var_7432_cast_fp16_1, y = const_205_promoted_to_fp16)[name = string("op_7434_cast_fp16")];
            int32 var_7436 = const()[name = string("op_7436"), val = int32(-2)];
            bool var_7437_interleave_0 = const()[name = string("op_7437_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_7437_cast_fp16 = concat(axis = var_7436, interleave = var_7437_interleave_0, values = (var_7434_cast_fp16, var_7432_cast_fp16_0))[name = string("op_7437_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_7438_cast_fp16 = mul(x = var_7437_cast_fp16, y = var_866_cast_fp16)[name = string("op_7438_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_205_cast_fp16 = add(x = var_7431_cast_fp16, y = var_7438_cast_fp16)[name = string("key_states_205_cast_fp16")];
            tensor<int32, [1]> expand_dims_240 = const()[name = string("expand_dims_240"), val = tensor<int32, [1]>([20])];
            tensor<int32, [1]> expand_dims_241 = const()[name = string("expand_dims_241"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_243 = const()[name = string("expand_dims_243"), val = tensor<int32, [1]>([0])];
            int32 concat_245_axis_0 = const()[name = string("concat_245_axis_0"), val = int32(0)];
            bool concat_245_interleave_0 = const()[name = string("concat_245_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_245 = concat(axis = concat_245_axis_0, interleave = concat_245_interleave_0, values = (expand_dims_240, expand_dims_241, position_id, expand_dims_243))[name = string("concat_245")];
            tensor<int32, [1]> expand_dims_244 = const()[name = string("expand_dims_244"), val = tensor<int32, [1]>([21])];
            tensor<int32, [1]> concat_246_values1_0 = const()[name = string("concat_246_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_246_values3_0 = const()[name = string("concat_246_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_246_axis_0 = const()[name = string("concat_246_axis_0"), val = int32(0)];
            bool concat_246_interleave_0 = const()[name = string("concat_246_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_246 = concat(axis = concat_246_axis_0, interleave = concat_246_interleave_0, values = (expand_dims_244, concat_246_values1_0, cache_position_end, concat_246_values3_0))[name = string("concat_246")];
            tensor<int32, [4]> key_states_207_perm_0 = const()[name = string("key_states_207_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_21_stride_0 = const()[name = string("key_cache_internal_tensor_assign_21_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_21_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_21_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_21_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_21_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_21_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_21_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_207_cast_fp16 = transpose(perm = key_states_207_perm_0, x = key_states_205_cast_fp16)[name = string("transpose_458")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_21_cast_fp16 = slice_update(begin = concat_245, begin_mask = key_cache_internal_tensor_assign_21_begin_mask_0, end = concat_246, end_mask = key_cache_internal_tensor_assign_21_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_21_squeeze_mask_0, stride = key_cache_internal_tensor_assign_21_stride_0, update = key_states_207_cast_fp16, x = coreml_update_state_318)[name = string("key_cache_internal_tensor_assign_21_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_21_cast_fp16, input = key_cache)[name = string("coreml_update_state_320_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_320 = read_state(input = key_cache)[name = string("coreml_update_state_320")];
            tensor<int32, [4]> value_states_123_perm_0 = const()[name = string("value_states_123_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_21_stride_0 = const()[name = string("value_cache_internal_tensor_assign_21_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_21_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_21_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_21_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_21_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_21_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_21_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_123_cast_fp16 = transpose(perm = value_states_123_perm_0, x = var_7414_cast_fp16)[name = string("transpose_457")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_21_cast_fp16 = slice_update(begin = concat_245, begin_mask = value_cache_internal_tensor_assign_21_begin_mask_0, end = concat_246, end_mask = value_cache_internal_tensor_assign_21_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_21_squeeze_mask_0, stride = value_cache_internal_tensor_assign_21_stride_0, update = value_states_123_cast_fp16, x = coreml_update_state_319)[name = string("value_cache_internal_tensor_assign_21_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_21_cast_fp16, input = value_cache)[name = string("coreml_update_state_321_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_321 = read_state(input = value_cache)[name = string("coreml_update_state_321")];
            tensor<int32, [4]> var_7508_begin_0 = const()[name = string("op_7508_begin_0"), val = tensor<int32, [4]>([20, 0, 0, 0])];
            tensor<int32, [4]> var_7508_end_0 = const()[name = string("op_7508_end_0"), val = tensor<int32, [4]>([21, 2, 2048, 128])];
            tensor<bool, [4]> var_7508_end_mask_0 = const()[name = string("op_7508_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7508_cast_fp16 = slice_by_index(begin = var_7508_begin_0, end = var_7508_end_0, end_mask = var_7508_end_mask_0, x = coreml_update_state_320)[name = string("op_7508_cast_fp16")];
            tensor<int32, [2]> tile_40 = const()[name = string("tile_40"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7511_axis_0 = const()[name = string("op_7511_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7511_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7511_cast_fp16_1 = split(axis = var_7511_axis_0, split_sizes = tile_40, x = var_7508_cast_fp16)[name = string("op_7511_cast_fp16")];
            tensor<int32, [4]> var_7518_begin_0 = const()[name = string("op_7518_begin_0"), val = tensor<int32, [4]>([20, 0, 0, 0])];
            tensor<int32, [4]> var_7518_end_0 = const()[name = string("op_7518_end_0"), val = tensor<int32, [4]>([21, 2, 2048, 128])];
            tensor<bool, [4]> var_7518_end_mask_0 = const()[name = string("op_7518_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7518_cast_fp16 = slice_by_index(begin = var_7518_begin_0, end = var_7518_end_0, end_mask = var_7518_end_mask_0, x = coreml_update_state_321)[name = string("op_7518_cast_fp16")];
            tensor<int32, [2]> tile_41 = const()[name = string("tile_41"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7521_axis_0 = const()[name = string("op_7521_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7521_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7521_cast_fp16_1 = split(axis = var_7521_axis_0, split_sizes = tile_41, x = var_7518_cast_fp16)[name = string("op_7521_cast_fp16")];
            tensor<int32, [2]> var_7524_split_sizes_0 = const()[name = string("op_7524_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_7524_axis_0 = const()[name = string("op_7524_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_7524_0, tensor<fp16, [1, 8, 128, 64]> var_7524_1 = split(axis = var_7524_axis_0, split_sizes = var_7524_split_sizes_0, x = query_states_123_cast_fp16)[name = string("op_7524")];
            bool attn_weights_321_transpose_x_0 = const()[name = string("attn_weights_321_transpose_x_0"), val = bool(false)];
            bool attn_weights_321_transpose_y_0 = const()[name = string("attn_weights_321_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_321_cast_fp16 = matmul(transpose_x = attn_weights_321_transpose_x_0, transpose_y = attn_weights_321_transpose_y_0, x = var_7511_cast_fp16_0, y = var_7524_0)[name = string("attn_weights_321_cast_fp16")];
            fp16 var_7527_to_fp16 = const()[name = string("op_7527_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_323_cast_fp16 = mul(x = attn_weights_321_cast_fp16, y = var_7527_to_fp16)[name = string("attn_weights_323_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_325_cast_fp16 = add(x = attn_weights_323_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_325_cast_fp16")];
            int32 var_7531 = const()[name = string("op_7531"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_327_cast_fp16 = softmax(axis = var_7531, x = attn_weights_325_cast_fp16)[name = string("attn_weights_327_cast_fp16")];
            bool var_7537_transpose_x_1 = const()[name = string("op_7537_transpose_x_1"), val = bool(true)];
            bool var_7537_transpose_y_1 = const()[name = string("op_7537_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_7537_cast_fp16 = matmul(transpose_x = var_7537_transpose_x_1, transpose_y = var_7537_transpose_y_1, x = attn_weights_327_cast_fp16, y = var_7521_cast_fp16_0)[name = string("op_7537_cast_fp16")];
            bool attn_weights_329_transpose_x_0 = const()[name = string("attn_weights_329_transpose_x_0"), val = bool(false)];
            bool attn_weights_329_transpose_y_0 = const()[name = string("attn_weights_329_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_329_cast_fp16 = matmul(transpose_x = attn_weights_329_transpose_x_0, transpose_y = attn_weights_329_transpose_y_0, x = var_7511_cast_fp16_1, y = var_7524_1)[name = string("attn_weights_329_cast_fp16")];
            fp16 var_7539_to_fp16 = const()[name = string("op_7539_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_331_cast_fp16 = mul(x = attn_weights_329_cast_fp16, y = var_7539_to_fp16)[name = string("attn_weights_331_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_333_cast_fp16 = add(x = attn_weights_331_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_333_cast_fp16")];
            int32 var_7543 = const()[name = string("op_7543"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_335_cast_fp16 = softmax(axis = var_7543, x = attn_weights_333_cast_fp16)[name = string("attn_weights_335_cast_fp16")];
            bool attn_output_161_transpose_x_1 = const()[name = string("attn_output_161_transpose_x_1"), val = bool(true)];
            bool attn_output_161_transpose_y_1 = const()[name = string("attn_output_161_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_161_cast_fp16 = matmul(transpose_x = attn_output_161_transpose_x_1, transpose_y = attn_output_161_transpose_y_1, x = attn_weights_335_cast_fp16, y = var_7521_cast_fp16_1)[name = string("attn_output_161_cast_fp16")];
            int32 var_7551 = const()[name = string("op_7551"), val = int32(1)];
            bool attn_output_163_interleave_0 = const()[name = string("attn_output_163_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_163_cast_fp16 = concat(axis = var_7551, interleave = attn_output_163_interleave_0, values = (var_7537_cast_fp16, attn_output_161_cast_fp16))[name = string("attn_output_163_cast_fp16")];
            tensor<int32, [4]> var_7555_perm_0 = const()[name = string("op_7555_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_251x = const()[name = string("concat_251x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_7555_cast_fp16 = transpose(perm = var_7555_perm_0, x = attn_output_163_cast_fp16)[name = string("transpose_456")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_167_cast_fp16 = reshape(shape = concat_251x, x = var_7555_cast_fp16)[name = string("attn_output_167_cast_fp16")];
            tensor<int32, [2]> hidden_states_203_strides_0 = const()[name = string("hidden_states_203_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_203_pad_type_0 = const()[name = string("hidden_states_203_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_203_pad_0 = const()[name = string("hidden_states_203_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_203_dilations_0 = const()[name = string("hidden_states_203_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_203_groups_0 = const()[name = string("hidden_states_203_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_203_cast_fp16 = conv(dilations = hidden_states_203_dilations_0, groups = hidden_states_203_groups_0, pad = hidden_states_203_pad_0, pad_type = hidden_states_203_pad_type_0, strides = hidden_states_203_strides_0, weight = layers_20_self_attn_o_proj_weight_cast_fp16, x = attn_output_167_cast_fp16)[name = string("hidden_states_203_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_205_cast_fp16 = add(x = hidden_states_199_cast_fp16, y = hidden_states_203_cast_fp16)[name = string("hidden_states_205_cast_fp16")];
            fp16 const_210_promoted_to_fp16 = const()[name = string("const_210_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_7588_cast_fp16 = mul(x = hidden_states_205_cast_fp16, y = const_210_promoted_to_fp16)[name = string("op_7588_cast_fp16")];
            int32 var_7586 = const()[name = string("op_7586"), val = int32(1)];
            bool doubled_165_interleave_0 = const()[name = string("doubled_165_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_165_cast_fp16 = concat(axis = var_7586, interleave = doubled_165_interleave_0, values = (hidden_states_205_cast_fp16, var_7588_cast_fp16))[name = string("doubled_165_cast_fp16")];
            tensor<int32, [1]> out_83_axes_0 = const()[name = string("out_83_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_83_gamma_0_to_fp16 = const()[name = string("out_83_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1494166848)))];
            fp16 var_7598_to_fp16 = const()[name = string("op_7598_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_83_cast_fp16 = layer_norm(axes = out_83_axes_0, epsilon = var_7598_to_fp16, gamma = out_83_gamma_0_to_fp16, x = doubled_165_cast_fp16)[name = string("out_83_cast_fp16")];
            tensor<int32, [2]> var_7609_split_sizes_0 = const()[name = string("op_7609_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7609_axis_0 = const()[name = string("op_7609_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_7609_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_7609_cast_fp16_1 = split(axis = var_7609_axis_0, split_sizes = var_7609_split_sizes_0, x = out_83_cast_fp16)[name = string("op_7609_cast_fp16")];
            tensor<int32, [2]> input_41_strides_0 = const()[name = string("input_41_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_41_pad_type_0 = const()[name = string("input_41_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_41_pad_0 = const()[name = string("input_41_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_41_dilations_0 = const()[name = string("input_41_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_41_groups_0 = const()[name = string("input_41_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_41_cast_fp16 = conv(dilations = input_41_dilations_0, groups = input_41_groups_0, pad = input_41_pad_0, pad_type = input_41_pad_type_0, strides = input_41_strides_0, weight = layers_20_mlp_gate_proj_weight_cast_fp16, x = var_7609_cast_fp16_0)[name = string("input_41_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_7626_cast_fp16 = silu(x = input_41_cast_fp16)[name = string("op_7626_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_20_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_20_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1494175104)))];
            tensor<int32, [2]> var_7632_strides_0 = const()[name = string("op_7632_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_7632_pad_type_0 = const()[name = string("op_7632_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_7632_pad_0 = const()[name = string("op_7632_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_7632_dilations_0 = const()[name = string("op_7632_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7632_groups_0 = const()[name = string("op_7632_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_7632_cast_fp16 = conv(dilations = var_7632_dilations_0, groups = var_7632_groups_0, pad = var_7632_pad_0, pad_type = var_7632_pad_type_0, strides = var_7632_strides_0, weight = layers_20_mlp_up_proj_weight_to_fp16, x = var_7609_cast_fp16_0)[name = string("op_7632_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_209_cast_fp16 = mul(x = var_7626_cast_fp16, y = var_7632_cast_fp16)[name = string("x_209_cast_fp16")];
            tensor<int32, [2]> hidden_states_207_strides_0 = const()[name = string("hidden_states_207_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_207_pad_type_0 = const()[name = string("hidden_states_207_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_207_pad_0 = const()[name = string("hidden_states_207_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_207_dilations_0 = const()[name = string("hidden_states_207_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_207_groups_0 = const()[name = string("hidden_states_207_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_207_cast_fp16 = conv(dilations = hidden_states_207_dilations_0, groups = hidden_states_207_groups_0, pad = hidden_states_207_pad_0, pad_type = hidden_states_207_pad_type_0, strides = hidden_states_207_strides_0, weight = layers_20_mlp_down_proj_weight_cast_fp16, x = x_209_cast_fp16)[name = string("hidden_states_207_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_209_cast_fp16 = add(x = hidden_states_205_cast_fp16, y = hidden_states_207_cast_fp16)[name = string("hidden_states_209_cast_fp16")];
            fp16 const_212_promoted_to_fp16 = const()[name = string("const_212_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_7650_cast_fp16 = mul(x = hidden_states_209_cast_fp16, y = const_212_promoted_to_fp16)[name = string("op_7650_cast_fp16")];
            int32 var_7648 = const()[name = string("op_7648"), val = int32(1)];
            bool doubled_169_interleave_0 = const()[name = string("doubled_169_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_169_cast_fp16 = concat(axis = var_7648, interleave = doubled_169_interleave_0, values = (hidden_states_209_cast_fp16, var_7650_cast_fp16))[name = string("doubled_169_cast_fp16")];
            tensor<int32, [1]> out_85_axes_0 = const()[name = string("out_85_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_85_gamma_0_to_fp16 = const()[name = string("out_85_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1519340992)))];
            fp16 var_7660_to_fp16 = const()[name = string("op_7660_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_85_cast_fp16 = layer_norm(axes = out_85_axes_0, epsilon = var_7660_to_fp16, gamma = out_85_gamma_0_to_fp16, x = doubled_169_cast_fp16)[name = string("out_85_cast_fp16")];
            tensor<int32, [2]> var_7671_split_sizes_0 = const()[name = string("op_7671_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7671_axis_0 = const()[name = string("op_7671_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_7671_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_7671_cast_fp16_1 = split(axis = var_7671_axis_0, split_sizes = var_7671_split_sizes_0, x = out_85_cast_fp16)[name = string("op_7671_cast_fp16")];
            tensor<int32, [2]> query_states_127_strides_0 = const()[name = string("query_states_127_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_127_pad_type_0 = const()[name = string("query_states_127_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_127_pad_0 = const()[name = string("query_states_127_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_127_dilations_0 = const()[name = string("query_states_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_127_groups_0 = const()[name = string("query_states_127_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_127_cast_fp16 = conv(dilations = query_states_127_dilations_0, groups = query_states_127_groups_0, pad = query_states_127_pad_0, pad_type = query_states_127_pad_type_0, strides = query_states_127_strides_0, weight = layers_21_self_attn_q_proj_weight_cast_fp16, x = var_7671_cast_fp16_0)[name = string("query_states_127_cast_fp16")];
            tensor<int32, [2]> key_states_211_strides_0 = const()[name = string("key_states_211_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_211_pad_type_0 = const()[name = string("key_states_211_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_211_pad_0 = const()[name = string("key_states_211_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_211_dilations_0 = const()[name = string("key_states_211_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_211_groups_0 = const()[name = string("key_states_211_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_211_cast_fp16 = conv(dilations = key_states_211_dilations_0, groups = key_states_211_groups_0, pad = key_states_211_pad_0, pad_type = key_states_211_pad_type_0, strides = key_states_211_strides_0, weight = layers_21_self_attn_k_proj_weight_cast_fp16, x = var_7671_cast_fp16_0)[name = string("key_states_211_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_21_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_21_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1519349248)))];
            tensor<int32, [2]> value_states_127_strides_0 = const()[name = string("value_states_127_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_127_pad_type_0 = const()[name = string("value_states_127_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_127_pad_0 = const()[name = string("value_states_127_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_127_dilations_0 = const()[name = string("value_states_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_127_groups_0 = const()[name = string("value_states_127_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_127_cast_fp16 = conv(dilations = value_states_127_dilations_0, groups = value_states_127_groups_0, pad = value_states_127_pad_0, pad_type = value_states_127_pad_type_0, strides = value_states_127_strides_0, weight = layers_21_self_attn_v_proj_weight_to_fp16, x = var_7671_cast_fp16_0)[name = string("value_states_127_cast_fp16")];
            tensor<int32, [4]> concat_252x = const()[name = string("concat_252x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_211_cast_fp16 = reshape(shape = concat_252x, x = query_states_127_cast_fp16)[name = string("x_211_cast_fp16")];
            tensor<int32, [4]> concat_253x = const()[name = string("concat_253x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_7728_cast_fp16 = reshape(shape = concat_253x, x = key_states_211_cast_fp16)[name = string("op_7728_cast_fp16")];
            tensor<int32, [4]> concat_254x = const()[name = string("concat_254x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_7735_cast_fp16 = reshape(shape = concat_254x, x = value_states_127_cast_fp16)[name = string("op_7735_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_7739_cast_fp16 = mul(x = x_211_cast_fp16, y = var_859_cast_fp16)[name = string("op_7739_cast_fp16")];
            tensor<int32, [2]> var_7740_split_sizes_0 = const()[name = string("op_7740_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7740_axis_0 = const()[name = string("op_7740_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_7740_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_7740_cast_fp16_1 = split(axis = var_7740_axis_0, split_sizes = var_7740_split_sizes_0, x = x_211_cast_fp16)[name = string("op_7740_cast_fp16")];
            fp16 const_214_promoted_to_fp16 = const()[name = string("const_214_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_7742_cast_fp16 = mul(x = var_7740_cast_fp16_1, y = const_214_promoted_to_fp16)[name = string("op_7742_cast_fp16")];
            int32 var_7744 = const()[name = string("op_7744"), val = int32(-2)];
            bool var_7745_interleave_0 = const()[name = string("op_7745_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_7745_cast_fp16 = concat(axis = var_7744, interleave = var_7745_interleave_0, values = (var_7742_cast_fp16, var_7740_cast_fp16_0))[name = string("op_7745_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_7746_cast_fp16 = mul(x = var_7745_cast_fp16, y = var_866_cast_fp16)[name = string("op_7746_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_129_cast_fp16 = add(x = var_7739_cast_fp16, y = var_7746_cast_fp16)[name = string("query_states_129_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_7752_cast_fp16 = mul(x = var_7728_cast_fp16, y = var_859_cast_fp16)[name = string("op_7752_cast_fp16")];
            tensor<int32, [2]> var_7753_split_sizes_0 = const()[name = string("op_7753_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7753_axis_0 = const()[name = string("op_7753_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_7753_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_7753_cast_fp16_1 = split(axis = var_7753_axis_0, split_sizes = var_7753_split_sizes_0, x = var_7728_cast_fp16)[name = string("op_7753_cast_fp16")];
            fp16 const_215_promoted_to_fp16 = const()[name = string("const_215_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_7755_cast_fp16 = mul(x = var_7753_cast_fp16_1, y = const_215_promoted_to_fp16)[name = string("op_7755_cast_fp16")];
            int32 var_7757 = const()[name = string("op_7757"), val = int32(-2)];
            bool var_7758_interleave_0 = const()[name = string("op_7758_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_7758_cast_fp16 = concat(axis = var_7757, interleave = var_7758_interleave_0, values = (var_7755_cast_fp16, var_7753_cast_fp16_0))[name = string("op_7758_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_7759_cast_fp16 = mul(x = var_7758_cast_fp16, y = var_866_cast_fp16)[name = string("op_7759_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_215_cast_fp16 = add(x = var_7752_cast_fp16, y = var_7759_cast_fp16)[name = string("key_states_215_cast_fp16")];
            tensor<int32, [1]> expand_dims_252 = const()[name = string("expand_dims_252"), val = tensor<int32, [1]>([21])];
            tensor<int32, [1]> expand_dims_253 = const()[name = string("expand_dims_253"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_255 = const()[name = string("expand_dims_255"), val = tensor<int32, [1]>([0])];
            int32 concat_257_axis_0 = const()[name = string("concat_257_axis_0"), val = int32(0)];
            bool concat_257_interleave_0 = const()[name = string("concat_257_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_257 = concat(axis = concat_257_axis_0, interleave = concat_257_interleave_0, values = (expand_dims_252, expand_dims_253, position_id, expand_dims_255))[name = string("concat_257")];
            tensor<int32, [1]> expand_dims_256 = const()[name = string("expand_dims_256"), val = tensor<int32, [1]>([22])];
            tensor<int32, [1]> concat_258_values1_0 = const()[name = string("concat_258_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_258_values3_0 = const()[name = string("concat_258_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_258_axis_0 = const()[name = string("concat_258_axis_0"), val = int32(0)];
            bool concat_258_interleave_0 = const()[name = string("concat_258_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_258 = concat(axis = concat_258_axis_0, interleave = concat_258_interleave_0, values = (expand_dims_256, concat_258_values1_0, cache_position_end, concat_258_values3_0))[name = string("concat_258")];
            tensor<int32, [4]> key_states_217_perm_0 = const()[name = string("key_states_217_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_22_stride_0 = const()[name = string("key_cache_internal_tensor_assign_22_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_22_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_22_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_22_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_22_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_22_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_22_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_217_cast_fp16 = transpose(perm = key_states_217_perm_0, x = key_states_215_cast_fp16)[name = string("transpose_455")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_22_cast_fp16 = slice_update(begin = concat_257, begin_mask = key_cache_internal_tensor_assign_22_begin_mask_0, end = concat_258, end_mask = key_cache_internal_tensor_assign_22_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_22_squeeze_mask_0, stride = key_cache_internal_tensor_assign_22_stride_0, update = key_states_217_cast_fp16, x = coreml_update_state_320)[name = string("key_cache_internal_tensor_assign_22_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_22_cast_fp16, input = key_cache)[name = string("coreml_update_state_322_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_322 = read_state(input = key_cache)[name = string("coreml_update_state_322")];
            tensor<int32, [4]> value_states_129_perm_0 = const()[name = string("value_states_129_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_22_stride_0 = const()[name = string("value_cache_internal_tensor_assign_22_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_22_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_22_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_22_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_22_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_22_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_22_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_129_cast_fp16 = transpose(perm = value_states_129_perm_0, x = var_7735_cast_fp16)[name = string("transpose_454")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_22_cast_fp16 = slice_update(begin = concat_257, begin_mask = value_cache_internal_tensor_assign_22_begin_mask_0, end = concat_258, end_mask = value_cache_internal_tensor_assign_22_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_22_squeeze_mask_0, stride = value_cache_internal_tensor_assign_22_stride_0, update = value_states_129_cast_fp16, x = coreml_update_state_321)[name = string("value_cache_internal_tensor_assign_22_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_22_cast_fp16, input = value_cache)[name = string("coreml_update_state_323_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_323 = read_state(input = value_cache)[name = string("coreml_update_state_323")];
            tensor<int32, [4]> var_7829_begin_0 = const()[name = string("op_7829_begin_0"), val = tensor<int32, [4]>([21, 0, 0, 0])];
            tensor<int32, [4]> var_7829_end_0 = const()[name = string("op_7829_end_0"), val = tensor<int32, [4]>([22, 2, 2048, 128])];
            tensor<bool, [4]> var_7829_end_mask_0 = const()[name = string("op_7829_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7829_cast_fp16 = slice_by_index(begin = var_7829_begin_0, end = var_7829_end_0, end_mask = var_7829_end_mask_0, x = coreml_update_state_322)[name = string("op_7829_cast_fp16")];
            tensor<int32, [2]> tile_42 = const()[name = string("tile_42"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7832_axis_0 = const()[name = string("op_7832_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7832_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7832_cast_fp16_1 = split(axis = var_7832_axis_0, split_sizes = tile_42, x = var_7829_cast_fp16)[name = string("op_7832_cast_fp16")];
            tensor<int32, [4]> var_7839_begin_0 = const()[name = string("op_7839_begin_0"), val = tensor<int32, [4]>([21, 0, 0, 0])];
            tensor<int32, [4]> var_7839_end_0 = const()[name = string("op_7839_end_0"), val = tensor<int32, [4]>([22, 2, 2048, 128])];
            tensor<bool, [4]> var_7839_end_mask_0 = const()[name = string("op_7839_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7839_cast_fp16 = slice_by_index(begin = var_7839_begin_0, end = var_7839_end_0, end_mask = var_7839_end_mask_0, x = coreml_update_state_323)[name = string("op_7839_cast_fp16")];
            tensor<int32, [2]> tile_43 = const()[name = string("tile_43"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7842_axis_0 = const()[name = string("op_7842_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7842_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7842_cast_fp16_1 = split(axis = var_7842_axis_0, split_sizes = tile_43, x = var_7839_cast_fp16)[name = string("op_7842_cast_fp16")];
            tensor<int32, [2]> var_7845_split_sizes_0 = const()[name = string("op_7845_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_7845_axis_0 = const()[name = string("op_7845_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_7845_0, tensor<fp16, [1, 8, 128, 64]> var_7845_1 = split(axis = var_7845_axis_0, split_sizes = var_7845_split_sizes_0, x = query_states_129_cast_fp16)[name = string("op_7845")];
            bool attn_weights_337_transpose_x_0 = const()[name = string("attn_weights_337_transpose_x_0"), val = bool(false)];
            bool attn_weights_337_transpose_y_0 = const()[name = string("attn_weights_337_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_337_cast_fp16 = matmul(transpose_x = attn_weights_337_transpose_x_0, transpose_y = attn_weights_337_transpose_y_0, x = var_7832_cast_fp16_0, y = var_7845_0)[name = string("attn_weights_337_cast_fp16")];
            fp16 var_7848_to_fp16 = const()[name = string("op_7848_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_339_cast_fp16 = mul(x = attn_weights_337_cast_fp16, y = var_7848_to_fp16)[name = string("attn_weights_339_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_341_cast_fp16 = add(x = attn_weights_339_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_341_cast_fp16")];
            int32 var_7852 = const()[name = string("op_7852"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_343_cast_fp16 = softmax(axis = var_7852, x = attn_weights_341_cast_fp16)[name = string("attn_weights_343_cast_fp16")];
            bool var_7858_transpose_x_1 = const()[name = string("op_7858_transpose_x_1"), val = bool(true)];
            bool var_7858_transpose_y_1 = const()[name = string("op_7858_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_7858_cast_fp16 = matmul(transpose_x = var_7858_transpose_x_1, transpose_y = var_7858_transpose_y_1, x = attn_weights_343_cast_fp16, y = var_7842_cast_fp16_0)[name = string("op_7858_cast_fp16")];
            bool attn_weights_345_transpose_x_0 = const()[name = string("attn_weights_345_transpose_x_0"), val = bool(false)];
            bool attn_weights_345_transpose_y_0 = const()[name = string("attn_weights_345_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_345_cast_fp16 = matmul(transpose_x = attn_weights_345_transpose_x_0, transpose_y = attn_weights_345_transpose_y_0, x = var_7832_cast_fp16_1, y = var_7845_1)[name = string("attn_weights_345_cast_fp16")];
            fp16 var_7860_to_fp16 = const()[name = string("op_7860_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_347_cast_fp16 = mul(x = attn_weights_345_cast_fp16, y = var_7860_to_fp16)[name = string("attn_weights_347_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_349_cast_fp16 = add(x = attn_weights_347_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_349_cast_fp16")];
            int32 var_7864 = const()[name = string("op_7864"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_351_cast_fp16 = softmax(axis = var_7864, x = attn_weights_349_cast_fp16)[name = string("attn_weights_351_cast_fp16")];
            bool attn_output_169_transpose_x_1 = const()[name = string("attn_output_169_transpose_x_1"), val = bool(true)];
            bool attn_output_169_transpose_y_1 = const()[name = string("attn_output_169_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_169_cast_fp16 = matmul(transpose_x = attn_output_169_transpose_x_1, transpose_y = attn_output_169_transpose_y_1, x = attn_weights_351_cast_fp16, y = var_7842_cast_fp16_1)[name = string("attn_output_169_cast_fp16")];
            int32 var_7872 = const()[name = string("op_7872"), val = int32(1)];
            bool attn_output_171_interleave_0 = const()[name = string("attn_output_171_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_171_cast_fp16 = concat(axis = var_7872, interleave = attn_output_171_interleave_0, values = (var_7858_cast_fp16, attn_output_169_cast_fp16))[name = string("attn_output_171_cast_fp16")];
            tensor<int32, [4]> var_7876_perm_0 = const()[name = string("op_7876_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_263x = const()[name = string("concat_263x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_7876_cast_fp16 = transpose(perm = var_7876_perm_0, x = attn_output_171_cast_fp16)[name = string("transpose_453")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_175_cast_fp16 = reshape(shape = concat_263x, x = var_7876_cast_fp16)[name = string("attn_output_175_cast_fp16")];
            tensor<int32, [2]> hidden_states_213_strides_0 = const()[name = string("hidden_states_213_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_213_pad_type_0 = const()[name = string("hidden_states_213_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_213_pad_0 = const()[name = string("hidden_states_213_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_213_dilations_0 = const()[name = string("hidden_states_213_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_213_groups_0 = const()[name = string("hidden_states_213_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_213_cast_fp16 = conv(dilations = hidden_states_213_dilations_0, groups = hidden_states_213_groups_0, pad = hidden_states_213_pad_0, pad_type = hidden_states_213_pad_type_0, strides = hidden_states_213_strides_0, weight = layers_21_self_attn_o_proj_weight_cast_fp16, x = attn_output_175_cast_fp16)[name = string("hidden_states_213_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_215_cast_fp16 = add(x = hidden_states_209_cast_fp16, y = hidden_states_213_cast_fp16)[name = string("hidden_states_215_cast_fp16")];
            fp16 const_220_promoted_to_fp16 = const()[name = string("const_220_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_7909_cast_fp16 = mul(x = hidden_states_215_cast_fp16, y = const_220_promoted_to_fp16)[name = string("op_7909_cast_fp16")];
            int32 var_7907 = const()[name = string("op_7907"), val = int32(1)];
            bool doubled_173_interleave_0 = const()[name = string("doubled_173_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_173_cast_fp16 = concat(axis = var_7907, interleave = doubled_173_interleave_0, values = (hidden_states_215_cast_fp16, var_7909_cast_fp16))[name = string("doubled_173_cast_fp16")];
            tensor<int32, [1]> out_87_axes_0 = const()[name = string("out_87_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_87_gamma_0_to_fp16 = const()[name = string("out_87_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1520397888)))];
            fp16 var_7919_to_fp16 = const()[name = string("op_7919_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_87_cast_fp16 = layer_norm(axes = out_87_axes_0, epsilon = var_7919_to_fp16, gamma = out_87_gamma_0_to_fp16, x = doubled_173_cast_fp16)[name = string("out_87_cast_fp16")];
            tensor<int32, [2]> var_7930_split_sizes_0 = const()[name = string("op_7930_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7930_axis_0 = const()[name = string("op_7930_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_7930_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_7930_cast_fp16_1 = split(axis = var_7930_axis_0, split_sizes = var_7930_split_sizes_0, x = out_87_cast_fp16)[name = string("op_7930_cast_fp16")];
            tensor<int32, [2]> input_43_strides_0 = const()[name = string("input_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_43_pad_type_0 = const()[name = string("input_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_43_pad_0 = const()[name = string("input_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_43_dilations_0 = const()[name = string("input_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_43_groups_0 = const()[name = string("input_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_43_cast_fp16 = conv(dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = layers_21_mlp_gate_proj_weight_cast_fp16, x = var_7930_cast_fp16_0)[name = string("input_43_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_7947_cast_fp16 = silu(x = input_43_cast_fp16)[name = string("op_7947_cast_fp16")];
            tensor<int32, [2]> var_7953_strides_0 = const()[name = string("op_7953_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_7953_pad_type_0 = const()[name = string("op_7953_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_7953_pad_0 = const()[name = string("op_7953_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_7953_dilations_0 = const()[name = string("op_7953_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7953_groups_0 = const()[name = string("op_7953_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_7953_cast_fp16 = conv(dilations = var_7953_dilations_0, groups = var_7953_groups_0, pad = var_7953_pad_0, pad_type = var_7953_pad_type_0, strides = var_7953_strides_0, weight = layers_21_mlp_up_proj_weight_cast_fp16, x = var_7930_cast_fp16_0)[name = string("op_7953_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_219_cast_fp16 = mul(x = var_7947_cast_fp16, y = var_7953_cast_fp16)[name = string("x_219_cast_fp16")];
            tensor<int32, [2]> hidden_states_217_strides_0 = const()[name = string("hidden_states_217_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_217_pad_type_0 = const()[name = string("hidden_states_217_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_217_pad_0 = const()[name = string("hidden_states_217_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_217_dilations_0 = const()[name = string("hidden_states_217_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_217_groups_0 = const()[name = string("hidden_states_217_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_217_cast_fp16 = conv(dilations = hidden_states_217_dilations_0, groups = hidden_states_217_groups_0, pad = hidden_states_217_pad_0, pad_type = hidden_states_217_pad_type_0, strides = hidden_states_217_strides_0, weight = layers_21_mlp_down_proj_weight_cast_fp16, x = x_219_cast_fp16)[name = string("hidden_states_217_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_219_cast_fp16 = add(x = hidden_states_215_cast_fp16, y = hidden_states_217_cast_fp16)[name = string("hidden_states_219_cast_fp16")];
            fp16 const_222_promoted_to_fp16 = const()[name = string("const_222_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_7971_cast_fp16 = mul(x = hidden_states_219_cast_fp16, y = const_222_promoted_to_fp16)[name = string("op_7971_cast_fp16")];
            int32 var_7969 = const()[name = string("op_7969"), val = int32(1)];
            bool doubled_177_interleave_0 = const()[name = string("doubled_177_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_177_cast_fp16 = concat(axis = var_7969, interleave = doubled_177_interleave_0, values = (hidden_states_219_cast_fp16, var_7971_cast_fp16))[name = string("doubled_177_cast_fp16")];
            tensor<int32, [1]> out_89_axes_0 = const()[name = string("out_89_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_89_gamma_0_to_fp16 = const()[name = string("out_89_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1520406144)))];
            fp16 var_7981_to_fp16 = const()[name = string("op_7981_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_89_cast_fp16 = layer_norm(axes = out_89_axes_0, epsilon = var_7981_to_fp16, gamma = out_89_gamma_0_to_fp16, x = doubled_177_cast_fp16)[name = string("out_89_cast_fp16")];
            tensor<int32, [2]> var_7992_split_sizes_0 = const()[name = string("op_7992_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7992_axis_0 = const()[name = string("op_7992_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_7992_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_7992_cast_fp16_1 = split(axis = var_7992_axis_0, split_sizes = var_7992_split_sizes_0, x = out_89_cast_fp16)[name = string("op_7992_cast_fp16")];
            tensor<int32, [2]> query_states_133_strides_0 = const()[name = string("query_states_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_133_pad_type_0 = const()[name = string("query_states_133_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_133_pad_0 = const()[name = string("query_states_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_133_dilations_0 = const()[name = string("query_states_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_133_groups_0 = const()[name = string("query_states_133_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_133_cast_fp16 = conv(dilations = query_states_133_dilations_0, groups = query_states_133_groups_0, pad = query_states_133_pad_0, pad_type = query_states_133_pad_type_0, strides = query_states_133_strides_0, weight = layers_22_self_attn_q_proj_weight_cast_fp16, x = var_7992_cast_fp16_0)[name = string("query_states_133_cast_fp16")];
            tensor<int32, [2]> key_states_221_strides_0 = const()[name = string("key_states_221_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_221_pad_type_0 = const()[name = string("key_states_221_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_221_pad_0 = const()[name = string("key_states_221_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_221_dilations_0 = const()[name = string("key_states_221_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_221_groups_0 = const()[name = string("key_states_221_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_221_cast_fp16 = conv(dilations = key_states_221_dilations_0, groups = key_states_221_groups_0, pad = key_states_221_pad_0, pad_type = key_states_221_pad_type_0, strides = key_states_221_strides_0, weight = layers_22_self_attn_k_proj_weight_cast_fp16, x = var_7992_cast_fp16_0)[name = string("key_states_221_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_22_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_22_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1520414400)))];
            tensor<int32, [2]> value_states_133_strides_0 = const()[name = string("value_states_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_133_pad_type_0 = const()[name = string("value_states_133_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_133_pad_0 = const()[name = string("value_states_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_133_dilations_0 = const()[name = string("value_states_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_133_groups_0 = const()[name = string("value_states_133_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_133_cast_fp16 = conv(dilations = value_states_133_dilations_0, groups = value_states_133_groups_0, pad = value_states_133_pad_0, pad_type = value_states_133_pad_type_0, strides = value_states_133_strides_0, weight = layers_22_self_attn_v_proj_weight_to_fp16, x = var_7992_cast_fp16_0)[name = string("value_states_133_cast_fp16")];
            tensor<int32, [4]> concat_264x = const()[name = string("concat_264x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_221_cast_fp16 = reshape(shape = concat_264x, x = query_states_133_cast_fp16)[name = string("x_221_cast_fp16")];
            tensor<int32, [4]> concat_265x = const()[name = string("concat_265x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_8049_cast_fp16 = reshape(shape = concat_265x, x = key_states_221_cast_fp16)[name = string("op_8049_cast_fp16")];
            tensor<int32, [4]> concat_266x = const()[name = string("concat_266x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_8056_cast_fp16 = reshape(shape = concat_266x, x = value_states_133_cast_fp16)[name = string("op_8056_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_8060_cast_fp16 = mul(x = x_221_cast_fp16, y = var_859_cast_fp16)[name = string("op_8060_cast_fp16")];
            tensor<int32, [2]> var_8061_split_sizes_0 = const()[name = string("op_8061_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8061_axis_0 = const()[name = string("op_8061_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_8061_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_8061_cast_fp16_1 = split(axis = var_8061_axis_0, split_sizes = var_8061_split_sizes_0, x = x_221_cast_fp16)[name = string("op_8061_cast_fp16")];
            fp16 const_224_promoted_to_fp16 = const()[name = string("const_224_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_8063_cast_fp16 = mul(x = var_8061_cast_fp16_1, y = const_224_promoted_to_fp16)[name = string("op_8063_cast_fp16")];
            int32 var_8065 = const()[name = string("op_8065"), val = int32(-2)];
            bool var_8066_interleave_0 = const()[name = string("op_8066_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_8066_cast_fp16 = concat(axis = var_8065, interleave = var_8066_interleave_0, values = (var_8063_cast_fp16, var_8061_cast_fp16_0))[name = string("op_8066_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_8067_cast_fp16 = mul(x = var_8066_cast_fp16, y = var_866_cast_fp16)[name = string("op_8067_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_135_cast_fp16 = add(x = var_8060_cast_fp16, y = var_8067_cast_fp16)[name = string("query_states_135_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_8073_cast_fp16 = mul(x = var_8049_cast_fp16, y = var_859_cast_fp16)[name = string("op_8073_cast_fp16")];
            tensor<int32, [2]> var_8074_split_sizes_0 = const()[name = string("op_8074_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8074_axis_0 = const()[name = string("op_8074_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_8074_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_8074_cast_fp16_1 = split(axis = var_8074_axis_0, split_sizes = var_8074_split_sizes_0, x = var_8049_cast_fp16)[name = string("op_8074_cast_fp16")];
            fp16 const_225_promoted_to_fp16 = const()[name = string("const_225_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_8076_cast_fp16 = mul(x = var_8074_cast_fp16_1, y = const_225_promoted_to_fp16)[name = string("op_8076_cast_fp16")];
            int32 var_8078 = const()[name = string("op_8078"), val = int32(-2)];
            bool var_8079_interleave_0 = const()[name = string("op_8079_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_8079_cast_fp16 = concat(axis = var_8078, interleave = var_8079_interleave_0, values = (var_8076_cast_fp16, var_8074_cast_fp16_0))[name = string("op_8079_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_8080_cast_fp16 = mul(x = var_8079_cast_fp16, y = var_866_cast_fp16)[name = string("op_8080_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_225_cast_fp16 = add(x = var_8073_cast_fp16, y = var_8080_cast_fp16)[name = string("key_states_225_cast_fp16")];
            tensor<int32, [1]> expand_dims_264 = const()[name = string("expand_dims_264"), val = tensor<int32, [1]>([22])];
            tensor<int32, [1]> expand_dims_265 = const()[name = string("expand_dims_265"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_267 = const()[name = string("expand_dims_267"), val = tensor<int32, [1]>([0])];
            int32 concat_269_axis_0 = const()[name = string("concat_269_axis_0"), val = int32(0)];
            bool concat_269_interleave_0 = const()[name = string("concat_269_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_269 = concat(axis = concat_269_axis_0, interleave = concat_269_interleave_0, values = (expand_dims_264, expand_dims_265, position_id, expand_dims_267))[name = string("concat_269")];
            tensor<int32, [1]> expand_dims_268 = const()[name = string("expand_dims_268"), val = tensor<int32, [1]>([23])];
            tensor<int32, [1]> concat_270_values1_0 = const()[name = string("concat_270_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_270_values3_0 = const()[name = string("concat_270_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_270_axis_0 = const()[name = string("concat_270_axis_0"), val = int32(0)];
            bool concat_270_interleave_0 = const()[name = string("concat_270_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_270 = concat(axis = concat_270_axis_0, interleave = concat_270_interleave_0, values = (expand_dims_268, concat_270_values1_0, cache_position_end, concat_270_values3_0))[name = string("concat_270")];
            tensor<int32, [4]> key_states_227_perm_0 = const()[name = string("key_states_227_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_23_stride_0 = const()[name = string("key_cache_internal_tensor_assign_23_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_23_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_23_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_23_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_23_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_23_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_23_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_227_cast_fp16 = transpose(perm = key_states_227_perm_0, x = key_states_225_cast_fp16)[name = string("transpose_452")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_23_cast_fp16 = slice_update(begin = concat_269, begin_mask = key_cache_internal_tensor_assign_23_begin_mask_0, end = concat_270, end_mask = key_cache_internal_tensor_assign_23_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_23_squeeze_mask_0, stride = key_cache_internal_tensor_assign_23_stride_0, update = key_states_227_cast_fp16, x = coreml_update_state_322)[name = string("key_cache_internal_tensor_assign_23_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_23_cast_fp16, input = key_cache)[name = string("coreml_update_state_324_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_324 = read_state(input = key_cache)[name = string("coreml_update_state_324")];
            tensor<int32, [4]> value_states_135_perm_0 = const()[name = string("value_states_135_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_23_stride_0 = const()[name = string("value_cache_internal_tensor_assign_23_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_23_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_23_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_23_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_23_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_23_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_23_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_135_cast_fp16 = transpose(perm = value_states_135_perm_0, x = var_8056_cast_fp16)[name = string("transpose_451")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_23_cast_fp16 = slice_update(begin = concat_269, begin_mask = value_cache_internal_tensor_assign_23_begin_mask_0, end = concat_270, end_mask = value_cache_internal_tensor_assign_23_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_23_squeeze_mask_0, stride = value_cache_internal_tensor_assign_23_stride_0, update = value_states_135_cast_fp16, x = coreml_update_state_323)[name = string("value_cache_internal_tensor_assign_23_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_23_cast_fp16, input = value_cache)[name = string("coreml_update_state_325_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_325 = read_state(input = value_cache)[name = string("coreml_update_state_325")];
            tensor<int32, [4]> var_8150_begin_0 = const()[name = string("op_8150_begin_0"), val = tensor<int32, [4]>([22, 0, 0, 0])];
            tensor<int32, [4]> var_8150_end_0 = const()[name = string("op_8150_end_0"), val = tensor<int32, [4]>([23, 2, 2048, 128])];
            tensor<bool, [4]> var_8150_end_mask_0 = const()[name = string("op_8150_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8150_cast_fp16 = slice_by_index(begin = var_8150_begin_0, end = var_8150_end_0, end_mask = var_8150_end_mask_0, x = coreml_update_state_324)[name = string("op_8150_cast_fp16")];
            tensor<int32, [2]> tile_44 = const()[name = string("tile_44"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8153_axis_0 = const()[name = string("op_8153_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8153_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8153_cast_fp16_1 = split(axis = var_8153_axis_0, split_sizes = tile_44, x = var_8150_cast_fp16)[name = string("op_8153_cast_fp16")];
            tensor<int32, [4]> var_8160_begin_0 = const()[name = string("op_8160_begin_0"), val = tensor<int32, [4]>([22, 0, 0, 0])];
            tensor<int32, [4]> var_8160_end_0 = const()[name = string("op_8160_end_0"), val = tensor<int32, [4]>([23, 2, 2048, 128])];
            tensor<bool, [4]> var_8160_end_mask_0 = const()[name = string("op_8160_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8160_cast_fp16 = slice_by_index(begin = var_8160_begin_0, end = var_8160_end_0, end_mask = var_8160_end_mask_0, x = coreml_update_state_325)[name = string("op_8160_cast_fp16")];
            tensor<int32, [2]> tile_45 = const()[name = string("tile_45"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8163_axis_0 = const()[name = string("op_8163_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8163_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8163_cast_fp16_1 = split(axis = var_8163_axis_0, split_sizes = tile_45, x = var_8160_cast_fp16)[name = string("op_8163_cast_fp16")];
            tensor<int32, [2]> var_8166_split_sizes_0 = const()[name = string("op_8166_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_8166_axis_0 = const()[name = string("op_8166_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_8166_0, tensor<fp16, [1, 8, 128, 64]> var_8166_1 = split(axis = var_8166_axis_0, split_sizes = var_8166_split_sizes_0, x = query_states_135_cast_fp16)[name = string("op_8166")];
            bool attn_weights_353_transpose_x_0 = const()[name = string("attn_weights_353_transpose_x_0"), val = bool(false)];
            bool attn_weights_353_transpose_y_0 = const()[name = string("attn_weights_353_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_353_cast_fp16 = matmul(transpose_x = attn_weights_353_transpose_x_0, transpose_y = attn_weights_353_transpose_y_0, x = var_8153_cast_fp16_0, y = var_8166_0)[name = string("attn_weights_353_cast_fp16")];
            fp16 var_8169_to_fp16 = const()[name = string("op_8169_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_355_cast_fp16 = mul(x = attn_weights_353_cast_fp16, y = var_8169_to_fp16)[name = string("attn_weights_355_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_357_cast_fp16 = add(x = attn_weights_355_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_357_cast_fp16")];
            int32 var_8173 = const()[name = string("op_8173"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_359_cast_fp16 = softmax(axis = var_8173, x = attn_weights_357_cast_fp16)[name = string("attn_weights_359_cast_fp16")];
            bool var_8179_transpose_x_1 = const()[name = string("op_8179_transpose_x_1"), val = bool(true)];
            bool var_8179_transpose_y_1 = const()[name = string("op_8179_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_8179_cast_fp16 = matmul(transpose_x = var_8179_transpose_x_1, transpose_y = var_8179_transpose_y_1, x = attn_weights_359_cast_fp16, y = var_8163_cast_fp16_0)[name = string("op_8179_cast_fp16")];
            bool attn_weights_361_transpose_x_0 = const()[name = string("attn_weights_361_transpose_x_0"), val = bool(false)];
            bool attn_weights_361_transpose_y_0 = const()[name = string("attn_weights_361_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_361_cast_fp16 = matmul(transpose_x = attn_weights_361_transpose_x_0, transpose_y = attn_weights_361_transpose_y_0, x = var_8153_cast_fp16_1, y = var_8166_1)[name = string("attn_weights_361_cast_fp16")];
            fp16 var_8181_to_fp16 = const()[name = string("op_8181_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_363_cast_fp16 = mul(x = attn_weights_361_cast_fp16, y = var_8181_to_fp16)[name = string("attn_weights_363_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_365_cast_fp16 = add(x = attn_weights_363_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_365_cast_fp16")];
            int32 var_8185 = const()[name = string("op_8185"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_367_cast_fp16 = softmax(axis = var_8185, x = attn_weights_365_cast_fp16)[name = string("attn_weights_367_cast_fp16")];
            bool attn_output_177_transpose_x_1 = const()[name = string("attn_output_177_transpose_x_1"), val = bool(true)];
            bool attn_output_177_transpose_y_1 = const()[name = string("attn_output_177_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_177_cast_fp16 = matmul(transpose_x = attn_output_177_transpose_x_1, transpose_y = attn_output_177_transpose_y_1, x = attn_weights_367_cast_fp16, y = var_8163_cast_fp16_1)[name = string("attn_output_177_cast_fp16")];
            int32 var_8193 = const()[name = string("op_8193"), val = int32(1)];
            bool attn_output_179_interleave_0 = const()[name = string("attn_output_179_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_179_cast_fp16 = concat(axis = var_8193, interleave = attn_output_179_interleave_0, values = (var_8179_cast_fp16, attn_output_177_cast_fp16))[name = string("attn_output_179_cast_fp16")];
            tensor<int32, [4]> var_8197_perm_0 = const()[name = string("op_8197_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_275x = const()[name = string("concat_275x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_8197_cast_fp16 = transpose(perm = var_8197_perm_0, x = attn_output_179_cast_fp16)[name = string("transpose_450")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_183_cast_fp16 = reshape(shape = concat_275x, x = var_8197_cast_fp16)[name = string("attn_output_183_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_22_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_22_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1521463040)))];
            tensor<int32, [2]> hidden_states_223_strides_0 = const()[name = string("hidden_states_223_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_223_pad_type_0 = const()[name = string("hidden_states_223_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_223_pad_0 = const()[name = string("hidden_states_223_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_223_dilations_0 = const()[name = string("hidden_states_223_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_223_groups_0 = const()[name = string("hidden_states_223_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_223_cast_fp16 = conv(dilations = hidden_states_223_dilations_0, groups = hidden_states_223_groups_0, pad = hidden_states_223_pad_0, pad_type = hidden_states_223_pad_type_0, strides = hidden_states_223_strides_0, weight = layers_22_self_attn_o_proj_weight_to_fp16, x = attn_output_183_cast_fp16)[name = string("hidden_states_223_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_225_cast_fp16 = add(x = hidden_states_219_cast_fp16, y = hidden_states_223_cast_fp16)[name = string("hidden_states_225_cast_fp16")];
            fp16 const_230_promoted_to_fp16 = const()[name = string("const_230_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_8230_cast_fp16 = mul(x = hidden_states_225_cast_fp16, y = const_230_promoted_to_fp16)[name = string("op_8230_cast_fp16")];
            int32 var_8228 = const()[name = string("op_8228"), val = int32(1)];
            bool doubled_181_interleave_0 = const()[name = string("doubled_181_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_181_cast_fp16 = concat(axis = var_8228, interleave = doubled_181_interleave_0, values = (hidden_states_225_cast_fp16, var_8230_cast_fp16))[name = string("doubled_181_cast_fp16")];
            tensor<int32, [1]> out_91_axes_0 = const()[name = string("out_91_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_91_gamma_0_to_fp16 = const()[name = string("out_91_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1529851712)))];
            fp16 var_8240_to_fp16 = const()[name = string("op_8240_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_91_cast_fp16 = layer_norm(axes = out_91_axes_0, epsilon = var_8240_to_fp16, gamma = out_91_gamma_0_to_fp16, x = doubled_181_cast_fp16)[name = string("out_91_cast_fp16")];
            tensor<int32, [2]> var_8251_split_sizes_0 = const()[name = string("op_8251_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8251_axis_0 = const()[name = string("op_8251_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_8251_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_8251_cast_fp16_1 = split(axis = var_8251_axis_0, split_sizes = var_8251_split_sizes_0, x = out_91_cast_fp16)[name = string("op_8251_cast_fp16")];
            tensor<int32, [2]> input_45_strides_0 = const()[name = string("input_45_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_45_pad_type_0 = const()[name = string("input_45_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_45_pad_0 = const()[name = string("input_45_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_45_dilations_0 = const()[name = string("input_45_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_45_groups_0 = const()[name = string("input_45_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_45_cast_fp16 = conv(dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = layers_22_mlp_gate_proj_weight_cast_fp16, x = var_8251_cast_fp16_0)[name = string("input_45_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_8268_cast_fp16 = silu(x = input_45_cast_fp16)[name = string("op_8268_cast_fp16")];
            tensor<int32, [2]> var_8274_strides_0 = const()[name = string("op_8274_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_8274_pad_type_0 = const()[name = string("op_8274_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_8274_pad_0 = const()[name = string("op_8274_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_8274_dilations_0 = const()[name = string("op_8274_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8274_groups_0 = const()[name = string("op_8274_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_8274_cast_fp16 = conv(dilations = var_8274_dilations_0, groups = var_8274_groups_0, pad = var_8274_pad_0, pad_type = var_8274_pad_type_0, strides = var_8274_strides_0, weight = layers_22_mlp_up_proj_weight_cast_fp16, x = var_8251_cast_fp16_0)[name = string("op_8274_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_229_cast_fp16 = mul(x = var_8268_cast_fp16, y = var_8274_cast_fp16)[name = string("x_229_cast_fp16")];
            tensor<int32, [2]> hidden_states_227_strides_0 = const()[name = string("hidden_states_227_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_227_pad_type_0 = const()[name = string("hidden_states_227_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_227_pad_0 = const()[name = string("hidden_states_227_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_227_dilations_0 = const()[name = string("hidden_states_227_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_227_groups_0 = const()[name = string("hidden_states_227_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_227_cast_fp16 = conv(dilations = hidden_states_227_dilations_0, groups = hidden_states_227_groups_0, pad = hidden_states_227_pad_0, pad_type = hidden_states_227_pad_type_0, strides = hidden_states_227_strides_0, weight = layers_22_mlp_down_proj_weight_cast_fp16, x = x_229_cast_fp16)[name = string("hidden_states_227_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_229_cast_fp16 = add(x = hidden_states_225_cast_fp16, y = hidden_states_227_cast_fp16)[name = string("hidden_states_229_cast_fp16")];
            fp16 const_232_promoted_to_fp16 = const()[name = string("const_232_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_8292_cast_fp16 = mul(x = hidden_states_229_cast_fp16, y = const_232_promoted_to_fp16)[name = string("op_8292_cast_fp16")];
            int32 var_8290 = const()[name = string("op_8290"), val = int32(1)];
            bool doubled_185_interleave_0 = const()[name = string("doubled_185_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_185_cast_fp16 = concat(axis = var_8290, interleave = doubled_185_interleave_0, values = (hidden_states_229_cast_fp16, var_8292_cast_fp16))[name = string("doubled_185_cast_fp16")];
            tensor<int32, [1]> out_93_axes_0 = const()[name = string("out_93_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_93_gamma_0_to_fp16 = const()[name = string("out_93_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1529859968)))];
            fp16 var_8302_to_fp16 = const()[name = string("op_8302_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_93_cast_fp16 = layer_norm(axes = out_93_axes_0, epsilon = var_8302_to_fp16, gamma = out_93_gamma_0_to_fp16, x = doubled_185_cast_fp16)[name = string("out_93_cast_fp16")];
            tensor<int32, [2]> var_8313_split_sizes_0 = const()[name = string("op_8313_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8313_axis_0 = const()[name = string("op_8313_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_8313_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_8313_cast_fp16_1 = split(axis = var_8313_axis_0, split_sizes = var_8313_split_sizes_0, x = out_93_cast_fp16)[name = string("op_8313_cast_fp16")];
            tensor<int32, [2]> query_states_139_strides_0 = const()[name = string("query_states_139_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_139_pad_type_0 = const()[name = string("query_states_139_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_139_pad_0 = const()[name = string("query_states_139_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_139_dilations_0 = const()[name = string("query_states_139_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_139_groups_0 = const()[name = string("query_states_139_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_139_cast_fp16 = conv(dilations = query_states_139_dilations_0, groups = query_states_139_groups_0, pad = query_states_139_pad_0, pad_type = query_states_139_pad_type_0, strides = query_states_139_strides_0, weight = layers_23_self_attn_q_proj_weight_cast_fp16, x = var_8313_cast_fp16_0)[name = string("query_states_139_cast_fp16")];
            tensor<int32, [2]> key_states_231_strides_0 = const()[name = string("key_states_231_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_231_pad_type_0 = const()[name = string("key_states_231_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_231_pad_0 = const()[name = string("key_states_231_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_231_dilations_0 = const()[name = string("key_states_231_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_231_groups_0 = const()[name = string("key_states_231_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_231_cast_fp16 = conv(dilations = key_states_231_dilations_0, groups = key_states_231_groups_0, pad = key_states_231_pad_0, pad_type = key_states_231_pad_type_0, strides = key_states_231_strides_0, weight = layers_23_self_attn_k_proj_weight_cast_fp16, x = var_8313_cast_fp16_0)[name = string("key_states_231_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_23_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_23_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1529868224)))];
            tensor<int32, [2]> value_states_139_strides_0 = const()[name = string("value_states_139_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_139_pad_type_0 = const()[name = string("value_states_139_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_139_pad_0 = const()[name = string("value_states_139_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_139_dilations_0 = const()[name = string("value_states_139_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_139_groups_0 = const()[name = string("value_states_139_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_139_cast_fp16 = conv(dilations = value_states_139_dilations_0, groups = value_states_139_groups_0, pad = value_states_139_pad_0, pad_type = value_states_139_pad_type_0, strides = value_states_139_strides_0, weight = layers_23_self_attn_v_proj_weight_to_fp16, x = var_8313_cast_fp16_0)[name = string("value_states_139_cast_fp16")];
            tensor<int32, [4]> concat_276x = const()[name = string("concat_276x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_231_cast_fp16 = reshape(shape = concat_276x, x = query_states_139_cast_fp16)[name = string("x_231_cast_fp16")];
            tensor<int32, [4]> concat_277x = const()[name = string("concat_277x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_8370_cast_fp16 = reshape(shape = concat_277x, x = key_states_231_cast_fp16)[name = string("op_8370_cast_fp16")];
            tensor<int32, [4]> concat_278x = const()[name = string("concat_278x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_8377_cast_fp16 = reshape(shape = concat_278x, x = value_states_139_cast_fp16)[name = string("op_8377_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_8381_cast_fp16 = mul(x = x_231_cast_fp16, y = var_859_cast_fp16)[name = string("op_8381_cast_fp16")];
            tensor<int32, [2]> var_8382_split_sizes_0 = const()[name = string("op_8382_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8382_axis_0 = const()[name = string("op_8382_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_8382_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_8382_cast_fp16_1 = split(axis = var_8382_axis_0, split_sizes = var_8382_split_sizes_0, x = x_231_cast_fp16)[name = string("op_8382_cast_fp16")];
            fp16 const_234_promoted_to_fp16 = const()[name = string("const_234_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_8384_cast_fp16 = mul(x = var_8382_cast_fp16_1, y = const_234_promoted_to_fp16)[name = string("op_8384_cast_fp16")];
            int32 var_8386 = const()[name = string("op_8386"), val = int32(-2)];
            bool var_8387_interleave_0 = const()[name = string("op_8387_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_8387_cast_fp16 = concat(axis = var_8386, interleave = var_8387_interleave_0, values = (var_8384_cast_fp16, var_8382_cast_fp16_0))[name = string("op_8387_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_8388_cast_fp16 = mul(x = var_8387_cast_fp16, y = var_866_cast_fp16)[name = string("op_8388_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_141_cast_fp16 = add(x = var_8381_cast_fp16, y = var_8388_cast_fp16)[name = string("query_states_141_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_8394_cast_fp16 = mul(x = var_8370_cast_fp16, y = var_859_cast_fp16)[name = string("op_8394_cast_fp16")];
            tensor<int32, [2]> var_8395_split_sizes_0 = const()[name = string("op_8395_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8395_axis_0 = const()[name = string("op_8395_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_8395_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_8395_cast_fp16_1 = split(axis = var_8395_axis_0, split_sizes = var_8395_split_sizes_0, x = var_8370_cast_fp16)[name = string("op_8395_cast_fp16")];
            fp16 const_235_promoted_to_fp16 = const()[name = string("const_235_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_8397_cast_fp16 = mul(x = var_8395_cast_fp16_1, y = const_235_promoted_to_fp16)[name = string("op_8397_cast_fp16")];
            int32 var_8399 = const()[name = string("op_8399"), val = int32(-2)];
            bool var_8400_interleave_0 = const()[name = string("op_8400_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_8400_cast_fp16 = concat(axis = var_8399, interleave = var_8400_interleave_0, values = (var_8397_cast_fp16, var_8395_cast_fp16_0))[name = string("op_8400_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_8401_cast_fp16 = mul(x = var_8400_cast_fp16, y = var_866_cast_fp16)[name = string("op_8401_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_235_cast_fp16 = add(x = var_8394_cast_fp16, y = var_8401_cast_fp16)[name = string("key_states_235_cast_fp16")];
            tensor<int32, [1]> expand_dims_276 = const()[name = string("expand_dims_276"), val = tensor<int32, [1]>([23])];
            tensor<int32, [1]> expand_dims_277 = const()[name = string("expand_dims_277"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_279 = const()[name = string("expand_dims_279"), val = tensor<int32, [1]>([0])];
            int32 concat_281_axis_0 = const()[name = string("concat_281_axis_0"), val = int32(0)];
            bool concat_281_interleave_0 = const()[name = string("concat_281_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_281 = concat(axis = concat_281_axis_0, interleave = concat_281_interleave_0, values = (expand_dims_276, expand_dims_277, position_id, expand_dims_279))[name = string("concat_281")];
            tensor<int32, [1]> expand_dims_280 = const()[name = string("expand_dims_280"), val = tensor<int32, [1]>([24])];
            tensor<int32, [1]> concat_282_values1_0 = const()[name = string("concat_282_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_282_values3_0 = const()[name = string("concat_282_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_282_axis_0 = const()[name = string("concat_282_axis_0"), val = int32(0)];
            bool concat_282_interleave_0 = const()[name = string("concat_282_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_282 = concat(axis = concat_282_axis_0, interleave = concat_282_interleave_0, values = (expand_dims_280, concat_282_values1_0, cache_position_end, concat_282_values3_0))[name = string("concat_282")];
            tensor<int32, [4]> key_states_237_perm_0 = const()[name = string("key_states_237_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_24_stride_0 = const()[name = string("key_cache_internal_tensor_assign_24_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_24_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_24_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_24_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_24_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_24_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_24_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_237_cast_fp16 = transpose(perm = key_states_237_perm_0, x = key_states_235_cast_fp16)[name = string("transpose_449")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_24_cast_fp16 = slice_update(begin = concat_281, begin_mask = key_cache_internal_tensor_assign_24_begin_mask_0, end = concat_282, end_mask = key_cache_internal_tensor_assign_24_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_24_squeeze_mask_0, stride = key_cache_internal_tensor_assign_24_stride_0, update = key_states_237_cast_fp16, x = coreml_update_state_324)[name = string("key_cache_internal_tensor_assign_24_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_24_cast_fp16, input = key_cache)[name = string("coreml_update_state_326_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_326 = read_state(input = key_cache)[name = string("coreml_update_state_326")];
            tensor<int32, [4]> value_states_141_perm_0 = const()[name = string("value_states_141_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_24_stride_0 = const()[name = string("value_cache_internal_tensor_assign_24_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_24_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_24_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_24_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_24_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_24_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_24_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_141_cast_fp16 = transpose(perm = value_states_141_perm_0, x = var_8377_cast_fp16)[name = string("transpose_448")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_24_cast_fp16 = slice_update(begin = concat_281, begin_mask = value_cache_internal_tensor_assign_24_begin_mask_0, end = concat_282, end_mask = value_cache_internal_tensor_assign_24_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_24_squeeze_mask_0, stride = value_cache_internal_tensor_assign_24_stride_0, update = value_states_141_cast_fp16, x = coreml_update_state_325)[name = string("value_cache_internal_tensor_assign_24_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_24_cast_fp16, input = value_cache)[name = string("coreml_update_state_327_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_327 = read_state(input = value_cache)[name = string("coreml_update_state_327")];
            tensor<int32, [4]> var_8471_begin_0 = const()[name = string("op_8471_begin_0"), val = tensor<int32, [4]>([23, 0, 0, 0])];
            tensor<int32, [4]> var_8471_end_0 = const()[name = string("op_8471_end_0"), val = tensor<int32, [4]>([24, 2, 2048, 128])];
            tensor<bool, [4]> var_8471_end_mask_0 = const()[name = string("op_8471_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8471_cast_fp16 = slice_by_index(begin = var_8471_begin_0, end = var_8471_end_0, end_mask = var_8471_end_mask_0, x = coreml_update_state_326)[name = string("op_8471_cast_fp16")];
            tensor<int32, [2]> tile_46 = const()[name = string("tile_46"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8474_axis_0 = const()[name = string("op_8474_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8474_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8474_cast_fp16_1 = split(axis = var_8474_axis_0, split_sizes = tile_46, x = var_8471_cast_fp16)[name = string("op_8474_cast_fp16")];
            tensor<int32, [4]> var_8481_begin_0 = const()[name = string("op_8481_begin_0"), val = tensor<int32, [4]>([23, 0, 0, 0])];
            tensor<int32, [4]> var_8481_end_0 = const()[name = string("op_8481_end_0"), val = tensor<int32, [4]>([24, 2, 2048, 128])];
            tensor<bool, [4]> var_8481_end_mask_0 = const()[name = string("op_8481_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8481_cast_fp16 = slice_by_index(begin = var_8481_begin_0, end = var_8481_end_0, end_mask = var_8481_end_mask_0, x = coreml_update_state_327)[name = string("op_8481_cast_fp16")];
            tensor<int32, [2]> tile_47 = const()[name = string("tile_47"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8484_axis_0 = const()[name = string("op_8484_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8484_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8484_cast_fp16_1 = split(axis = var_8484_axis_0, split_sizes = tile_47, x = var_8481_cast_fp16)[name = string("op_8484_cast_fp16")];
            tensor<int32, [2]> var_8487_split_sizes_0 = const()[name = string("op_8487_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_8487_axis_0 = const()[name = string("op_8487_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_8487_0, tensor<fp16, [1, 8, 128, 64]> var_8487_1 = split(axis = var_8487_axis_0, split_sizes = var_8487_split_sizes_0, x = query_states_141_cast_fp16)[name = string("op_8487")];
            bool attn_weights_369_transpose_x_0 = const()[name = string("attn_weights_369_transpose_x_0"), val = bool(false)];
            bool attn_weights_369_transpose_y_0 = const()[name = string("attn_weights_369_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_369_cast_fp16 = matmul(transpose_x = attn_weights_369_transpose_x_0, transpose_y = attn_weights_369_transpose_y_0, x = var_8474_cast_fp16_0, y = var_8487_0)[name = string("attn_weights_369_cast_fp16")];
            fp16 var_8490_to_fp16 = const()[name = string("op_8490_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_371_cast_fp16 = mul(x = attn_weights_369_cast_fp16, y = var_8490_to_fp16)[name = string("attn_weights_371_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_373_cast_fp16 = add(x = attn_weights_371_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_373_cast_fp16")];
            int32 var_8494 = const()[name = string("op_8494"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_375_cast_fp16 = softmax(axis = var_8494, x = attn_weights_373_cast_fp16)[name = string("attn_weights_375_cast_fp16")];
            bool var_8500_transpose_x_1 = const()[name = string("op_8500_transpose_x_1"), val = bool(true)];
            bool var_8500_transpose_y_1 = const()[name = string("op_8500_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_8500_cast_fp16 = matmul(transpose_x = var_8500_transpose_x_1, transpose_y = var_8500_transpose_y_1, x = attn_weights_375_cast_fp16, y = var_8484_cast_fp16_0)[name = string("op_8500_cast_fp16")];
            bool attn_weights_377_transpose_x_0 = const()[name = string("attn_weights_377_transpose_x_0"), val = bool(false)];
            bool attn_weights_377_transpose_y_0 = const()[name = string("attn_weights_377_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_377_cast_fp16 = matmul(transpose_x = attn_weights_377_transpose_x_0, transpose_y = attn_weights_377_transpose_y_0, x = var_8474_cast_fp16_1, y = var_8487_1)[name = string("attn_weights_377_cast_fp16")];
            fp16 var_8502_to_fp16 = const()[name = string("op_8502_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_379_cast_fp16 = mul(x = attn_weights_377_cast_fp16, y = var_8502_to_fp16)[name = string("attn_weights_379_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_381_cast_fp16 = add(x = attn_weights_379_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_381_cast_fp16")];
            int32 var_8506 = const()[name = string("op_8506"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_383_cast_fp16 = softmax(axis = var_8506, x = attn_weights_381_cast_fp16)[name = string("attn_weights_383_cast_fp16")];
            bool attn_output_185_transpose_x_1 = const()[name = string("attn_output_185_transpose_x_1"), val = bool(true)];
            bool attn_output_185_transpose_y_1 = const()[name = string("attn_output_185_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_185_cast_fp16 = matmul(transpose_x = attn_output_185_transpose_x_1, transpose_y = attn_output_185_transpose_y_1, x = attn_weights_383_cast_fp16, y = var_8484_cast_fp16_1)[name = string("attn_output_185_cast_fp16")];
            int32 var_8514 = const()[name = string("op_8514"), val = int32(1)];
            bool attn_output_187_interleave_0 = const()[name = string("attn_output_187_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_187_cast_fp16 = concat(axis = var_8514, interleave = attn_output_187_interleave_0, values = (var_8500_cast_fp16, attn_output_185_cast_fp16))[name = string("attn_output_187_cast_fp16")];
            tensor<int32, [4]> var_8518_perm_0 = const()[name = string("op_8518_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_287x = const()[name = string("concat_287x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_8518_cast_fp16 = transpose(perm = var_8518_perm_0, x = attn_output_187_cast_fp16)[name = string("transpose_447")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_191_cast_fp16 = reshape(shape = concat_287x, x = var_8518_cast_fp16)[name = string("attn_output_191_cast_fp16")];
            tensor<int32, [2]> hidden_states_233_strides_0 = const()[name = string("hidden_states_233_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_233_pad_type_0 = const()[name = string("hidden_states_233_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_233_pad_0 = const()[name = string("hidden_states_233_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_233_dilations_0 = const()[name = string("hidden_states_233_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_233_groups_0 = const()[name = string("hidden_states_233_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_233_cast_fp16 = conv(dilations = hidden_states_233_dilations_0, groups = hidden_states_233_groups_0, pad = hidden_states_233_pad_0, pad_type = hidden_states_233_pad_type_0, strides = hidden_states_233_strides_0, weight = layers_23_self_attn_o_proj_weight_cast_fp16, x = attn_output_191_cast_fp16)[name = string("hidden_states_233_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_235_cast_fp16 = add(x = hidden_states_229_cast_fp16, y = hidden_states_233_cast_fp16)[name = string("hidden_states_235_cast_fp16")];
            fp16 const_240_promoted_to_fp16 = const()[name = string("const_240_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_8551_cast_fp16 = mul(x = hidden_states_235_cast_fp16, y = const_240_promoted_to_fp16)[name = string("op_8551_cast_fp16")];
            int32 var_8549 = const()[name = string("op_8549"), val = int32(1)];
            bool doubled_189_interleave_0 = const()[name = string("doubled_189_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_189_cast_fp16 = concat(axis = var_8549, interleave = doubled_189_interleave_0, values = (hidden_states_235_cast_fp16, var_8551_cast_fp16))[name = string("doubled_189_cast_fp16")];
            tensor<int32, [1]> out_95_axes_0 = const()[name = string("out_95_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_95_gamma_0_to_fp16 = const()[name = string("out_95_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1530916864)))];
            fp16 var_8561_to_fp16 = const()[name = string("op_8561_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_95_cast_fp16 = layer_norm(axes = out_95_axes_0, epsilon = var_8561_to_fp16, gamma = out_95_gamma_0_to_fp16, x = doubled_189_cast_fp16)[name = string("out_95_cast_fp16")];
            tensor<int32, [2]> var_8572_split_sizes_0 = const()[name = string("op_8572_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8572_axis_0 = const()[name = string("op_8572_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_8572_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_8572_cast_fp16_1 = split(axis = var_8572_axis_0, split_sizes = var_8572_split_sizes_0, x = out_95_cast_fp16)[name = string("op_8572_cast_fp16")];
            tensor<int32, [2]> input_47_strides_0 = const()[name = string("input_47_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_47_pad_type_0 = const()[name = string("input_47_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_47_dilations_0 = const()[name = string("input_47_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_47_groups_0 = const()[name = string("input_47_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_47_cast_fp16 = conv(dilations = input_47_dilations_0, groups = input_47_groups_0, pad = input_47_pad_0, pad_type = input_47_pad_type_0, strides = input_47_strides_0, weight = layers_23_mlp_gate_proj_weight_cast_fp16, x = var_8572_cast_fp16_0)[name = string("input_47_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_8589_cast_fp16 = silu(x = input_47_cast_fp16)[name = string("op_8589_cast_fp16")];
            tensor<int32, [2]> var_8595_strides_0 = const()[name = string("op_8595_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_8595_pad_type_0 = const()[name = string("op_8595_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_8595_pad_0 = const()[name = string("op_8595_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_8595_dilations_0 = const()[name = string("op_8595_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8595_groups_0 = const()[name = string("op_8595_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_8595_cast_fp16 = conv(dilations = var_8595_dilations_0, groups = var_8595_groups_0, pad = var_8595_pad_0, pad_type = var_8595_pad_type_0, strides = var_8595_strides_0, weight = layers_23_mlp_up_proj_weight_cast_fp16, x = var_8572_cast_fp16_0)[name = string("op_8595_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_239_cast_fp16 = mul(x = var_8589_cast_fp16, y = var_8595_cast_fp16)[name = string("x_239_cast_fp16")];
            tensor<int32, [2]> hidden_states_237_strides_0 = const()[name = string("hidden_states_237_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_237_pad_type_0 = const()[name = string("hidden_states_237_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_237_pad_0 = const()[name = string("hidden_states_237_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_237_dilations_0 = const()[name = string("hidden_states_237_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_237_groups_0 = const()[name = string("hidden_states_237_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_237_cast_fp16 = conv(dilations = hidden_states_237_dilations_0, groups = hidden_states_237_groups_0, pad = hidden_states_237_pad_0, pad_type = hidden_states_237_pad_type_0, strides = hidden_states_237_strides_0, weight = layers_23_mlp_down_proj_weight_cast_fp16, x = x_239_cast_fp16)[name = string("hidden_states_237_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_239_cast_fp16 = add(x = hidden_states_235_cast_fp16, y = hidden_states_237_cast_fp16)[name = string("hidden_states_239_cast_fp16")];
            fp16 const_242_promoted_to_fp16 = const()[name = string("const_242_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_8613_cast_fp16 = mul(x = hidden_states_239_cast_fp16, y = const_242_promoted_to_fp16)[name = string("op_8613_cast_fp16")];
            int32 var_8611 = const()[name = string("op_8611"), val = int32(1)];
            bool doubled_193_interleave_0 = const()[name = string("doubled_193_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_193_cast_fp16 = concat(axis = var_8611, interleave = doubled_193_interleave_0, values = (hidden_states_239_cast_fp16, var_8613_cast_fp16))[name = string("doubled_193_cast_fp16")];
            tensor<int32, [1]> out_97_axes_0 = const()[name = string("out_97_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_97_gamma_0_to_fp16 = const()[name = string("out_97_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1530925120)))];
            fp16 var_8623_to_fp16 = const()[name = string("op_8623_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_97_cast_fp16 = layer_norm(axes = out_97_axes_0, epsilon = var_8623_to_fp16, gamma = out_97_gamma_0_to_fp16, x = doubled_193_cast_fp16)[name = string("out_97_cast_fp16")];
            tensor<int32, [2]> var_8634_split_sizes_0 = const()[name = string("op_8634_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8634_axis_0 = const()[name = string("op_8634_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_8634_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_8634_cast_fp16_1 = split(axis = var_8634_axis_0, split_sizes = var_8634_split_sizes_0, x = out_97_cast_fp16)[name = string("op_8634_cast_fp16")];
            tensor<int32, [2]> query_states_145_strides_0 = const()[name = string("query_states_145_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_145_pad_type_0 = const()[name = string("query_states_145_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_145_pad_0 = const()[name = string("query_states_145_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_145_dilations_0 = const()[name = string("query_states_145_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_145_groups_0 = const()[name = string("query_states_145_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_145_cast_fp16 = conv(dilations = query_states_145_dilations_0, groups = query_states_145_groups_0, pad = query_states_145_pad_0, pad_type = query_states_145_pad_type_0, strides = query_states_145_strides_0, weight = layers_24_self_attn_q_proj_weight_cast_fp16, x = var_8634_cast_fp16_0)[name = string("query_states_145_cast_fp16")];
            tensor<int32, [2]> key_states_241_strides_0 = const()[name = string("key_states_241_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_241_pad_type_0 = const()[name = string("key_states_241_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_241_pad_0 = const()[name = string("key_states_241_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_241_dilations_0 = const()[name = string("key_states_241_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_241_groups_0 = const()[name = string("key_states_241_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_241_cast_fp16 = conv(dilations = key_states_241_dilations_0, groups = key_states_241_groups_0, pad = key_states_241_pad_0, pad_type = key_states_241_pad_type_0, strides = key_states_241_strides_0, weight = layers_24_self_attn_k_proj_weight_cast_fp16, x = var_8634_cast_fp16_0)[name = string("key_states_241_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_24_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_24_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1530933376)))];
            tensor<int32, [2]> value_states_145_strides_0 = const()[name = string("value_states_145_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_145_pad_type_0 = const()[name = string("value_states_145_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_145_pad_0 = const()[name = string("value_states_145_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_145_dilations_0 = const()[name = string("value_states_145_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_145_groups_0 = const()[name = string("value_states_145_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_145_cast_fp16 = conv(dilations = value_states_145_dilations_0, groups = value_states_145_groups_0, pad = value_states_145_pad_0, pad_type = value_states_145_pad_type_0, strides = value_states_145_strides_0, weight = layers_24_self_attn_v_proj_weight_to_fp16, x = var_8634_cast_fp16_0)[name = string("value_states_145_cast_fp16")];
            tensor<int32, [4]> concat_288x = const()[name = string("concat_288x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_241_cast_fp16 = reshape(shape = concat_288x, x = query_states_145_cast_fp16)[name = string("x_241_cast_fp16")];
            tensor<int32, [4]> concat_289x = const()[name = string("concat_289x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_8691_cast_fp16 = reshape(shape = concat_289x, x = key_states_241_cast_fp16)[name = string("op_8691_cast_fp16")];
            tensor<int32, [4]> concat_290x = const()[name = string("concat_290x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_8698_cast_fp16 = reshape(shape = concat_290x, x = value_states_145_cast_fp16)[name = string("op_8698_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_8702_cast_fp16 = mul(x = x_241_cast_fp16, y = var_859_cast_fp16)[name = string("op_8702_cast_fp16")];
            tensor<int32, [2]> var_8703_split_sizes_0 = const()[name = string("op_8703_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8703_axis_0 = const()[name = string("op_8703_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_8703_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_8703_cast_fp16_1 = split(axis = var_8703_axis_0, split_sizes = var_8703_split_sizes_0, x = x_241_cast_fp16)[name = string("op_8703_cast_fp16")];
            fp16 const_244_promoted_to_fp16 = const()[name = string("const_244_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_8705_cast_fp16 = mul(x = var_8703_cast_fp16_1, y = const_244_promoted_to_fp16)[name = string("op_8705_cast_fp16")];
            int32 var_8707 = const()[name = string("op_8707"), val = int32(-2)];
            bool var_8708_interleave_0 = const()[name = string("op_8708_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_8708_cast_fp16 = concat(axis = var_8707, interleave = var_8708_interleave_0, values = (var_8705_cast_fp16, var_8703_cast_fp16_0))[name = string("op_8708_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_8709_cast_fp16 = mul(x = var_8708_cast_fp16, y = var_866_cast_fp16)[name = string("op_8709_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_147_cast_fp16 = add(x = var_8702_cast_fp16, y = var_8709_cast_fp16)[name = string("query_states_147_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_8715_cast_fp16 = mul(x = var_8691_cast_fp16, y = var_859_cast_fp16)[name = string("op_8715_cast_fp16")];
            tensor<int32, [2]> var_8716_split_sizes_0 = const()[name = string("op_8716_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8716_axis_0 = const()[name = string("op_8716_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_8716_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_8716_cast_fp16_1 = split(axis = var_8716_axis_0, split_sizes = var_8716_split_sizes_0, x = var_8691_cast_fp16)[name = string("op_8716_cast_fp16")];
            fp16 const_245_promoted_to_fp16 = const()[name = string("const_245_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_8718_cast_fp16 = mul(x = var_8716_cast_fp16_1, y = const_245_promoted_to_fp16)[name = string("op_8718_cast_fp16")];
            int32 var_8720 = const()[name = string("op_8720"), val = int32(-2)];
            bool var_8721_interleave_0 = const()[name = string("op_8721_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_8721_cast_fp16 = concat(axis = var_8720, interleave = var_8721_interleave_0, values = (var_8718_cast_fp16, var_8716_cast_fp16_0))[name = string("op_8721_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_8722_cast_fp16 = mul(x = var_8721_cast_fp16, y = var_866_cast_fp16)[name = string("op_8722_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_245_cast_fp16 = add(x = var_8715_cast_fp16, y = var_8722_cast_fp16)[name = string("key_states_245_cast_fp16")];
            tensor<int32, [1]> expand_dims_288 = const()[name = string("expand_dims_288"), val = tensor<int32, [1]>([24])];
            tensor<int32, [1]> expand_dims_289 = const()[name = string("expand_dims_289"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_291 = const()[name = string("expand_dims_291"), val = tensor<int32, [1]>([0])];
            int32 concat_293_axis_0 = const()[name = string("concat_293_axis_0"), val = int32(0)];
            bool concat_293_interleave_0 = const()[name = string("concat_293_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_293 = concat(axis = concat_293_axis_0, interleave = concat_293_interleave_0, values = (expand_dims_288, expand_dims_289, position_id, expand_dims_291))[name = string("concat_293")];
            tensor<int32, [1]> expand_dims_292 = const()[name = string("expand_dims_292"), val = tensor<int32, [1]>([25])];
            tensor<int32, [1]> concat_294_values1_0 = const()[name = string("concat_294_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_294_values3_0 = const()[name = string("concat_294_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_294_axis_0 = const()[name = string("concat_294_axis_0"), val = int32(0)];
            bool concat_294_interleave_0 = const()[name = string("concat_294_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_294 = concat(axis = concat_294_axis_0, interleave = concat_294_interleave_0, values = (expand_dims_292, concat_294_values1_0, cache_position_end, concat_294_values3_0))[name = string("concat_294")];
            tensor<int32, [4]> key_states_247_perm_0 = const()[name = string("key_states_247_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_25_stride_0 = const()[name = string("key_cache_internal_tensor_assign_25_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_25_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_25_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_25_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_25_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_25_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_25_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_247_cast_fp16 = transpose(perm = key_states_247_perm_0, x = key_states_245_cast_fp16)[name = string("transpose_446")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_25_cast_fp16 = slice_update(begin = concat_293, begin_mask = key_cache_internal_tensor_assign_25_begin_mask_0, end = concat_294, end_mask = key_cache_internal_tensor_assign_25_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_25_squeeze_mask_0, stride = key_cache_internal_tensor_assign_25_stride_0, update = key_states_247_cast_fp16, x = coreml_update_state_326)[name = string("key_cache_internal_tensor_assign_25_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_25_cast_fp16, input = key_cache)[name = string("coreml_update_state_328_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_328 = read_state(input = key_cache)[name = string("coreml_update_state_328")];
            tensor<int32, [4]> value_states_147_perm_0 = const()[name = string("value_states_147_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_25_stride_0 = const()[name = string("value_cache_internal_tensor_assign_25_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_25_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_25_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_25_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_25_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_25_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_25_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_147_cast_fp16 = transpose(perm = value_states_147_perm_0, x = var_8698_cast_fp16)[name = string("transpose_445")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_25_cast_fp16 = slice_update(begin = concat_293, begin_mask = value_cache_internal_tensor_assign_25_begin_mask_0, end = concat_294, end_mask = value_cache_internal_tensor_assign_25_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_25_squeeze_mask_0, stride = value_cache_internal_tensor_assign_25_stride_0, update = value_states_147_cast_fp16, x = coreml_update_state_327)[name = string("value_cache_internal_tensor_assign_25_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_25_cast_fp16, input = value_cache)[name = string("coreml_update_state_329_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_329 = read_state(input = value_cache)[name = string("coreml_update_state_329")];
            tensor<int32, [4]> var_8792_begin_0 = const()[name = string("op_8792_begin_0"), val = tensor<int32, [4]>([24, 0, 0, 0])];
            tensor<int32, [4]> var_8792_end_0 = const()[name = string("op_8792_end_0"), val = tensor<int32, [4]>([25, 2, 2048, 128])];
            tensor<bool, [4]> var_8792_end_mask_0 = const()[name = string("op_8792_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8792_cast_fp16 = slice_by_index(begin = var_8792_begin_0, end = var_8792_end_0, end_mask = var_8792_end_mask_0, x = coreml_update_state_328)[name = string("op_8792_cast_fp16")];
            tensor<int32, [2]> tile_48 = const()[name = string("tile_48"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8795_axis_0 = const()[name = string("op_8795_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8795_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8795_cast_fp16_1 = split(axis = var_8795_axis_0, split_sizes = tile_48, x = var_8792_cast_fp16)[name = string("op_8795_cast_fp16")];
            tensor<int32, [4]> var_8802_begin_0 = const()[name = string("op_8802_begin_0"), val = tensor<int32, [4]>([24, 0, 0, 0])];
            tensor<int32, [4]> var_8802_end_0 = const()[name = string("op_8802_end_0"), val = tensor<int32, [4]>([25, 2, 2048, 128])];
            tensor<bool, [4]> var_8802_end_mask_0 = const()[name = string("op_8802_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8802_cast_fp16 = slice_by_index(begin = var_8802_begin_0, end = var_8802_end_0, end_mask = var_8802_end_mask_0, x = coreml_update_state_329)[name = string("op_8802_cast_fp16")];
            tensor<int32, [2]> tile_49 = const()[name = string("tile_49"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8805_axis_0 = const()[name = string("op_8805_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8805_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8805_cast_fp16_1 = split(axis = var_8805_axis_0, split_sizes = tile_49, x = var_8802_cast_fp16)[name = string("op_8805_cast_fp16")];
            tensor<int32, [2]> var_8808_split_sizes_0 = const()[name = string("op_8808_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_8808_axis_0 = const()[name = string("op_8808_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_8808_0, tensor<fp16, [1, 8, 128, 64]> var_8808_1 = split(axis = var_8808_axis_0, split_sizes = var_8808_split_sizes_0, x = query_states_147_cast_fp16)[name = string("op_8808")];
            bool attn_weights_385_transpose_x_0 = const()[name = string("attn_weights_385_transpose_x_0"), val = bool(false)];
            bool attn_weights_385_transpose_y_0 = const()[name = string("attn_weights_385_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_385_cast_fp16 = matmul(transpose_x = attn_weights_385_transpose_x_0, transpose_y = attn_weights_385_transpose_y_0, x = var_8795_cast_fp16_0, y = var_8808_0)[name = string("attn_weights_385_cast_fp16")];
            fp16 var_8811_to_fp16 = const()[name = string("op_8811_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_387_cast_fp16 = mul(x = attn_weights_385_cast_fp16, y = var_8811_to_fp16)[name = string("attn_weights_387_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_389_cast_fp16 = add(x = attn_weights_387_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_389_cast_fp16")];
            int32 var_8815 = const()[name = string("op_8815"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_391_cast_fp16 = softmax(axis = var_8815, x = attn_weights_389_cast_fp16)[name = string("attn_weights_391_cast_fp16")];
            bool var_8821_transpose_x_1 = const()[name = string("op_8821_transpose_x_1"), val = bool(true)];
            bool var_8821_transpose_y_1 = const()[name = string("op_8821_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_8821_cast_fp16 = matmul(transpose_x = var_8821_transpose_x_1, transpose_y = var_8821_transpose_y_1, x = attn_weights_391_cast_fp16, y = var_8805_cast_fp16_0)[name = string("op_8821_cast_fp16")];
            bool attn_weights_393_transpose_x_0 = const()[name = string("attn_weights_393_transpose_x_0"), val = bool(false)];
            bool attn_weights_393_transpose_y_0 = const()[name = string("attn_weights_393_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_393_cast_fp16 = matmul(transpose_x = attn_weights_393_transpose_x_0, transpose_y = attn_weights_393_transpose_y_0, x = var_8795_cast_fp16_1, y = var_8808_1)[name = string("attn_weights_393_cast_fp16")];
            fp16 var_8823_to_fp16 = const()[name = string("op_8823_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_395_cast_fp16 = mul(x = attn_weights_393_cast_fp16, y = var_8823_to_fp16)[name = string("attn_weights_395_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_397_cast_fp16 = add(x = attn_weights_395_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_397_cast_fp16")];
            int32 var_8827 = const()[name = string("op_8827"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_399_cast_fp16 = softmax(axis = var_8827, x = attn_weights_397_cast_fp16)[name = string("attn_weights_399_cast_fp16")];
            bool attn_output_193_transpose_x_1 = const()[name = string("attn_output_193_transpose_x_1"), val = bool(true)];
            bool attn_output_193_transpose_y_1 = const()[name = string("attn_output_193_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_193_cast_fp16 = matmul(transpose_x = attn_output_193_transpose_x_1, transpose_y = attn_output_193_transpose_y_1, x = attn_weights_399_cast_fp16, y = var_8805_cast_fp16_1)[name = string("attn_output_193_cast_fp16")];
            int32 var_8835 = const()[name = string("op_8835"), val = int32(1)];
            bool attn_output_195_interleave_0 = const()[name = string("attn_output_195_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_195_cast_fp16 = concat(axis = var_8835, interleave = attn_output_195_interleave_0, values = (var_8821_cast_fp16, attn_output_193_cast_fp16))[name = string("attn_output_195_cast_fp16")];
            tensor<int32, [4]> var_8839_perm_0 = const()[name = string("op_8839_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_299x = const()[name = string("concat_299x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_8839_cast_fp16 = transpose(perm = var_8839_perm_0, x = attn_output_195_cast_fp16)[name = string("transpose_444")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_199_cast_fp16 = reshape(shape = concat_299x, x = var_8839_cast_fp16)[name = string("attn_output_199_cast_fp16")];
            tensor<int32, [2]> hidden_states_243_strides_0 = const()[name = string("hidden_states_243_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_243_pad_type_0 = const()[name = string("hidden_states_243_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_243_pad_0 = const()[name = string("hidden_states_243_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_243_dilations_0 = const()[name = string("hidden_states_243_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_243_groups_0 = const()[name = string("hidden_states_243_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_243_cast_fp16 = conv(dilations = hidden_states_243_dilations_0, groups = hidden_states_243_groups_0, pad = hidden_states_243_pad_0, pad_type = hidden_states_243_pad_type_0, strides = hidden_states_243_strides_0, weight = layers_24_self_attn_o_proj_weight_cast_fp16, x = attn_output_199_cast_fp16)[name = string("hidden_states_243_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_245_cast_fp16 = add(x = hidden_states_239_cast_fp16, y = hidden_states_243_cast_fp16)[name = string("hidden_states_245_cast_fp16")];
            fp16 const_250_promoted_to_fp16 = const()[name = string("const_250_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_8872_cast_fp16 = mul(x = hidden_states_245_cast_fp16, y = const_250_promoted_to_fp16)[name = string("op_8872_cast_fp16")];
            int32 var_8870 = const()[name = string("op_8870"), val = int32(1)];
            bool doubled_197_interleave_0 = const()[name = string("doubled_197_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_197_cast_fp16 = concat(axis = var_8870, interleave = doubled_197_interleave_0, values = (hidden_states_245_cast_fp16, var_8872_cast_fp16))[name = string("doubled_197_cast_fp16")];
            tensor<int32, [1]> out_99_axes_0 = const()[name = string("out_99_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_99_gamma_0_to_fp16 = const()[name = string("out_99_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1531982016)))];
            fp16 var_8882_to_fp16 = const()[name = string("op_8882_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_99_cast_fp16 = layer_norm(axes = out_99_axes_0, epsilon = var_8882_to_fp16, gamma = out_99_gamma_0_to_fp16, x = doubled_197_cast_fp16)[name = string("out_99_cast_fp16")];
            tensor<int32, [2]> var_8893_split_sizes_0 = const()[name = string("op_8893_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8893_axis_0 = const()[name = string("op_8893_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_8893_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_8893_cast_fp16_1 = split(axis = var_8893_axis_0, split_sizes = var_8893_split_sizes_0, x = out_99_cast_fp16)[name = string("op_8893_cast_fp16")];
            tensor<int32, [2]> input_49_strides_0 = const()[name = string("input_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_49_pad_type_0 = const()[name = string("input_49_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_49_pad_0 = const()[name = string("input_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_49_dilations_0 = const()[name = string("input_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_49_groups_0 = const()[name = string("input_49_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_49_cast_fp16 = conv(dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = layers_24_mlp_gate_proj_weight_cast_fp16, x = var_8893_cast_fp16_0)[name = string("input_49_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_8910_cast_fp16 = silu(x = input_49_cast_fp16)[name = string("op_8910_cast_fp16")];
            tensor<int32, [2]> var_8916_strides_0 = const()[name = string("op_8916_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_8916_pad_type_0 = const()[name = string("op_8916_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_8916_pad_0 = const()[name = string("op_8916_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_8916_dilations_0 = const()[name = string("op_8916_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8916_groups_0 = const()[name = string("op_8916_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_8916_cast_fp16 = conv(dilations = var_8916_dilations_0, groups = var_8916_groups_0, pad = var_8916_pad_0, pad_type = var_8916_pad_type_0, strides = var_8916_strides_0, weight = layers_24_mlp_up_proj_weight_cast_fp16, x = var_8893_cast_fp16_0)[name = string("op_8916_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_249_cast_fp16 = mul(x = var_8910_cast_fp16, y = var_8916_cast_fp16)[name = string("x_249_cast_fp16")];
            tensor<int32, [2]> hidden_states_247_strides_0 = const()[name = string("hidden_states_247_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_247_pad_type_0 = const()[name = string("hidden_states_247_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_247_pad_0 = const()[name = string("hidden_states_247_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_247_dilations_0 = const()[name = string("hidden_states_247_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_247_groups_0 = const()[name = string("hidden_states_247_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_247_cast_fp16 = conv(dilations = hidden_states_247_dilations_0, groups = hidden_states_247_groups_0, pad = hidden_states_247_pad_0, pad_type = hidden_states_247_pad_type_0, strides = hidden_states_247_strides_0, weight = layers_24_mlp_down_proj_weight_cast_fp16, x = x_249_cast_fp16)[name = string("hidden_states_247_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_249_cast_fp16 = add(x = hidden_states_245_cast_fp16, y = hidden_states_247_cast_fp16)[name = string("hidden_states_249_cast_fp16")];
            fp16 const_252_promoted_to_fp16 = const()[name = string("const_252_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_8934_cast_fp16 = mul(x = hidden_states_249_cast_fp16, y = const_252_promoted_to_fp16)[name = string("op_8934_cast_fp16")];
            int32 var_8932 = const()[name = string("op_8932"), val = int32(1)];
            bool doubled_201_interleave_0 = const()[name = string("doubled_201_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_201_cast_fp16 = concat(axis = var_8932, interleave = doubled_201_interleave_0, values = (hidden_states_249_cast_fp16, var_8934_cast_fp16))[name = string("doubled_201_cast_fp16")];
            tensor<int32, [1]> out_101_axes_0 = const()[name = string("out_101_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_101_gamma_0_to_fp16 = const()[name = string("out_101_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1531990272)))];
            fp16 var_8944_to_fp16 = const()[name = string("op_8944_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_101_cast_fp16 = layer_norm(axes = out_101_axes_0, epsilon = var_8944_to_fp16, gamma = out_101_gamma_0_to_fp16, x = doubled_201_cast_fp16)[name = string("out_101_cast_fp16")];
            tensor<int32, [2]> var_8955_split_sizes_0 = const()[name = string("op_8955_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8955_axis_0 = const()[name = string("op_8955_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_8955_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_8955_cast_fp16_1 = split(axis = var_8955_axis_0, split_sizes = var_8955_split_sizes_0, x = out_101_cast_fp16)[name = string("op_8955_cast_fp16")];
            tensor<int32, [2]> query_states_151_strides_0 = const()[name = string("query_states_151_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_151_pad_type_0 = const()[name = string("query_states_151_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_151_pad_0 = const()[name = string("query_states_151_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_151_dilations_0 = const()[name = string("query_states_151_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_151_groups_0 = const()[name = string("query_states_151_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_151_cast_fp16 = conv(dilations = query_states_151_dilations_0, groups = query_states_151_groups_0, pad = query_states_151_pad_0, pad_type = query_states_151_pad_type_0, strides = query_states_151_strides_0, weight = layers_25_self_attn_q_proj_weight_cast_fp16, x = var_8955_cast_fp16_0)[name = string("query_states_151_cast_fp16")];
            tensor<int32, [2]> key_states_251_strides_0 = const()[name = string("key_states_251_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_251_pad_type_0 = const()[name = string("key_states_251_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_251_pad_0 = const()[name = string("key_states_251_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_251_dilations_0 = const()[name = string("key_states_251_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_251_groups_0 = const()[name = string("key_states_251_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_251_cast_fp16 = conv(dilations = key_states_251_dilations_0, groups = key_states_251_groups_0, pad = key_states_251_pad_0, pad_type = key_states_251_pad_type_0, strides = key_states_251_strides_0, weight = layers_25_self_attn_k_proj_weight_cast_fp16, x = var_8955_cast_fp16_0)[name = string("key_states_251_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_25_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_25_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1531998528)))];
            tensor<int32, [2]> value_states_151_strides_0 = const()[name = string("value_states_151_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_151_pad_type_0 = const()[name = string("value_states_151_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_151_pad_0 = const()[name = string("value_states_151_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_151_dilations_0 = const()[name = string("value_states_151_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_151_groups_0 = const()[name = string("value_states_151_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_151_cast_fp16 = conv(dilations = value_states_151_dilations_0, groups = value_states_151_groups_0, pad = value_states_151_pad_0, pad_type = value_states_151_pad_type_0, strides = value_states_151_strides_0, weight = layers_25_self_attn_v_proj_weight_to_fp16, x = var_8955_cast_fp16_0)[name = string("value_states_151_cast_fp16")];
            tensor<int32, [4]> concat_300x = const()[name = string("concat_300x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_251_cast_fp16 = reshape(shape = concat_300x, x = query_states_151_cast_fp16)[name = string("x_251_cast_fp16")];
            tensor<int32, [4]> concat_301x = const()[name = string("concat_301x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_9012_cast_fp16 = reshape(shape = concat_301x, x = key_states_251_cast_fp16)[name = string("op_9012_cast_fp16")];
            tensor<int32, [4]> concat_302x = const()[name = string("concat_302x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_9019_cast_fp16 = reshape(shape = concat_302x, x = value_states_151_cast_fp16)[name = string("op_9019_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_9023_cast_fp16 = mul(x = x_251_cast_fp16, y = var_859_cast_fp16)[name = string("op_9023_cast_fp16")];
            tensor<int32, [2]> var_9024_split_sizes_0 = const()[name = string("op_9024_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9024_axis_0 = const()[name = string("op_9024_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_9024_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_9024_cast_fp16_1 = split(axis = var_9024_axis_0, split_sizes = var_9024_split_sizes_0, x = x_251_cast_fp16)[name = string("op_9024_cast_fp16")];
            fp16 const_254_promoted_to_fp16 = const()[name = string("const_254_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_9026_cast_fp16 = mul(x = var_9024_cast_fp16_1, y = const_254_promoted_to_fp16)[name = string("op_9026_cast_fp16")];
            int32 var_9028 = const()[name = string("op_9028"), val = int32(-2)];
            bool var_9029_interleave_0 = const()[name = string("op_9029_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_9029_cast_fp16 = concat(axis = var_9028, interleave = var_9029_interleave_0, values = (var_9026_cast_fp16, var_9024_cast_fp16_0))[name = string("op_9029_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_9030_cast_fp16 = mul(x = var_9029_cast_fp16, y = var_866_cast_fp16)[name = string("op_9030_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_153_cast_fp16 = add(x = var_9023_cast_fp16, y = var_9030_cast_fp16)[name = string("query_states_153_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_9036_cast_fp16 = mul(x = var_9012_cast_fp16, y = var_859_cast_fp16)[name = string("op_9036_cast_fp16")];
            tensor<int32, [2]> var_9037_split_sizes_0 = const()[name = string("op_9037_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9037_axis_0 = const()[name = string("op_9037_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_9037_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_9037_cast_fp16_1 = split(axis = var_9037_axis_0, split_sizes = var_9037_split_sizes_0, x = var_9012_cast_fp16)[name = string("op_9037_cast_fp16")];
            fp16 const_255_promoted_to_fp16 = const()[name = string("const_255_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_9039_cast_fp16 = mul(x = var_9037_cast_fp16_1, y = const_255_promoted_to_fp16)[name = string("op_9039_cast_fp16")];
            int32 var_9041 = const()[name = string("op_9041"), val = int32(-2)];
            bool var_9042_interleave_0 = const()[name = string("op_9042_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_9042_cast_fp16 = concat(axis = var_9041, interleave = var_9042_interleave_0, values = (var_9039_cast_fp16, var_9037_cast_fp16_0))[name = string("op_9042_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_9043_cast_fp16 = mul(x = var_9042_cast_fp16, y = var_866_cast_fp16)[name = string("op_9043_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_255_cast_fp16 = add(x = var_9036_cast_fp16, y = var_9043_cast_fp16)[name = string("key_states_255_cast_fp16")];
            tensor<int32, [1]> expand_dims_300 = const()[name = string("expand_dims_300"), val = tensor<int32, [1]>([25])];
            tensor<int32, [1]> expand_dims_301 = const()[name = string("expand_dims_301"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_303 = const()[name = string("expand_dims_303"), val = tensor<int32, [1]>([0])];
            int32 concat_305_axis_0 = const()[name = string("concat_305_axis_0"), val = int32(0)];
            bool concat_305_interleave_0 = const()[name = string("concat_305_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_305 = concat(axis = concat_305_axis_0, interleave = concat_305_interleave_0, values = (expand_dims_300, expand_dims_301, position_id, expand_dims_303))[name = string("concat_305")];
            tensor<int32, [1]> expand_dims_304 = const()[name = string("expand_dims_304"), val = tensor<int32, [1]>([26])];
            tensor<int32, [1]> concat_306_values1_0 = const()[name = string("concat_306_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_306_values3_0 = const()[name = string("concat_306_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_306_axis_0 = const()[name = string("concat_306_axis_0"), val = int32(0)];
            bool concat_306_interleave_0 = const()[name = string("concat_306_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_306 = concat(axis = concat_306_axis_0, interleave = concat_306_interleave_0, values = (expand_dims_304, concat_306_values1_0, cache_position_end, concat_306_values3_0))[name = string("concat_306")];
            tensor<int32, [4]> key_states_257_perm_0 = const()[name = string("key_states_257_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_26_stride_0 = const()[name = string("key_cache_internal_tensor_assign_26_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_26_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_26_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_26_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_26_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_26_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_26_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_257_cast_fp16 = transpose(perm = key_states_257_perm_0, x = key_states_255_cast_fp16)[name = string("transpose_443")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_26_cast_fp16 = slice_update(begin = concat_305, begin_mask = key_cache_internal_tensor_assign_26_begin_mask_0, end = concat_306, end_mask = key_cache_internal_tensor_assign_26_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_26_squeeze_mask_0, stride = key_cache_internal_tensor_assign_26_stride_0, update = key_states_257_cast_fp16, x = coreml_update_state_328)[name = string("key_cache_internal_tensor_assign_26_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_26_cast_fp16, input = key_cache)[name = string("coreml_update_state_330_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_330 = read_state(input = key_cache)[name = string("coreml_update_state_330")];
            tensor<int32, [4]> value_states_153_perm_0 = const()[name = string("value_states_153_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_26_stride_0 = const()[name = string("value_cache_internal_tensor_assign_26_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_26_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_26_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_26_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_26_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_26_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_26_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_153_cast_fp16 = transpose(perm = value_states_153_perm_0, x = var_9019_cast_fp16)[name = string("transpose_442")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_26_cast_fp16 = slice_update(begin = concat_305, begin_mask = value_cache_internal_tensor_assign_26_begin_mask_0, end = concat_306, end_mask = value_cache_internal_tensor_assign_26_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_26_squeeze_mask_0, stride = value_cache_internal_tensor_assign_26_stride_0, update = value_states_153_cast_fp16, x = coreml_update_state_329)[name = string("value_cache_internal_tensor_assign_26_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_26_cast_fp16, input = value_cache)[name = string("coreml_update_state_331_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_331 = read_state(input = value_cache)[name = string("coreml_update_state_331")];
            tensor<int32, [4]> var_9113_begin_0 = const()[name = string("op_9113_begin_0"), val = tensor<int32, [4]>([25, 0, 0, 0])];
            tensor<int32, [4]> var_9113_end_0 = const()[name = string("op_9113_end_0"), val = tensor<int32, [4]>([26, 2, 2048, 128])];
            tensor<bool, [4]> var_9113_end_mask_0 = const()[name = string("op_9113_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9113_cast_fp16 = slice_by_index(begin = var_9113_begin_0, end = var_9113_end_0, end_mask = var_9113_end_mask_0, x = coreml_update_state_330)[name = string("op_9113_cast_fp16")];
            tensor<int32, [2]> tile_50 = const()[name = string("tile_50"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9116_axis_0 = const()[name = string("op_9116_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9116_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9116_cast_fp16_1 = split(axis = var_9116_axis_0, split_sizes = tile_50, x = var_9113_cast_fp16)[name = string("op_9116_cast_fp16")];
            tensor<int32, [4]> var_9123_begin_0 = const()[name = string("op_9123_begin_0"), val = tensor<int32, [4]>([25, 0, 0, 0])];
            tensor<int32, [4]> var_9123_end_0 = const()[name = string("op_9123_end_0"), val = tensor<int32, [4]>([26, 2, 2048, 128])];
            tensor<bool, [4]> var_9123_end_mask_0 = const()[name = string("op_9123_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9123_cast_fp16 = slice_by_index(begin = var_9123_begin_0, end = var_9123_end_0, end_mask = var_9123_end_mask_0, x = coreml_update_state_331)[name = string("op_9123_cast_fp16")];
            tensor<int32, [2]> tile_51 = const()[name = string("tile_51"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9126_axis_0 = const()[name = string("op_9126_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9126_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9126_cast_fp16_1 = split(axis = var_9126_axis_0, split_sizes = tile_51, x = var_9123_cast_fp16)[name = string("op_9126_cast_fp16")];
            tensor<int32, [2]> var_9129_split_sizes_0 = const()[name = string("op_9129_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_9129_axis_0 = const()[name = string("op_9129_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_9129_0, tensor<fp16, [1, 8, 128, 64]> var_9129_1 = split(axis = var_9129_axis_0, split_sizes = var_9129_split_sizes_0, x = query_states_153_cast_fp16)[name = string("op_9129")];
            bool attn_weights_401_transpose_x_0 = const()[name = string("attn_weights_401_transpose_x_0"), val = bool(false)];
            bool attn_weights_401_transpose_y_0 = const()[name = string("attn_weights_401_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_401_cast_fp16 = matmul(transpose_x = attn_weights_401_transpose_x_0, transpose_y = attn_weights_401_transpose_y_0, x = var_9116_cast_fp16_0, y = var_9129_0)[name = string("attn_weights_401_cast_fp16")];
            fp16 var_9132_to_fp16 = const()[name = string("op_9132_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_403_cast_fp16 = mul(x = attn_weights_401_cast_fp16, y = var_9132_to_fp16)[name = string("attn_weights_403_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_405_cast_fp16 = add(x = attn_weights_403_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_405_cast_fp16")];
            int32 var_9136 = const()[name = string("op_9136"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_407_cast_fp16 = softmax(axis = var_9136, x = attn_weights_405_cast_fp16)[name = string("attn_weights_407_cast_fp16")];
            bool var_9142_transpose_x_1 = const()[name = string("op_9142_transpose_x_1"), val = bool(true)];
            bool var_9142_transpose_y_1 = const()[name = string("op_9142_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_9142_cast_fp16 = matmul(transpose_x = var_9142_transpose_x_1, transpose_y = var_9142_transpose_y_1, x = attn_weights_407_cast_fp16, y = var_9126_cast_fp16_0)[name = string("op_9142_cast_fp16")];
            bool attn_weights_409_transpose_x_0 = const()[name = string("attn_weights_409_transpose_x_0"), val = bool(false)];
            bool attn_weights_409_transpose_y_0 = const()[name = string("attn_weights_409_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_409_cast_fp16 = matmul(transpose_x = attn_weights_409_transpose_x_0, transpose_y = attn_weights_409_transpose_y_0, x = var_9116_cast_fp16_1, y = var_9129_1)[name = string("attn_weights_409_cast_fp16")];
            fp16 var_9144_to_fp16 = const()[name = string("op_9144_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_411_cast_fp16 = mul(x = attn_weights_409_cast_fp16, y = var_9144_to_fp16)[name = string("attn_weights_411_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_413_cast_fp16 = add(x = attn_weights_411_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_413_cast_fp16")];
            int32 var_9148 = const()[name = string("op_9148"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_415_cast_fp16 = softmax(axis = var_9148, x = attn_weights_413_cast_fp16)[name = string("attn_weights_415_cast_fp16")];
            bool attn_output_201_transpose_x_1 = const()[name = string("attn_output_201_transpose_x_1"), val = bool(true)];
            bool attn_output_201_transpose_y_1 = const()[name = string("attn_output_201_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_201_cast_fp16 = matmul(transpose_x = attn_output_201_transpose_x_1, transpose_y = attn_output_201_transpose_y_1, x = attn_weights_415_cast_fp16, y = var_9126_cast_fp16_1)[name = string("attn_output_201_cast_fp16")];
            int32 var_9156 = const()[name = string("op_9156"), val = int32(1)];
            bool attn_output_203_interleave_0 = const()[name = string("attn_output_203_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_203_cast_fp16 = concat(axis = var_9156, interleave = attn_output_203_interleave_0, values = (var_9142_cast_fp16, attn_output_201_cast_fp16))[name = string("attn_output_203_cast_fp16")];
            tensor<int32, [4]> var_9160_perm_0 = const()[name = string("op_9160_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_311x = const()[name = string("concat_311x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_9160_cast_fp16 = transpose(perm = var_9160_perm_0, x = attn_output_203_cast_fp16)[name = string("transpose_441")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_207_cast_fp16 = reshape(shape = concat_311x, x = var_9160_cast_fp16)[name = string("attn_output_207_cast_fp16")];
            tensor<int32, [2]> hidden_states_253_strides_0 = const()[name = string("hidden_states_253_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_253_pad_type_0 = const()[name = string("hidden_states_253_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_253_pad_0 = const()[name = string("hidden_states_253_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_253_dilations_0 = const()[name = string("hidden_states_253_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_253_groups_0 = const()[name = string("hidden_states_253_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_253_cast_fp16 = conv(dilations = hidden_states_253_dilations_0, groups = hidden_states_253_groups_0, pad = hidden_states_253_pad_0, pad_type = hidden_states_253_pad_type_0, strides = hidden_states_253_strides_0, weight = layers_25_self_attn_o_proj_weight_cast_fp16, x = attn_output_207_cast_fp16)[name = string("hidden_states_253_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_255_cast_fp16 = add(x = hidden_states_249_cast_fp16, y = hidden_states_253_cast_fp16)[name = string("hidden_states_255_cast_fp16")];
            fp16 const_260_promoted_to_fp16 = const()[name = string("const_260_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_9193_cast_fp16 = mul(x = hidden_states_255_cast_fp16, y = const_260_promoted_to_fp16)[name = string("op_9193_cast_fp16")];
            int32 var_9191 = const()[name = string("op_9191"), val = int32(1)];
            bool doubled_205_interleave_0 = const()[name = string("doubled_205_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_205_cast_fp16 = concat(axis = var_9191, interleave = doubled_205_interleave_0, values = (hidden_states_255_cast_fp16, var_9193_cast_fp16))[name = string("doubled_205_cast_fp16")];
            tensor<int32, [1]> out_103_axes_0 = const()[name = string("out_103_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_103_gamma_0_to_fp16 = const()[name = string("out_103_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1533047168)))];
            fp16 var_9203_to_fp16 = const()[name = string("op_9203_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_103_cast_fp16 = layer_norm(axes = out_103_axes_0, epsilon = var_9203_to_fp16, gamma = out_103_gamma_0_to_fp16, x = doubled_205_cast_fp16)[name = string("out_103_cast_fp16")];
            tensor<int32, [2]> var_9214_split_sizes_0 = const()[name = string("op_9214_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9214_axis_0 = const()[name = string("op_9214_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_9214_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_9214_cast_fp16_1 = split(axis = var_9214_axis_0, split_sizes = var_9214_split_sizes_0, x = out_103_cast_fp16)[name = string("op_9214_cast_fp16")];
            tensor<int32, [2]> input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_51_cast_fp16 = conv(dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = layers_25_mlp_gate_proj_weight_cast_fp16, x = var_9214_cast_fp16_0)[name = string("input_51_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_9231_cast_fp16 = silu(x = input_51_cast_fp16)[name = string("op_9231_cast_fp16")];
            tensor<int32, [2]> var_9237_strides_0 = const()[name = string("op_9237_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_9237_pad_type_0 = const()[name = string("op_9237_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_9237_pad_0 = const()[name = string("op_9237_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_9237_dilations_0 = const()[name = string("op_9237_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9237_groups_0 = const()[name = string("op_9237_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_9237_cast_fp16 = conv(dilations = var_9237_dilations_0, groups = var_9237_groups_0, pad = var_9237_pad_0, pad_type = var_9237_pad_type_0, strides = var_9237_strides_0, weight = layers_25_mlp_up_proj_weight_cast_fp16, x = var_9214_cast_fp16_0)[name = string("op_9237_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_259_cast_fp16 = mul(x = var_9231_cast_fp16, y = var_9237_cast_fp16)[name = string("x_259_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_25_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_25_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1533055424)))];
            tensor<int32, [2]> hidden_states_257_strides_0 = const()[name = string("hidden_states_257_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_257_pad_type_0 = const()[name = string("hidden_states_257_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_257_pad_0 = const()[name = string("hidden_states_257_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_257_dilations_0 = const()[name = string("hidden_states_257_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_257_groups_0 = const()[name = string("hidden_states_257_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_257_cast_fp16 = conv(dilations = hidden_states_257_dilations_0, groups = hidden_states_257_groups_0, pad = hidden_states_257_pad_0, pad_type = hidden_states_257_pad_type_0, strides = hidden_states_257_strides_0, weight = layers_25_mlp_down_proj_weight_to_fp16, x = x_259_cast_fp16)[name = string("hidden_states_257_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_259_cast_fp16 = add(x = hidden_states_255_cast_fp16, y = hidden_states_257_cast_fp16)[name = string("hidden_states_259_cast_fp16")];
            fp16 const_262_promoted_to_fp16 = const()[name = string("const_262_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_9255_cast_fp16 = mul(x = hidden_states_259_cast_fp16, y = const_262_promoted_to_fp16)[name = string("op_9255_cast_fp16")];
            int32 var_9253 = const()[name = string("op_9253"), val = int32(1)];
            bool doubled_209_interleave_0 = const()[name = string("doubled_209_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_209_cast_fp16 = concat(axis = var_9253, interleave = doubled_209_interleave_0, values = (hidden_states_259_cast_fp16, var_9255_cast_fp16))[name = string("doubled_209_cast_fp16")];
            tensor<int32, [1]> out_105_axes_0 = const()[name = string("out_105_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_105_gamma_0_to_fp16 = const()[name = string("out_105_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1558221312)))];
            fp16 var_9265_to_fp16 = const()[name = string("op_9265_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_105_cast_fp16 = layer_norm(axes = out_105_axes_0, epsilon = var_9265_to_fp16, gamma = out_105_gamma_0_to_fp16, x = doubled_209_cast_fp16)[name = string("out_105_cast_fp16")];
            tensor<int32, [2]> var_9276_split_sizes_0 = const()[name = string("op_9276_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9276_axis_0 = const()[name = string("op_9276_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_9276_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_9276_cast_fp16_1 = split(axis = var_9276_axis_0, split_sizes = var_9276_split_sizes_0, x = out_105_cast_fp16)[name = string("op_9276_cast_fp16")];
            tensor<int32, [2]> query_states_157_strides_0 = const()[name = string("query_states_157_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_157_pad_type_0 = const()[name = string("query_states_157_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_157_pad_0 = const()[name = string("query_states_157_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_157_dilations_0 = const()[name = string("query_states_157_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_157_groups_0 = const()[name = string("query_states_157_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_157_cast_fp16 = conv(dilations = query_states_157_dilations_0, groups = query_states_157_groups_0, pad = query_states_157_pad_0, pad_type = query_states_157_pad_type_0, strides = query_states_157_strides_0, weight = layers_26_self_attn_q_proj_weight_cast_fp16, x = var_9276_cast_fp16_0)[name = string("query_states_157_cast_fp16")];
            tensor<int32, [2]> key_states_261_strides_0 = const()[name = string("key_states_261_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_261_pad_type_0 = const()[name = string("key_states_261_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_261_pad_0 = const()[name = string("key_states_261_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_261_dilations_0 = const()[name = string("key_states_261_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_261_groups_0 = const()[name = string("key_states_261_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_261_cast_fp16 = conv(dilations = key_states_261_dilations_0, groups = key_states_261_groups_0, pad = key_states_261_pad_0, pad_type = key_states_261_pad_type_0, strides = key_states_261_strides_0, weight = layers_26_self_attn_k_proj_weight_cast_fp16, x = var_9276_cast_fp16_0)[name = string("key_states_261_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_26_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_26_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1558229568)))];
            tensor<int32, [2]> value_states_157_strides_0 = const()[name = string("value_states_157_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_157_pad_type_0 = const()[name = string("value_states_157_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_157_pad_0 = const()[name = string("value_states_157_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_157_dilations_0 = const()[name = string("value_states_157_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_157_groups_0 = const()[name = string("value_states_157_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_157_cast_fp16 = conv(dilations = value_states_157_dilations_0, groups = value_states_157_groups_0, pad = value_states_157_pad_0, pad_type = value_states_157_pad_type_0, strides = value_states_157_strides_0, weight = layers_26_self_attn_v_proj_weight_to_fp16, x = var_9276_cast_fp16_0)[name = string("value_states_157_cast_fp16")];
            tensor<int32, [4]> concat_312x = const()[name = string("concat_312x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_261_cast_fp16 = reshape(shape = concat_312x, x = query_states_157_cast_fp16)[name = string("x_261_cast_fp16")];
            tensor<int32, [4]> concat_313x = const()[name = string("concat_313x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_9333_cast_fp16 = reshape(shape = concat_313x, x = key_states_261_cast_fp16)[name = string("op_9333_cast_fp16")];
            tensor<int32, [4]> concat_314x = const()[name = string("concat_314x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_9340_cast_fp16 = reshape(shape = concat_314x, x = value_states_157_cast_fp16)[name = string("op_9340_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_9344_cast_fp16 = mul(x = x_261_cast_fp16, y = var_859_cast_fp16)[name = string("op_9344_cast_fp16")];
            tensor<int32, [2]> var_9345_split_sizes_0 = const()[name = string("op_9345_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9345_axis_0 = const()[name = string("op_9345_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_9345_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_9345_cast_fp16_1 = split(axis = var_9345_axis_0, split_sizes = var_9345_split_sizes_0, x = x_261_cast_fp16)[name = string("op_9345_cast_fp16")];
            fp16 const_264_promoted_to_fp16 = const()[name = string("const_264_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_9347_cast_fp16 = mul(x = var_9345_cast_fp16_1, y = const_264_promoted_to_fp16)[name = string("op_9347_cast_fp16")];
            int32 var_9349 = const()[name = string("op_9349"), val = int32(-2)];
            bool var_9350_interleave_0 = const()[name = string("op_9350_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_9350_cast_fp16 = concat(axis = var_9349, interleave = var_9350_interleave_0, values = (var_9347_cast_fp16, var_9345_cast_fp16_0))[name = string("op_9350_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_9351_cast_fp16 = mul(x = var_9350_cast_fp16, y = var_866_cast_fp16)[name = string("op_9351_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_159_cast_fp16 = add(x = var_9344_cast_fp16, y = var_9351_cast_fp16)[name = string("query_states_159_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_9357_cast_fp16 = mul(x = var_9333_cast_fp16, y = var_859_cast_fp16)[name = string("op_9357_cast_fp16")];
            tensor<int32, [2]> var_9358_split_sizes_0 = const()[name = string("op_9358_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9358_axis_0 = const()[name = string("op_9358_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_9358_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_9358_cast_fp16_1 = split(axis = var_9358_axis_0, split_sizes = var_9358_split_sizes_0, x = var_9333_cast_fp16)[name = string("op_9358_cast_fp16")];
            fp16 const_265_promoted_to_fp16 = const()[name = string("const_265_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_9360_cast_fp16 = mul(x = var_9358_cast_fp16_1, y = const_265_promoted_to_fp16)[name = string("op_9360_cast_fp16")];
            int32 var_9362 = const()[name = string("op_9362"), val = int32(-2)];
            bool var_9363_interleave_0 = const()[name = string("op_9363_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_9363_cast_fp16 = concat(axis = var_9362, interleave = var_9363_interleave_0, values = (var_9360_cast_fp16, var_9358_cast_fp16_0))[name = string("op_9363_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_9364_cast_fp16 = mul(x = var_9363_cast_fp16, y = var_866_cast_fp16)[name = string("op_9364_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_265_cast_fp16 = add(x = var_9357_cast_fp16, y = var_9364_cast_fp16)[name = string("key_states_265_cast_fp16")];
            tensor<int32, [1]> expand_dims_312 = const()[name = string("expand_dims_312"), val = tensor<int32, [1]>([26])];
            tensor<int32, [1]> expand_dims_313 = const()[name = string("expand_dims_313"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_315 = const()[name = string("expand_dims_315"), val = tensor<int32, [1]>([0])];
            int32 concat_317_axis_0 = const()[name = string("concat_317_axis_0"), val = int32(0)];
            bool concat_317_interleave_0 = const()[name = string("concat_317_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_317 = concat(axis = concat_317_axis_0, interleave = concat_317_interleave_0, values = (expand_dims_312, expand_dims_313, position_id, expand_dims_315))[name = string("concat_317")];
            tensor<int32, [1]> expand_dims_316 = const()[name = string("expand_dims_316"), val = tensor<int32, [1]>([27])];
            tensor<int32, [1]> concat_318_values1_0 = const()[name = string("concat_318_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_318_values3_0 = const()[name = string("concat_318_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_318_axis_0 = const()[name = string("concat_318_axis_0"), val = int32(0)];
            bool concat_318_interleave_0 = const()[name = string("concat_318_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_318 = concat(axis = concat_318_axis_0, interleave = concat_318_interleave_0, values = (expand_dims_316, concat_318_values1_0, cache_position_end, concat_318_values3_0))[name = string("concat_318")];
            tensor<int32, [4]> key_states_267_perm_0 = const()[name = string("key_states_267_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_27_stride_0 = const()[name = string("key_cache_internal_tensor_assign_27_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_27_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_27_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_27_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_27_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_27_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_27_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_267_cast_fp16 = transpose(perm = key_states_267_perm_0, x = key_states_265_cast_fp16)[name = string("transpose_440")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_27_cast_fp16 = slice_update(begin = concat_317, begin_mask = key_cache_internal_tensor_assign_27_begin_mask_0, end = concat_318, end_mask = key_cache_internal_tensor_assign_27_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_27_squeeze_mask_0, stride = key_cache_internal_tensor_assign_27_stride_0, update = key_states_267_cast_fp16, x = coreml_update_state_330)[name = string("key_cache_internal_tensor_assign_27_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_27_cast_fp16, input = key_cache)[name = string("coreml_update_state_332_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_332 = read_state(input = key_cache)[name = string("coreml_update_state_332")];
            tensor<int32, [4]> value_states_159_perm_0 = const()[name = string("value_states_159_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_27_stride_0 = const()[name = string("value_cache_internal_tensor_assign_27_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_27_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_27_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_27_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_27_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_27_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_27_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_159_cast_fp16 = transpose(perm = value_states_159_perm_0, x = var_9340_cast_fp16)[name = string("transpose_439")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_27_cast_fp16 = slice_update(begin = concat_317, begin_mask = value_cache_internal_tensor_assign_27_begin_mask_0, end = concat_318, end_mask = value_cache_internal_tensor_assign_27_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_27_squeeze_mask_0, stride = value_cache_internal_tensor_assign_27_stride_0, update = value_states_159_cast_fp16, x = coreml_update_state_331)[name = string("value_cache_internal_tensor_assign_27_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_27_cast_fp16, input = value_cache)[name = string("coreml_update_state_333_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_333 = read_state(input = value_cache)[name = string("coreml_update_state_333")];
            tensor<int32, [4]> var_9434_begin_0 = const()[name = string("op_9434_begin_0"), val = tensor<int32, [4]>([26, 0, 0, 0])];
            tensor<int32, [4]> var_9434_end_0 = const()[name = string("op_9434_end_0"), val = tensor<int32, [4]>([27, 2, 2048, 128])];
            tensor<bool, [4]> var_9434_end_mask_0 = const()[name = string("op_9434_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9434_cast_fp16 = slice_by_index(begin = var_9434_begin_0, end = var_9434_end_0, end_mask = var_9434_end_mask_0, x = coreml_update_state_332)[name = string("op_9434_cast_fp16")];
            tensor<int32, [2]> tile_52 = const()[name = string("tile_52"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9437_axis_0 = const()[name = string("op_9437_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9437_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9437_cast_fp16_1 = split(axis = var_9437_axis_0, split_sizes = tile_52, x = var_9434_cast_fp16)[name = string("op_9437_cast_fp16")];
            tensor<int32, [4]> var_9444_begin_0 = const()[name = string("op_9444_begin_0"), val = tensor<int32, [4]>([26, 0, 0, 0])];
            tensor<int32, [4]> var_9444_end_0 = const()[name = string("op_9444_end_0"), val = tensor<int32, [4]>([27, 2, 2048, 128])];
            tensor<bool, [4]> var_9444_end_mask_0 = const()[name = string("op_9444_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9444_cast_fp16 = slice_by_index(begin = var_9444_begin_0, end = var_9444_end_0, end_mask = var_9444_end_mask_0, x = coreml_update_state_333)[name = string("op_9444_cast_fp16")];
            tensor<int32, [2]> tile_53 = const()[name = string("tile_53"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9447_axis_0 = const()[name = string("op_9447_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9447_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9447_cast_fp16_1 = split(axis = var_9447_axis_0, split_sizes = tile_53, x = var_9444_cast_fp16)[name = string("op_9447_cast_fp16")];
            tensor<int32, [2]> var_9450_split_sizes_0 = const()[name = string("op_9450_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_9450_axis_0 = const()[name = string("op_9450_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_9450_0, tensor<fp16, [1, 8, 128, 64]> var_9450_1 = split(axis = var_9450_axis_0, split_sizes = var_9450_split_sizes_0, x = query_states_159_cast_fp16)[name = string("op_9450")];
            bool attn_weights_417_transpose_x_0 = const()[name = string("attn_weights_417_transpose_x_0"), val = bool(false)];
            bool attn_weights_417_transpose_y_0 = const()[name = string("attn_weights_417_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_417_cast_fp16 = matmul(transpose_x = attn_weights_417_transpose_x_0, transpose_y = attn_weights_417_transpose_y_0, x = var_9437_cast_fp16_0, y = var_9450_0)[name = string("attn_weights_417_cast_fp16")];
            fp16 var_9453_to_fp16 = const()[name = string("op_9453_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_419_cast_fp16 = mul(x = attn_weights_417_cast_fp16, y = var_9453_to_fp16)[name = string("attn_weights_419_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_421_cast_fp16 = add(x = attn_weights_419_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_421_cast_fp16")];
            int32 var_9457 = const()[name = string("op_9457"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_423_cast_fp16 = softmax(axis = var_9457, x = attn_weights_421_cast_fp16)[name = string("attn_weights_423_cast_fp16")];
            bool var_9463_transpose_x_1 = const()[name = string("op_9463_transpose_x_1"), val = bool(true)];
            bool var_9463_transpose_y_1 = const()[name = string("op_9463_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_9463_cast_fp16 = matmul(transpose_x = var_9463_transpose_x_1, transpose_y = var_9463_transpose_y_1, x = attn_weights_423_cast_fp16, y = var_9447_cast_fp16_0)[name = string("op_9463_cast_fp16")];
            bool attn_weights_425_transpose_x_0 = const()[name = string("attn_weights_425_transpose_x_0"), val = bool(false)];
            bool attn_weights_425_transpose_y_0 = const()[name = string("attn_weights_425_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_425_cast_fp16 = matmul(transpose_x = attn_weights_425_transpose_x_0, transpose_y = attn_weights_425_transpose_y_0, x = var_9437_cast_fp16_1, y = var_9450_1)[name = string("attn_weights_425_cast_fp16")];
            fp16 var_9465_to_fp16 = const()[name = string("op_9465_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_427_cast_fp16 = mul(x = attn_weights_425_cast_fp16, y = var_9465_to_fp16)[name = string("attn_weights_427_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_429_cast_fp16 = add(x = attn_weights_427_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_429_cast_fp16")];
            int32 var_9469 = const()[name = string("op_9469"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_431_cast_fp16 = softmax(axis = var_9469, x = attn_weights_429_cast_fp16)[name = string("attn_weights_431_cast_fp16")];
            bool attn_output_209_transpose_x_1 = const()[name = string("attn_output_209_transpose_x_1"), val = bool(true)];
            bool attn_output_209_transpose_y_1 = const()[name = string("attn_output_209_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_209_cast_fp16 = matmul(transpose_x = attn_output_209_transpose_x_1, transpose_y = attn_output_209_transpose_y_1, x = attn_weights_431_cast_fp16, y = var_9447_cast_fp16_1)[name = string("attn_output_209_cast_fp16")];
            int32 var_9477 = const()[name = string("op_9477"), val = int32(1)];
            bool attn_output_211_interleave_0 = const()[name = string("attn_output_211_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_211_cast_fp16 = concat(axis = var_9477, interleave = attn_output_211_interleave_0, values = (var_9463_cast_fp16, attn_output_209_cast_fp16))[name = string("attn_output_211_cast_fp16")];
            tensor<int32, [4]> var_9481_perm_0 = const()[name = string("op_9481_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_323x = const()[name = string("concat_323x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_9481_cast_fp16 = transpose(perm = var_9481_perm_0, x = attn_output_211_cast_fp16)[name = string("transpose_438")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_215_cast_fp16 = reshape(shape = concat_323x, x = var_9481_cast_fp16)[name = string("attn_output_215_cast_fp16")];
            tensor<int32, [2]> hidden_states_263_strides_0 = const()[name = string("hidden_states_263_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_263_pad_type_0 = const()[name = string("hidden_states_263_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_263_pad_0 = const()[name = string("hidden_states_263_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_263_dilations_0 = const()[name = string("hidden_states_263_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_263_groups_0 = const()[name = string("hidden_states_263_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_263_cast_fp16 = conv(dilations = hidden_states_263_dilations_0, groups = hidden_states_263_groups_0, pad = hidden_states_263_pad_0, pad_type = hidden_states_263_pad_type_0, strides = hidden_states_263_strides_0, weight = layers_26_self_attn_o_proj_weight_cast_fp16, x = attn_output_215_cast_fp16)[name = string("hidden_states_263_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_265_cast_fp16 = add(x = hidden_states_259_cast_fp16, y = hidden_states_263_cast_fp16)[name = string("hidden_states_265_cast_fp16")];
            fp16 const_270_promoted_to_fp16 = const()[name = string("const_270_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_9514_cast_fp16 = mul(x = hidden_states_265_cast_fp16, y = const_270_promoted_to_fp16)[name = string("op_9514_cast_fp16")];
            int32 var_9512 = const()[name = string("op_9512"), val = int32(1)];
            bool doubled_213_interleave_0 = const()[name = string("doubled_213_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_213_cast_fp16 = concat(axis = var_9512, interleave = doubled_213_interleave_0, values = (hidden_states_265_cast_fp16, var_9514_cast_fp16))[name = string("doubled_213_cast_fp16")];
            tensor<int32, [1]> out_107_axes_0 = const()[name = string("out_107_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_107_gamma_0_to_fp16 = const()[name = string("out_107_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1559278208)))];
            fp16 var_9524_to_fp16 = const()[name = string("op_9524_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_107_cast_fp16 = layer_norm(axes = out_107_axes_0, epsilon = var_9524_to_fp16, gamma = out_107_gamma_0_to_fp16, x = doubled_213_cast_fp16)[name = string("out_107_cast_fp16")];
            tensor<int32, [2]> var_9535_split_sizes_0 = const()[name = string("op_9535_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9535_axis_0 = const()[name = string("op_9535_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_9535_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_9535_cast_fp16_1 = split(axis = var_9535_axis_0, split_sizes = var_9535_split_sizes_0, x = out_107_cast_fp16)[name = string("op_9535_cast_fp16")];
            tensor<int32, [2]> input_53_strides_0 = const()[name = string("input_53_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_53_pad_type_0 = const()[name = string("input_53_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_53_dilations_0 = const()[name = string("input_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_53_groups_0 = const()[name = string("input_53_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_53_cast_fp16 = conv(dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = layers_26_mlp_gate_proj_weight_cast_fp16, x = var_9535_cast_fp16_0)[name = string("input_53_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_9552_cast_fp16 = silu(x = input_53_cast_fp16)[name = string("op_9552_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_26_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_26_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1559286464)))];
            tensor<int32, [2]> var_9558_strides_0 = const()[name = string("op_9558_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_9558_pad_type_0 = const()[name = string("op_9558_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_9558_pad_0 = const()[name = string("op_9558_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_9558_dilations_0 = const()[name = string("op_9558_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9558_groups_0 = const()[name = string("op_9558_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_9558_cast_fp16 = conv(dilations = var_9558_dilations_0, groups = var_9558_groups_0, pad = var_9558_pad_0, pad_type = var_9558_pad_type_0, strides = var_9558_strides_0, weight = layers_26_mlp_up_proj_weight_to_fp16, x = var_9535_cast_fp16_0)[name = string("op_9558_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_269_cast_fp16 = mul(x = var_9552_cast_fp16, y = var_9558_cast_fp16)[name = string("x_269_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_26_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_26_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1584452352)))];
            tensor<int32, [2]> hidden_states_267_strides_0 = const()[name = string("hidden_states_267_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_267_pad_type_0 = const()[name = string("hidden_states_267_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_267_pad_0 = const()[name = string("hidden_states_267_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_267_dilations_0 = const()[name = string("hidden_states_267_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_267_groups_0 = const()[name = string("hidden_states_267_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_267_cast_fp16 = conv(dilations = hidden_states_267_dilations_0, groups = hidden_states_267_groups_0, pad = hidden_states_267_pad_0, pad_type = hidden_states_267_pad_type_0, strides = hidden_states_267_strides_0, weight = layers_26_mlp_down_proj_weight_to_fp16, x = x_269_cast_fp16)[name = string("hidden_states_267_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_269_cast_fp16 = add(x = hidden_states_265_cast_fp16, y = hidden_states_267_cast_fp16)[name = string("hidden_states_269_cast_fp16")];
            fp16 const_272_promoted_to_fp16 = const()[name = string("const_272_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_9576_cast_fp16 = mul(x = hidden_states_269_cast_fp16, y = const_272_promoted_to_fp16)[name = string("op_9576_cast_fp16")];
            int32 var_9574 = const()[name = string("op_9574"), val = int32(1)];
            bool doubled_217_interleave_0 = const()[name = string("doubled_217_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_217_cast_fp16 = concat(axis = var_9574, interleave = doubled_217_interleave_0, values = (hidden_states_269_cast_fp16, var_9576_cast_fp16))[name = string("doubled_217_cast_fp16")];
            tensor<int32, [1]> out_109_axes_0 = const()[name = string("out_109_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_109_gamma_0_to_fp16 = const()[name = string("out_109_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1609618240)))];
            fp16 var_9586_to_fp16 = const()[name = string("op_9586_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_109_cast_fp16 = layer_norm(axes = out_109_axes_0, epsilon = var_9586_to_fp16, gamma = out_109_gamma_0_to_fp16, x = doubled_217_cast_fp16)[name = string("out_109_cast_fp16")];
            tensor<int32, [2]> var_9597_split_sizes_0 = const()[name = string("op_9597_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9597_axis_0 = const()[name = string("op_9597_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_9597_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_9597_cast_fp16_1 = split(axis = var_9597_axis_0, split_sizes = var_9597_split_sizes_0, x = out_109_cast_fp16)[name = string("op_9597_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_27_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1609626496)))];
            tensor<int32, [2]> query_states_163_strides_0 = const()[name = string("query_states_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_163_pad_type_0 = const()[name = string("query_states_163_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_163_pad_0 = const()[name = string("query_states_163_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_163_dilations_0 = const()[name = string("query_states_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_163_groups_0 = const()[name = string("query_states_163_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> query_states_163_cast_fp16 = conv(dilations = query_states_163_dilations_0, groups = query_states_163_groups_0, pad = query_states_163_pad_0, pad_type = query_states_163_pad_type_0, strides = query_states_163_strides_0, weight = layers_27_self_attn_q_proj_weight_to_fp16, x = var_9597_cast_fp16_0)[name = string("query_states_163_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_27_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1618015168)))];
            tensor<int32, [2]> key_states_271_strides_0 = const()[name = string("key_states_271_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_271_pad_type_0 = const()[name = string("key_states_271_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_271_pad_0 = const()[name = string("key_states_271_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_271_dilations_0 = const()[name = string("key_states_271_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_271_groups_0 = const()[name = string("key_states_271_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> key_states_271_cast_fp16 = conv(dilations = key_states_271_dilations_0, groups = key_states_271_groups_0, pad = key_states_271_pad_0, pad_type = key_states_271_pad_type_0, strides = key_states_271_strides_0, weight = layers_27_self_attn_k_proj_weight_to_fp16, x = var_9597_cast_fp16_0)[name = string("key_states_271_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_27_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1619063808)))];
            tensor<int32, [2]> value_states_163_strides_0 = const()[name = string("value_states_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_163_pad_type_0 = const()[name = string("value_states_163_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_163_pad_0 = const()[name = string("value_states_163_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_163_dilations_0 = const()[name = string("value_states_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_163_groups_0 = const()[name = string("value_states_163_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 64]> value_states_163_cast_fp16 = conv(dilations = value_states_163_dilations_0, groups = value_states_163_groups_0, pad = value_states_163_pad_0, pad_type = value_states_163_pad_type_0, strides = value_states_163_strides_0, weight = layers_27_self_attn_v_proj_weight_to_fp16, x = var_9597_cast_fp16_0)[name = string("value_states_163_cast_fp16")];
            tensor<int32, [4]> concat_324x = const()[name = string("concat_324x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 64]> x_271_cast_fp16 = reshape(shape = concat_324x, x = query_states_163_cast_fp16)[name = string("x_271_cast_fp16")];
            tensor<int32, [4]> concat_325x = const()[name = string("concat_325x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_9654_cast_fp16 = reshape(shape = concat_325x, x = key_states_271_cast_fp16)[name = string("op_9654_cast_fp16")];
            tensor<int32, [4]> concat_326x = const()[name = string("concat_326x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 64]> var_9661_cast_fp16 = reshape(shape = concat_326x, x = value_states_163_cast_fp16)[name = string("op_9661_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_9665_cast_fp16 = mul(x = x_271_cast_fp16, y = var_859_cast_fp16)[name = string("op_9665_cast_fp16")];
            tensor<int32, [2]> var_9666_split_sizes_0 = const()[name = string("op_9666_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9666_axis_0 = const()[name = string("op_9666_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 64]> var_9666_cast_fp16_0, tensor<fp16, [1, 16, 64, 64]> var_9666_cast_fp16_1 = split(axis = var_9666_axis_0, split_sizes = var_9666_split_sizes_0, x = x_271_cast_fp16)[name = string("op_9666_cast_fp16")];
            fp16 const_274_promoted_to_fp16 = const()[name = string("const_274_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 64]> var_9668_cast_fp16 = mul(x = var_9666_cast_fp16_1, y = const_274_promoted_to_fp16)[name = string("op_9668_cast_fp16")];
            int32 var_9670 = const()[name = string("op_9670"), val = int32(-2)];
            bool var_9671_interleave_0 = const()[name = string("op_9671_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 64]> var_9671_cast_fp16 = concat(axis = var_9670, interleave = var_9671_interleave_0, values = (var_9668_cast_fp16, var_9666_cast_fp16_0))[name = string("op_9671_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> var_9672_cast_fp16 = mul(x = var_9671_cast_fp16, y = var_866_cast_fp16)[name = string("op_9672_cast_fp16")];
            tensor<fp16, [1, 16, 128, 64]> query_states_165_cast_fp16 = add(x = var_9665_cast_fp16, y = var_9672_cast_fp16)[name = string("query_states_165_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_9678_cast_fp16 = mul(x = var_9654_cast_fp16, y = var_859_cast_fp16)[name = string("op_9678_cast_fp16")];
            tensor<int32, [2]> var_9679_split_sizes_0 = const()[name = string("op_9679_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9679_axis_0 = const()[name = string("op_9679_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 64]> var_9679_cast_fp16_0, tensor<fp16, [1, 2, 64, 64]> var_9679_cast_fp16_1 = split(axis = var_9679_axis_0, split_sizes = var_9679_split_sizes_0, x = var_9654_cast_fp16)[name = string("op_9679_cast_fp16")];
            fp16 const_275_promoted_to_fp16 = const()[name = string("const_275_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 64]> var_9681_cast_fp16 = mul(x = var_9679_cast_fp16_1, y = const_275_promoted_to_fp16)[name = string("op_9681_cast_fp16")];
            int32 var_9683 = const()[name = string("op_9683"), val = int32(-2)];
            bool var_9684_interleave_0 = const()[name = string("op_9684_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 64]> var_9684_cast_fp16 = concat(axis = var_9683, interleave = var_9684_interleave_0, values = (var_9681_cast_fp16, var_9679_cast_fp16_0))[name = string("op_9684_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> var_9685_cast_fp16 = mul(x = var_9684_cast_fp16, y = var_866_cast_fp16)[name = string("op_9685_cast_fp16")];
            tensor<fp16, [1, 2, 128, 64]> key_states_275_cast_fp16 = add(x = var_9678_cast_fp16, y = var_9685_cast_fp16)[name = string("key_states_275_cast_fp16")];
            tensor<int32, [1]> expand_dims_324 = const()[name = string("expand_dims_324"), val = tensor<int32, [1]>([27])];
            tensor<int32, [1]> expand_dims_325 = const()[name = string("expand_dims_325"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_327 = const()[name = string("expand_dims_327"), val = tensor<int32, [1]>([0])];
            int32 concat_329_axis_0 = const()[name = string("concat_329_axis_0"), val = int32(0)];
            bool concat_329_interleave_0 = const()[name = string("concat_329_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_329 = concat(axis = concat_329_axis_0, interleave = concat_329_interleave_0, values = (expand_dims_324, expand_dims_325, position_id, expand_dims_327))[name = string("concat_329")];
            tensor<int32, [1]> expand_dims_328 = const()[name = string("expand_dims_328"), val = tensor<int32, [1]>([28])];
            tensor<int32, [1]> concat_330_values1_0 = const()[name = string("concat_330_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_330_values3_0 = const()[name = string("concat_330_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_330_axis_0 = const()[name = string("concat_330_axis_0"), val = int32(0)];
            bool concat_330_interleave_0 = const()[name = string("concat_330_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_330 = concat(axis = concat_330_axis_0, interleave = concat_330_interleave_0, values = (expand_dims_328, concat_330_values1_0, cache_position_end, concat_330_values3_0))[name = string("concat_330")];
            tensor<int32, [4]> key_states_277_perm_0 = const()[name = string("key_states_277_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_28_stride_0 = const()[name = string("key_cache_internal_tensor_assign_28_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_28_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_28_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_28_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_28_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_28_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_28_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> key_states_277_cast_fp16 = transpose(perm = key_states_277_perm_0, x = key_states_275_cast_fp16)[name = string("transpose_437")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_28_cast_fp16 = slice_update(begin = concat_329, begin_mask = key_cache_internal_tensor_assign_28_begin_mask_0, end = concat_330, end_mask = key_cache_internal_tensor_assign_28_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_28_squeeze_mask_0, stride = key_cache_internal_tensor_assign_28_stride_0, update = key_states_277_cast_fp16, x = coreml_update_state_332)[name = string("key_cache_internal_tensor_assign_28_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_28_cast_fp16, input = key_cache)[name = string("coreml_update_state_334_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_334 = read_state(input = key_cache)[name = string("coreml_update_state_334")];
            tensor<int32, [4]> value_states_165_perm_0 = const()[name = string("value_states_165_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_28_stride_0 = const()[name = string("value_cache_internal_tensor_assign_28_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_28_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_28_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_28_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_28_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_28_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_28_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 64, 128]> value_states_165_cast_fp16 = transpose(perm = value_states_165_perm_0, x = var_9661_cast_fp16)[name = string("transpose_436")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_28_cast_fp16 = slice_update(begin = concat_329, begin_mask = value_cache_internal_tensor_assign_28_begin_mask_0, end = concat_330, end_mask = value_cache_internal_tensor_assign_28_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_28_squeeze_mask_0, stride = value_cache_internal_tensor_assign_28_stride_0, update = value_states_165_cast_fp16, x = coreml_update_state_333)[name = string("value_cache_internal_tensor_assign_28_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_28_cast_fp16, input = value_cache)[name = string("coreml_update_state_335_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_335 = read_state(input = value_cache)[name = string("coreml_update_state_335")];
            tensor<int32, [4]> var_9755_begin_0 = const()[name = string("op_9755_begin_0"), val = tensor<int32, [4]>([27, 0, 0, 0])];
            tensor<int32, [4]> var_9755_end_0 = const()[name = string("op_9755_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_9755_end_mask_0 = const()[name = string("op_9755_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9755_cast_fp16 = slice_by_index(begin = var_9755_begin_0, end = var_9755_end_0, end_mask = var_9755_end_mask_0, x = coreml_update_state_334)[name = string("op_9755_cast_fp16")];
            tensor<int32, [2]> tile_54 = const()[name = string("tile_54"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9758_axis_0 = const()[name = string("op_9758_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9758_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9758_cast_fp16_1 = split(axis = var_9758_axis_0, split_sizes = tile_54, x = var_9755_cast_fp16)[name = string("op_9758_cast_fp16")];
            tensor<int32, [4]> var_9765_begin_0 = const()[name = string("op_9765_begin_0"), val = tensor<int32, [4]>([27, 0, 0, 0])];
            tensor<int32, [4]> var_9765_end_0 = const()[name = string("op_9765_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_9765_end_mask_0 = const()[name = string("op_9765_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9765_cast_fp16 = slice_by_index(begin = var_9765_begin_0, end = var_9765_end_0, end_mask = var_9765_end_mask_0, x = coreml_update_state_335)[name = string("op_9765_cast_fp16")];
            tensor<int32, [2]> tile_55 = const()[name = string("tile_55"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9768_axis_0 = const()[name = string("op_9768_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9768_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9768_cast_fp16_1 = split(axis = var_9768_axis_0, split_sizes = tile_55, x = var_9765_cast_fp16)[name = string("op_9768_cast_fp16")];
            tensor<int32, [2]> var_9771_split_sizes_0 = const()[name = string("op_9771_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_9771_axis_0 = const()[name = string("op_9771_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 64]> var_9771_0, tensor<fp16, [1, 8, 128, 64]> var_9771_1 = split(axis = var_9771_axis_0, split_sizes = var_9771_split_sizes_0, x = query_states_165_cast_fp16)[name = string("op_9771")];
            bool attn_weights_433_transpose_x_0 = const()[name = string("attn_weights_433_transpose_x_0"), val = bool(false)];
            bool attn_weights_433_transpose_y_0 = const()[name = string("attn_weights_433_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_433_cast_fp16 = matmul(transpose_x = attn_weights_433_transpose_x_0, transpose_y = attn_weights_433_transpose_y_0, x = var_9758_cast_fp16_0, y = var_9771_0)[name = string("attn_weights_433_cast_fp16")];
            fp16 var_9774_to_fp16 = const()[name = string("op_9774_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_435_cast_fp16 = mul(x = attn_weights_433_cast_fp16, y = var_9774_to_fp16)[name = string("attn_weights_435_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_437_cast_fp16 = add(x = attn_weights_435_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_437_cast_fp16")];
            int32 var_9778 = const()[name = string("op_9778"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_439_cast_fp16 = softmax(axis = var_9778, x = attn_weights_437_cast_fp16)[name = string("attn_weights_439_cast_fp16")];
            bool var_9784_transpose_x_1 = const()[name = string("op_9784_transpose_x_1"), val = bool(true)];
            bool var_9784_transpose_y_1 = const()[name = string("op_9784_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> var_9784_cast_fp16 = matmul(transpose_x = var_9784_transpose_x_1, transpose_y = var_9784_transpose_y_1, x = attn_weights_439_cast_fp16, y = var_9768_cast_fp16_0)[name = string("op_9784_cast_fp16")];
            bool attn_weights_441_transpose_x_0 = const()[name = string("attn_weights_441_transpose_x_0"), val = bool(false)];
            bool attn_weights_441_transpose_y_0 = const()[name = string("attn_weights_441_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_441_cast_fp16 = matmul(transpose_x = attn_weights_441_transpose_x_0, transpose_y = attn_weights_441_transpose_y_0, x = var_9758_cast_fp16_1, y = var_9771_1)[name = string("attn_weights_441_cast_fp16")];
            fp16 var_9786_to_fp16 = const()[name = string("op_9786_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_443_cast_fp16 = mul(x = attn_weights_441_cast_fp16, y = var_9786_to_fp16)[name = string("attn_weights_443_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_445_cast_fp16 = add(x = attn_weights_443_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_445_cast_fp16")];
            int32 var_9790 = const()[name = string("op_9790"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 64]> attn_weights_cast_fp16 = softmax(axis = var_9790, x = attn_weights_445_cast_fp16)[name = string("attn_weights_cast_fp16")];
            bool attn_output_217_transpose_x_1 = const()[name = string("attn_output_217_transpose_x_1"), val = bool(true)];
            bool attn_output_217_transpose_y_1 = const()[name = string("attn_output_217_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 64, 128]> attn_output_217_cast_fp16 = matmul(transpose_x = attn_output_217_transpose_x_1, transpose_y = attn_output_217_transpose_y_1, x = attn_weights_cast_fp16, y = var_9768_cast_fp16_1)[name = string("attn_output_217_cast_fp16")];
            int32 var_9798 = const()[name = string("op_9798"), val = int32(1)];
            bool attn_output_219_interleave_0 = const()[name = string("attn_output_219_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 64, 128]> attn_output_219_cast_fp16 = concat(axis = var_9798, interleave = attn_output_219_interleave_0, values = (var_9784_cast_fp16, attn_output_217_cast_fp16))[name = string("attn_output_219_cast_fp16")];
            tensor<int32, [4]> var_9802_perm_0 = const()[name = string("op_9802_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_335x = const()[name = string("concat_335x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 64]> var_9802_cast_fp16 = transpose(perm = var_9802_perm_0, x = attn_output_219_cast_fp16)[name = string("transpose_435")];
            tensor<fp16, [1, 2048, 1, 64]> attn_output_cast_fp16 = reshape(shape = concat_335x, x = var_9802_cast_fp16)[name = string("attn_output_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_27_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1620112448)))];
            tensor<int32, [2]> hidden_states_273_strides_0 = const()[name = string("hidden_states_273_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_273_pad_type_0 = const()[name = string("hidden_states_273_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_273_pad_0 = const()[name = string("hidden_states_273_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_273_dilations_0 = const()[name = string("hidden_states_273_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_273_groups_0 = const()[name = string("hidden_states_273_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_273_cast_fp16 = conv(dilations = hidden_states_273_dilations_0, groups = hidden_states_273_groups_0, pad = hidden_states_273_pad_0, pad_type = hidden_states_273_pad_type_0, strides = hidden_states_273_strides_0, weight = layers_27_self_attn_o_proj_weight_to_fp16, x = attn_output_cast_fp16)[name = string("hidden_states_273_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_275_cast_fp16 = add(x = hidden_states_269_cast_fp16, y = hidden_states_273_cast_fp16)[name = string("hidden_states_275_cast_fp16")];
            fp16 const_280_promoted_to_fp16 = const()[name = string("const_280_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_9835_cast_fp16 = mul(x = hidden_states_275_cast_fp16, y = const_280_promoted_to_fp16)[name = string("op_9835_cast_fp16")];
            int32 var_9833 = const()[name = string("op_9833"), val = int32(1)];
            bool doubled_221_interleave_0 = const()[name = string("doubled_221_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_221_cast_fp16 = concat(axis = var_9833, interleave = doubled_221_interleave_0, values = (hidden_states_275_cast_fp16, var_9835_cast_fp16))[name = string("doubled_221_cast_fp16")];
            tensor<int32, [1]> out_111_axes_0 = const()[name = string("out_111_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_111_gamma_0_to_fp16 = const()[name = string("out_111_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1628501120)))];
            fp16 var_9845_to_fp16 = const()[name = string("op_9845_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_111_cast_fp16 = layer_norm(axes = out_111_axes_0, epsilon = var_9845_to_fp16, gamma = out_111_gamma_0_to_fp16, x = doubled_221_cast_fp16)[name = string("out_111_cast_fp16")];
            tensor<int32, [2]> var_9856_split_sizes_0 = const()[name = string("op_9856_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9856_axis_0 = const()[name = string("op_9856_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> var_9856_cast_fp16_0, tensor<fp16, [1, 2048, 1, 64]> var_9856_cast_fp16_1 = split(axis = var_9856_axis_0, split_sizes = var_9856_split_sizes_0, x = out_111_cast_fp16)[name = string("op_9856_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_27_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_27_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1628509376)))];
            tensor<int32, [2]> input_strides_0 = const()[name = string("input_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_pad_type_0 = const()[name = string("input_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_pad_0 = const()[name = string("input_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_dilations_0 = const()[name = string("input_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_groups_0 = const()[name = string("input_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> input_cast_fp16 = conv(dilations = input_dilations_0, groups = input_groups_0, pad = input_pad_0, pad_type = input_pad_type_0, strides = input_strides_0, weight = layers_27_mlp_gate_proj_weight_to_fp16, x = var_9856_cast_fp16_0)[name = string("input_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> var_9873_cast_fp16 = silu(x = input_cast_fp16)[name = string("op_9873_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_27_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_27_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1653675264)))];
            tensor<int32, [2]> var_9879_strides_0 = const()[name = string("op_9879_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_9879_pad_type_0 = const()[name = string("op_9879_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_9879_pad_0 = const()[name = string("op_9879_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_9879_dilations_0 = const()[name = string("op_9879_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9879_groups_0 = const()[name = string("op_9879_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 64]> var_9879_cast_fp16 = conv(dilations = var_9879_dilations_0, groups = var_9879_groups_0, pad = var_9879_pad_0, pad_type = var_9879_pad_type_0, strides = var_9879_strides_0, weight = layers_27_mlp_up_proj_weight_to_fp16, x = var_9856_cast_fp16_0)[name = string("op_9879_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 64]> x_cast_fp16 = mul(x = var_9873_cast_fp16, y = var_9879_cast_fp16)[name = string("x_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_27_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_27_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1678841152)))];
            tensor<int32, [2]> hidden_states_277_strides_0 = const()[name = string("hidden_states_277_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_277_pad_type_0 = const()[name = string("hidden_states_277_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_277_pad_0 = const()[name = string("hidden_states_277_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_277_dilations_0 = const()[name = string("hidden_states_277_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_277_groups_0 = const()[name = string("hidden_states_277_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_277_cast_fp16 = conv(dilations = hidden_states_277_dilations_0, groups = hidden_states_277_groups_0, pad = hidden_states_277_pad_0, pad_type = hidden_states_277_pad_type_0, strides = hidden_states_277_strides_0, weight = layers_27_mlp_down_proj_weight_to_fp16, x = x_cast_fp16)[name = string("hidden_states_277_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states_cast_fp16 = add(x = hidden_states_275_cast_fp16, y = hidden_states_277_cast_fp16)[name = string("hidden_states_cast_fp16")];
            fp16 const_282_promoted_to_fp16 = const()[name = string("const_282_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 64]> var_9897_cast_fp16 = mul(x = hidden_states_cast_fp16, y = const_282_promoted_to_fp16)[name = string("op_9897_cast_fp16")];
            int32 var_9895 = const()[name = string("op_9895"), val = int32(1)];
            bool doubled_225_interleave_0 = const()[name = string("doubled_225_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 64]> doubled_225_cast_fp16 = concat(axis = var_9895, interleave = doubled_225_interleave_0, values = (hidden_states_cast_fp16, var_9897_cast_fp16))[name = string("doubled_225_cast_fp16")];
            tensor<int32, [1]> out_axes_0 = const()[name = string("out_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_gamma_0_to_fp16 = const()[name = string("out_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1704007040)))];
            fp16 var_9907_to_fp16 = const()[name = string("op_9907_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 64]> out_cast_fp16 = layer_norm(axes = out_axes_0, epsilon = var_9907_to_fp16, gamma = out_gamma_0_to_fp16, x = doubled_225_cast_fp16)[name = string("out_cast_fp16")];
            tensor<int32, [2]> var_9918_split_sizes_0 = const()[name = string("op_9918_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9918_axis_0 = const()[name = string("op_9918_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 64]> hidden_states, tensor<fp16, [1, 2048, 1, 64]> var_9918_cast_fp16_1 = split(axis = var_9918_axis_0, split_sizes = var_9918_split_sizes_0, x = out_cast_fp16)[name = string("op_9918_cast_fp16")];
        } -> (hidden_states);
    func length_8<ios18>(tensor<fp32, [1, 2048, 1, 8]> inputs_embeds, state<tensor<fp16, [28, 2, 2048, 128]>> key_cache, tensor<int32, [1]> position_id, tensor<int32, [8]> position_index_seed, state<tensor<fp16, [28, 2, 2048, 128]>> value_cache) {
            tensor<fp16, [256, 2048, 1, 1]> layers_1_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524992))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524416))))[name = string("layers_1_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_1_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(525312))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13120640))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13108288))))[name = string("layers_1_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_2_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13126848))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13651776))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13651200))))[name = string("layers_2_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_2_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13652096))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26247424))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26235072))))[name = string("layers_2_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_3_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26253632))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26778560))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26777984))))[name = string("layers_3_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_3_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26778880))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30977408))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30973248))))[name = string("layers_3_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_3_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30979520))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43566656))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43562496))))[name = string("layers_3_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_4_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43568768))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44093696))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44093120))))[name = string("layers_4_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_4_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44094016))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48292544))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48288384))))[name = string("layers_4_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_4_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48294656))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60889984))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60877632))))[name = string("layers_4_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_4_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60896192))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73491520))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73479168))))[name = string("layers_4_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_4_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73497728))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86084864))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86080704))))[name = string("layers_4_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_5_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86086976))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86611904))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86611328))))[name = string("layers_5_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_5_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86612224))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90810752))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90806592))))[name = string("layers_5_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_5_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90812864))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103408192))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103395840))))[name = string("layers_5_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_5_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103414400))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116001536))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115997376))))[name = string("layers_5_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_6_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116003648))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116528576))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116528000))))[name = string("layers_6_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_6_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116528896))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120727424))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120723264))))[name = string("layers_6_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_6_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120729536))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133324864))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133312512))))[name = string("layers_6_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_6_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133331072))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145926400))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145914048))))[name = string("layers_6_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_6_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145932608))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158519744))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158515584))))[name = string("layers_6_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_7_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158521856))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159046784))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159046208))))[name = string("layers_7_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_7_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159047104))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163245632))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163241472))))[name = string("layers_7_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_7_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163247744))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175843072))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175830720))))[name = string("layers_7_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_8_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175849280))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176374208))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176373632))))[name = string("layers_8_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_8_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176374528))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180573056))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180568896))))[name = string("layers_8_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_8_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180575168))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193170496))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193158144))))[name = string("layers_8_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_8_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193176704))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205772032))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205759680))))[name = string("layers_8_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_8_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205778240))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218365376))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218361216))))[name = string("layers_8_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_9_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218367488))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218892416))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218891840))))[name = string("layers_9_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_9_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218892736))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223091264))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223087104))))[name = string("layers_9_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_9_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223093376))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235688704))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235676352))))[name = string("layers_9_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_9_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235694912))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248290240))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248277888))))[name = string("layers_9_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_9_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248296448))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260883584))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260879424))))[name = string("layers_9_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_10_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260885696))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261410624))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261410048))))[name = string("layers_10_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_10_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261410944))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265609472))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265605312))))[name = string("layers_10_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_10_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265611584))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278206912))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278194560))))[name = string("layers_10_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_10_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278213120))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290808448))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290796096))))[name = string("layers_10_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_10_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290814656))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303401792))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303397632))))[name = string("layers_10_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_11_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303403904))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307602432))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307598272))))[name = string("layers_11_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_11_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307604544))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308129472))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308128896))))[name = string("layers_11_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_11_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308129792))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308654720))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308654144))))[name = string("layers_11_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_11_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308655040))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312853568))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312849408))))[name = string("layers_11_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_11_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312855680))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325451008))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325438656))))[name = string("layers_11_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_11_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325457216))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338052544))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338040192))))[name = string("layers_11_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_11_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338058752))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350645888))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350641728))))[name = string("layers_11_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_12_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350648000))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354846528))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354842368))))[name = string("layers_12_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_12_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354848640))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355373568))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355372992))))[name = string("layers_12_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_12_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355373888))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355898816))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355898240))))[name = string("layers_12_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_12_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355899136))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360097664))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360093504))))[name = string("layers_12_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_12_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360099776))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372695104))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372682752))))[name = string("layers_12_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_12_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372701312))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385296640))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385284288))))[name = string("layers_12_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_12_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385302848))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397889984))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397885824))))[name = string("layers_12_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_13_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397892096))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402090624))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402086464))))[name = string("layers_13_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_13_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402092736))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402617664))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402617088))))[name = string("layers_13_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_13_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402617984))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403142912))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403142336))))[name = string("layers_13_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_13_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403143232))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407341760))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407337600))))[name = string("layers_13_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_13_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407343872))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419939200))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419926848))))[name = string("layers_13_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_13_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419945408))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432532544))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432528384))))[name = string("layers_13_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_14_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432534656))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436733184))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436729024))))[name = string("layers_14_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_14_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436735296))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437260224))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437259648))))[name = string("layers_14_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_14_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437260544))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441459072))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441454912))))[name = string("layers_14_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_14_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441461184))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454056512))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454044160))))[name = string("layers_14_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_14_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454062720))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(466658048))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(466645696))))[name = string("layers_14_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_14_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(466664256))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(479251392))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(479247232))))[name = string("layers_14_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_15_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(479253504))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483452032))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483447872))))[name = string("layers_15_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_15_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483454144))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483979072))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483978496))))[name = string("layers_15_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_15_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483979392))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484504320))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484503744))))[name = string("layers_15_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_15_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484504640))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488703168))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488699008))))[name = string("layers_15_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_15_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488705280))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(501300608))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(501288256))))[name = string("layers_15_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_15_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(501306816))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513902144))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513889792))))[name = string("layers_15_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_15_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513908352))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526495488))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526491328))))[name = string("layers_15_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_16_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526497600))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530696128))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530691968))))[name = string("layers_16_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_16_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530698240))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531223168))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531222592))))[name = string("layers_16_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_16_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531223488))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531748416))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531747840))))[name = string("layers_16_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_16_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531748736))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535947264))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535943104))))[name = string("layers_16_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_16_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535949376))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(548536512))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(548532352))))[name = string("layers_16_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_17_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(548538624))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552737152))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552732992))))[name = string("layers_17_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_17_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552739264))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553264192))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553263616))))[name = string("layers_17_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_17_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553264512))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553789440))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553788864))))[name = string("layers_17_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_17_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553789760))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557988288))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557984128))))[name = string("layers_17_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_17_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557990400))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570585728))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570573376))))[name = string("layers_17_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_17_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570591936))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583187264))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583174912))))[name = string("layers_17_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_17_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583193472))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(595780608))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(595776448))))[name = string("layers_17_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_18_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(595782720))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(599981248))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(599977088))))[name = string("layers_18_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_18_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(599983360))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(600508288))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(600507712))))[name = string("layers_18_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_18_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(600508608))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(601033536))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(601032960))))[name = string("layers_18_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_18_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(601033856))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(605232384))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(605228224))))[name = string("layers_18_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_18_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(605234496))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(617829824))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(617817472))))[name = string("layers_18_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_18_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(617836032))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(630431360))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(630419008))))[name = string("layers_18_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_18_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(630437568))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(643024704))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(643020544))))[name = string("layers_18_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_19_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(643026816))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647225344))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647221184))))[name = string("layers_19_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_19_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647227456))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647752384))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647751808))))[name = string("layers_19_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_19_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647752704))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(660348032))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(660335680))))[name = string("layers_19_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_19_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(660354240))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(672949568))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(672937216))))[name = string("layers_19_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_19_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(672955776))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(685542912))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(685538752))))[name = string("layers_19_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_20_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(685545024))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(689743552))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(689739392))))[name = string("layers_20_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_20_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(689745664))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(690270592))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(690270016))))[name = string("layers_20_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_20_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(690270912))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(694469440))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(694465280))))[name = string("layers_20_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_20_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(694471552))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(707066880))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(707054528))))[name = string("layers_20_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_20_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(707073088))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(719660224))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(719656064))))[name = string("layers_20_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_21_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(719662336))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(723860864))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(723856704))))[name = string("layers_21_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_21_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(723862976))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724387904))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724387328))))[name = string("layers_21_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_21_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724388224))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(728586752))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(728582592))))[name = string("layers_21_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_21_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(728588864))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(741184192))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(741171840))))[name = string("layers_21_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_21_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(741190400))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(753785728))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(753773376))))[name = string("layers_21_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_21_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(753791936))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(766379072))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(766374912))))[name = string("layers_21_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_22_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(766381184))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(770579712))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(770575552))))[name = string("layers_22_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_22_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(770581824))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(771106752))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(771106176))))[name = string("layers_22_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_22_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(771107072))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783702400))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783690048))))[name = string("layers_22_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_22_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783708608))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(796303936))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(796291584))))[name = string("layers_22_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_22_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(796310144))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808897280))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808893120))))[name = string("layers_22_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_23_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808899392))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813097920))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813093760))))[name = string("layers_23_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_23_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813100032))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813624960))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813624384))))[name = string("layers_23_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_23_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813625280))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(817823808))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(817819648))))[name = string("layers_23_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_23_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(817825920))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(830421248))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(830408896))))[name = string("layers_23_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_23_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(830427456))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(843022784))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(843010432))))[name = string("layers_23_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_23_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(843028992))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855616128))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855611968))))[name = string("layers_23_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_24_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855618240))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(859816768))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(859812608))))[name = string("layers_24_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_24_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(859818880))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(860343808))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(860343232))))[name = string("layers_24_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_24_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(860344128))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(864542656))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(864538496))))[name = string("layers_24_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_24_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(864544768))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(877140096))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(877127744))))[name = string("layers_24_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_24_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(877146304))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889741632))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889729280))))[name = string("layers_24_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_24_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889747840))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(902334976))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(902330816))))[name = string("layers_24_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_25_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(902337088))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(906535616))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(906531456))))[name = string("layers_25_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_25_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(906537728))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(907062656))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(907062080))))[name = string("layers_25_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_25_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(907062976))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(911261504))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(911257344))))[name = string("layers_25_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_25_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(911263616))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(923858944))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(923846592))))[name = string("layers_25_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_25_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(923865152))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936460480))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936448128))))[name = string("layers_25_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_26_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936466688))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(940665216))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(940661056))))[name = string("layers_26_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_26_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(940667328))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(941192256))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(941191680))))[name = string("layers_26_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_26_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(941192576))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(945391104))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(945386944))))[name = string("layers_26_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_26_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(945393216))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(957988544))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(957976192))))[name = string("layers_26_mlp_gate_proj_weight_cast_fp16")];
            int32 gather_0_cast_uint16_to_int32 = const()[name = string("gather_0_cast_uint16_to_int32"), val = int32(8)];
            tensor<int32, [1]> cache_position_end = add(x = position_id, y = gather_0_cast_uint16_to_int32)[name = string("cache_position_end")];
            fp16 fill_like_0_value_0_to_fp16 = const()[name = string("fill_like_0_value_0_to_fp16"), val = fp16(0x1p+0)];
            tensor<fp16, [8]> fill_like_0_cast_fp16 = fill_like(ref_tensor = position_index_seed, value = fill_like_0_value_0_to_fp16)[name = string("fill_like_0_cast_fp16")];
            int32 var_831 = const()[name = string("op_831"), val = int32(0)];
            bool var_833_exclusive_0 = const()[name = string("op_833_exclusive_0"), val = bool(false)];
            bool var_833_reverse_0 = const()[name = string("op_833_reverse_0"), val = bool(false)];
            tensor<fp16, [8]> var_833_cast_fp16 = cumsum(axis = var_831, exclusive = var_833_exclusive_0, reverse = var_833_reverse_0, x = fill_like_0_cast_fp16)[name = string("op_833_cast_fp16")];
            fp16 var_835_promoted_to_fp16 = const()[name = string("op_835_promoted_to_fp16"), val = fp16(0x1p+0)];
            tensor<fp16, [8]> position_offsets_cast_fp16 = sub(x = var_833_cast_fp16, y = var_835_promoted_to_fp16)[name = string("position_offsets_cast_fp16")];
            tensor<int32, [1]> var_838_axes_0 = const()[name = string("op_838_axes_0"), val = tensor<int32, [1]>([0])];
            tensor<fp16, [1, 8]> var_838_cast_fp16 = expand_dims(axes = var_838_axes_0, x = position_offsets_cast_fp16)[name = string("op_838_cast_fp16")];
            string position_id_promoted_to_fp16_dtype_0 = const()[name = string("position_id_promoted_to_fp16_dtype_0"), val = string("fp16")];
            tensor<fp16, [1]> position_id_to_fp16 = cast(dtype = position_id_promoted_to_fp16_dtype_0, x = position_id)[name = string("cast_11")];
            tensor<fp16, [1, 8]> position_ids_1_cast_fp16 = add(x = var_838_cast_fp16, y = position_id_to_fp16)[name = string("position_ids_1_cast_fp16")];
            string position_ids_dtype_0 = const()[name = string("position_ids_dtype_0"), val = string("int32")];
            int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)];
            tensor<int32, [1, 8]> position_ids_1_cast_fp16_to_int32 = cast(dtype = position_ids_dtype_0, x = position_ids_1_cast_fp16)[name = string("cast_10")];
            tensor<bool, [1, 8]> greater_equal_0 = greater_equal(x = position_ids_1_cast_fp16_to_int32, y = greater_equal_0_y_0)[name = string("greater_equal_0")];
            int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(32768)];
            tensor<int32, [1, 8]> add_0 = add(x = position_ids_1_cast_fp16_to_int32, y = slice_by_index_0)[name = string("add_0")];
            tensor<int32, [1, 8]> select_0 = select(a = position_ids_1_cast_fp16_to_int32, b = add_0, cond = greater_equal_0)[name = string("select_0")];
            tensor<fp16, [32768, 128]> rope_emb_cos_cached_to_fp16 = const()[name = string("rope_emb_cos_cached_to_fp16"), val = tensor<fp16, [32768, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(957994752)))];
            int32 cos_1_batch_dims_0 = const()[name = string("cos_1_batch_dims_0"), val = int32(0)];
            bool cos_1_validate_indices_0 = const()[name = string("cos_1_validate_indices_0"), val = bool(false)];
            int32 greater_equal_4_y_0 = const()[name = string("greater_equal_4_y_0"), val = int32(0)];
            tensor<bool, [1, 8]> greater_equal_4 = greater_equal(x = select_0, y = greater_equal_4_y_0)[name = string("greater_equal_4")];
            int32 slice_by_index_4 = const()[name = string("slice_by_index_4"), val = int32(32768)];
            tensor<int32, [1, 8]> add_4 = add(x = select_0, y = slice_by_index_4)[name = string("add_4")];
            tensor<int32, [1, 8]> select_4 = select(a = select_0, b = add_4, cond = greater_equal_4)[name = string("select_4")];
            int32 cos_1_cast_fp16_axis_2 = const()[name = string("cos_1_cast_fp16_axis_2"), val = int32(0)];
            tensor<fp16, [1, 8, 128]> cos_1_cast_fp16 = gather(axis = cos_1_cast_fp16_axis_2, batch_dims = cos_1_batch_dims_0, indices = select_4, validate_indices = cos_1_validate_indices_0, x = rope_emb_cos_cached_to_fp16)[name = string("cos_1_cast_fp16")];
            tensor<fp16, [32768, 128]> rope_emb_sin_cached_to_fp16 = const()[name = string("rope_emb_sin_cached_to_fp16"), val = tensor<fp16, [32768, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(966383424)))];
            int32 sin_1_batch_dims_0 = const()[name = string("sin_1_batch_dims_0"), val = int32(0)];
            bool sin_1_validate_indices_0 = const()[name = string("sin_1_validate_indices_0"), val = bool(false)];
            int32 sin_1_cast_fp16_axis_2 = const()[name = string("sin_1_cast_fp16_axis_2"), val = int32(0)];
            tensor<fp16, [1, 8, 128]> sin_1_cast_fp16 = gather(axis = sin_1_cast_fp16_axis_2, batch_dims = sin_1_batch_dims_0, indices = select_4, validate_indices = sin_1_validate_indices_0, x = rope_emb_sin_cached_to_fp16)[name = string("sin_1_cast_fp16")];
            tensor<int32, [3]> var_857_perm_0 = const()[name = string("op_857_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<int32, [1]> var_859_axes_0 = const()[name = string("op_859_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 128, 8]> var_857_cast_fp16 = transpose(perm = var_857_perm_0, x = cos_1_cast_fp16)[name = string("transpose_260")];
            tensor<fp16, [1, 1, 128, 8]> var_859_cast_fp16 = expand_dims(axes = var_859_axes_0, x = var_857_cast_fp16)[name = string("op_859_cast_fp16")];
            tensor<int32, [3]> var_864_perm_0 = const()[name = string("op_864_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<int32, [1]> var_866_axes_0 = const()[name = string("op_866_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 128, 8]> var_864_cast_fp16 = transpose(perm = var_864_perm_0, x = sin_1_cast_fp16)[name = string("transpose_259")];
            tensor<fp16, [1, 1, 128, 8]> var_866_cast_fp16 = expand_dims(axes = var_866_axes_0, x = var_864_cast_fp16)[name = string("op_866_cast_fp16")];
            tensor<int32, [1]> var_885_axes_0 = const()[name = string("op_885_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1, 8, 1]> var_885 = expand_dims(axes = var_885_axes_0, x = position_ids_1_cast_fp16_to_int32)[name = string("op_885")];
            tensor<int32, [1, 1, 2048]> var_878 = const()[name = string("op_878"), val = tensor<int32, [1, 1, 2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(974772096)))];
            tensor<bool, [1, 8, 2048]> var_886 = greater(x = var_878, y = var_885)[name = string("op_886")];
            string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")];
            tensor<int32, [1]> var_893_axes_0 = const()[name = string("op_893_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 8, 2048]> var_886_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = var_886)[name = string("cast_9")];
            tensor<fp16, [1, 1, 8, 2048]> var_893_cast_fp16 = expand_dims(axes = var_893_axes_0, x = var_886_to_fp16)[name = string("op_893_cast_fp16")];
            tensor<int32, [4]> mask_perm_0 = const()[name = string("mask_perm_0"), val = tensor<int32, [4]>([0, 1, -1, -2])];
            fp16 var_897_promoted_to_fp16 = const()[name = string("op_897_promoted_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1, 2048, 8]> mask_cast_fp16 = transpose(perm = mask_perm_0, x = var_893_cast_fp16)[name = string("transpose_258")];
            tensor<bool, [1, 1, 2048, 8]> var_898_cast_fp16 = equal(x = mask_cast_fp16, y = var_897_promoted_to_fp16)[name = string("op_898_cast_fp16")];
            fp16 var_899_to_fp16 = const()[name = string("op_899_to_fp16"), val = fp16(-inf)];
            tensor<fp16, [1, 1, 2048, 8]> attn_mask_1_cast_fp16 = select(a = mask_cast_fp16, b = var_899_to_fp16, cond = var_898_cast_fp16)[name = string("attn_mask_1_cast_fp16")];
            string inputs_embeds_to_fp16_dtype_0 = const()[name = string("inputs_embeds_to_fp16_dtype_0"), val = string("fp16")];
            fp16 const_2_promoted_to_fp16 = const()[name = string("const_2_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> inputs_embeds_to_fp16 = cast(dtype = inputs_embeds_to_fp16_dtype_0, x = inputs_embeds)[name = string("cast_8")];
            tensor<fp16, [1, 2048, 1, 8]> var_909_cast_fp16 = mul(x = inputs_embeds_to_fp16, y = const_2_promoted_to_fp16)[name = string("op_909_cast_fp16")];
            int32 var_907 = const()[name = string("op_907"), val = int32(1)];
            bool doubled_1_interleave_0 = const()[name = string("doubled_1_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_1_cast_fp16 = concat(axis = var_907, interleave = doubled_1_interleave_0, values = (inputs_embeds_to_fp16, var_909_cast_fp16))[name = string("doubled_1_cast_fp16")];
            tensor<int32, [1]> out_1_axes_0 = const()[name = string("out_1_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_1_gamma_0_to_fp16 = const()[name = string("out_1_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(974780352)))];
            fp16 var_919_to_fp16 = const()[name = string("op_919_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_1_cast_fp16 = layer_norm(axes = out_1_axes_0, epsilon = var_919_to_fp16, gamma = out_1_gamma_0_to_fp16, x = doubled_1_cast_fp16)[name = string("out_1_cast_fp16")];
            tensor<int32, [2]> var_930_split_sizes_0 = const()[name = string("op_930_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_930_axis_0 = const()[name = string("op_930_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_930_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_930_cast_fp16_1 = split(axis = var_930_axis_0, split_sizes = var_930_split_sizes_0, x = out_1_cast_fp16)[name = string("op_930_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_0_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(974788608)))];
            tensor<int32, [2]> query_states_1_strides_0 = const()[name = string("query_states_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_1_pad_type_0 = const()[name = string("query_states_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_1_pad_0 = const()[name = string("query_states_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_1_dilations_0 = const()[name = string("query_states_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_1_groups_0 = const()[name = string("query_states_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_1_cast_fp16 = conv(dilations = query_states_1_dilations_0, groups = query_states_1_groups_0, pad = query_states_1_pad_0, pad_type = query_states_1_pad_type_0, strides = query_states_1_strides_0, weight = layers_0_self_attn_q_proj_weight_to_fp16, x = var_930_cast_fp16_0)[name = string("query_states_1_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_0_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(983177280)))];
            tensor<int32, [2]> key_states_1_strides_0 = const()[name = string("key_states_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_1_pad_type_0 = const()[name = string("key_states_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_1_pad_0 = const()[name = string("key_states_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_1_dilations_0 = const()[name = string("key_states_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_1_groups_0 = const()[name = string("key_states_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_1_cast_fp16 = conv(dilations = key_states_1_dilations_0, groups = key_states_1_groups_0, pad = key_states_1_pad_0, pad_type = key_states_1_pad_type_0, strides = key_states_1_strides_0, weight = layers_0_self_attn_k_proj_weight_to_fp16, x = var_930_cast_fp16_0)[name = string("key_states_1_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_0_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(984225920)))];
            tensor<int32, [2]> value_states_1_strides_0 = const()[name = string("value_states_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_1_pad_type_0 = const()[name = string("value_states_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_1_pad_0 = const()[name = string("value_states_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_1_dilations_0 = const()[name = string("value_states_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_1_groups_0 = const()[name = string("value_states_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_1_cast_fp16 = conv(dilations = value_states_1_dilations_0, groups = value_states_1_groups_0, pad = value_states_1_pad_0, pad_type = value_states_1_pad_type_0, strides = value_states_1_strides_0, weight = layers_0_self_attn_v_proj_weight_to_fp16, x = var_930_cast_fp16_0)[name = string("value_states_1_cast_fp16")];
            tensor<int32, [4]> concat_0x = const()[name = string("concat_0x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_1_cast_fp16 = reshape(shape = concat_0x, x = query_states_1_cast_fp16)[name = string("x_1_cast_fp16")];
            tensor<int32, [4]> concat_1x = const()[name = string("concat_1x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_987_cast_fp16 = reshape(shape = concat_1x, x = key_states_1_cast_fp16)[name = string("op_987_cast_fp16")];
            tensor<int32, [4]> concat_2x = const()[name = string("concat_2x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_994_cast_fp16 = reshape(shape = concat_2x, x = value_states_1_cast_fp16)[name = string("op_994_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_998_cast_fp16 = mul(x = x_1_cast_fp16, y = var_859_cast_fp16)[name = string("op_998_cast_fp16")];
            tensor<int32, [2]> var_999_split_sizes_0 = const()[name = string("op_999_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_999_axis_0 = const()[name = string("op_999_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_999_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_999_cast_fp16_1 = split(axis = var_999_axis_0, split_sizes = var_999_split_sizes_0, x = x_1_cast_fp16)[name = string("op_999_cast_fp16")];
            fp16 const_4_promoted_to_fp16 = const()[name = string("const_4_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_1001_cast_fp16 = mul(x = var_999_cast_fp16_1, y = const_4_promoted_to_fp16)[name = string("op_1001_cast_fp16")];
            int32 var_1003 = const()[name = string("op_1003"), val = int32(-2)];
            bool var_1004_interleave_0 = const()[name = string("op_1004_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_1004_cast_fp16 = concat(axis = var_1003, interleave = var_1004_interleave_0, values = (var_1001_cast_fp16, var_999_cast_fp16_0))[name = string("op_1004_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_1005_cast_fp16 = mul(x = var_1004_cast_fp16, y = var_866_cast_fp16)[name = string("op_1005_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_3_cast_fp16 = add(x = var_998_cast_fp16, y = var_1005_cast_fp16)[name = string("query_states_3_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_1011_cast_fp16 = mul(x = var_987_cast_fp16, y = var_859_cast_fp16)[name = string("op_1011_cast_fp16")];
            tensor<int32, [2]> var_1012_split_sizes_0 = const()[name = string("op_1012_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1012_axis_0 = const()[name = string("op_1012_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_1012_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_1012_cast_fp16_1 = split(axis = var_1012_axis_0, split_sizes = var_1012_split_sizes_0, x = var_987_cast_fp16)[name = string("op_1012_cast_fp16")];
            fp16 const_5_promoted_to_fp16 = const()[name = string("const_5_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_1014_cast_fp16 = mul(x = var_1012_cast_fp16_1, y = const_5_promoted_to_fp16)[name = string("op_1014_cast_fp16")];
            int32 var_1016 = const()[name = string("op_1016"), val = int32(-2)];
            bool var_1017_interleave_0 = const()[name = string("op_1017_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_1017_cast_fp16 = concat(axis = var_1016, interleave = var_1017_interleave_0, values = (var_1014_cast_fp16, var_1012_cast_fp16_0))[name = string("op_1017_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_1018_cast_fp16 = mul(x = var_1017_cast_fp16, y = var_866_cast_fp16)[name = string("op_1018_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_5_cast_fp16 = add(x = var_1011_cast_fp16, y = var_1018_cast_fp16)[name = string("key_states_5_cast_fp16")];
            tensor<fp16, [28, 2, 2048, 128]> read_state_0 = read_state(input = key_cache)[name = string("read_state_0")];
            tensor<int32, [1]> expand_dims_0 = const()[name = string("expand_dims_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_1 = const()[name = string("expand_dims_1"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_3 = const()[name = string("expand_dims_3"), val = tensor<int32, [1]>([0])];
            int32 concat_5_axis_0 = const()[name = string("concat_5_axis_0"), val = int32(0)];
            bool concat_5_interleave_0 = const()[name = string("concat_5_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_5 = concat(axis = concat_5_axis_0, interleave = concat_5_interleave_0, values = (expand_dims_0, expand_dims_1, position_id, expand_dims_3))[name = string("concat_5")];
            tensor<int32, [1]> expand_dims_4 = const()[name = string("expand_dims_4"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> concat_6_values1_0 = const()[name = string("concat_6_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_6_values3_0 = const()[name = string("concat_6_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_6_axis_0 = const()[name = string("concat_6_axis_0"), val = int32(0)];
            bool concat_6_interleave_0 = const()[name = string("concat_6_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_6 = concat(axis = concat_6_axis_0, interleave = concat_6_interleave_0, values = (expand_dims_4, concat_6_values1_0, cache_position_end, concat_6_values3_0))[name = string("concat_6")];
            tensor<int32, [4]> key_states_7_perm_0 = const()[name = string("key_states_7_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_1_stride_0 = const()[name = string("key_cache_internal_tensor_assign_1_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_1_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_1_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_1_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_1_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_1_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_1_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_7_cast_fp16 = transpose(perm = key_states_7_perm_0, x = key_states_5_cast_fp16)[name = string("transpose_257")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_1_cast_fp16 = slice_update(begin = concat_5, begin_mask = key_cache_internal_tensor_assign_1_begin_mask_0, end = concat_6, end_mask = key_cache_internal_tensor_assign_1_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_1_squeeze_mask_0, stride = key_cache_internal_tensor_assign_1_stride_0, update = key_states_7_cast_fp16, x = read_state_0)[name = string("key_cache_internal_tensor_assign_1_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_1_cast_fp16, input = key_cache)[name = string("coreml_update_state_112_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_112 = read_state(input = key_cache)[name = string("coreml_update_state_112")];
            tensor<fp16, [28, 2, 2048, 128]> read_state_1 = read_state(input = value_cache)[name = string("read_state_1")];
            tensor<int32, [4]> value_states_3_perm_0 = const()[name = string("value_states_3_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_1_stride_0 = const()[name = string("value_cache_internal_tensor_assign_1_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_1_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_1_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_1_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_1_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_1_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_1_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_3_cast_fp16 = transpose(perm = value_states_3_perm_0, x = var_994_cast_fp16)[name = string("transpose_256")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_1_cast_fp16 = slice_update(begin = concat_5, begin_mask = value_cache_internal_tensor_assign_1_begin_mask_0, end = concat_6, end_mask = value_cache_internal_tensor_assign_1_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_1_squeeze_mask_0, stride = value_cache_internal_tensor_assign_1_stride_0, update = value_states_3_cast_fp16, x = read_state_1)[name = string("value_cache_internal_tensor_assign_1_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_1_cast_fp16, input = value_cache)[name = string("coreml_update_state_113_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_113 = read_state(input = value_cache)[name = string("coreml_update_state_113")];
            tensor<int32, [4]> var_1088_begin_0 = const()[name = string("op_1088_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> var_1088_end_0 = const()[name = string("op_1088_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_1088_end_mask_0 = const()[name = string("op_1088_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1088_cast_fp16 = slice_by_index(begin = var_1088_begin_0, end = var_1088_end_0, end_mask = var_1088_end_mask_0, x = coreml_update_state_112)[name = string("op_1088_cast_fp16")];
            tensor<int32, [2]> tile_0 = const()[name = string("tile_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1091_axis_0 = const()[name = string("op_1091_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1091_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1091_cast_fp16_1 = split(axis = var_1091_axis_0, split_sizes = tile_0, x = var_1088_cast_fp16)[name = string("op_1091_cast_fp16")];
            tensor<int32, [4]> var_1098_begin_0 = const()[name = string("op_1098_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> var_1098_end_0 = const()[name = string("op_1098_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_1098_end_mask_0 = const()[name = string("op_1098_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1098_cast_fp16 = slice_by_index(begin = var_1098_begin_0, end = var_1098_end_0, end_mask = var_1098_end_mask_0, x = coreml_update_state_113)[name = string("op_1098_cast_fp16")];
            tensor<int32, [2]> tile_1 = const()[name = string("tile_1"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1101_axis_0 = const()[name = string("op_1101_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1101_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1101_cast_fp16_1 = split(axis = var_1101_axis_0, split_sizes = tile_1, x = var_1098_cast_fp16)[name = string("op_1101_cast_fp16")];
            tensor<int32, [2]> var_1104_split_sizes_0 = const()[name = string("op_1104_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_1104_axis_0 = const()[name = string("op_1104_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_1104_0, tensor<fp16, [1, 8, 128, 8]> var_1104_1 = split(axis = var_1104_axis_0, split_sizes = var_1104_split_sizes_0, x = query_states_3_cast_fp16)[name = string("op_1104")];
            bool attn_weights_1_transpose_x_0 = const()[name = string("attn_weights_1_transpose_x_0"), val = bool(false)];
            bool attn_weights_1_transpose_y_0 = const()[name = string("attn_weights_1_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_1_cast_fp16 = matmul(transpose_x = attn_weights_1_transpose_x_0, transpose_y = attn_weights_1_transpose_y_0, x = var_1091_cast_fp16_0, y = var_1104_0)[name = string("attn_weights_1_cast_fp16")];
            fp16 var_1107_to_fp16 = const()[name = string("op_1107_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_3_cast_fp16 = mul(x = attn_weights_1_cast_fp16, y = var_1107_to_fp16)[name = string("attn_weights_3_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_5_cast_fp16 = add(x = attn_weights_3_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_5_cast_fp16")];
            int32 var_1111 = const()[name = string("op_1111"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_7_cast_fp16 = softmax(axis = var_1111, x = attn_weights_5_cast_fp16)[name = string("attn_weights_7_cast_fp16")];
            bool var_1117_transpose_x_1 = const()[name = string("op_1117_transpose_x_1"), val = bool(true)];
            bool var_1117_transpose_y_1 = const()[name = string("op_1117_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_1117_cast_fp16 = matmul(transpose_x = var_1117_transpose_x_1, transpose_y = var_1117_transpose_y_1, x = attn_weights_7_cast_fp16, y = var_1101_cast_fp16_0)[name = string("op_1117_cast_fp16")];
            bool attn_weights_9_transpose_x_0 = const()[name = string("attn_weights_9_transpose_x_0"), val = bool(false)];
            bool attn_weights_9_transpose_y_0 = const()[name = string("attn_weights_9_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_9_cast_fp16 = matmul(transpose_x = attn_weights_9_transpose_x_0, transpose_y = attn_weights_9_transpose_y_0, x = var_1091_cast_fp16_1, y = var_1104_1)[name = string("attn_weights_9_cast_fp16")];
            fp16 var_1119_to_fp16 = const()[name = string("op_1119_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_11_cast_fp16 = mul(x = attn_weights_9_cast_fp16, y = var_1119_to_fp16)[name = string("attn_weights_11_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_13_cast_fp16 = add(x = attn_weights_11_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_13_cast_fp16")];
            int32 var_1123 = const()[name = string("op_1123"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_15_cast_fp16 = softmax(axis = var_1123, x = attn_weights_13_cast_fp16)[name = string("attn_weights_15_cast_fp16")];
            bool attn_output_1_transpose_x_1 = const()[name = string("attn_output_1_transpose_x_1"), val = bool(true)];
            bool attn_output_1_transpose_y_1 = const()[name = string("attn_output_1_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_1_cast_fp16 = matmul(transpose_x = attn_output_1_transpose_x_1, transpose_y = attn_output_1_transpose_y_1, x = attn_weights_15_cast_fp16, y = var_1101_cast_fp16_1)[name = string("attn_output_1_cast_fp16")];
            int32 var_1131 = const()[name = string("op_1131"), val = int32(1)];
            bool attn_output_3_interleave_0 = const()[name = string("attn_output_3_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_3_cast_fp16 = concat(axis = var_1131, interleave = attn_output_3_interleave_0, values = (var_1117_cast_fp16, attn_output_1_cast_fp16))[name = string("attn_output_3_cast_fp16")];
            tensor<int32, [4]> var_1135_perm_0 = const()[name = string("op_1135_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_11x = const()[name = string("concat_11x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_1135_cast_fp16 = transpose(perm = var_1135_perm_0, x = attn_output_3_cast_fp16)[name = string("transpose_255")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_7_cast_fp16 = reshape(shape = concat_11x, x = var_1135_cast_fp16)[name = string("attn_output_7_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_0_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(985274560)))];
            tensor<int32, [2]> hidden_states_3_strides_0 = const()[name = string("hidden_states_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_3_pad_type_0 = const()[name = string("hidden_states_3_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_3_pad_0 = const()[name = string("hidden_states_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_3_dilations_0 = const()[name = string("hidden_states_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_3_groups_0 = const()[name = string("hidden_states_3_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_3_cast_fp16 = conv(dilations = hidden_states_3_dilations_0, groups = hidden_states_3_groups_0, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = hidden_states_3_strides_0, weight = layers_0_self_attn_o_proj_weight_to_fp16, x = attn_output_7_cast_fp16)[name = string("hidden_states_3_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_5_cast_fp16 = add(x = inputs_embeds_to_fp16, y = hidden_states_3_cast_fp16)[name = string("hidden_states_5_cast_fp16")];
            fp16 const_10_promoted_to_fp16 = const()[name = string("const_10_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_1168_cast_fp16 = mul(x = hidden_states_5_cast_fp16, y = const_10_promoted_to_fp16)[name = string("op_1168_cast_fp16")];
            int32 var_1166 = const()[name = string("op_1166"), val = int32(1)];
            bool doubled_5_interleave_0 = const()[name = string("doubled_5_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_5_cast_fp16 = concat(axis = var_1166, interleave = doubled_5_interleave_0, values = (hidden_states_5_cast_fp16, var_1168_cast_fp16))[name = string("doubled_5_cast_fp16")];
            tensor<int32, [1]> out_3_axes_0 = const()[name = string("out_3_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_3_gamma_0_to_fp16 = const()[name = string("out_3_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(993663232)))];
            fp16 var_1178_to_fp16 = const()[name = string("op_1178_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_3_cast_fp16 = layer_norm(axes = out_3_axes_0, epsilon = var_1178_to_fp16, gamma = out_3_gamma_0_to_fp16, x = doubled_5_cast_fp16)[name = string("out_3_cast_fp16")];
            tensor<int32, [2]> var_1189_split_sizes_0 = const()[name = string("op_1189_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1189_axis_0 = const()[name = string("op_1189_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_1189_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_1189_cast_fp16_1 = split(axis = var_1189_axis_0, split_sizes = var_1189_split_sizes_0, x = out_3_cast_fp16)[name = string("op_1189_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_0_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_0_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(993671488)))];
            tensor<int32, [2]> input_1_strides_0 = const()[name = string("input_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_1_pad_type_0 = const()[name = string("input_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_1_pad_0 = const()[name = string("input_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_1_dilations_0 = const()[name = string("input_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_1_groups_0 = const()[name = string("input_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_1_cast_fp16 = conv(dilations = input_1_dilations_0, groups = input_1_groups_0, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = input_1_strides_0, weight = layers_0_mlp_gate_proj_weight_to_fp16, x = var_1189_cast_fp16_0)[name = string("input_1_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_1206_cast_fp16 = silu(x = input_1_cast_fp16)[name = string("op_1206_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_0_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_0_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1018837376)))];
            tensor<int32, [2]> var_1212_strides_0 = const()[name = string("op_1212_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_1212_pad_type_0 = const()[name = string("op_1212_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_1212_pad_0 = const()[name = string("op_1212_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_1212_dilations_0 = const()[name = string("op_1212_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1212_groups_0 = const()[name = string("op_1212_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_1212_cast_fp16 = conv(dilations = var_1212_dilations_0, groups = var_1212_groups_0, pad = var_1212_pad_0, pad_type = var_1212_pad_type_0, strides = var_1212_strides_0, weight = layers_0_mlp_up_proj_weight_to_fp16, x = var_1189_cast_fp16_0)[name = string("op_1212_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_9_cast_fp16 = mul(x = var_1206_cast_fp16, y = var_1212_cast_fp16)[name = string("x_9_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_0_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_0_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1044003264)))];
            tensor<int32, [2]> hidden_states_7_strides_0 = const()[name = string("hidden_states_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_7_pad_type_0 = const()[name = string("hidden_states_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_7_pad_0 = const()[name = string("hidden_states_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_7_dilations_0 = const()[name = string("hidden_states_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_7_groups_0 = const()[name = string("hidden_states_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_7_cast_fp16 = conv(dilations = hidden_states_7_dilations_0, groups = hidden_states_7_groups_0, pad = hidden_states_7_pad_0, pad_type = hidden_states_7_pad_type_0, strides = hidden_states_7_strides_0, weight = layers_0_mlp_down_proj_weight_to_fp16, x = x_9_cast_fp16)[name = string("hidden_states_7_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_9_cast_fp16 = add(x = hidden_states_5_cast_fp16, y = hidden_states_7_cast_fp16)[name = string("hidden_states_9_cast_fp16")];
            fp16 const_12_promoted_to_fp16 = const()[name = string("const_12_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_1230_cast_fp16 = mul(x = hidden_states_9_cast_fp16, y = const_12_promoted_to_fp16)[name = string("op_1230_cast_fp16")];
            int32 var_1228 = const()[name = string("op_1228"), val = int32(1)];
            bool doubled_9_interleave_0 = const()[name = string("doubled_9_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_9_cast_fp16 = concat(axis = var_1228, interleave = doubled_9_interleave_0, values = (hidden_states_9_cast_fp16, var_1230_cast_fp16))[name = string("doubled_9_cast_fp16")];
            tensor<int32, [1]> out_5_axes_0 = const()[name = string("out_5_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_5_gamma_0_to_fp16 = const()[name = string("out_5_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1069169152)))];
            fp16 var_1240_to_fp16 = const()[name = string("op_1240_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_5_cast_fp16 = layer_norm(axes = out_5_axes_0, epsilon = var_1240_to_fp16, gamma = out_5_gamma_0_to_fp16, x = doubled_9_cast_fp16)[name = string("out_5_cast_fp16")];
            tensor<int32, [2]> var_1251_split_sizes_0 = const()[name = string("op_1251_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1251_axis_0 = const()[name = string("op_1251_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_1251_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_1251_cast_fp16_1 = split(axis = var_1251_axis_0, split_sizes = var_1251_split_sizes_0, x = out_5_cast_fp16)[name = string("op_1251_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_1_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_1_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1069177408)))];
            tensor<int32, [2]> query_states_7_strides_0 = const()[name = string("query_states_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_7_pad_type_0 = const()[name = string("query_states_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_7_pad_0 = const()[name = string("query_states_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_7_dilations_0 = const()[name = string("query_states_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_7_groups_0 = const()[name = string("query_states_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_7_cast_fp16 = conv(dilations = query_states_7_dilations_0, groups = query_states_7_groups_0, pad = query_states_7_pad_0, pad_type = query_states_7_pad_type_0, strides = query_states_7_strides_0, weight = layers_1_self_attn_q_proj_weight_to_fp16, x = var_1251_cast_fp16_0)[name = string("query_states_7_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_1_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_1_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1077566080)))];
            tensor<int32, [2]> key_states_11_strides_0 = const()[name = string("key_states_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_11_pad_type_0 = const()[name = string("key_states_11_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_11_pad_0 = const()[name = string("key_states_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_11_dilations_0 = const()[name = string("key_states_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_11_groups_0 = const()[name = string("key_states_11_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_11_cast_fp16 = conv(dilations = key_states_11_dilations_0, groups = key_states_11_groups_0, pad = key_states_11_pad_0, pad_type = key_states_11_pad_type_0, strides = key_states_11_strides_0, weight = layers_1_self_attn_k_proj_weight_to_fp16, x = var_1251_cast_fp16_0)[name = string("key_states_11_cast_fp16")];
            tensor<int32, [2]> value_states_7_strides_0 = const()[name = string("value_states_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_7_pad_type_0 = const()[name = string("value_states_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_7_pad_0 = const()[name = string("value_states_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_7_dilations_0 = const()[name = string("value_states_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_7_groups_0 = const()[name = string("value_states_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_7_cast_fp16 = conv(dilations = value_states_7_dilations_0, groups = value_states_7_groups_0, pad = value_states_7_pad_0, pad_type = value_states_7_pad_type_0, strides = value_states_7_strides_0, weight = layers_1_self_attn_v_proj_weight_cast_fp16, x = var_1251_cast_fp16_0)[name = string("value_states_7_cast_fp16")];
            tensor<int32, [4]> concat_12x = const()[name = string("concat_12x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_11_cast_fp16 = reshape(shape = concat_12x, x = query_states_7_cast_fp16)[name = string("x_11_cast_fp16")];
            tensor<int32, [4]> concat_13x = const()[name = string("concat_13x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_1308_cast_fp16 = reshape(shape = concat_13x, x = key_states_11_cast_fp16)[name = string("op_1308_cast_fp16")];
            tensor<int32, [4]> concat_14x = const()[name = string("concat_14x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_1315_cast_fp16 = reshape(shape = concat_14x, x = value_states_7_cast_fp16)[name = string("op_1315_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_1319_cast_fp16 = mul(x = x_11_cast_fp16, y = var_859_cast_fp16)[name = string("op_1319_cast_fp16")];
            tensor<int32, [2]> var_1320_split_sizes_0 = const()[name = string("op_1320_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1320_axis_0 = const()[name = string("op_1320_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_1320_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_1320_cast_fp16_1 = split(axis = var_1320_axis_0, split_sizes = var_1320_split_sizes_0, x = x_11_cast_fp16)[name = string("op_1320_cast_fp16")];
            fp16 const_14_promoted_to_fp16 = const()[name = string("const_14_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_1322_cast_fp16 = mul(x = var_1320_cast_fp16_1, y = const_14_promoted_to_fp16)[name = string("op_1322_cast_fp16")];
            int32 var_1324 = const()[name = string("op_1324"), val = int32(-2)];
            bool var_1325_interleave_0 = const()[name = string("op_1325_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_1325_cast_fp16 = concat(axis = var_1324, interleave = var_1325_interleave_0, values = (var_1322_cast_fp16, var_1320_cast_fp16_0))[name = string("op_1325_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_1326_cast_fp16 = mul(x = var_1325_cast_fp16, y = var_866_cast_fp16)[name = string("op_1326_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_9_cast_fp16 = add(x = var_1319_cast_fp16, y = var_1326_cast_fp16)[name = string("query_states_9_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_1332_cast_fp16 = mul(x = var_1308_cast_fp16, y = var_859_cast_fp16)[name = string("op_1332_cast_fp16")];
            tensor<int32, [2]> var_1333_split_sizes_0 = const()[name = string("op_1333_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1333_axis_0 = const()[name = string("op_1333_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_1333_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_1333_cast_fp16_1 = split(axis = var_1333_axis_0, split_sizes = var_1333_split_sizes_0, x = var_1308_cast_fp16)[name = string("op_1333_cast_fp16")];
            fp16 const_15_promoted_to_fp16 = const()[name = string("const_15_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_1335_cast_fp16 = mul(x = var_1333_cast_fp16_1, y = const_15_promoted_to_fp16)[name = string("op_1335_cast_fp16")];
            int32 var_1337 = const()[name = string("op_1337"), val = int32(-2)];
            bool var_1338_interleave_0 = const()[name = string("op_1338_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_1338_cast_fp16 = concat(axis = var_1337, interleave = var_1338_interleave_0, values = (var_1335_cast_fp16, var_1333_cast_fp16_0))[name = string("op_1338_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_1339_cast_fp16 = mul(x = var_1338_cast_fp16, y = var_866_cast_fp16)[name = string("op_1339_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_15_cast_fp16 = add(x = var_1332_cast_fp16, y = var_1339_cast_fp16)[name = string("key_states_15_cast_fp16")];
            tensor<int32, [1]> expand_dims_12 = const()[name = string("expand_dims_12"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> expand_dims_13 = const()[name = string("expand_dims_13"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_15 = const()[name = string("expand_dims_15"), val = tensor<int32, [1]>([0])];
            int32 concat_17_axis_0 = const()[name = string("concat_17_axis_0"), val = int32(0)];
            bool concat_17_interleave_0 = const()[name = string("concat_17_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_17 = concat(axis = concat_17_axis_0, interleave = concat_17_interleave_0, values = (expand_dims_12, expand_dims_13, position_id, expand_dims_15))[name = string("concat_17")];
            tensor<int32, [1]> expand_dims_16 = const()[name = string("expand_dims_16"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> concat_18_values1_0 = const()[name = string("concat_18_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_18_values3_0 = const()[name = string("concat_18_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_18_axis_0 = const()[name = string("concat_18_axis_0"), val = int32(0)];
            bool concat_18_interleave_0 = const()[name = string("concat_18_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_18 = concat(axis = concat_18_axis_0, interleave = concat_18_interleave_0, values = (expand_dims_16, concat_18_values1_0, cache_position_end, concat_18_values3_0))[name = string("concat_18")];
            tensor<int32, [4]> key_states_17_perm_0 = const()[name = string("key_states_17_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_2_stride_0 = const()[name = string("key_cache_internal_tensor_assign_2_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_2_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_2_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_2_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_2_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_2_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_2_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_17_cast_fp16 = transpose(perm = key_states_17_perm_0, x = key_states_15_cast_fp16)[name = string("transpose_254")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_2_cast_fp16 = slice_update(begin = concat_17, begin_mask = key_cache_internal_tensor_assign_2_begin_mask_0, end = concat_18, end_mask = key_cache_internal_tensor_assign_2_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_2_squeeze_mask_0, stride = key_cache_internal_tensor_assign_2_stride_0, update = key_states_17_cast_fp16, x = coreml_update_state_112)[name = string("key_cache_internal_tensor_assign_2_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_2_cast_fp16, input = key_cache)[name = string("coreml_update_state_114_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_114 = read_state(input = key_cache)[name = string("coreml_update_state_114")];
            tensor<int32, [4]> value_states_9_perm_0 = const()[name = string("value_states_9_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_2_stride_0 = const()[name = string("value_cache_internal_tensor_assign_2_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_2_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_2_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_2_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_2_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_2_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_2_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_9_cast_fp16 = transpose(perm = value_states_9_perm_0, x = var_1315_cast_fp16)[name = string("transpose_253")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_2_cast_fp16 = slice_update(begin = concat_17, begin_mask = value_cache_internal_tensor_assign_2_begin_mask_0, end = concat_18, end_mask = value_cache_internal_tensor_assign_2_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_2_squeeze_mask_0, stride = value_cache_internal_tensor_assign_2_stride_0, update = value_states_9_cast_fp16, x = coreml_update_state_113)[name = string("value_cache_internal_tensor_assign_2_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_2_cast_fp16, input = value_cache)[name = string("coreml_update_state_115_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_115 = read_state(input = value_cache)[name = string("coreml_update_state_115")];
            tensor<int32, [4]> var_1409_begin_0 = const()[name = string("op_1409_begin_0"), val = tensor<int32, [4]>([1, 0, 0, 0])];
            tensor<int32, [4]> var_1409_end_0 = const()[name = string("op_1409_end_0"), val = tensor<int32, [4]>([2, 2, 2048, 128])];
            tensor<bool, [4]> var_1409_end_mask_0 = const()[name = string("op_1409_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1409_cast_fp16 = slice_by_index(begin = var_1409_begin_0, end = var_1409_end_0, end_mask = var_1409_end_mask_0, x = coreml_update_state_114)[name = string("op_1409_cast_fp16")];
            tensor<int32, [2]> tile_2 = const()[name = string("tile_2"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1412_axis_0 = const()[name = string("op_1412_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1412_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1412_cast_fp16_1 = split(axis = var_1412_axis_0, split_sizes = tile_2, x = var_1409_cast_fp16)[name = string("op_1412_cast_fp16")];
            tensor<int32, [4]> var_1419_begin_0 = const()[name = string("op_1419_begin_0"), val = tensor<int32, [4]>([1, 0, 0, 0])];
            tensor<int32, [4]> var_1419_end_0 = const()[name = string("op_1419_end_0"), val = tensor<int32, [4]>([2, 2, 2048, 128])];
            tensor<bool, [4]> var_1419_end_mask_0 = const()[name = string("op_1419_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1419_cast_fp16 = slice_by_index(begin = var_1419_begin_0, end = var_1419_end_0, end_mask = var_1419_end_mask_0, x = coreml_update_state_115)[name = string("op_1419_cast_fp16")];
            tensor<int32, [2]> tile_3 = const()[name = string("tile_3"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1422_axis_0 = const()[name = string("op_1422_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1422_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1422_cast_fp16_1 = split(axis = var_1422_axis_0, split_sizes = tile_3, x = var_1419_cast_fp16)[name = string("op_1422_cast_fp16")];
            tensor<int32, [2]> var_1425_split_sizes_0 = const()[name = string("op_1425_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_1425_axis_0 = const()[name = string("op_1425_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_1425_0, tensor<fp16, [1, 8, 128, 8]> var_1425_1 = split(axis = var_1425_axis_0, split_sizes = var_1425_split_sizes_0, x = query_states_9_cast_fp16)[name = string("op_1425")];
            bool attn_weights_17_transpose_x_0 = const()[name = string("attn_weights_17_transpose_x_0"), val = bool(false)];
            bool attn_weights_17_transpose_y_0 = const()[name = string("attn_weights_17_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_17_cast_fp16 = matmul(transpose_x = attn_weights_17_transpose_x_0, transpose_y = attn_weights_17_transpose_y_0, x = var_1412_cast_fp16_0, y = var_1425_0)[name = string("attn_weights_17_cast_fp16")];
            fp16 var_1428_to_fp16 = const()[name = string("op_1428_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_19_cast_fp16 = mul(x = attn_weights_17_cast_fp16, y = var_1428_to_fp16)[name = string("attn_weights_19_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_21_cast_fp16 = add(x = attn_weights_19_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_21_cast_fp16")];
            int32 var_1432 = const()[name = string("op_1432"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_23_cast_fp16 = softmax(axis = var_1432, x = attn_weights_21_cast_fp16)[name = string("attn_weights_23_cast_fp16")];
            bool var_1438_transpose_x_1 = const()[name = string("op_1438_transpose_x_1"), val = bool(true)];
            bool var_1438_transpose_y_1 = const()[name = string("op_1438_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_1438_cast_fp16 = matmul(transpose_x = var_1438_transpose_x_1, transpose_y = var_1438_transpose_y_1, x = attn_weights_23_cast_fp16, y = var_1422_cast_fp16_0)[name = string("op_1438_cast_fp16")];
            bool attn_weights_25_transpose_x_0 = const()[name = string("attn_weights_25_transpose_x_0"), val = bool(false)];
            bool attn_weights_25_transpose_y_0 = const()[name = string("attn_weights_25_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_25_cast_fp16 = matmul(transpose_x = attn_weights_25_transpose_x_0, transpose_y = attn_weights_25_transpose_y_0, x = var_1412_cast_fp16_1, y = var_1425_1)[name = string("attn_weights_25_cast_fp16")];
            fp16 var_1440_to_fp16 = const()[name = string("op_1440_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_27_cast_fp16 = mul(x = attn_weights_25_cast_fp16, y = var_1440_to_fp16)[name = string("attn_weights_27_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_29_cast_fp16 = add(x = attn_weights_27_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_29_cast_fp16")];
            int32 var_1444 = const()[name = string("op_1444"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_31_cast_fp16 = softmax(axis = var_1444, x = attn_weights_29_cast_fp16)[name = string("attn_weights_31_cast_fp16")];
            bool attn_output_9_transpose_x_1 = const()[name = string("attn_output_9_transpose_x_1"), val = bool(true)];
            bool attn_output_9_transpose_y_1 = const()[name = string("attn_output_9_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_9_cast_fp16 = matmul(transpose_x = attn_output_9_transpose_x_1, transpose_y = attn_output_9_transpose_y_1, x = attn_weights_31_cast_fp16, y = var_1422_cast_fp16_1)[name = string("attn_output_9_cast_fp16")];
            int32 var_1452 = const()[name = string("op_1452"), val = int32(1)];
            bool attn_output_11_interleave_0 = const()[name = string("attn_output_11_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_11_cast_fp16 = concat(axis = var_1452, interleave = attn_output_11_interleave_0, values = (var_1438_cast_fp16, attn_output_9_cast_fp16))[name = string("attn_output_11_cast_fp16")];
            tensor<int32, [4]> var_1456_perm_0 = const()[name = string("op_1456_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_23x = const()[name = string("concat_23x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_1456_cast_fp16 = transpose(perm = var_1456_perm_0, x = attn_output_11_cast_fp16)[name = string("transpose_252")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_15_cast_fp16 = reshape(shape = concat_23x, x = var_1456_cast_fp16)[name = string("attn_output_15_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_1_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_1_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1078614720)))];
            tensor<int32, [2]> hidden_states_13_strides_0 = const()[name = string("hidden_states_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_13_pad_type_0 = const()[name = string("hidden_states_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_13_pad_0 = const()[name = string("hidden_states_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_13_dilations_0 = const()[name = string("hidden_states_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_13_groups_0 = const()[name = string("hidden_states_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_13_cast_fp16 = conv(dilations = hidden_states_13_dilations_0, groups = hidden_states_13_groups_0, pad = hidden_states_13_pad_0, pad_type = hidden_states_13_pad_type_0, strides = hidden_states_13_strides_0, weight = layers_1_self_attn_o_proj_weight_to_fp16, x = attn_output_15_cast_fp16)[name = string("hidden_states_13_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_15_cast_fp16 = add(x = hidden_states_9_cast_fp16, y = hidden_states_13_cast_fp16)[name = string("hidden_states_15_cast_fp16")];
            fp16 const_20_promoted_to_fp16 = const()[name = string("const_20_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_1489_cast_fp16 = mul(x = hidden_states_15_cast_fp16, y = const_20_promoted_to_fp16)[name = string("op_1489_cast_fp16")];
            int32 var_1487 = const()[name = string("op_1487"), val = int32(1)];
            bool doubled_13_interleave_0 = const()[name = string("doubled_13_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_13_cast_fp16 = concat(axis = var_1487, interleave = doubled_13_interleave_0, values = (hidden_states_15_cast_fp16, var_1489_cast_fp16))[name = string("doubled_13_cast_fp16")];
            tensor<int32, [1]> out_7_axes_0 = const()[name = string("out_7_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_7_gamma_0_to_fp16 = const()[name = string("out_7_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1087003392)))];
            fp16 var_1499_to_fp16 = const()[name = string("op_1499_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_7_cast_fp16 = layer_norm(axes = out_7_axes_0, epsilon = var_1499_to_fp16, gamma = out_7_gamma_0_to_fp16, x = doubled_13_cast_fp16)[name = string("out_7_cast_fp16")];
            tensor<int32, [2]> var_1510_split_sizes_0 = const()[name = string("op_1510_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1510_axis_0 = const()[name = string("op_1510_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_1510_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_1510_cast_fp16_1 = split(axis = var_1510_axis_0, split_sizes = var_1510_split_sizes_0, x = out_7_cast_fp16)[name = string("op_1510_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_1_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_1_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1087011648)))];
            tensor<int32, [2]> input_3_strides_0 = const()[name = string("input_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_3_dilations_0 = const()[name = string("input_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_3_groups_0 = const()[name = string("input_3_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_3_cast_fp16 = conv(dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = layers_1_mlp_gate_proj_weight_to_fp16, x = var_1510_cast_fp16_0)[name = string("input_3_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_1527_cast_fp16 = silu(x = input_3_cast_fp16)[name = string("op_1527_cast_fp16")];
            tensor<int32, [2]> var_1533_strides_0 = const()[name = string("op_1533_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_1533_pad_type_0 = const()[name = string("op_1533_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_1533_pad_0 = const()[name = string("op_1533_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_1533_dilations_0 = const()[name = string("op_1533_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1533_groups_0 = const()[name = string("op_1533_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_1533_cast_fp16 = conv(dilations = var_1533_dilations_0, groups = var_1533_groups_0, pad = var_1533_pad_0, pad_type = var_1533_pad_type_0, strides = var_1533_strides_0, weight = layers_1_mlp_up_proj_weight_cast_fp16, x = var_1510_cast_fp16_0)[name = string("op_1533_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_19_cast_fp16 = mul(x = var_1527_cast_fp16, y = var_1533_cast_fp16)[name = string("x_19_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_1_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_1_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1112177536)))];
            tensor<int32, [2]> hidden_states_17_strides_0 = const()[name = string("hidden_states_17_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_17_pad_type_0 = const()[name = string("hidden_states_17_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_17_pad_0 = const()[name = string("hidden_states_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_17_dilations_0 = const()[name = string("hidden_states_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_17_groups_0 = const()[name = string("hidden_states_17_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_17_cast_fp16 = conv(dilations = hidden_states_17_dilations_0, groups = hidden_states_17_groups_0, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = hidden_states_17_strides_0, weight = layers_1_mlp_down_proj_weight_to_fp16, x = x_19_cast_fp16)[name = string("hidden_states_17_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_19_cast_fp16 = add(x = hidden_states_15_cast_fp16, y = hidden_states_17_cast_fp16)[name = string("hidden_states_19_cast_fp16")];
            fp16 const_22_promoted_to_fp16 = const()[name = string("const_22_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_1551_cast_fp16 = mul(x = hidden_states_19_cast_fp16, y = const_22_promoted_to_fp16)[name = string("op_1551_cast_fp16")];
            int32 var_1549 = const()[name = string("op_1549"), val = int32(1)];
            bool doubled_17_interleave_0 = const()[name = string("doubled_17_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_17_cast_fp16 = concat(axis = var_1549, interleave = doubled_17_interleave_0, values = (hidden_states_19_cast_fp16, var_1551_cast_fp16))[name = string("doubled_17_cast_fp16")];
            tensor<int32, [1]> out_9_axes_0 = const()[name = string("out_9_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_9_gamma_0_to_fp16 = const()[name = string("out_9_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1137343424)))];
            fp16 var_1561_to_fp16 = const()[name = string("op_1561_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_9_cast_fp16 = layer_norm(axes = out_9_axes_0, epsilon = var_1561_to_fp16, gamma = out_9_gamma_0_to_fp16, x = doubled_17_cast_fp16)[name = string("out_9_cast_fp16")];
            tensor<int32, [2]> var_1572_split_sizes_0 = const()[name = string("op_1572_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1572_axis_0 = const()[name = string("op_1572_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_1572_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_1572_cast_fp16_1 = split(axis = var_1572_axis_0, split_sizes = var_1572_split_sizes_0, x = out_9_cast_fp16)[name = string("op_1572_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_2_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_2_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1137351680)))];
            tensor<int32, [2]> query_states_13_strides_0 = const()[name = string("query_states_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_13_pad_type_0 = const()[name = string("query_states_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_13_pad_0 = const()[name = string("query_states_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_13_dilations_0 = const()[name = string("query_states_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_13_groups_0 = const()[name = string("query_states_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_13_cast_fp16 = conv(dilations = query_states_13_dilations_0, groups = query_states_13_groups_0, pad = query_states_13_pad_0, pad_type = query_states_13_pad_type_0, strides = query_states_13_strides_0, weight = layers_2_self_attn_q_proj_weight_to_fp16, x = var_1572_cast_fp16_0)[name = string("query_states_13_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_2_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_2_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1145740352)))];
            tensor<int32, [2]> key_states_21_strides_0 = const()[name = string("key_states_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_21_pad_type_0 = const()[name = string("key_states_21_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_21_pad_0 = const()[name = string("key_states_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_21_dilations_0 = const()[name = string("key_states_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_21_groups_0 = const()[name = string("key_states_21_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_21_cast_fp16 = conv(dilations = key_states_21_dilations_0, groups = key_states_21_groups_0, pad = key_states_21_pad_0, pad_type = key_states_21_pad_type_0, strides = key_states_21_strides_0, weight = layers_2_self_attn_k_proj_weight_to_fp16, x = var_1572_cast_fp16_0)[name = string("key_states_21_cast_fp16")];
            tensor<int32, [2]> value_states_13_strides_0 = const()[name = string("value_states_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_13_pad_type_0 = const()[name = string("value_states_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_13_pad_0 = const()[name = string("value_states_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_13_dilations_0 = const()[name = string("value_states_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_13_groups_0 = const()[name = string("value_states_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_13_cast_fp16 = conv(dilations = value_states_13_dilations_0, groups = value_states_13_groups_0, pad = value_states_13_pad_0, pad_type = value_states_13_pad_type_0, strides = value_states_13_strides_0, weight = layers_2_self_attn_v_proj_weight_cast_fp16, x = var_1572_cast_fp16_0)[name = string("value_states_13_cast_fp16")];
            tensor<int32, [4]> concat_24x = const()[name = string("concat_24x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_21_cast_fp16 = reshape(shape = concat_24x, x = query_states_13_cast_fp16)[name = string("x_21_cast_fp16")];
            tensor<int32, [4]> concat_25x = const()[name = string("concat_25x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_1629_cast_fp16 = reshape(shape = concat_25x, x = key_states_21_cast_fp16)[name = string("op_1629_cast_fp16")];
            tensor<int32, [4]> concat_26x = const()[name = string("concat_26x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_1636_cast_fp16 = reshape(shape = concat_26x, x = value_states_13_cast_fp16)[name = string("op_1636_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_1640_cast_fp16 = mul(x = x_21_cast_fp16, y = var_859_cast_fp16)[name = string("op_1640_cast_fp16")];
            tensor<int32, [2]> var_1641_split_sizes_0 = const()[name = string("op_1641_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1641_axis_0 = const()[name = string("op_1641_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_1641_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_1641_cast_fp16_1 = split(axis = var_1641_axis_0, split_sizes = var_1641_split_sizes_0, x = x_21_cast_fp16)[name = string("op_1641_cast_fp16")];
            fp16 const_24_promoted_to_fp16 = const()[name = string("const_24_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_1643_cast_fp16 = mul(x = var_1641_cast_fp16_1, y = const_24_promoted_to_fp16)[name = string("op_1643_cast_fp16")];
            int32 var_1645 = const()[name = string("op_1645"), val = int32(-2)];
            bool var_1646_interleave_0 = const()[name = string("op_1646_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_1646_cast_fp16 = concat(axis = var_1645, interleave = var_1646_interleave_0, values = (var_1643_cast_fp16, var_1641_cast_fp16_0))[name = string("op_1646_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_1647_cast_fp16 = mul(x = var_1646_cast_fp16, y = var_866_cast_fp16)[name = string("op_1647_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_15_cast_fp16 = add(x = var_1640_cast_fp16, y = var_1647_cast_fp16)[name = string("query_states_15_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_1653_cast_fp16 = mul(x = var_1629_cast_fp16, y = var_859_cast_fp16)[name = string("op_1653_cast_fp16")];
            tensor<int32, [2]> var_1654_split_sizes_0 = const()[name = string("op_1654_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1654_axis_0 = const()[name = string("op_1654_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_1654_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_1654_cast_fp16_1 = split(axis = var_1654_axis_0, split_sizes = var_1654_split_sizes_0, x = var_1629_cast_fp16)[name = string("op_1654_cast_fp16")];
            fp16 const_25_promoted_to_fp16 = const()[name = string("const_25_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_1656_cast_fp16 = mul(x = var_1654_cast_fp16_1, y = const_25_promoted_to_fp16)[name = string("op_1656_cast_fp16")];
            int32 var_1658 = const()[name = string("op_1658"), val = int32(-2)];
            bool var_1659_interleave_0 = const()[name = string("op_1659_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_1659_cast_fp16 = concat(axis = var_1658, interleave = var_1659_interleave_0, values = (var_1656_cast_fp16, var_1654_cast_fp16_0))[name = string("op_1659_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_1660_cast_fp16 = mul(x = var_1659_cast_fp16, y = var_866_cast_fp16)[name = string("op_1660_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_25_cast_fp16 = add(x = var_1653_cast_fp16, y = var_1660_cast_fp16)[name = string("key_states_25_cast_fp16")];
            tensor<int32, [1]> expand_dims_24 = const()[name = string("expand_dims_24"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> expand_dims_25 = const()[name = string("expand_dims_25"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_27 = const()[name = string("expand_dims_27"), val = tensor<int32, [1]>([0])];
            int32 concat_29_axis_0 = const()[name = string("concat_29_axis_0"), val = int32(0)];
            bool concat_29_interleave_0 = const()[name = string("concat_29_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_29 = concat(axis = concat_29_axis_0, interleave = concat_29_interleave_0, values = (expand_dims_24, expand_dims_25, position_id, expand_dims_27))[name = string("concat_29")];
            tensor<int32, [1]> expand_dims_28 = const()[name = string("expand_dims_28"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> concat_30_values1_0 = const()[name = string("concat_30_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_30_values3_0 = const()[name = string("concat_30_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_30_axis_0 = const()[name = string("concat_30_axis_0"), val = int32(0)];
            bool concat_30_interleave_0 = const()[name = string("concat_30_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_30 = concat(axis = concat_30_axis_0, interleave = concat_30_interleave_0, values = (expand_dims_28, concat_30_values1_0, cache_position_end, concat_30_values3_0))[name = string("concat_30")];
            tensor<int32, [4]> key_states_27_perm_0 = const()[name = string("key_states_27_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_3_stride_0 = const()[name = string("key_cache_internal_tensor_assign_3_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_3_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_3_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_3_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_3_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_3_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_3_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_27_cast_fp16 = transpose(perm = key_states_27_perm_0, x = key_states_25_cast_fp16)[name = string("transpose_251")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_3_cast_fp16 = slice_update(begin = concat_29, begin_mask = key_cache_internal_tensor_assign_3_begin_mask_0, end = concat_30, end_mask = key_cache_internal_tensor_assign_3_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_3_squeeze_mask_0, stride = key_cache_internal_tensor_assign_3_stride_0, update = key_states_27_cast_fp16, x = coreml_update_state_114)[name = string("key_cache_internal_tensor_assign_3_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_3_cast_fp16, input = key_cache)[name = string("coreml_update_state_116_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_116 = read_state(input = key_cache)[name = string("coreml_update_state_116")];
            tensor<int32, [4]> value_states_15_perm_0 = const()[name = string("value_states_15_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_3_stride_0 = const()[name = string("value_cache_internal_tensor_assign_3_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_3_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_3_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_3_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_3_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_3_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_3_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_15_cast_fp16 = transpose(perm = value_states_15_perm_0, x = var_1636_cast_fp16)[name = string("transpose_250")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_3_cast_fp16 = slice_update(begin = concat_29, begin_mask = value_cache_internal_tensor_assign_3_begin_mask_0, end = concat_30, end_mask = value_cache_internal_tensor_assign_3_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_3_squeeze_mask_0, stride = value_cache_internal_tensor_assign_3_stride_0, update = value_states_15_cast_fp16, x = coreml_update_state_115)[name = string("value_cache_internal_tensor_assign_3_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_3_cast_fp16, input = value_cache)[name = string("coreml_update_state_117_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_117 = read_state(input = value_cache)[name = string("coreml_update_state_117")];
            tensor<int32, [4]> var_1730_begin_0 = const()[name = string("op_1730_begin_0"), val = tensor<int32, [4]>([2, 0, 0, 0])];
            tensor<int32, [4]> var_1730_end_0 = const()[name = string("op_1730_end_0"), val = tensor<int32, [4]>([3, 2, 2048, 128])];
            tensor<bool, [4]> var_1730_end_mask_0 = const()[name = string("op_1730_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1730_cast_fp16 = slice_by_index(begin = var_1730_begin_0, end = var_1730_end_0, end_mask = var_1730_end_mask_0, x = coreml_update_state_116)[name = string("op_1730_cast_fp16")];
            tensor<int32, [2]> tile_4 = const()[name = string("tile_4"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1733_axis_0 = const()[name = string("op_1733_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1733_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1733_cast_fp16_1 = split(axis = var_1733_axis_0, split_sizes = tile_4, x = var_1730_cast_fp16)[name = string("op_1733_cast_fp16")];
            tensor<int32, [4]> var_1740_begin_0 = const()[name = string("op_1740_begin_0"), val = tensor<int32, [4]>([2, 0, 0, 0])];
            tensor<int32, [4]> var_1740_end_0 = const()[name = string("op_1740_end_0"), val = tensor<int32, [4]>([3, 2, 2048, 128])];
            tensor<bool, [4]> var_1740_end_mask_0 = const()[name = string("op_1740_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1740_cast_fp16 = slice_by_index(begin = var_1740_begin_0, end = var_1740_end_0, end_mask = var_1740_end_mask_0, x = coreml_update_state_117)[name = string("op_1740_cast_fp16")];
            tensor<int32, [2]> tile_5 = const()[name = string("tile_5"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1743_axis_0 = const()[name = string("op_1743_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1743_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1743_cast_fp16_1 = split(axis = var_1743_axis_0, split_sizes = tile_5, x = var_1740_cast_fp16)[name = string("op_1743_cast_fp16")];
            tensor<int32, [2]> var_1746_split_sizes_0 = const()[name = string("op_1746_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_1746_axis_0 = const()[name = string("op_1746_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_1746_0, tensor<fp16, [1, 8, 128, 8]> var_1746_1 = split(axis = var_1746_axis_0, split_sizes = var_1746_split_sizes_0, x = query_states_15_cast_fp16)[name = string("op_1746")];
            bool attn_weights_33_transpose_x_0 = const()[name = string("attn_weights_33_transpose_x_0"), val = bool(false)];
            bool attn_weights_33_transpose_y_0 = const()[name = string("attn_weights_33_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_33_cast_fp16 = matmul(transpose_x = attn_weights_33_transpose_x_0, transpose_y = attn_weights_33_transpose_y_0, x = var_1733_cast_fp16_0, y = var_1746_0)[name = string("attn_weights_33_cast_fp16")];
            fp16 var_1749_to_fp16 = const()[name = string("op_1749_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_35_cast_fp16 = mul(x = attn_weights_33_cast_fp16, y = var_1749_to_fp16)[name = string("attn_weights_35_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_37_cast_fp16 = add(x = attn_weights_35_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_37_cast_fp16")];
            int32 var_1753 = const()[name = string("op_1753"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_39_cast_fp16 = softmax(axis = var_1753, x = attn_weights_37_cast_fp16)[name = string("attn_weights_39_cast_fp16")];
            bool var_1759_transpose_x_1 = const()[name = string("op_1759_transpose_x_1"), val = bool(true)];
            bool var_1759_transpose_y_1 = const()[name = string("op_1759_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_1759_cast_fp16 = matmul(transpose_x = var_1759_transpose_x_1, transpose_y = var_1759_transpose_y_1, x = attn_weights_39_cast_fp16, y = var_1743_cast_fp16_0)[name = string("op_1759_cast_fp16")];
            bool attn_weights_41_transpose_x_0 = const()[name = string("attn_weights_41_transpose_x_0"), val = bool(false)];
            bool attn_weights_41_transpose_y_0 = const()[name = string("attn_weights_41_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_41_cast_fp16 = matmul(transpose_x = attn_weights_41_transpose_x_0, transpose_y = attn_weights_41_transpose_y_0, x = var_1733_cast_fp16_1, y = var_1746_1)[name = string("attn_weights_41_cast_fp16")];
            fp16 var_1761_to_fp16 = const()[name = string("op_1761_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_43_cast_fp16 = mul(x = attn_weights_41_cast_fp16, y = var_1761_to_fp16)[name = string("attn_weights_43_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_45_cast_fp16 = add(x = attn_weights_43_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_45_cast_fp16")];
            int32 var_1765 = const()[name = string("op_1765"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_47_cast_fp16 = softmax(axis = var_1765, x = attn_weights_45_cast_fp16)[name = string("attn_weights_47_cast_fp16")];
            bool attn_output_17_transpose_x_1 = const()[name = string("attn_output_17_transpose_x_1"), val = bool(true)];
            bool attn_output_17_transpose_y_1 = const()[name = string("attn_output_17_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_17_cast_fp16 = matmul(transpose_x = attn_output_17_transpose_x_1, transpose_y = attn_output_17_transpose_y_1, x = attn_weights_47_cast_fp16, y = var_1743_cast_fp16_1)[name = string("attn_output_17_cast_fp16")];
            int32 var_1773 = const()[name = string("op_1773"), val = int32(1)];
            bool attn_output_19_interleave_0 = const()[name = string("attn_output_19_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_19_cast_fp16 = concat(axis = var_1773, interleave = attn_output_19_interleave_0, values = (var_1759_cast_fp16, attn_output_17_cast_fp16))[name = string("attn_output_19_cast_fp16")];
            tensor<int32, [4]> var_1777_perm_0 = const()[name = string("op_1777_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_35x = const()[name = string("concat_35x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_1777_cast_fp16 = transpose(perm = var_1777_perm_0, x = attn_output_19_cast_fp16)[name = string("transpose_249")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_23_cast_fp16 = reshape(shape = concat_35x, x = var_1777_cast_fp16)[name = string("attn_output_23_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_2_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_2_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1146788992)))];
            tensor<int32, [2]> hidden_states_23_strides_0 = const()[name = string("hidden_states_23_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_23_pad_type_0 = const()[name = string("hidden_states_23_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_23_pad_0 = const()[name = string("hidden_states_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_23_dilations_0 = const()[name = string("hidden_states_23_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_23_groups_0 = const()[name = string("hidden_states_23_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_23_cast_fp16 = conv(dilations = hidden_states_23_dilations_0, groups = hidden_states_23_groups_0, pad = hidden_states_23_pad_0, pad_type = hidden_states_23_pad_type_0, strides = hidden_states_23_strides_0, weight = layers_2_self_attn_o_proj_weight_to_fp16, x = attn_output_23_cast_fp16)[name = string("hidden_states_23_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_25_cast_fp16 = add(x = hidden_states_19_cast_fp16, y = hidden_states_23_cast_fp16)[name = string("hidden_states_25_cast_fp16")];
            fp16 const_30_promoted_to_fp16 = const()[name = string("const_30_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_1810_cast_fp16 = mul(x = hidden_states_25_cast_fp16, y = const_30_promoted_to_fp16)[name = string("op_1810_cast_fp16")];
            int32 var_1808 = const()[name = string("op_1808"), val = int32(1)];
            bool doubled_21_interleave_0 = const()[name = string("doubled_21_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_21_cast_fp16 = concat(axis = var_1808, interleave = doubled_21_interleave_0, values = (hidden_states_25_cast_fp16, var_1810_cast_fp16))[name = string("doubled_21_cast_fp16")];
            tensor<int32, [1]> out_11_axes_0 = const()[name = string("out_11_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_11_gamma_0_to_fp16 = const()[name = string("out_11_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1155177664)))];
            fp16 var_1820_to_fp16 = const()[name = string("op_1820_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_11_cast_fp16 = layer_norm(axes = out_11_axes_0, epsilon = var_1820_to_fp16, gamma = out_11_gamma_0_to_fp16, x = doubled_21_cast_fp16)[name = string("out_11_cast_fp16")];
            tensor<int32, [2]> var_1831_split_sizes_0 = const()[name = string("op_1831_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1831_axis_0 = const()[name = string("op_1831_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_1831_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_1831_cast_fp16_1 = split(axis = var_1831_axis_0, split_sizes = var_1831_split_sizes_0, x = out_11_cast_fp16)[name = string("op_1831_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_2_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_2_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1155185920)))];
            tensor<int32, [2]> input_5_strides_0 = const()[name = string("input_5_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_5_pad_type_0 = const()[name = string("input_5_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_5_dilations_0 = const()[name = string("input_5_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_5_groups_0 = const()[name = string("input_5_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_5_cast_fp16 = conv(dilations = input_5_dilations_0, groups = input_5_groups_0, pad = input_5_pad_0, pad_type = input_5_pad_type_0, strides = input_5_strides_0, weight = layers_2_mlp_gate_proj_weight_to_fp16, x = var_1831_cast_fp16_0)[name = string("input_5_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_1848_cast_fp16 = silu(x = input_5_cast_fp16)[name = string("op_1848_cast_fp16")];
            tensor<int32, [2]> var_1854_strides_0 = const()[name = string("op_1854_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_1854_pad_type_0 = const()[name = string("op_1854_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_1854_pad_0 = const()[name = string("op_1854_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_1854_dilations_0 = const()[name = string("op_1854_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1854_groups_0 = const()[name = string("op_1854_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_1854_cast_fp16 = conv(dilations = var_1854_dilations_0, groups = var_1854_groups_0, pad = var_1854_pad_0, pad_type = var_1854_pad_type_0, strides = var_1854_strides_0, weight = layers_2_mlp_up_proj_weight_cast_fp16, x = var_1831_cast_fp16_0)[name = string("op_1854_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_29_cast_fp16 = mul(x = var_1848_cast_fp16, y = var_1854_cast_fp16)[name = string("x_29_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_2_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_2_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1180351808)))];
            tensor<int32, [2]> hidden_states_27_strides_0 = const()[name = string("hidden_states_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_27_pad_type_0 = const()[name = string("hidden_states_27_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_27_pad_0 = const()[name = string("hidden_states_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_27_dilations_0 = const()[name = string("hidden_states_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_27_groups_0 = const()[name = string("hidden_states_27_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_27_cast_fp16 = conv(dilations = hidden_states_27_dilations_0, groups = hidden_states_27_groups_0, pad = hidden_states_27_pad_0, pad_type = hidden_states_27_pad_type_0, strides = hidden_states_27_strides_0, weight = layers_2_mlp_down_proj_weight_to_fp16, x = x_29_cast_fp16)[name = string("hidden_states_27_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_29_cast_fp16 = add(x = hidden_states_25_cast_fp16, y = hidden_states_27_cast_fp16)[name = string("hidden_states_29_cast_fp16")];
            fp16 const_32_promoted_to_fp16 = const()[name = string("const_32_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_1872_cast_fp16 = mul(x = hidden_states_29_cast_fp16, y = const_32_promoted_to_fp16)[name = string("op_1872_cast_fp16")];
            int32 var_1870 = const()[name = string("op_1870"), val = int32(1)];
            bool doubled_25_interleave_0 = const()[name = string("doubled_25_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_25_cast_fp16 = concat(axis = var_1870, interleave = doubled_25_interleave_0, values = (hidden_states_29_cast_fp16, var_1872_cast_fp16))[name = string("doubled_25_cast_fp16")];
            tensor<int32, [1]> out_13_axes_0 = const()[name = string("out_13_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_13_gamma_0_to_fp16 = const()[name = string("out_13_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1205517696)))];
            fp16 var_1882_to_fp16 = const()[name = string("op_1882_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_13_cast_fp16 = layer_norm(axes = out_13_axes_0, epsilon = var_1882_to_fp16, gamma = out_13_gamma_0_to_fp16, x = doubled_25_cast_fp16)[name = string("out_13_cast_fp16")];
            tensor<int32, [2]> var_1893_split_sizes_0 = const()[name = string("op_1893_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1893_axis_0 = const()[name = string("op_1893_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_1893_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_1893_cast_fp16_1 = split(axis = var_1893_axis_0, split_sizes = var_1893_split_sizes_0, x = out_13_cast_fp16)[name = string("op_1893_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_3_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_3_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1205525952)))];
            tensor<int32, [2]> query_states_19_strides_0 = const()[name = string("query_states_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_19_pad_type_0 = const()[name = string("query_states_19_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_19_pad_0 = const()[name = string("query_states_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_19_dilations_0 = const()[name = string("query_states_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_19_groups_0 = const()[name = string("query_states_19_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_19_cast_fp16 = conv(dilations = query_states_19_dilations_0, groups = query_states_19_groups_0, pad = query_states_19_pad_0, pad_type = query_states_19_pad_type_0, strides = query_states_19_strides_0, weight = layers_3_self_attn_q_proj_weight_to_fp16, x = var_1893_cast_fp16_0)[name = string("query_states_19_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_3_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_3_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1213914624)))];
            tensor<int32, [2]> key_states_31_strides_0 = const()[name = string("key_states_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_31_pad_type_0 = const()[name = string("key_states_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_31_pad_0 = const()[name = string("key_states_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_31_dilations_0 = const()[name = string("key_states_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_31_groups_0 = const()[name = string("key_states_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_31_cast_fp16 = conv(dilations = key_states_31_dilations_0, groups = key_states_31_groups_0, pad = key_states_31_pad_0, pad_type = key_states_31_pad_type_0, strides = key_states_31_strides_0, weight = layers_3_self_attn_k_proj_weight_to_fp16, x = var_1893_cast_fp16_0)[name = string("key_states_31_cast_fp16")];
            tensor<int32, [2]> value_states_19_strides_0 = const()[name = string("value_states_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_19_pad_type_0 = const()[name = string("value_states_19_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_19_pad_0 = const()[name = string("value_states_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_19_dilations_0 = const()[name = string("value_states_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_19_groups_0 = const()[name = string("value_states_19_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_19_cast_fp16 = conv(dilations = value_states_19_dilations_0, groups = value_states_19_groups_0, pad = value_states_19_pad_0, pad_type = value_states_19_pad_type_0, strides = value_states_19_strides_0, weight = layers_3_self_attn_v_proj_weight_cast_fp16, x = var_1893_cast_fp16_0)[name = string("value_states_19_cast_fp16")];
            tensor<int32, [4]> concat_36x = const()[name = string("concat_36x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_31_cast_fp16 = reshape(shape = concat_36x, x = query_states_19_cast_fp16)[name = string("x_31_cast_fp16")];
            tensor<int32, [4]> concat_37x = const()[name = string("concat_37x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_1950_cast_fp16 = reshape(shape = concat_37x, x = key_states_31_cast_fp16)[name = string("op_1950_cast_fp16")];
            tensor<int32, [4]> concat_38x = const()[name = string("concat_38x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_1957_cast_fp16 = reshape(shape = concat_38x, x = value_states_19_cast_fp16)[name = string("op_1957_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_1961_cast_fp16 = mul(x = x_31_cast_fp16, y = var_859_cast_fp16)[name = string("op_1961_cast_fp16")];
            tensor<int32, [2]> var_1962_split_sizes_0 = const()[name = string("op_1962_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1962_axis_0 = const()[name = string("op_1962_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_1962_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_1962_cast_fp16_1 = split(axis = var_1962_axis_0, split_sizes = var_1962_split_sizes_0, x = x_31_cast_fp16)[name = string("op_1962_cast_fp16")];
            fp16 const_34_promoted_to_fp16 = const()[name = string("const_34_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_1964_cast_fp16 = mul(x = var_1962_cast_fp16_1, y = const_34_promoted_to_fp16)[name = string("op_1964_cast_fp16")];
            int32 var_1966 = const()[name = string("op_1966"), val = int32(-2)];
            bool var_1967_interleave_0 = const()[name = string("op_1967_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_1967_cast_fp16 = concat(axis = var_1966, interleave = var_1967_interleave_0, values = (var_1964_cast_fp16, var_1962_cast_fp16_0))[name = string("op_1967_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_1968_cast_fp16 = mul(x = var_1967_cast_fp16, y = var_866_cast_fp16)[name = string("op_1968_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_21_cast_fp16 = add(x = var_1961_cast_fp16, y = var_1968_cast_fp16)[name = string("query_states_21_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_1974_cast_fp16 = mul(x = var_1950_cast_fp16, y = var_859_cast_fp16)[name = string("op_1974_cast_fp16")];
            tensor<int32, [2]> var_1975_split_sizes_0 = const()[name = string("op_1975_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1975_axis_0 = const()[name = string("op_1975_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_1975_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_1975_cast_fp16_1 = split(axis = var_1975_axis_0, split_sizes = var_1975_split_sizes_0, x = var_1950_cast_fp16)[name = string("op_1975_cast_fp16")];
            fp16 const_35_promoted_to_fp16 = const()[name = string("const_35_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_1977_cast_fp16 = mul(x = var_1975_cast_fp16_1, y = const_35_promoted_to_fp16)[name = string("op_1977_cast_fp16")];
            int32 var_1979 = const()[name = string("op_1979"), val = int32(-2)];
            bool var_1980_interleave_0 = const()[name = string("op_1980_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_1980_cast_fp16 = concat(axis = var_1979, interleave = var_1980_interleave_0, values = (var_1977_cast_fp16, var_1975_cast_fp16_0))[name = string("op_1980_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_1981_cast_fp16 = mul(x = var_1980_cast_fp16, y = var_866_cast_fp16)[name = string("op_1981_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_35_cast_fp16 = add(x = var_1974_cast_fp16, y = var_1981_cast_fp16)[name = string("key_states_35_cast_fp16")];
            tensor<int32, [1]> expand_dims_36 = const()[name = string("expand_dims_36"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> expand_dims_37 = const()[name = string("expand_dims_37"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_39 = const()[name = string("expand_dims_39"), val = tensor<int32, [1]>([0])];
            int32 concat_41_axis_0 = const()[name = string("concat_41_axis_0"), val = int32(0)];
            bool concat_41_interleave_0 = const()[name = string("concat_41_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_41 = concat(axis = concat_41_axis_0, interleave = concat_41_interleave_0, values = (expand_dims_36, expand_dims_37, position_id, expand_dims_39))[name = string("concat_41")];
            tensor<int32, [1]> expand_dims_40 = const()[name = string("expand_dims_40"), val = tensor<int32, [1]>([4])];
            tensor<int32, [1]> concat_42_values1_0 = const()[name = string("concat_42_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_42_values3_0 = const()[name = string("concat_42_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_42_axis_0 = const()[name = string("concat_42_axis_0"), val = int32(0)];
            bool concat_42_interleave_0 = const()[name = string("concat_42_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_42 = concat(axis = concat_42_axis_0, interleave = concat_42_interleave_0, values = (expand_dims_40, concat_42_values1_0, cache_position_end, concat_42_values3_0))[name = string("concat_42")];
            tensor<int32, [4]> key_states_37_perm_0 = const()[name = string("key_states_37_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_4_stride_0 = const()[name = string("key_cache_internal_tensor_assign_4_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_4_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_4_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_4_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_4_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_4_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_4_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_37_cast_fp16 = transpose(perm = key_states_37_perm_0, x = key_states_35_cast_fp16)[name = string("transpose_248")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_4_cast_fp16 = slice_update(begin = concat_41, begin_mask = key_cache_internal_tensor_assign_4_begin_mask_0, end = concat_42, end_mask = key_cache_internal_tensor_assign_4_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_4_squeeze_mask_0, stride = key_cache_internal_tensor_assign_4_stride_0, update = key_states_37_cast_fp16, x = coreml_update_state_116)[name = string("key_cache_internal_tensor_assign_4_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_4_cast_fp16, input = key_cache)[name = string("coreml_update_state_118_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_118 = read_state(input = key_cache)[name = string("coreml_update_state_118")];
            tensor<int32, [4]> value_states_21_perm_0 = const()[name = string("value_states_21_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_4_stride_0 = const()[name = string("value_cache_internal_tensor_assign_4_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_4_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_4_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_4_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_4_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_4_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_4_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_21_cast_fp16 = transpose(perm = value_states_21_perm_0, x = var_1957_cast_fp16)[name = string("transpose_247")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_4_cast_fp16 = slice_update(begin = concat_41, begin_mask = value_cache_internal_tensor_assign_4_begin_mask_0, end = concat_42, end_mask = value_cache_internal_tensor_assign_4_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_4_squeeze_mask_0, stride = value_cache_internal_tensor_assign_4_stride_0, update = value_states_21_cast_fp16, x = coreml_update_state_117)[name = string("value_cache_internal_tensor_assign_4_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_4_cast_fp16, input = value_cache)[name = string("coreml_update_state_119_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_119 = read_state(input = value_cache)[name = string("coreml_update_state_119")];
            tensor<int32, [4]> var_2051_begin_0 = const()[name = string("op_2051_begin_0"), val = tensor<int32, [4]>([3, 0, 0, 0])];
            tensor<int32, [4]> var_2051_end_0 = const()[name = string("op_2051_end_0"), val = tensor<int32, [4]>([4, 2, 2048, 128])];
            tensor<bool, [4]> var_2051_end_mask_0 = const()[name = string("op_2051_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2051_cast_fp16 = slice_by_index(begin = var_2051_begin_0, end = var_2051_end_0, end_mask = var_2051_end_mask_0, x = coreml_update_state_118)[name = string("op_2051_cast_fp16")];
            tensor<int32, [2]> tile_6 = const()[name = string("tile_6"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2054_axis_0 = const()[name = string("op_2054_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2054_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2054_cast_fp16_1 = split(axis = var_2054_axis_0, split_sizes = tile_6, x = var_2051_cast_fp16)[name = string("op_2054_cast_fp16")];
            tensor<int32, [4]> var_2061_begin_0 = const()[name = string("op_2061_begin_0"), val = tensor<int32, [4]>([3, 0, 0, 0])];
            tensor<int32, [4]> var_2061_end_0 = const()[name = string("op_2061_end_0"), val = tensor<int32, [4]>([4, 2, 2048, 128])];
            tensor<bool, [4]> var_2061_end_mask_0 = const()[name = string("op_2061_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2061_cast_fp16 = slice_by_index(begin = var_2061_begin_0, end = var_2061_end_0, end_mask = var_2061_end_mask_0, x = coreml_update_state_119)[name = string("op_2061_cast_fp16")];
            tensor<int32, [2]> tile_7 = const()[name = string("tile_7"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2064_axis_0 = const()[name = string("op_2064_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2064_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2064_cast_fp16_1 = split(axis = var_2064_axis_0, split_sizes = tile_7, x = var_2061_cast_fp16)[name = string("op_2064_cast_fp16")];
            tensor<int32, [2]> var_2067_split_sizes_0 = const()[name = string("op_2067_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_2067_axis_0 = const()[name = string("op_2067_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_2067_0, tensor<fp16, [1, 8, 128, 8]> var_2067_1 = split(axis = var_2067_axis_0, split_sizes = var_2067_split_sizes_0, x = query_states_21_cast_fp16)[name = string("op_2067")];
            bool attn_weights_49_transpose_x_0 = const()[name = string("attn_weights_49_transpose_x_0"), val = bool(false)];
            bool attn_weights_49_transpose_y_0 = const()[name = string("attn_weights_49_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_49_cast_fp16 = matmul(transpose_x = attn_weights_49_transpose_x_0, transpose_y = attn_weights_49_transpose_y_0, x = var_2054_cast_fp16_0, y = var_2067_0)[name = string("attn_weights_49_cast_fp16")];
            fp16 var_2070_to_fp16 = const()[name = string("op_2070_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_51_cast_fp16 = mul(x = attn_weights_49_cast_fp16, y = var_2070_to_fp16)[name = string("attn_weights_51_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_53_cast_fp16 = add(x = attn_weights_51_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_53_cast_fp16")];
            int32 var_2074 = const()[name = string("op_2074"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_55_cast_fp16 = softmax(axis = var_2074, x = attn_weights_53_cast_fp16)[name = string("attn_weights_55_cast_fp16")];
            bool var_2080_transpose_x_1 = const()[name = string("op_2080_transpose_x_1"), val = bool(true)];
            bool var_2080_transpose_y_1 = const()[name = string("op_2080_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_2080_cast_fp16 = matmul(transpose_x = var_2080_transpose_x_1, transpose_y = var_2080_transpose_y_1, x = attn_weights_55_cast_fp16, y = var_2064_cast_fp16_0)[name = string("op_2080_cast_fp16")];
            bool attn_weights_57_transpose_x_0 = const()[name = string("attn_weights_57_transpose_x_0"), val = bool(false)];
            bool attn_weights_57_transpose_y_0 = const()[name = string("attn_weights_57_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_57_cast_fp16 = matmul(transpose_x = attn_weights_57_transpose_x_0, transpose_y = attn_weights_57_transpose_y_0, x = var_2054_cast_fp16_1, y = var_2067_1)[name = string("attn_weights_57_cast_fp16")];
            fp16 var_2082_to_fp16 = const()[name = string("op_2082_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_59_cast_fp16 = mul(x = attn_weights_57_cast_fp16, y = var_2082_to_fp16)[name = string("attn_weights_59_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_61_cast_fp16 = add(x = attn_weights_59_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_61_cast_fp16")];
            int32 var_2086 = const()[name = string("op_2086"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_63_cast_fp16 = softmax(axis = var_2086, x = attn_weights_61_cast_fp16)[name = string("attn_weights_63_cast_fp16")];
            bool attn_output_25_transpose_x_1 = const()[name = string("attn_output_25_transpose_x_1"), val = bool(true)];
            bool attn_output_25_transpose_y_1 = const()[name = string("attn_output_25_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_25_cast_fp16 = matmul(transpose_x = attn_output_25_transpose_x_1, transpose_y = attn_output_25_transpose_y_1, x = attn_weights_63_cast_fp16, y = var_2064_cast_fp16_1)[name = string("attn_output_25_cast_fp16")];
            int32 var_2094 = const()[name = string("op_2094"), val = int32(1)];
            bool attn_output_27_interleave_0 = const()[name = string("attn_output_27_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_27_cast_fp16 = concat(axis = var_2094, interleave = attn_output_27_interleave_0, values = (var_2080_cast_fp16, attn_output_25_cast_fp16))[name = string("attn_output_27_cast_fp16")];
            tensor<int32, [4]> var_2098_perm_0 = const()[name = string("op_2098_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_47x = const()[name = string("concat_47x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_2098_cast_fp16 = transpose(perm = var_2098_perm_0, x = attn_output_27_cast_fp16)[name = string("transpose_246")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_31_cast_fp16 = reshape(shape = concat_47x, x = var_2098_cast_fp16)[name = string("attn_output_31_cast_fp16")];
            tensor<int32, [2]> hidden_states_33_strides_0 = const()[name = string("hidden_states_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_33_pad_type_0 = const()[name = string("hidden_states_33_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_33_pad_0 = const()[name = string("hidden_states_33_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_33_dilations_0 = const()[name = string("hidden_states_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_33_groups_0 = const()[name = string("hidden_states_33_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_33_cast_fp16 = conv(dilations = hidden_states_33_dilations_0, groups = hidden_states_33_groups_0, pad = hidden_states_33_pad_0, pad_type = hidden_states_33_pad_type_0, strides = hidden_states_33_strides_0, weight = layers_3_self_attn_o_proj_weight_cast_fp16, x = attn_output_31_cast_fp16)[name = string("hidden_states_33_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_35_cast_fp16 = add(x = hidden_states_29_cast_fp16, y = hidden_states_33_cast_fp16)[name = string("hidden_states_35_cast_fp16")];
            fp16 const_40_promoted_to_fp16 = const()[name = string("const_40_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_2131_cast_fp16 = mul(x = hidden_states_35_cast_fp16, y = const_40_promoted_to_fp16)[name = string("op_2131_cast_fp16")];
            int32 var_2129 = const()[name = string("op_2129"), val = int32(1)];
            bool doubled_29_interleave_0 = const()[name = string("doubled_29_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_29_cast_fp16 = concat(axis = var_2129, interleave = doubled_29_interleave_0, values = (hidden_states_35_cast_fp16, var_2131_cast_fp16))[name = string("doubled_29_cast_fp16")];
            tensor<int32, [1]> out_15_axes_0 = const()[name = string("out_15_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_15_gamma_0_to_fp16 = const()[name = string("out_15_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1214963264)))];
            fp16 var_2141_to_fp16 = const()[name = string("op_2141_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_15_cast_fp16 = layer_norm(axes = out_15_axes_0, epsilon = var_2141_to_fp16, gamma = out_15_gamma_0_to_fp16, x = doubled_29_cast_fp16)[name = string("out_15_cast_fp16")];
            tensor<int32, [2]> var_2152_split_sizes_0 = const()[name = string("op_2152_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2152_axis_0 = const()[name = string("op_2152_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_2152_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_2152_cast_fp16_1 = split(axis = var_2152_axis_0, split_sizes = var_2152_split_sizes_0, x = out_15_cast_fp16)[name = string("op_2152_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_3_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_3_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1214971520)))];
            tensor<int32, [2]> input_7_strides_0 = const()[name = string("input_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_7_pad_type_0 = const()[name = string("input_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_7_pad_0 = const()[name = string("input_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_7_dilations_0 = const()[name = string("input_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_7_groups_0 = const()[name = string("input_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_7_cast_fp16 = conv(dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = layers_3_mlp_gate_proj_weight_to_fp16, x = var_2152_cast_fp16_0)[name = string("input_7_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_2169_cast_fp16 = silu(x = input_7_cast_fp16)[name = string("op_2169_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_3_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_3_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1240137408)))];
            tensor<int32, [2]> var_2175_strides_0 = const()[name = string("op_2175_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_2175_pad_type_0 = const()[name = string("op_2175_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_2175_pad_0 = const()[name = string("op_2175_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_2175_dilations_0 = const()[name = string("op_2175_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2175_groups_0 = const()[name = string("op_2175_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_2175_cast_fp16 = conv(dilations = var_2175_dilations_0, groups = var_2175_groups_0, pad = var_2175_pad_0, pad_type = var_2175_pad_type_0, strides = var_2175_strides_0, weight = layers_3_mlp_up_proj_weight_to_fp16, x = var_2152_cast_fp16_0)[name = string("op_2175_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_39_cast_fp16 = mul(x = var_2169_cast_fp16, y = var_2175_cast_fp16)[name = string("x_39_cast_fp16")];
            tensor<int32, [2]> hidden_states_37_strides_0 = const()[name = string("hidden_states_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_37_pad_type_0 = const()[name = string("hidden_states_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_37_pad_0 = const()[name = string("hidden_states_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_37_dilations_0 = const()[name = string("hidden_states_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_37_groups_0 = const()[name = string("hidden_states_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_37_cast_fp16 = conv(dilations = hidden_states_37_dilations_0, groups = hidden_states_37_groups_0, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = hidden_states_37_strides_0, weight = layers_3_mlp_down_proj_weight_cast_fp16, x = x_39_cast_fp16)[name = string("hidden_states_37_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_39_cast_fp16 = add(x = hidden_states_35_cast_fp16, y = hidden_states_37_cast_fp16)[name = string("hidden_states_39_cast_fp16")];
            fp16 const_42_promoted_to_fp16 = const()[name = string("const_42_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_2193_cast_fp16 = mul(x = hidden_states_39_cast_fp16, y = const_42_promoted_to_fp16)[name = string("op_2193_cast_fp16")];
            int32 var_2191 = const()[name = string("op_2191"), val = int32(1)];
            bool doubled_33_interleave_0 = const()[name = string("doubled_33_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_33_cast_fp16 = concat(axis = var_2191, interleave = doubled_33_interleave_0, values = (hidden_states_39_cast_fp16, var_2193_cast_fp16))[name = string("doubled_33_cast_fp16")];
            tensor<int32, [1]> out_17_axes_0 = const()[name = string("out_17_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_17_gamma_0_to_fp16 = const()[name = string("out_17_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1265303296)))];
            fp16 var_2203_to_fp16 = const()[name = string("op_2203_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_17_cast_fp16 = layer_norm(axes = out_17_axes_0, epsilon = var_2203_to_fp16, gamma = out_17_gamma_0_to_fp16, x = doubled_33_cast_fp16)[name = string("out_17_cast_fp16")];
            tensor<int32, [2]> var_2214_split_sizes_0 = const()[name = string("op_2214_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2214_axis_0 = const()[name = string("op_2214_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_2214_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_2214_cast_fp16_1 = split(axis = var_2214_axis_0, split_sizes = var_2214_split_sizes_0, x = out_17_cast_fp16)[name = string("op_2214_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_4_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_4_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1265311552)))];
            tensor<int32, [2]> query_states_25_strides_0 = const()[name = string("query_states_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_25_pad_type_0 = const()[name = string("query_states_25_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_25_pad_0 = const()[name = string("query_states_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_25_dilations_0 = const()[name = string("query_states_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_25_groups_0 = const()[name = string("query_states_25_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_25_cast_fp16 = conv(dilations = query_states_25_dilations_0, groups = query_states_25_groups_0, pad = query_states_25_pad_0, pad_type = query_states_25_pad_type_0, strides = query_states_25_strides_0, weight = layers_4_self_attn_q_proj_weight_to_fp16, x = var_2214_cast_fp16_0)[name = string("query_states_25_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_4_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_4_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1273700224)))];
            tensor<int32, [2]> key_states_41_strides_0 = const()[name = string("key_states_41_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_41_pad_type_0 = const()[name = string("key_states_41_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_41_pad_0 = const()[name = string("key_states_41_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_41_dilations_0 = const()[name = string("key_states_41_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_41_groups_0 = const()[name = string("key_states_41_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_41_cast_fp16 = conv(dilations = key_states_41_dilations_0, groups = key_states_41_groups_0, pad = key_states_41_pad_0, pad_type = key_states_41_pad_type_0, strides = key_states_41_strides_0, weight = layers_4_self_attn_k_proj_weight_to_fp16, x = var_2214_cast_fp16_0)[name = string("key_states_41_cast_fp16")];
            tensor<int32, [2]> value_states_25_strides_0 = const()[name = string("value_states_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_25_pad_type_0 = const()[name = string("value_states_25_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_25_pad_0 = const()[name = string("value_states_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_25_dilations_0 = const()[name = string("value_states_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_25_groups_0 = const()[name = string("value_states_25_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_25_cast_fp16 = conv(dilations = value_states_25_dilations_0, groups = value_states_25_groups_0, pad = value_states_25_pad_0, pad_type = value_states_25_pad_type_0, strides = value_states_25_strides_0, weight = layers_4_self_attn_v_proj_weight_cast_fp16, x = var_2214_cast_fp16_0)[name = string("value_states_25_cast_fp16")];
            tensor<int32, [4]> concat_48x = const()[name = string("concat_48x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_41_cast_fp16 = reshape(shape = concat_48x, x = query_states_25_cast_fp16)[name = string("x_41_cast_fp16")];
            tensor<int32, [4]> concat_49x = const()[name = string("concat_49x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_2271_cast_fp16 = reshape(shape = concat_49x, x = key_states_41_cast_fp16)[name = string("op_2271_cast_fp16")];
            tensor<int32, [4]> concat_50x = const()[name = string("concat_50x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_2278_cast_fp16 = reshape(shape = concat_50x, x = value_states_25_cast_fp16)[name = string("op_2278_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_2282_cast_fp16 = mul(x = x_41_cast_fp16, y = var_859_cast_fp16)[name = string("op_2282_cast_fp16")];
            tensor<int32, [2]> var_2283_split_sizes_0 = const()[name = string("op_2283_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2283_axis_0 = const()[name = string("op_2283_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_2283_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_2283_cast_fp16_1 = split(axis = var_2283_axis_0, split_sizes = var_2283_split_sizes_0, x = x_41_cast_fp16)[name = string("op_2283_cast_fp16")];
            fp16 const_44_promoted_to_fp16 = const()[name = string("const_44_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_2285_cast_fp16 = mul(x = var_2283_cast_fp16_1, y = const_44_promoted_to_fp16)[name = string("op_2285_cast_fp16")];
            int32 var_2287 = const()[name = string("op_2287"), val = int32(-2)];
            bool var_2288_interleave_0 = const()[name = string("op_2288_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_2288_cast_fp16 = concat(axis = var_2287, interleave = var_2288_interleave_0, values = (var_2285_cast_fp16, var_2283_cast_fp16_0))[name = string("op_2288_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_2289_cast_fp16 = mul(x = var_2288_cast_fp16, y = var_866_cast_fp16)[name = string("op_2289_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_27_cast_fp16 = add(x = var_2282_cast_fp16, y = var_2289_cast_fp16)[name = string("query_states_27_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_2295_cast_fp16 = mul(x = var_2271_cast_fp16, y = var_859_cast_fp16)[name = string("op_2295_cast_fp16")];
            tensor<int32, [2]> var_2296_split_sizes_0 = const()[name = string("op_2296_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2296_axis_0 = const()[name = string("op_2296_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_2296_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_2296_cast_fp16_1 = split(axis = var_2296_axis_0, split_sizes = var_2296_split_sizes_0, x = var_2271_cast_fp16)[name = string("op_2296_cast_fp16")];
            fp16 const_45_promoted_to_fp16 = const()[name = string("const_45_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_2298_cast_fp16 = mul(x = var_2296_cast_fp16_1, y = const_45_promoted_to_fp16)[name = string("op_2298_cast_fp16")];
            int32 var_2300 = const()[name = string("op_2300"), val = int32(-2)];
            bool var_2301_interleave_0 = const()[name = string("op_2301_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_2301_cast_fp16 = concat(axis = var_2300, interleave = var_2301_interleave_0, values = (var_2298_cast_fp16, var_2296_cast_fp16_0))[name = string("op_2301_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_2302_cast_fp16 = mul(x = var_2301_cast_fp16, y = var_866_cast_fp16)[name = string("op_2302_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_45_cast_fp16 = add(x = var_2295_cast_fp16, y = var_2302_cast_fp16)[name = string("key_states_45_cast_fp16")];
            tensor<int32, [1]> expand_dims_48 = const()[name = string("expand_dims_48"), val = tensor<int32, [1]>([4])];
            tensor<int32, [1]> expand_dims_49 = const()[name = string("expand_dims_49"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_51 = const()[name = string("expand_dims_51"), val = tensor<int32, [1]>([0])];
            int32 concat_53_axis_0 = const()[name = string("concat_53_axis_0"), val = int32(0)];
            bool concat_53_interleave_0 = const()[name = string("concat_53_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_53 = concat(axis = concat_53_axis_0, interleave = concat_53_interleave_0, values = (expand_dims_48, expand_dims_49, position_id, expand_dims_51))[name = string("concat_53")];
            tensor<int32, [1]> expand_dims_52 = const()[name = string("expand_dims_52"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> concat_54_values1_0 = const()[name = string("concat_54_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_54_values3_0 = const()[name = string("concat_54_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_54_axis_0 = const()[name = string("concat_54_axis_0"), val = int32(0)];
            bool concat_54_interleave_0 = const()[name = string("concat_54_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_54 = concat(axis = concat_54_axis_0, interleave = concat_54_interleave_0, values = (expand_dims_52, concat_54_values1_0, cache_position_end, concat_54_values3_0))[name = string("concat_54")];
            tensor<int32, [4]> key_states_47_perm_0 = const()[name = string("key_states_47_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_5_stride_0 = const()[name = string("key_cache_internal_tensor_assign_5_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_5_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_5_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_5_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_5_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_5_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_5_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_47_cast_fp16 = transpose(perm = key_states_47_perm_0, x = key_states_45_cast_fp16)[name = string("transpose_245")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_5_cast_fp16 = slice_update(begin = concat_53, begin_mask = key_cache_internal_tensor_assign_5_begin_mask_0, end = concat_54, end_mask = key_cache_internal_tensor_assign_5_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_5_squeeze_mask_0, stride = key_cache_internal_tensor_assign_5_stride_0, update = key_states_47_cast_fp16, x = coreml_update_state_118)[name = string("key_cache_internal_tensor_assign_5_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_5_cast_fp16, input = key_cache)[name = string("coreml_update_state_120_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_120 = read_state(input = key_cache)[name = string("coreml_update_state_120")];
            tensor<int32, [4]> value_states_27_perm_0 = const()[name = string("value_states_27_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_5_stride_0 = const()[name = string("value_cache_internal_tensor_assign_5_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_5_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_5_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_5_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_5_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_5_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_5_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_27_cast_fp16 = transpose(perm = value_states_27_perm_0, x = var_2278_cast_fp16)[name = string("transpose_244")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_5_cast_fp16 = slice_update(begin = concat_53, begin_mask = value_cache_internal_tensor_assign_5_begin_mask_0, end = concat_54, end_mask = value_cache_internal_tensor_assign_5_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_5_squeeze_mask_0, stride = value_cache_internal_tensor_assign_5_stride_0, update = value_states_27_cast_fp16, x = coreml_update_state_119)[name = string("value_cache_internal_tensor_assign_5_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_5_cast_fp16, input = value_cache)[name = string("coreml_update_state_121_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_121 = read_state(input = value_cache)[name = string("coreml_update_state_121")];
            tensor<int32, [4]> var_2372_begin_0 = const()[name = string("op_2372_begin_0"), val = tensor<int32, [4]>([4, 0, 0, 0])];
            tensor<int32, [4]> var_2372_end_0 = const()[name = string("op_2372_end_0"), val = tensor<int32, [4]>([5, 2, 2048, 128])];
            tensor<bool, [4]> var_2372_end_mask_0 = const()[name = string("op_2372_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2372_cast_fp16 = slice_by_index(begin = var_2372_begin_0, end = var_2372_end_0, end_mask = var_2372_end_mask_0, x = coreml_update_state_120)[name = string("op_2372_cast_fp16")];
            tensor<int32, [2]> tile_8 = const()[name = string("tile_8"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2375_axis_0 = const()[name = string("op_2375_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2375_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2375_cast_fp16_1 = split(axis = var_2375_axis_0, split_sizes = tile_8, x = var_2372_cast_fp16)[name = string("op_2375_cast_fp16")];
            tensor<int32, [4]> var_2382_begin_0 = const()[name = string("op_2382_begin_0"), val = tensor<int32, [4]>([4, 0, 0, 0])];
            tensor<int32, [4]> var_2382_end_0 = const()[name = string("op_2382_end_0"), val = tensor<int32, [4]>([5, 2, 2048, 128])];
            tensor<bool, [4]> var_2382_end_mask_0 = const()[name = string("op_2382_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2382_cast_fp16 = slice_by_index(begin = var_2382_begin_0, end = var_2382_end_0, end_mask = var_2382_end_mask_0, x = coreml_update_state_121)[name = string("op_2382_cast_fp16")];
            tensor<int32, [2]> tile_9 = const()[name = string("tile_9"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2385_axis_0 = const()[name = string("op_2385_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2385_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2385_cast_fp16_1 = split(axis = var_2385_axis_0, split_sizes = tile_9, x = var_2382_cast_fp16)[name = string("op_2385_cast_fp16")];
            tensor<int32, [2]> var_2388_split_sizes_0 = const()[name = string("op_2388_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_2388_axis_0 = const()[name = string("op_2388_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_2388_0, tensor<fp16, [1, 8, 128, 8]> var_2388_1 = split(axis = var_2388_axis_0, split_sizes = var_2388_split_sizes_0, x = query_states_27_cast_fp16)[name = string("op_2388")];
            bool attn_weights_65_transpose_x_0 = const()[name = string("attn_weights_65_transpose_x_0"), val = bool(false)];
            bool attn_weights_65_transpose_y_0 = const()[name = string("attn_weights_65_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_65_cast_fp16 = matmul(transpose_x = attn_weights_65_transpose_x_0, transpose_y = attn_weights_65_transpose_y_0, x = var_2375_cast_fp16_0, y = var_2388_0)[name = string("attn_weights_65_cast_fp16")];
            fp16 var_2391_to_fp16 = const()[name = string("op_2391_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_67_cast_fp16 = mul(x = attn_weights_65_cast_fp16, y = var_2391_to_fp16)[name = string("attn_weights_67_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_69_cast_fp16 = add(x = attn_weights_67_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_69_cast_fp16")];
            int32 var_2395 = const()[name = string("op_2395"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_71_cast_fp16 = softmax(axis = var_2395, x = attn_weights_69_cast_fp16)[name = string("attn_weights_71_cast_fp16")];
            bool var_2401_transpose_x_1 = const()[name = string("op_2401_transpose_x_1"), val = bool(true)];
            bool var_2401_transpose_y_1 = const()[name = string("op_2401_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_2401_cast_fp16 = matmul(transpose_x = var_2401_transpose_x_1, transpose_y = var_2401_transpose_y_1, x = attn_weights_71_cast_fp16, y = var_2385_cast_fp16_0)[name = string("op_2401_cast_fp16")];
            bool attn_weights_73_transpose_x_0 = const()[name = string("attn_weights_73_transpose_x_0"), val = bool(false)];
            bool attn_weights_73_transpose_y_0 = const()[name = string("attn_weights_73_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_73_cast_fp16 = matmul(transpose_x = attn_weights_73_transpose_x_0, transpose_y = attn_weights_73_transpose_y_0, x = var_2375_cast_fp16_1, y = var_2388_1)[name = string("attn_weights_73_cast_fp16")];
            fp16 var_2403_to_fp16 = const()[name = string("op_2403_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_75_cast_fp16 = mul(x = attn_weights_73_cast_fp16, y = var_2403_to_fp16)[name = string("attn_weights_75_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_77_cast_fp16 = add(x = attn_weights_75_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_77_cast_fp16")];
            int32 var_2407 = const()[name = string("op_2407"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_79_cast_fp16 = softmax(axis = var_2407, x = attn_weights_77_cast_fp16)[name = string("attn_weights_79_cast_fp16")];
            bool attn_output_33_transpose_x_1 = const()[name = string("attn_output_33_transpose_x_1"), val = bool(true)];
            bool attn_output_33_transpose_y_1 = const()[name = string("attn_output_33_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_33_cast_fp16 = matmul(transpose_x = attn_output_33_transpose_x_1, transpose_y = attn_output_33_transpose_y_1, x = attn_weights_79_cast_fp16, y = var_2385_cast_fp16_1)[name = string("attn_output_33_cast_fp16")];
            int32 var_2415 = const()[name = string("op_2415"), val = int32(1)];
            bool attn_output_35_interleave_0 = const()[name = string("attn_output_35_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_35_cast_fp16 = concat(axis = var_2415, interleave = attn_output_35_interleave_0, values = (var_2401_cast_fp16, attn_output_33_cast_fp16))[name = string("attn_output_35_cast_fp16")];
            tensor<int32, [4]> var_2419_perm_0 = const()[name = string("op_2419_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_59x = const()[name = string("concat_59x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_2419_cast_fp16 = transpose(perm = var_2419_perm_0, x = attn_output_35_cast_fp16)[name = string("transpose_243")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_39_cast_fp16 = reshape(shape = concat_59x, x = var_2419_cast_fp16)[name = string("attn_output_39_cast_fp16")];
            tensor<int32, [2]> hidden_states_43_strides_0 = const()[name = string("hidden_states_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_43_pad_type_0 = const()[name = string("hidden_states_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_43_pad_0 = const()[name = string("hidden_states_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_43_dilations_0 = const()[name = string("hidden_states_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_43_groups_0 = const()[name = string("hidden_states_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_43_cast_fp16 = conv(dilations = hidden_states_43_dilations_0, groups = hidden_states_43_groups_0, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = hidden_states_43_strides_0, weight = layers_4_self_attn_o_proj_weight_cast_fp16, x = attn_output_39_cast_fp16)[name = string("hidden_states_43_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_45_cast_fp16 = add(x = hidden_states_39_cast_fp16, y = hidden_states_43_cast_fp16)[name = string("hidden_states_45_cast_fp16")];
            fp16 const_50_promoted_to_fp16 = const()[name = string("const_50_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_2452_cast_fp16 = mul(x = hidden_states_45_cast_fp16, y = const_50_promoted_to_fp16)[name = string("op_2452_cast_fp16")];
            int32 var_2450 = const()[name = string("op_2450"), val = int32(1)];
            bool doubled_37_interleave_0 = const()[name = string("doubled_37_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_37_cast_fp16 = concat(axis = var_2450, interleave = doubled_37_interleave_0, values = (hidden_states_45_cast_fp16, var_2452_cast_fp16))[name = string("doubled_37_cast_fp16")];
            tensor<int32, [1]> out_19_axes_0 = const()[name = string("out_19_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_19_gamma_0_to_fp16 = const()[name = string("out_19_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1274748864)))];
            fp16 var_2462_to_fp16 = const()[name = string("op_2462_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_19_cast_fp16 = layer_norm(axes = out_19_axes_0, epsilon = var_2462_to_fp16, gamma = out_19_gamma_0_to_fp16, x = doubled_37_cast_fp16)[name = string("out_19_cast_fp16")];
            tensor<int32, [2]> var_2473_split_sizes_0 = const()[name = string("op_2473_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2473_axis_0 = const()[name = string("op_2473_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_2473_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_2473_cast_fp16_1 = split(axis = var_2473_axis_0, split_sizes = var_2473_split_sizes_0, x = out_19_cast_fp16)[name = string("op_2473_cast_fp16")];
            tensor<int32, [2]> input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_9_cast_fp16 = conv(dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = layers_4_mlp_gate_proj_weight_cast_fp16, x = var_2473_cast_fp16_0)[name = string("input_9_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_2490_cast_fp16 = silu(x = input_9_cast_fp16)[name = string("op_2490_cast_fp16")];
            tensor<int32, [2]> var_2496_strides_0 = const()[name = string("op_2496_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_2496_pad_type_0 = const()[name = string("op_2496_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_2496_pad_0 = const()[name = string("op_2496_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_2496_dilations_0 = const()[name = string("op_2496_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2496_groups_0 = const()[name = string("op_2496_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_2496_cast_fp16 = conv(dilations = var_2496_dilations_0, groups = var_2496_groups_0, pad = var_2496_pad_0, pad_type = var_2496_pad_type_0, strides = var_2496_strides_0, weight = layers_4_mlp_up_proj_weight_cast_fp16, x = var_2473_cast_fp16_0)[name = string("op_2496_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_49_cast_fp16 = mul(x = var_2490_cast_fp16, y = var_2496_cast_fp16)[name = string("x_49_cast_fp16")];
            tensor<int32, [2]> hidden_states_47_strides_0 = const()[name = string("hidden_states_47_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_47_pad_type_0 = const()[name = string("hidden_states_47_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_47_pad_0 = const()[name = string("hidden_states_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_47_dilations_0 = const()[name = string("hidden_states_47_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_47_groups_0 = const()[name = string("hidden_states_47_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_47_cast_fp16 = conv(dilations = hidden_states_47_dilations_0, groups = hidden_states_47_groups_0, pad = hidden_states_47_pad_0, pad_type = hidden_states_47_pad_type_0, strides = hidden_states_47_strides_0, weight = layers_4_mlp_down_proj_weight_cast_fp16, x = x_49_cast_fp16)[name = string("hidden_states_47_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_49_cast_fp16 = add(x = hidden_states_45_cast_fp16, y = hidden_states_47_cast_fp16)[name = string("hidden_states_49_cast_fp16")];
            fp16 const_52_promoted_to_fp16 = const()[name = string("const_52_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_2514_cast_fp16 = mul(x = hidden_states_49_cast_fp16, y = const_52_promoted_to_fp16)[name = string("op_2514_cast_fp16")];
            int32 var_2512 = const()[name = string("op_2512"), val = int32(1)];
            bool doubled_41_interleave_0 = const()[name = string("doubled_41_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_41_cast_fp16 = concat(axis = var_2512, interleave = doubled_41_interleave_0, values = (hidden_states_49_cast_fp16, var_2514_cast_fp16))[name = string("doubled_41_cast_fp16")];
            tensor<int32, [1]> out_21_axes_0 = const()[name = string("out_21_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_21_gamma_0_to_fp16 = const()[name = string("out_21_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1274757120)))];
            fp16 var_2524_to_fp16 = const()[name = string("op_2524_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_21_cast_fp16 = layer_norm(axes = out_21_axes_0, epsilon = var_2524_to_fp16, gamma = out_21_gamma_0_to_fp16, x = doubled_41_cast_fp16)[name = string("out_21_cast_fp16")];
            tensor<int32, [2]> var_2535_split_sizes_0 = const()[name = string("op_2535_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2535_axis_0 = const()[name = string("op_2535_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_2535_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_2535_cast_fp16_1 = split(axis = var_2535_axis_0, split_sizes = var_2535_split_sizes_0, x = out_21_cast_fp16)[name = string("op_2535_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_5_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_5_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1274765376)))];
            tensor<int32, [2]> query_states_31_strides_0 = const()[name = string("query_states_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_31_pad_type_0 = const()[name = string("query_states_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_31_pad_0 = const()[name = string("query_states_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_31_dilations_0 = const()[name = string("query_states_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_31_groups_0 = const()[name = string("query_states_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_31_cast_fp16 = conv(dilations = query_states_31_dilations_0, groups = query_states_31_groups_0, pad = query_states_31_pad_0, pad_type = query_states_31_pad_type_0, strides = query_states_31_strides_0, weight = layers_5_self_attn_q_proj_weight_to_fp16, x = var_2535_cast_fp16_0)[name = string("query_states_31_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_5_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_5_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1283154048)))];
            tensor<int32, [2]> key_states_51_strides_0 = const()[name = string("key_states_51_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_51_pad_type_0 = const()[name = string("key_states_51_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_51_pad_0 = const()[name = string("key_states_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_51_dilations_0 = const()[name = string("key_states_51_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_51_groups_0 = const()[name = string("key_states_51_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_51_cast_fp16 = conv(dilations = key_states_51_dilations_0, groups = key_states_51_groups_0, pad = key_states_51_pad_0, pad_type = key_states_51_pad_type_0, strides = key_states_51_strides_0, weight = layers_5_self_attn_k_proj_weight_to_fp16, x = var_2535_cast_fp16_0)[name = string("key_states_51_cast_fp16")];
            tensor<int32, [2]> value_states_31_strides_0 = const()[name = string("value_states_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_31_pad_type_0 = const()[name = string("value_states_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_31_pad_0 = const()[name = string("value_states_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_31_dilations_0 = const()[name = string("value_states_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_31_groups_0 = const()[name = string("value_states_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_31_cast_fp16 = conv(dilations = value_states_31_dilations_0, groups = value_states_31_groups_0, pad = value_states_31_pad_0, pad_type = value_states_31_pad_type_0, strides = value_states_31_strides_0, weight = layers_5_self_attn_v_proj_weight_cast_fp16, x = var_2535_cast_fp16_0)[name = string("value_states_31_cast_fp16")];
            tensor<int32, [4]> concat_60x = const()[name = string("concat_60x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_51_cast_fp16 = reshape(shape = concat_60x, x = query_states_31_cast_fp16)[name = string("x_51_cast_fp16")];
            tensor<int32, [4]> concat_61x = const()[name = string("concat_61x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_2592_cast_fp16 = reshape(shape = concat_61x, x = key_states_51_cast_fp16)[name = string("op_2592_cast_fp16")];
            tensor<int32, [4]> concat_62x = const()[name = string("concat_62x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_2599_cast_fp16 = reshape(shape = concat_62x, x = value_states_31_cast_fp16)[name = string("op_2599_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_2603_cast_fp16 = mul(x = x_51_cast_fp16, y = var_859_cast_fp16)[name = string("op_2603_cast_fp16")];
            tensor<int32, [2]> var_2604_split_sizes_0 = const()[name = string("op_2604_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2604_axis_0 = const()[name = string("op_2604_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_2604_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_2604_cast_fp16_1 = split(axis = var_2604_axis_0, split_sizes = var_2604_split_sizes_0, x = x_51_cast_fp16)[name = string("op_2604_cast_fp16")];
            fp16 const_54_promoted_to_fp16 = const()[name = string("const_54_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_2606_cast_fp16 = mul(x = var_2604_cast_fp16_1, y = const_54_promoted_to_fp16)[name = string("op_2606_cast_fp16")];
            int32 var_2608 = const()[name = string("op_2608"), val = int32(-2)];
            bool var_2609_interleave_0 = const()[name = string("op_2609_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_2609_cast_fp16 = concat(axis = var_2608, interleave = var_2609_interleave_0, values = (var_2606_cast_fp16, var_2604_cast_fp16_0))[name = string("op_2609_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_2610_cast_fp16 = mul(x = var_2609_cast_fp16, y = var_866_cast_fp16)[name = string("op_2610_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_33_cast_fp16 = add(x = var_2603_cast_fp16, y = var_2610_cast_fp16)[name = string("query_states_33_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_2616_cast_fp16 = mul(x = var_2592_cast_fp16, y = var_859_cast_fp16)[name = string("op_2616_cast_fp16")];
            tensor<int32, [2]> var_2617_split_sizes_0 = const()[name = string("op_2617_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2617_axis_0 = const()[name = string("op_2617_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_2617_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_2617_cast_fp16_1 = split(axis = var_2617_axis_0, split_sizes = var_2617_split_sizes_0, x = var_2592_cast_fp16)[name = string("op_2617_cast_fp16")];
            fp16 const_55_promoted_to_fp16 = const()[name = string("const_55_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_2619_cast_fp16 = mul(x = var_2617_cast_fp16_1, y = const_55_promoted_to_fp16)[name = string("op_2619_cast_fp16")];
            int32 var_2621 = const()[name = string("op_2621"), val = int32(-2)];
            bool var_2622_interleave_0 = const()[name = string("op_2622_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_2622_cast_fp16 = concat(axis = var_2621, interleave = var_2622_interleave_0, values = (var_2619_cast_fp16, var_2617_cast_fp16_0))[name = string("op_2622_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_2623_cast_fp16 = mul(x = var_2622_cast_fp16, y = var_866_cast_fp16)[name = string("op_2623_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_55_cast_fp16 = add(x = var_2616_cast_fp16, y = var_2623_cast_fp16)[name = string("key_states_55_cast_fp16")];
            tensor<int32, [1]> expand_dims_60 = const()[name = string("expand_dims_60"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> expand_dims_61 = const()[name = string("expand_dims_61"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_63 = const()[name = string("expand_dims_63"), val = tensor<int32, [1]>([0])];
            int32 concat_65_axis_0 = const()[name = string("concat_65_axis_0"), val = int32(0)];
            bool concat_65_interleave_0 = const()[name = string("concat_65_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_65 = concat(axis = concat_65_axis_0, interleave = concat_65_interleave_0, values = (expand_dims_60, expand_dims_61, position_id, expand_dims_63))[name = string("concat_65")];
            tensor<int32, [1]> expand_dims_64 = const()[name = string("expand_dims_64"), val = tensor<int32, [1]>([6])];
            tensor<int32, [1]> concat_66_values1_0 = const()[name = string("concat_66_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_66_values3_0 = const()[name = string("concat_66_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_66_axis_0 = const()[name = string("concat_66_axis_0"), val = int32(0)];
            bool concat_66_interleave_0 = const()[name = string("concat_66_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_66 = concat(axis = concat_66_axis_0, interleave = concat_66_interleave_0, values = (expand_dims_64, concat_66_values1_0, cache_position_end, concat_66_values3_0))[name = string("concat_66")];
            tensor<int32, [4]> key_states_57_perm_0 = const()[name = string("key_states_57_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_6_stride_0 = const()[name = string("key_cache_internal_tensor_assign_6_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_6_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_6_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_6_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_6_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_6_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_6_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_57_cast_fp16 = transpose(perm = key_states_57_perm_0, x = key_states_55_cast_fp16)[name = string("transpose_242")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_6_cast_fp16 = slice_update(begin = concat_65, begin_mask = key_cache_internal_tensor_assign_6_begin_mask_0, end = concat_66, end_mask = key_cache_internal_tensor_assign_6_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_6_squeeze_mask_0, stride = key_cache_internal_tensor_assign_6_stride_0, update = key_states_57_cast_fp16, x = coreml_update_state_120)[name = string("key_cache_internal_tensor_assign_6_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_6_cast_fp16, input = key_cache)[name = string("coreml_update_state_122_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_122 = read_state(input = key_cache)[name = string("coreml_update_state_122")];
            tensor<int32, [4]> value_states_33_perm_0 = const()[name = string("value_states_33_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_6_stride_0 = const()[name = string("value_cache_internal_tensor_assign_6_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_6_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_6_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_6_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_6_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_6_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_6_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_33_cast_fp16 = transpose(perm = value_states_33_perm_0, x = var_2599_cast_fp16)[name = string("transpose_241")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_6_cast_fp16 = slice_update(begin = concat_65, begin_mask = value_cache_internal_tensor_assign_6_begin_mask_0, end = concat_66, end_mask = value_cache_internal_tensor_assign_6_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_6_squeeze_mask_0, stride = value_cache_internal_tensor_assign_6_stride_0, update = value_states_33_cast_fp16, x = coreml_update_state_121)[name = string("value_cache_internal_tensor_assign_6_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_6_cast_fp16, input = value_cache)[name = string("coreml_update_state_123_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_123 = read_state(input = value_cache)[name = string("coreml_update_state_123")];
            tensor<int32, [4]> var_2693_begin_0 = const()[name = string("op_2693_begin_0"), val = tensor<int32, [4]>([5, 0, 0, 0])];
            tensor<int32, [4]> var_2693_end_0 = const()[name = string("op_2693_end_0"), val = tensor<int32, [4]>([6, 2, 2048, 128])];
            tensor<bool, [4]> var_2693_end_mask_0 = const()[name = string("op_2693_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2693_cast_fp16 = slice_by_index(begin = var_2693_begin_0, end = var_2693_end_0, end_mask = var_2693_end_mask_0, x = coreml_update_state_122)[name = string("op_2693_cast_fp16")];
            tensor<int32, [2]> tile_10 = const()[name = string("tile_10"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2696_axis_0 = const()[name = string("op_2696_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2696_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2696_cast_fp16_1 = split(axis = var_2696_axis_0, split_sizes = tile_10, x = var_2693_cast_fp16)[name = string("op_2696_cast_fp16")];
            tensor<int32, [4]> var_2703_begin_0 = const()[name = string("op_2703_begin_0"), val = tensor<int32, [4]>([5, 0, 0, 0])];
            tensor<int32, [4]> var_2703_end_0 = const()[name = string("op_2703_end_0"), val = tensor<int32, [4]>([6, 2, 2048, 128])];
            tensor<bool, [4]> var_2703_end_mask_0 = const()[name = string("op_2703_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2703_cast_fp16 = slice_by_index(begin = var_2703_begin_0, end = var_2703_end_0, end_mask = var_2703_end_mask_0, x = coreml_update_state_123)[name = string("op_2703_cast_fp16")];
            tensor<int32, [2]> tile_11 = const()[name = string("tile_11"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2706_axis_0 = const()[name = string("op_2706_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2706_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2706_cast_fp16_1 = split(axis = var_2706_axis_0, split_sizes = tile_11, x = var_2703_cast_fp16)[name = string("op_2706_cast_fp16")];
            tensor<int32, [2]> var_2709_split_sizes_0 = const()[name = string("op_2709_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_2709_axis_0 = const()[name = string("op_2709_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_2709_0, tensor<fp16, [1, 8, 128, 8]> var_2709_1 = split(axis = var_2709_axis_0, split_sizes = var_2709_split_sizes_0, x = query_states_33_cast_fp16)[name = string("op_2709")];
            bool attn_weights_81_transpose_x_0 = const()[name = string("attn_weights_81_transpose_x_0"), val = bool(false)];
            bool attn_weights_81_transpose_y_0 = const()[name = string("attn_weights_81_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_81_cast_fp16 = matmul(transpose_x = attn_weights_81_transpose_x_0, transpose_y = attn_weights_81_transpose_y_0, x = var_2696_cast_fp16_0, y = var_2709_0)[name = string("attn_weights_81_cast_fp16")];
            fp16 var_2712_to_fp16 = const()[name = string("op_2712_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_83_cast_fp16 = mul(x = attn_weights_81_cast_fp16, y = var_2712_to_fp16)[name = string("attn_weights_83_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_85_cast_fp16 = add(x = attn_weights_83_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_85_cast_fp16")];
            int32 var_2716 = const()[name = string("op_2716"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_87_cast_fp16 = softmax(axis = var_2716, x = attn_weights_85_cast_fp16)[name = string("attn_weights_87_cast_fp16")];
            bool var_2722_transpose_x_1 = const()[name = string("op_2722_transpose_x_1"), val = bool(true)];
            bool var_2722_transpose_y_1 = const()[name = string("op_2722_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_2722_cast_fp16 = matmul(transpose_x = var_2722_transpose_x_1, transpose_y = var_2722_transpose_y_1, x = attn_weights_87_cast_fp16, y = var_2706_cast_fp16_0)[name = string("op_2722_cast_fp16")];
            bool attn_weights_89_transpose_x_0 = const()[name = string("attn_weights_89_transpose_x_0"), val = bool(false)];
            bool attn_weights_89_transpose_y_0 = const()[name = string("attn_weights_89_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_89_cast_fp16 = matmul(transpose_x = attn_weights_89_transpose_x_0, transpose_y = attn_weights_89_transpose_y_0, x = var_2696_cast_fp16_1, y = var_2709_1)[name = string("attn_weights_89_cast_fp16")];
            fp16 var_2724_to_fp16 = const()[name = string("op_2724_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_91_cast_fp16 = mul(x = attn_weights_89_cast_fp16, y = var_2724_to_fp16)[name = string("attn_weights_91_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_93_cast_fp16 = add(x = attn_weights_91_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_93_cast_fp16")];
            int32 var_2728 = const()[name = string("op_2728"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_95_cast_fp16 = softmax(axis = var_2728, x = attn_weights_93_cast_fp16)[name = string("attn_weights_95_cast_fp16")];
            bool attn_output_41_transpose_x_1 = const()[name = string("attn_output_41_transpose_x_1"), val = bool(true)];
            bool attn_output_41_transpose_y_1 = const()[name = string("attn_output_41_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_41_cast_fp16 = matmul(transpose_x = attn_output_41_transpose_x_1, transpose_y = attn_output_41_transpose_y_1, x = attn_weights_95_cast_fp16, y = var_2706_cast_fp16_1)[name = string("attn_output_41_cast_fp16")];
            int32 var_2736 = const()[name = string("op_2736"), val = int32(1)];
            bool attn_output_43_interleave_0 = const()[name = string("attn_output_43_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_43_cast_fp16 = concat(axis = var_2736, interleave = attn_output_43_interleave_0, values = (var_2722_cast_fp16, attn_output_41_cast_fp16))[name = string("attn_output_43_cast_fp16")];
            tensor<int32, [4]> var_2740_perm_0 = const()[name = string("op_2740_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_71x = const()[name = string("concat_71x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_2740_cast_fp16 = transpose(perm = var_2740_perm_0, x = attn_output_43_cast_fp16)[name = string("transpose_240")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_47_cast_fp16 = reshape(shape = concat_71x, x = var_2740_cast_fp16)[name = string("attn_output_47_cast_fp16")];
            tensor<int32, [2]> hidden_states_53_strides_0 = const()[name = string("hidden_states_53_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_53_pad_type_0 = const()[name = string("hidden_states_53_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_53_pad_0 = const()[name = string("hidden_states_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_53_dilations_0 = const()[name = string("hidden_states_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_53_groups_0 = const()[name = string("hidden_states_53_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_53_cast_fp16 = conv(dilations = hidden_states_53_dilations_0, groups = hidden_states_53_groups_0, pad = hidden_states_53_pad_0, pad_type = hidden_states_53_pad_type_0, strides = hidden_states_53_strides_0, weight = layers_5_self_attn_o_proj_weight_cast_fp16, x = attn_output_47_cast_fp16)[name = string("hidden_states_53_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_55_cast_fp16 = add(x = hidden_states_49_cast_fp16, y = hidden_states_53_cast_fp16)[name = string("hidden_states_55_cast_fp16")];
            fp16 const_60_promoted_to_fp16 = const()[name = string("const_60_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_2773_cast_fp16 = mul(x = hidden_states_55_cast_fp16, y = const_60_promoted_to_fp16)[name = string("op_2773_cast_fp16")];
            int32 var_2771 = const()[name = string("op_2771"), val = int32(1)];
            bool doubled_45_interleave_0 = const()[name = string("doubled_45_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_45_cast_fp16 = concat(axis = var_2771, interleave = doubled_45_interleave_0, values = (hidden_states_55_cast_fp16, var_2773_cast_fp16))[name = string("doubled_45_cast_fp16")];
            tensor<int32, [1]> out_23_axes_0 = const()[name = string("out_23_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_23_gamma_0_to_fp16 = const()[name = string("out_23_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1284202688)))];
            fp16 var_2783_to_fp16 = const()[name = string("op_2783_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_23_cast_fp16 = layer_norm(axes = out_23_axes_0, epsilon = var_2783_to_fp16, gamma = out_23_gamma_0_to_fp16, x = doubled_45_cast_fp16)[name = string("out_23_cast_fp16")];
            tensor<int32, [2]> var_2794_split_sizes_0 = const()[name = string("op_2794_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2794_axis_0 = const()[name = string("op_2794_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_2794_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_2794_cast_fp16_1 = split(axis = var_2794_axis_0, split_sizes = var_2794_split_sizes_0, x = out_23_cast_fp16)[name = string("op_2794_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_5_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_5_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1284210944)))];
            tensor<int32, [2]> input_11_strides_0 = const()[name = string("input_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_11_pad_type_0 = const()[name = string("input_11_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_11_dilations_0 = const()[name = string("input_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_11_groups_0 = const()[name = string("input_11_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_11_cast_fp16 = conv(dilations = input_11_dilations_0, groups = input_11_groups_0, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = input_11_strides_0, weight = layers_5_mlp_gate_proj_weight_to_fp16, x = var_2794_cast_fp16_0)[name = string("input_11_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_2811_cast_fp16 = silu(x = input_11_cast_fp16)[name = string("op_2811_cast_fp16")];
            tensor<int32, [2]> var_2817_strides_0 = const()[name = string("op_2817_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_2817_pad_type_0 = const()[name = string("op_2817_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_2817_pad_0 = const()[name = string("op_2817_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_2817_dilations_0 = const()[name = string("op_2817_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2817_groups_0 = const()[name = string("op_2817_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_2817_cast_fp16 = conv(dilations = var_2817_dilations_0, groups = var_2817_groups_0, pad = var_2817_pad_0, pad_type = var_2817_pad_type_0, strides = var_2817_strides_0, weight = layers_5_mlp_up_proj_weight_cast_fp16, x = var_2794_cast_fp16_0)[name = string("op_2817_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_59_cast_fp16 = mul(x = var_2811_cast_fp16, y = var_2817_cast_fp16)[name = string("x_59_cast_fp16")];
            tensor<int32, [2]> hidden_states_57_strides_0 = const()[name = string("hidden_states_57_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_57_pad_type_0 = const()[name = string("hidden_states_57_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_57_pad_0 = const()[name = string("hidden_states_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_57_dilations_0 = const()[name = string("hidden_states_57_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_57_groups_0 = const()[name = string("hidden_states_57_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_57_cast_fp16 = conv(dilations = hidden_states_57_dilations_0, groups = hidden_states_57_groups_0, pad = hidden_states_57_pad_0, pad_type = hidden_states_57_pad_type_0, strides = hidden_states_57_strides_0, weight = layers_5_mlp_down_proj_weight_cast_fp16, x = x_59_cast_fp16)[name = string("hidden_states_57_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_59_cast_fp16 = add(x = hidden_states_55_cast_fp16, y = hidden_states_57_cast_fp16)[name = string("hidden_states_59_cast_fp16")];
            fp16 const_62_promoted_to_fp16 = const()[name = string("const_62_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_2835_cast_fp16 = mul(x = hidden_states_59_cast_fp16, y = const_62_promoted_to_fp16)[name = string("op_2835_cast_fp16")];
            int32 var_2833 = const()[name = string("op_2833"), val = int32(1)];
            bool doubled_49_interleave_0 = const()[name = string("doubled_49_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_49_cast_fp16 = concat(axis = var_2833, interleave = doubled_49_interleave_0, values = (hidden_states_59_cast_fp16, var_2835_cast_fp16))[name = string("doubled_49_cast_fp16")];
            tensor<int32, [1]> out_25_axes_0 = const()[name = string("out_25_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_25_gamma_0_to_fp16 = const()[name = string("out_25_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1309376832)))];
            fp16 var_2845_to_fp16 = const()[name = string("op_2845_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_25_cast_fp16 = layer_norm(axes = out_25_axes_0, epsilon = var_2845_to_fp16, gamma = out_25_gamma_0_to_fp16, x = doubled_49_cast_fp16)[name = string("out_25_cast_fp16")];
            tensor<int32, [2]> var_2856_split_sizes_0 = const()[name = string("op_2856_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2856_axis_0 = const()[name = string("op_2856_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_2856_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_2856_cast_fp16_1 = split(axis = var_2856_axis_0, split_sizes = var_2856_split_sizes_0, x = out_25_cast_fp16)[name = string("op_2856_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_6_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_6_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1309385088)))];
            tensor<int32, [2]> query_states_37_strides_0 = const()[name = string("query_states_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_37_pad_type_0 = const()[name = string("query_states_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_37_pad_0 = const()[name = string("query_states_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_37_dilations_0 = const()[name = string("query_states_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_37_groups_0 = const()[name = string("query_states_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_37_cast_fp16 = conv(dilations = query_states_37_dilations_0, groups = query_states_37_groups_0, pad = query_states_37_pad_0, pad_type = query_states_37_pad_type_0, strides = query_states_37_strides_0, weight = layers_6_self_attn_q_proj_weight_to_fp16, x = var_2856_cast_fp16_0)[name = string("query_states_37_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_6_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_6_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1317773760)))];
            tensor<int32, [2]> key_states_61_strides_0 = const()[name = string("key_states_61_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_61_pad_type_0 = const()[name = string("key_states_61_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_61_pad_0 = const()[name = string("key_states_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_61_dilations_0 = const()[name = string("key_states_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_61_groups_0 = const()[name = string("key_states_61_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_61_cast_fp16 = conv(dilations = key_states_61_dilations_0, groups = key_states_61_groups_0, pad = key_states_61_pad_0, pad_type = key_states_61_pad_type_0, strides = key_states_61_strides_0, weight = layers_6_self_attn_k_proj_weight_to_fp16, x = var_2856_cast_fp16_0)[name = string("key_states_61_cast_fp16")];
            tensor<int32, [2]> value_states_37_strides_0 = const()[name = string("value_states_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_37_pad_type_0 = const()[name = string("value_states_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_37_pad_0 = const()[name = string("value_states_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_37_dilations_0 = const()[name = string("value_states_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_37_groups_0 = const()[name = string("value_states_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_37_cast_fp16 = conv(dilations = value_states_37_dilations_0, groups = value_states_37_groups_0, pad = value_states_37_pad_0, pad_type = value_states_37_pad_type_0, strides = value_states_37_strides_0, weight = layers_6_self_attn_v_proj_weight_cast_fp16, x = var_2856_cast_fp16_0)[name = string("value_states_37_cast_fp16")];
            tensor<int32, [4]> concat_72x = const()[name = string("concat_72x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_61_cast_fp16 = reshape(shape = concat_72x, x = query_states_37_cast_fp16)[name = string("x_61_cast_fp16")];
            tensor<int32, [4]> concat_73x = const()[name = string("concat_73x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_2913_cast_fp16 = reshape(shape = concat_73x, x = key_states_61_cast_fp16)[name = string("op_2913_cast_fp16")];
            tensor<int32, [4]> concat_74x = const()[name = string("concat_74x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_2920_cast_fp16 = reshape(shape = concat_74x, x = value_states_37_cast_fp16)[name = string("op_2920_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_2924_cast_fp16 = mul(x = x_61_cast_fp16, y = var_859_cast_fp16)[name = string("op_2924_cast_fp16")];
            tensor<int32, [2]> var_2925_split_sizes_0 = const()[name = string("op_2925_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2925_axis_0 = const()[name = string("op_2925_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_2925_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_2925_cast_fp16_1 = split(axis = var_2925_axis_0, split_sizes = var_2925_split_sizes_0, x = x_61_cast_fp16)[name = string("op_2925_cast_fp16")];
            fp16 const_64_promoted_to_fp16 = const()[name = string("const_64_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_2927_cast_fp16 = mul(x = var_2925_cast_fp16_1, y = const_64_promoted_to_fp16)[name = string("op_2927_cast_fp16")];
            int32 var_2929 = const()[name = string("op_2929"), val = int32(-2)];
            bool var_2930_interleave_0 = const()[name = string("op_2930_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_2930_cast_fp16 = concat(axis = var_2929, interleave = var_2930_interleave_0, values = (var_2927_cast_fp16, var_2925_cast_fp16_0))[name = string("op_2930_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_2931_cast_fp16 = mul(x = var_2930_cast_fp16, y = var_866_cast_fp16)[name = string("op_2931_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_39_cast_fp16 = add(x = var_2924_cast_fp16, y = var_2931_cast_fp16)[name = string("query_states_39_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_2937_cast_fp16 = mul(x = var_2913_cast_fp16, y = var_859_cast_fp16)[name = string("op_2937_cast_fp16")];
            tensor<int32, [2]> var_2938_split_sizes_0 = const()[name = string("op_2938_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2938_axis_0 = const()[name = string("op_2938_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_2938_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_2938_cast_fp16_1 = split(axis = var_2938_axis_0, split_sizes = var_2938_split_sizes_0, x = var_2913_cast_fp16)[name = string("op_2938_cast_fp16")];
            fp16 const_65_promoted_to_fp16 = const()[name = string("const_65_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_2940_cast_fp16 = mul(x = var_2938_cast_fp16_1, y = const_65_promoted_to_fp16)[name = string("op_2940_cast_fp16")];
            int32 var_2942 = const()[name = string("op_2942"), val = int32(-2)];
            bool var_2943_interleave_0 = const()[name = string("op_2943_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_2943_cast_fp16 = concat(axis = var_2942, interleave = var_2943_interleave_0, values = (var_2940_cast_fp16, var_2938_cast_fp16_0))[name = string("op_2943_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_2944_cast_fp16 = mul(x = var_2943_cast_fp16, y = var_866_cast_fp16)[name = string("op_2944_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_65_cast_fp16 = add(x = var_2937_cast_fp16, y = var_2944_cast_fp16)[name = string("key_states_65_cast_fp16")];
            tensor<int32, [1]> expand_dims_72 = const()[name = string("expand_dims_72"), val = tensor<int32, [1]>([6])];
            tensor<int32, [1]> expand_dims_73 = const()[name = string("expand_dims_73"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_75 = const()[name = string("expand_dims_75"), val = tensor<int32, [1]>([0])];
            int32 concat_77_axis_0 = const()[name = string("concat_77_axis_0"), val = int32(0)];
            bool concat_77_interleave_0 = const()[name = string("concat_77_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_77 = concat(axis = concat_77_axis_0, interleave = concat_77_interleave_0, values = (expand_dims_72, expand_dims_73, position_id, expand_dims_75))[name = string("concat_77")];
            tensor<int32, [1]> expand_dims_76 = const()[name = string("expand_dims_76"), val = tensor<int32, [1]>([7])];
            tensor<int32, [1]> concat_78_values1_0 = const()[name = string("concat_78_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_78_values3_0 = const()[name = string("concat_78_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_78_axis_0 = const()[name = string("concat_78_axis_0"), val = int32(0)];
            bool concat_78_interleave_0 = const()[name = string("concat_78_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_78 = concat(axis = concat_78_axis_0, interleave = concat_78_interleave_0, values = (expand_dims_76, concat_78_values1_0, cache_position_end, concat_78_values3_0))[name = string("concat_78")];
            tensor<int32, [4]> key_states_67_perm_0 = const()[name = string("key_states_67_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_7_stride_0 = const()[name = string("key_cache_internal_tensor_assign_7_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_7_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_7_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_7_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_7_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_7_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_7_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_67_cast_fp16 = transpose(perm = key_states_67_perm_0, x = key_states_65_cast_fp16)[name = string("transpose_239")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_7_cast_fp16 = slice_update(begin = concat_77, begin_mask = key_cache_internal_tensor_assign_7_begin_mask_0, end = concat_78, end_mask = key_cache_internal_tensor_assign_7_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_7_squeeze_mask_0, stride = key_cache_internal_tensor_assign_7_stride_0, update = key_states_67_cast_fp16, x = coreml_update_state_122)[name = string("key_cache_internal_tensor_assign_7_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_7_cast_fp16, input = key_cache)[name = string("coreml_update_state_124_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_124 = read_state(input = key_cache)[name = string("coreml_update_state_124")];
            tensor<int32, [4]> value_states_39_perm_0 = const()[name = string("value_states_39_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_7_stride_0 = const()[name = string("value_cache_internal_tensor_assign_7_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_7_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_7_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_7_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_7_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_7_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_7_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_39_cast_fp16 = transpose(perm = value_states_39_perm_0, x = var_2920_cast_fp16)[name = string("transpose_238")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_7_cast_fp16 = slice_update(begin = concat_77, begin_mask = value_cache_internal_tensor_assign_7_begin_mask_0, end = concat_78, end_mask = value_cache_internal_tensor_assign_7_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_7_squeeze_mask_0, stride = value_cache_internal_tensor_assign_7_stride_0, update = value_states_39_cast_fp16, x = coreml_update_state_123)[name = string("value_cache_internal_tensor_assign_7_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_7_cast_fp16, input = value_cache)[name = string("coreml_update_state_125_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_125 = read_state(input = value_cache)[name = string("coreml_update_state_125")];
            tensor<int32, [4]> var_3014_begin_0 = const()[name = string("op_3014_begin_0"), val = tensor<int32, [4]>([6, 0, 0, 0])];
            tensor<int32, [4]> var_3014_end_0 = const()[name = string("op_3014_end_0"), val = tensor<int32, [4]>([7, 2, 2048, 128])];
            tensor<bool, [4]> var_3014_end_mask_0 = const()[name = string("op_3014_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3014_cast_fp16 = slice_by_index(begin = var_3014_begin_0, end = var_3014_end_0, end_mask = var_3014_end_mask_0, x = coreml_update_state_124)[name = string("op_3014_cast_fp16")];
            tensor<int32, [2]> tile_12 = const()[name = string("tile_12"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3017_axis_0 = const()[name = string("op_3017_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3017_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3017_cast_fp16_1 = split(axis = var_3017_axis_0, split_sizes = tile_12, x = var_3014_cast_fp16)[name = string("op_3017_cast_fp16")];
            tensor<int32, [4]> var_3024_begin_0 = const()[name = string("op_3024_begin_0"), val = tensor<int32, [4]>([6, 0, 0, 0])];
            tensor<int32, [4]> var_3024_end_0 = const()[name = string("op_3024_end_0"), val = tensor<int32, [4]>([7, 2, 2048, 128])];
            tensor<bool, [4]> var_3024_end_mask_0 = const()[name = string("op_3024_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3024_cast_fp16 = slice_by_index(begin = var_3024_begin_0, end = var_3024_end_0, end_mask = var_3024_end_mask_0, x = coreml_update_state_125)[name = string("op_3024_cast_fp16")];
            tensor<int32, [2]> tile_13 = const()[name = string("tile_13"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3027_axis_0 = const()[name = string("op_3027_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3027_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3027_cast_fp16_1 = split(axis = var_3027_axis_0, split_sizes = tile_13, x = var_3024_cast_fp16)[name = string("op_3027_cast_fp16")];
            tensor<int32, [2]> var_3030_split_sizes_0 = const()[name = string("op_3030_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3030_axis_0 = const()[name = string("op_3030_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_3030_0, tensor<fp16, [1, 8, 128, 8]> var_3030_1 = split(axis = var_3030_axis_0, split_sizes = var_3030_split_sizes_0, x = query_states_39_cast_fp16)[name = string("op_3030")];
            bool attn_weights_97_transpose_x_0 = const()[name = string("attn_weights_97_transpose_x_0"), val = bool(false)];
            bool attn_weights_97_transpose_y_0 = const()[name = string("attn_weights_97_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_97_cast_fp16 = matmul(transpose_x = attn_weights_97_transpose_x_0, transpose_y = attn_weights_97_transpose_y_0, x = var_3017_cast_fp16_0, y = var_3030_0)[name = string("attn_weights_97_cast_fp16")];
            fp16 var_3033_to_fp16 = const()[name = string("op_3033_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_99_cast_fp16 = mul(x = attn_weights_97_cast_fp16, y = var_3033_to_fp16)[name = string("attn_weights_99_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_101_cast_fp16 = add(x = attn_weights_99_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_101_cast_fp16")];
            int32 var_3037 = const()[name = string("op_3037"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_103_cast_fp16 = softmax(axis = var_3037, x = attn_weights_101_cast_fp16)[name = string("attn_weights_103_cast_fp16")];
            bool var_3043_transpose_x_1 = const()[name = string("op_3043_transpose_x_1"), val = bool(true)];
            bool var_3043_transpose_y_1 = const()[name = string("op_3043_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_3043_cast_fp16 = matmul(transpose_x = var_3043_transpose_x_1, transpose_y = var_3043_transpose_y_1, x = attn_weights_103_cast_fp16, y = var_3027_cast_fp16_0)[name = string("op_3043_cast_fp16")];
            bool attn_weights_105_transpose_x_0 = const()[name = string("attn_weights_105_transpose_x_0"), val = bool(false)];
            bool attn_weights_105_transpose_y_0 = const()[name = string("attn_weights_105_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_105_cast_fp16 = matmul(transpose_x = attn_weights_105_transpose_x_0, transpose_y = attn_weights_105_transpose_y_0, x = var_3017_cast_fp16_1, y = var_3030_1)[name = string("attn_weights_105_cast_fp16")];
            fp16 var_3045_to_fp16 = const()[name = string("op_3045_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_107_cast_fp16 = mul(x = attn_weights_105_cast_fp16, y = var_3045_to_fp16)[name = string("attn_weights_107_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_109_cast_fp16 = add(x = attn_weights_107_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_109_cast_fp16")];
            int32 var_3049 = const()[name = string("op_3049"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_111_cast_fp16 = softmax(axis = var_3049, x = attn_weights_109_cast_fp16)[name = string("attn_weights_111_cast_fp16")];
            bool attn_output_49_transpose_x_1 = const()[name = string("attn_output_49_transpose_x_1"), val = bool(true)];
            bool attn_output_49_transpose_y_1 = const()[name = string("attn_output_49_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_49_cast_fp16 = matmul(transpose_x = attn_output_49_transpose_x_1, transpose_y = attn_output_49_transpose_y_1, x = attn_weights_111_cast_fp16, y = var_3027_cast_fp16_1)[name = string("attn_output_49_cast_fp16")];
            int32 var_3057 = const()[name = string("op_3057"), val = int32(1)];
            bool attn_output_51_interleave_0 = const()[name = string("attn_output_51_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_51_cast_fp16 = concat(axis = var_3057, interleave = attn_output_51_interleave_0, values = (var_3043_cast_fp16, attn_output_49_cast_fp16))[name = string("attn_output_51_cast_fp16")];
            tensor<int32, [4]> var_3061_perm_0 = const()[name = string("op_3061_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_83x = const()[name = string("concat_83x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_3061_cast_fp16 = transpose(perm = var_3061_perm_0, x = attn_output_51_cast_fp16)[name = string("transpose_237")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_55_cast_fp16 = reshape(shape = concat_83x, x = var_3061_cast_fp16)[name = string("attn_output_55_cast_fp16")];
            tensor<int32, [2]> hidden_states_63_strides_0 = const()[name = string("hidden_states_63_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_63_pad_type_0 = const()[name = string("hidden_states_63_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_63_pad_0 = const()[name = string("hidden_states_63_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_63_dilations_0 = const()[name = string("hidden_states_63_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_63_groups_0 = const()[name = string("hidden_states_63_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_63_cast_fp16 = conv(dilations = hidden_states_63_dilations_0, groups = hidden_states_63_groups_0, pad = hidden_states_63_pad_0, pad_type = hidden_states_63_pad_type_0, strides = hidden_states_63_strides_0, weight = layers_6_self_attn_o_proj_weight_cast_fp16, x = attn_output_55_cast_fp16)[name = string("hidden_states_63_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_65_cast_fp16 = add(x = hidden_states_59_cast_fp16, y = hidden_states_63_cast_fp16)[name = string("hidden_states_65_cast_fp16")];
            fp16 const_70_promoted_to_fp16 = const()[name = string("const_70_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_3094_cast_fp16 = mul(x = hidden_states_65_cast_fp16, y = const_70_promoted_to_fp16)[name = string("op_3094_cast_fp16")];
            int32 var_3092 = const()[name = string("op_3092"), val = int32(1)];
            bool doubled_53_interleave_0 = const()[name = string("doubled_53_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_53_cast_fp16 = concat(axis = var_3092, interleave = doubled_53_interleave_0, values = (hidden_states_65_cast_fp16, var_3094_cast_fp16))[name = string("doubled_53_cast_fp16")];
            tensor<int32, [1]> out_27_axes_0 = const()[name = string("out_27_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_27_gamma_0_to_fp16 = const()[name = string("out_27_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1318822400)))];
            fp16 var_3104_to_fp16 = const()[name = string("op_3104_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_27_cast_fp16 = layer_norm(axes = out_27_axes_0, epsilon = var_3104_to_fp16, gamma = out_27_gamma_0_to_fp16, x = doubled_53_cast_fp16)[name = string("out_27_cast_fp16")];
            tensor<int32, [2]> var_3115_split_sizes_0 = const()[name = string("op_3115_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3115_axis_0 = const()[name = string("op_3115_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_3115_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_3115_cast_fp16_1 = split(axis = var_3115_axis_0, split_sizes = var_3115_split_sizes_0, x = out_27_cast_fp16)[name = string("op_3115_cast_fp16")];
            tensor<int32, [2]> input_13_strides_0 = const()[name = string("input_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_13_pad_type_0 = const()[name = string("input_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_13_pad_0 = const()[name = string("input_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_13_dilations_0 = const()[name = string("input_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_13_groups_0 = const()[name = string("input_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_13_cast_fp16 = conv(dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = layers_6_mlp_gate_proj_weight_cast_fp16, x = var_3115_cast_fp16_0)[name = string("input_13_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_3132_cast_fp16 = silu(x = input_13_cast_fp16)[name = string("op_3132_cast_fp16")];
            tensor<int32, [2]> var_3138_strides_0 = const()[name = string("op_3138_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_3138_pad_type_0 = const()[name = string("op_3138_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_3138_pad_0 = const()[name = string("op_3138_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_3138_dilations_0 = const()[name = string("op_3138_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3138_groups_0 = const()[name = string("op_3138_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_3138_cast_fp16 = conv(dilations = var_3138_dilations_0, groups = var_3138_groups_0, pad = var_3138_pad_0, pad_type = var_3138_pad_type_0, strides = var_3138_strides_0, weight = layers_6_mlp_up_proj_weight_cast_fp16, x = var_3115_cast_fp16_0)[name = string("op_3138_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_69_cast_fp16 = mul(x = var_3132_cast_fp16, y = var_3138_cast_fp16)[name = string("x_69_cast_fp16")];
            tensor<int32, [2]> hidden_states_67_strides_0 = const()[name = string("hidden_states_67_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_67_pad_type_0 = const()[name = string("hidden_states_67_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_67_pad_0 = const()[name = string("hidden_states_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_67_dilations_0 = const()[name = string("hidden_states_67_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_67_groups_0 = const()[name = string("hidden_states_67_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_67_cast_fp16 = conv(dilations = hidden_states_67_dilations_0, groups = hidden_states_67_groups_0, pad = hidden_states_67_pad_0, pad_type = hidden_states_67_pad_type_0, strides = hidden_states_67_strides_0, weight = layers_6_mlp_down_proj_weight_cast_fp16, x = x_69_cast_fp16)[name = string("hidden_states_67_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_69_cast_fp16 = add(x = hidden_states_65_cast_fp16, y = hidden_states_67_cast_fp16)[name = string("hidden_states_69_cast_fp16")];
            fp16 const_72_promoted_to_fp16 = const()[name = string("const_72_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_3156_cast_fp16 = mul(x = hidden_states_69_cast_fp16, y = const_72_promoted_to_fp16)[name = string("op_3156_cast_fp16")];
            int32 var_3154 = const()[name = string("op_3154"), val = int32(1)];
            bool doubled_57_interleave_0 = const()[name = string("doubled_57_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_57_cast_fp16 = concat(axis = var_3154, interleave = doubled_57_interleave_0, values = (hidden_states_69_cast_fp16, var_3156_cast_fp16))[name = string("doubled_57_cast_fp16")];
            tensor<int32, [1]> out_29_axes_0 = const()[name = string("out_29_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_29_gamma_0_to_fp16 = const()[name = string("out_29_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1318830656)))];
            fp16 var_3166_to_fp16 = const()[name = string("op_3166_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_29_cast_fp16 = layer_norm(axes = out_29_axes_0, epsilon = var_3166_to_fp16, gamma = out_29_gamma_0_to_fp16, x = doubled_57_cast_fp16)[name = string("out_29_cast_fp16")];
            tensor<int32, [2]> var_3177_split_sizes_0 = const()[name = string("op_3177_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3177_axis_0 = const()[name = string("op_3177_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_3177_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_3177_cast_fp16_1 = split(axis = var_3177_axis_0, split_sizes = var_3177_split_sizes_0, x = out_29_cast_fp16)[name = string("op_3177_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_7_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_7_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1318838912)))];
            tensor<int32, [2]> query_states_43_strides_0 = const()[name = string("query_states_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_43_pad_type_0 = const()[name = string("query_states_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_43_pad_0 = const()[name = string("query_states_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_43_dilations_0 = const()[name = string("query_states_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_43_groups_0 = const()[name = string("query_states_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_43_cast_fp16 = conv(dilations = query_states_43_dilations_0, groups = query_states_43_groups_0, pad = query_states_43_pad_0, pad_type = query_states_43_pad_type_0, strides = query_states_43_strides_0, weight = layers_7_self_attn_q_proj_weight_to_fp16, x = var_3177_cast_fp16_0)[name = string("query_states_43_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_7_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_7_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1327227584)))];
            tensor<int32, [2]> key_states_71_strides_0 = const()[name = string("key_states_71_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_71_pad_type_0 = const()[name = string("key_states_71_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_71_pad_0 = const()[name = string("key_states_71_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_71_dilations_0 = const()[name = string("key_states_71_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_71_groups_0 = const()[name = string("key_states_71_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_71_cast_fp16 = conv(dilations = key_states_71_dilations_0, groups = key_states_71_groups_0, pad = key_states_71_pad_0, pad_type = key_states_71_pad_type_0, strides = key_states_71_strides_0, weight = layers_7_self_attn_k_proj_weight_to_fp16, x = var_3177_cast_fp16_0)[name = string("key_states_71_cast_fp16")];
            tensor<int32, [2]> value_states_43_strides_0 = const()[name = string("value_states_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_43_pad_type_0 = const()[name = string("value_states_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_43_pad_0 = const()[name = string("value_states_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_43_dilations_0 = const()[name = string("value_states_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_43_groups_0 = const()[name = string("value_states_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_43_cast_fp16 = conv(dilations = value_states_43_dilations_0, groups = value_states_43_groups_0, pad = value_states_43_pad_0, pad_type = value_states_43_pad_type_0, strides = value_states_43_strides_0, weight = layers_7_self_attn_v_proj_weight_cast_fp16, x = var_3177_cast_fp16_0)[name = string("value_states_43_cast_fp16")];
            tensor<int32, [4]> concat_84x = const()[name = string("concat_84x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_71_cast_fp16 = reshape(shape = concat_84x, x = query_states_43_cast_fp16)[name = string("x_71_cast_fp16")];
            tensor<int32, [4]> concat_85x = const()[name = string("concat_85x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_3234_cast_fp16 = reshape(shape = concat_85x, x = key_states_71_cast_fp16)[name = string("op_3234_cast_fp16")];
            tensor<int32, [4]> concat_86x = const()[name = string("concat_86x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_3241_cast_fp16 = reshape(shape = concat_86x, x = value_states_43_cast_fp16)[name = string("op_3241_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_3245_cast_fp16 = mul(x = x_71_cast_fp16, y = var_859_cast_fp16)[name = string("op_3245_cast_fp16")];
            tensor<int32, [2]> var_3246_split_sizes_0 = const()[name = string("op_3246_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3246_axis_0 = const()[name = string("op_3246_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_3246_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_3246_cast_fp16_1 = split(axis = var_3246_axis_0, split_sizes = var_3246_split_sizes_0, x = x_71_cast_fp16)[name = string("op_3246_cast_fp16")];
            fp16 const_74_promoted_to_fp16 = const()[name = string("const_74_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_3248_cast_fp16 = mul(x = var_3246_cast_fp16_1, y = const_74_promoted_to_fp16)[name = string("op_3248_cast_fp16")];
            int32 var_3250 = const()[name = string("op_3250"), val = int32(-2)];
            bool var_3251_interleave_0 = const()[name = string("op_3251_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_3251_cast_fp16 = concat(axis = var_3250, interleave = var_3251_interleave_0, values = (var_3248_cast_fp16, var_3246_cast_fp16_0))[name = string("op_3251_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_3252_cast_fp16 = mul(x = var_3251_cast_fp16, y = var_866_cast_fp16)[name = string("op_3252_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_45_cast_fp16 = add(x = var_3245_cast_fp16, y = var_3252_cast_fp16)[name = string("query_states_45_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_3258_cast_fp16 = mul(x = var_3234_cast_fp16, y = var_859_cast_fp16)[name = string("op_3258_cast_fp16")];
            tensor<int32, [2]> var_3259_split_sizes_0 = const()[name = string("op_3259_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3259_axis_0 = const()[name = string("op_3259_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_3259_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_3259_cast_fp16_1 = split(axis = var_3259_axis_0, split_sizes = var_3259_split_sizes_0, x = var_3234_cast_fp16)[name = string("op_3259_cast_fp16")];
            fp16 const_75_promoted_to_fp16 = const()[name = string("const_75_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_3261_cast_fp16 = mul(x = var_3259_cast_fp16_1, y = const_75_promoted_to_fp16)[name = string("op_3261_cast_fp16")];
            int32 var_3263 = const()[name = string("op_3263"), val = int32(-2)];
            bool var_3264_interleave_0 = const()[name = string("op_3264_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_3264_cast_fp16 = concat(axis = var_3263, interleave = var_3264_interleave_0, values = (var_3261_cast_fp16, var_3259_cast_fp16_0))[name = string("op_3264_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_3265_cast_fp16 = mul(x = var_3264_cast_fp16, y = var_866_cast_fp16)[name = string("op_3265_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_75_cast_fp16 = add(x = var_3258_cast_fp16, y = var_3265_cast_fp16)[name = string("key_states_75_cast_fp16")];
            tensor<int32, [1]> expand_dims_84 = const()[name = string("expand_dims_84"), val = tensor<int32, [1]>([7])];
            tensor<int32, [1]> expand_dims_85 = const()[name = string("expand_dims_85"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_87 = const()[name = string("expand_dims_87"), val = tensor<int32, [1]>([0])];
            int32 concat_89_axis_0 = const()[name = string("concat_89_axis_0"), val = int32(0)];
            bool concat_89_interleave_0 = const()[name = string("concat_89_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_89 = concat(axis = concat_89_axis_0, interleave = concat_89_interleave_0, values = (expand_dims_84, expand_dims_85, position_id, expand_dims_87))[name = string("concat_89")];
            tensor<int32, [1]> expand_dims_88 = const()[name = string("expand_dims_88"), val = tensor<int32, [1]>([8])];
            tensor<int32, [1]> concat_90_values1_0 = const()[name = string("concat_90_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_90_values3_0 = const()[name = string("concat_90_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_90_axis_0 = const()[name = string("concat_90_axis_0"), val = int32(0)];
            bool concat_90_interleave_0 = const()[name = string("concat_90_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_90 = concat(axis = concat_90_axis_0, interleave = concat_90_interleave_0, values = (expand_dims_88, concat_90_values1_0, cache_position_end, concat_90_values3_0))[name = string("concat_90")];
            tensor<int32, [4]> key_states_77_perm_0 = const()[name = string("key_states_77_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_8_stride_0 = const()[name = string("key_cache_internal_tensor_assign_8_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_8_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_8_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_8_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_8_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_8_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_8_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_77_cast_fp16 = transpose(perm = key_states_77_perm_0, x = key_states_75_cast_fp16)[name = string("transpose_236")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_8_cast_fp16 = slice_update(begin = concat_89, begin_mask = key_cache_internal_tensor_assign_8_begin_mask_0, end = concat_90, end_mask = key_cache_internal_tensor_assign_8_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_8_squeeze_mask_0, stride = key_cache_internal_tensor_assign_8_stride_0, update = key_states_77_cast_fp16, x = coreml_update_state_124)[name = string("key_cache_internal_tensor_assign_8_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_8_cast_fp16, input = key_cache)[name = string("coreml_update_state_126_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_126 = read_state(input = key_cache)[name = string("coreml_update_state_126")];
            tensor<int32, [4]> value_states_45_perm_0 = const()[name = string("value_states_45_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_8_stride_0 = const()[name = string("value_cache_internal_tensor_assign_8_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_8_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_8_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_8_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_8_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_8_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_8_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_45_cast_fp16 = transpose(perm = value_states_45_perm_0, x = var_3241_cast_fp16)[name = string("transpose_235")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_8_cast_fp16 = slice_update(begin = concat_89, begin_mask = value_cache_internal_tensor_assign_8_begin_mask_0, end = concat_90, end_mask = value_cache_internal_tensor_assign_8_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_8_squeeze_mask_0, stride = value_cache_internal_tensor_assign_8_stride_0, update = value_states_45_cast_fp16, x = coreml_update_state_125)[name = string("value_cache_internal_tensor_assign_8_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_8_cast_fp16, input = value_cache)[name = string("coreml_update_state_127_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_127 = read_state(input = value_cache)[name = string("coreml_update_state_127")];
            tensor<int32, [4]> var_3335_begin_0 = const()[name = string("op_3335_begin_0"), val = tensor<int32, [4]>([7, 0, 0, 0])];
            tensor<int32, [4]> var_3335_end_0 = const()[name = string("op_3335_end_0"), val = tensor<int32, [4]>([8, 2, 2048, 128])];
            tensor<bool, [4]> var_3335_end_mask_0 = const()[name = string("op_3335_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3335_cast_fp16 = slice_by_index(begin = var_3335_begin_0, end = var_3335_end_0, end_mask = var_3335_end_mask_0, x = coreml_update_state_126)[name = string("op_3335_cast_fp16")];
            tensor<int32, [2]> tile_14 = const()[name = string("tile_14"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3338_axis_0 = const()[name = string("op_3338_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3338_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3338_cast_fp16_1 = split(axis = var_3338_axis_0, split_sizes = tile_14, x = var_3335_cast_fp16)[name = string("op_3338_cast_fp16")];
            tensor<int32, [4]> var_3345_begin_0 = const()[name = string("op_3345_begin_0"), val = tensor<int32, [4]>([7, 0, 0, 0])];
            tensor<int32, [4]> var_3345_end_0 = const()[name = string("op_3345_end_0"), val = tensor<int32, [4]>([8, 2, 2048, 128])];
            tensor<bool, [4]> var_3345_end_mask_0 = const()[name = string("op_3345_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3345_cast_fp16 = slice_by_index(begin = var_3345_begin_0, end = var_3345_end_0, end_mask = var_3345_end_mask_0, x = coreml_update_state_127)[name = string("op_3345_cast_fp16")];
            tensor<int32, [2]> tile_15 = const()[name = string("tile_15"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3348_axis_0 = const()[name = string("op_3348_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3348_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3348_cast_fp16_1 = split(axis = var_3348_axis_0, split_sizes = tile_15, x = var_3345_cast_fp16)[name = string("op_3348_cast_fp16")];
            tensor<int32, [2]> var_3351_split_sizes_0 = const()[name = string("op_3351_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3351_axis_0 = const()[name = string("op_3351_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_3351_0, tensor<fp16, [1, 8, 128, 8]> var_3351_1 = split(axis = var_3351_axis_0, split_sizes = var_3351_split_sizes_0, x = query_states_45_cast_fp16)[name = string("op_3351")];
            bool attn_weights_113_transpose_x_0 = const()[name = string("attn_weights_113_transpose_x_0"), val = bool(false)];
            bool attn_weights_113_transpose_y_0 = const()[name = string("attn_weights_113_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_113_cast_fp16 = matmul(transpose_x = attn_weights_113_transpose_x_0, transpose_y = attn_weights_113_transpose_y_0, x = var_3338_cast_fp16_0, y = var_3351_0)[name = string("attn_weights_113_cast_fp16")];
            fp16 var_3354_to_fp16 = const()[name = string("op_3354_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_115_cast_fp16 = mul(x = attn_weights_113_cast_fp16, y = var_3354_to_fp16)[name = string("attn_weights_115_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_117_cast_fp16 = add(x = attn_weights_115_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_117_cast_fp16")];
            int32 var_3358 = const()[name = string("op_3358"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_119_cast_fp16 = softmax(axis = var_3358, x = attn_weights_117_cast_fp16)[name = string("attn_weights_119_cast_fp16")];
            bool var_3364_transpose_x_1 = const()[name = string("op_3364_transpose_x_1"), val = bool(true)];
            bool var_3364_transpose_y_1 = const()[name = string("op_3364_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_3364_cast_fp16 = matmul(transpose_x = var_3364_transpose_x_1, transpose_y = var_3364_transpose_y_1, x = attn_weights_119_cast_fp16, y = var_3348_cast_fp16_0)[name = string("op_3364_cast_fp16")];
            bool attn_weights_121_transpose_x_0 = const()[name = string("attn_weights_121_transpose_x_0"), val = bool(false)];
            bool attn_weights_121_transpose_y_0 = const()[name = string("attn_weights_121_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_121_cast_fp16 = matmul(transpose_x = attn_weights_121_transpose_x_0, transpose_y = attn_weights_121_transpose_y_0, x = var_3338_cast_fp16_1, y = var_3351_1)[name = string("attn_weights_121_cast_fp16")];
            fp16 var_3366_to_fp16 = const()[name = string("op_3366_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_123_cast_fp16 = mul(x = attn_weights_121_cast_fp16, y = var_3366_to_fp16)[name = string("attn_weights_123_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_125_cast_fp16 = add(x = attn_weights_123_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_125_cast_fp16")];
            int32 var_3370 = const()[name = string("op_3370"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_127_cast_fp16 = softmax(axis = var_3370, x = attn_weights_125_cast_fp16)[name = string("attn_weights_127_cast_fp16")];
            bool attn_output_57_transpose_x_1 = const()[name = string("attn_output_57_transpose_x_1"), val = bool(true)];
            bool attn_output_57_transpose_y_1 = const()[name = string("attn_output_57_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_57_cast_fp16 = matmul(transpose_x = attn_output_57_transpose_x_1, transpose_y = attn_output_57_transpose_y_1, x = attn_weights_127_cast_fp16, y = var_3348_cast_fp16_1)[name = string("attn_output_57_cast_fp16")];
            int32 var_3378 = const()[name = string("op_3378"), val = int32(1)];
            bool attn_output_59_interleave_0 = const()[name = string("attn_output_59_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_59_cast_fp16 = concat(axis = var_3378, interleave = attn_output_59_interleave_0, values = (var_3364_cast_fp16, attn_output_57_cast_fp16))[name = string("attn_output_59_cast_fp16")];
            tensor<int32, [4]> var_3382_perm_0 = const()[name = string("op_3382_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_95x = const()[name = string("concat_95x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_3382_cast_fp16 = transpose(perm = var_3382_perm_0, x = attn_output_59_cast_fp16)[name = string("transpose_234")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_63_cast_fp16 = reshape(shape = concat_95x, x = var_3382_cast_fp16)[name = string("attn_output_63_cast_fp16")];
            tensor<int32, [2]> hidden_states_73_strides_0 = const()[name = string("hidden_states_73_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_73_pad_type_0 = const()[name = string("hidden_states_73_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_73_pad_0 = const()[name = string("hidden_states_73_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_73_dilations_0 = const()[name = string("hidden_states_73_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_73_groups_0 = const()[name = string("hidden_states_73_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_73_cast_fp16 = conv(dilations = hidden_states_73_dilations_0, groups = hidden_states_73_groups_0, pad = hidden_states_73_pad_0, pad_type = hidden_states_73_pad_type_0, strides = hidden_states_73_strides_0, weight = layers_7_self_attn_o_proj_weight_cast_fp16, x = attn_output_63_cast_fp16)[name = string("hidden_states_73_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_75_cast_fp16 = add(x = hidden_states_69_cast_fp16, y = hidden_states_73_cast_fp16)[name = string("hidden_states_75_cast_fp16")];
            fp16 const_80_promoted_to_fp16 = const()[name = string("const_80_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_3415_cast_fp16 = mul(x = hidden_states_75_cast_fp16, y = const_80_promoted_to_fp16)[name = string("op_3415_cast_fp16")];
            int32 var_3413 = const()[name = string("op_3413"), val = int32(1)];
            bool doubled_61_interleave_0 = const()[name = string("doubled_61_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_61_cast_fp16 = concat(axis = var_3413, interleave = doubled_61_interleave_0, values = (hidden_states_75_cast_fp16, var_3415_cast_fp16))[name = string("doubled_61_cast_fp16")];
            tensor<int32, [1]> out_31_axes_0 = const()[name = string("out_31_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_31_gamma_0_to_fp16 = const()[name = string("out_31_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1328276224)))];
            fp16 var_3425_to_fp16 = const()[name = string("op_3425_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_31_cast_fp16 = layer_norm(axes = out_31_axes_0, epsilon = var_3425_to_fp16, gamma = out_31_gamma_0_to_fp16, x = doubled_61_cast_fp16)[name = string("out_31_cast_fp16")];
            tensor<int32, [2]> var_3436_split_sizes_0 = const()[name = string("op_3436_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3436_axis_0 = const()[name = string("op_3436_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_3436_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_3436_cast_fp16_1 = split(axis = var_3436_axis_0, split_sizes = var_3436_split_sizes_0, x = out_31_cast_fp16)[name = string("op_3436_cast_fp16")];
            tensor<int32, [2]> input_15_strides_0 = const()[name = string("input_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_15_pad_type_0 = const()[name = string("input_15_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_15_dilations_0 = const()[name = string("input_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_15_groups_0 = const()[name = string("input_15_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_15_cast_fp16 = conv(dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = layers_7_mlp_gate_proj_weight_cast_fp16, x = var_3436_cast_fp16_0)[name = string("input_15_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_3453_cast_fp16 = silu(x = input_15_cast_fp16)[name = string("op_3453_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_7_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_7_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1328284480)))];
            tensor<int32, [2]> var_3459_strides_0 = const()[name = string("op_3459_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_3459_pad_type_0 = const()[name = string("op_3459_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_3459_pad_0 = const()[name = string("op_3459_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_3459_dilations_0 = const()[name = string("op_3459_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3459_groups_0 = const()[name = string("op_3459_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_3459_cast_fp16 = conv(dilations = var_3459_dilations_0, groups = var_3459_groups_0, pad = var_3459_pad_0, pad_type = var_3459_pad_type_0, strides = var_3459_strides_0, weight = layers_7_mlp_up_proj_weight_to_fp16, x = var_3436_cast_fp16_0)[name = string("op_3459_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_79_cast_fp16 = mul(x = var_3453_cast_fp16, y = var_3459_cast_fp16)[name = string("x_79_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_7_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_7_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353450368)))];
            tensor<int32, [2]> hidden_states_77_strides_0 = const()[name = string("hidden_states_77_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_77_pad_type_0 = const()[name = string("hidden_states_77_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_77_pad_0 = const()[name = string("hidden_states_77_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_77_dilations_0 = const()[name = string("hidden_states_77_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_77_groups_0 = const()[name = string("hidden_states_77_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_77_cast_fp16 = conv(dilations = hidden_states_77_dilations_0, groups = hidden_states_77_groups_0, pad = hidden_states_77_pad_0, pad_type = hidden_states_77_pad_type_0, strides = hidden_states_77_strides_0, weight = layers_7_mlp_down_proj_weight_to_fp16, x = x_79_cast_fp16)[name = string("hidden_states_77_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_79_cast_fp16 = add(x = hidden_states_75_cast_fp16, y = hidden_states_77_cast_fp16)[name = string("hidden_states_79_cast_fp16")];
            fp16 const_82_promoted_to_fp16 = const()[name = string("const_82_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_3477_cast_fp16 = mul(x = hidden_states_79_cast_fp16, y = const_82_promoted_to_fp16)[name = string("op_3477_cast_fp16")];
            int32 var_3475 = const()[name = string("op_3475"), val = int32(1)];
            bool doubled_65_interleave_0 = const()[name = string("doubled_65_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_65_cast_fp16 = concat(axis = var_3475, interleave = doubled_65_interleave_0, values = (hidden_states_79_cast_fp16, var_3477_cast_fp16))[name = string("doubled_65_cast_fp16")];
            tensor<int32, [1]> out_33_axes_0 = const()[name = string("out_33_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_33_gamma_0_to_fp16 = const()[name = string("out_33_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1378616256)))];
            fp16 var_3487_to_fp16 = const()[name = string("op_3487_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_33_cast_fp16 = layer_norm(axes = out_33_axes_0, epsilon = var_3487_to_fp16, gamma = out_33_gamma_0_to_fp16, x = doubled_65_cast_fp16)[name = string("out_33_cast_fp16")];
            tensor<int32, [2]> var_3498_split_sizes_0 = const()[name = string("op_3498_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3498_axis_0 = const()[name = string("op_3498_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_3498_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_3498_cast_fp16_1 = split(axis = var_3498_axis_0, split_sizes = var_3498_split_sizes_0, x = out_33_cast_fp16)[name = string("op_3498_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_8_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_8_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1378624512)))];
            tensor<int32, [2]> query_states_49_strides_0 = const()[name = string("query_states_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_49_pad_type_0 = const()[name = string("query_states_49_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_49_pad_0 = const()[name = string("query_states_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_49_dilations_0 = const()[name = string("query_states_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_49_groups_0 = const()[name = string("query_states_49_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_49_cast_fp16 = conv(dilations = query_states_49_dilations_0, groups = query_states_49_groups_0, pad = query_states_49_pad_0, pad_type = query_states_49_pad_type_0, strides = query_states_49_strides_0, weight = layers_8_self_attn_q_proj_weight_to_fp16, x = var_3498_cast_fp16_0)[name = string("query_states_49_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_8_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_8_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1387013184)))];
            tensor<int32, [2]> key_states_81_strides_0 = const()[name = string("key_states_81_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_81_pad_type_0 = const()[name = string("key_states_81_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_81_pad_0 = const()[name = string("key_states_81_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_81_dilations_0 = const()[name = string("key_states_81_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_81_groups_0 = const()[name = string("key_states_81_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_81_cast_fp16 = conv(dilations = key_states_81_dilations_0, groups = key_states_81_groups_0, pad = key_states_81_pad_0, pad_type = key_states_81_pad_type_0, strides = key_states_81_strides_0, weight = layers_8_self_attn_k_proj_weight_to_fp16, x = var_3498_cast_fp16_0)[name = string("key_states_81_cast_fp16")];
            tensor<int32, [2]> value_states_49_strides_0 = const()[name = string("value_states_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_49_pad_type_0 = const()[name = string("value_states_49_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_49_pad_0 = const()[name = string("value_states_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_49_dilations_0 = const()[name = string("value_states_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_49_groups_0 = const()[name = string("value_states_49_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_49_cast_fp16 = conv(dilations = value_states_49_dilations_0, groups = value_states_49_groups_0, pad = value_states_49_pad_0, pad_type = value_states_49_pad_type_0, strides = value_states_49_strides_0, weight = layers_8_self_attn_v_proj_weight_cast_fp16, x = var_3498_cast_fp16_0)[name = string("value_states_49_cast_fp16")];
            tensor<int32, [4]> concat_96x = const()[name = string("concat_96x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_81_cast_fp16 = reshape(shape = concat_96x, x = query_states_49_cast_fp16)[name = string("x_81_cast_fp16")];
            tensor<int32, [4]> concat_97x = const()[name = string("concat_97x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_3555_cast_fp16 = reshape(shape = concat_97x, x = key_states_81_cast_fp16)[name = string("op_3555_cast_fp16")];
            tensor<int32, [4]> concat_98x = const()[name = string("concat_98x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_3562_cast_fp16 = reshape(shape = concat_98x, x = value_states_49_cast_fp16)[name = string("op_3562_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_3566_cast_fp16 = mul(x = x_81_cast_fp16, y = var_859_cast_fp16)[name = string("op_3566_cast_fp16")];
            tensor<int32, [2]> var_3567_split_sizes_0 = const()[name = string("op_3567_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3567_axis_0 = const()[name = string("op_3567_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_3567_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_3567_cast_fp16_1 = split(axis = var_3567_axis_0, split_sizes = var_3567_split_sizes_0, x = x_81_cast_fp16)[name = string("op_3567_cast_fp16")];
            fp16 const_84_promoted_to_fp16 = const()[name = string("const_84_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_3569_cast_fp16 = mul(x = var_3567_cast_fp16_1, y = const_84_promoted_to_fp16)[name = string("op_3569_cast_fp16")];
            int32 var_3571 = const()[name = string("op_3571"), val = int32(-2)];
            bool var_3572_interleave_0 = const()[name = string("op_3572_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_3572_cast_fp16 = concat(axis = var_3571, interleave = var_3572_interleave_0, values = (var_3569_cast_fp16, var_3567_cast_fp16_0))[name = string("op_3572_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_3573_cast_fp16 = mul(x = var_3572_cast_fp16, y = var_866_cast_fp16)[name = string("op_3573_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_51_cast_fp16 = add(x = var_3566_cast_fp16, y = var_3573_cast_fp16)[name = string("query_states_51_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_3579_cast_fp16 = mul(x = var_3555_cast_fp16, y = var_859_cast_fp16)[name = string("op_3579_cast_fp16")];
            tensor<int32, [2]> var_3580_split_sizes_0 = const()[name = string("op_3580_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3580_axis_0 = const()[name = string("op_3580_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_3580_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_3580_cast_fp16_1 = split(axis = var_3580_axis_0, split_sizes = var_3580_split_sizes_0, x = var_3555_cast_fp16)[name = string("op_3580_cast_fp16")];
            fp16 const_85_promoted_to_fp16 = const()[name = string("const_85_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_3582_cast_fp16 = mul(x = var_3580_cast_fp16_1, y = const_85_promoted_to_fp16)[name = string("op_3582_cast_fp16")];
            int32 var_3584 = const()[name = string("op_3584"), val = int32(-2)];
            bool var_3585_interleave_0 = const()[name = string("op_3585_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_3585_cast_fp16 = concat(axis = var_3584, interleave = var_3585_interleave_0, values = (var_3582_cast_fp16, var_3580_cast_fp16_0))[name = string("op_3585_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_3586_cast_fp16 = mul(x = var_3585_cast_fp16, y = var_866_cast_fp16)[name = string("op_3586_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_85_cast_fp16 = add(x = var_3579_cast_fp16, y = var_3586_cast_fp16)[name = string("key_states_85_cast_fp16")];
            tensor<int32, [1]> expand_dims_96 = const()[name = string("expand_dims_96"), val = tensor<int32, [1]>([8])];
            tensor<int32, [1]> expand_dims_97 = const()[name = string("expand_dims_97"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_99 = const()[name = string("expand_dims_99"), val = tensor<int32, [1]>([0])];
            int32 concat_101_axis_0 = const()[name = string("concat_101_axis_0"), val = int32(0)];
            bool concat_101_interleave_0 = const()[name = string("concat_101_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_101 = concat(axis = concat_101_axis_0, interleave = concat_101_interleave_0, values = (expand_dims_96, expand_dims_97, position_id, expand_dims_99))[name = string("concat_101")];
            tensor<int32, [1]> expand_dims_100 = const()[name = string("expand_dims_100"), val = tensor<int32, [1]>([9])];
            tensor<int32, [1]> concat_102_values1_0 = const()[name = string("concat_102_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_102_values3_0 = const()[name = string("concat_102_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_102_axis_0 = const()[name = string("concat_102_axis_0"), val = int32(0)];
            bool concat_102_interleave_0 = const()[name = string("concat_102_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_102 = concat(axis = concat_102_axis_0, interleave = concat_102_interleave_0, values = (expand_dims_100, concat_102_values1_0, cache_position_end, concat_102_values3_0))[name = string("concat_102")];
            tensor<int32, [4]> key_states_87_perm_0 = const()[name = string("key_states_87_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_9_stride_0 = const()[name = string("key_cache_internal_tensor_assign_9_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_9_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_9_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_9_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_9_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_9_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_9_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_87_cast_fp16 = transpose(perm = key_states_87_perm_0, x = key_states_85_cast_fp16)[name = string("transpose_233")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_9_cast_fp16 = slice_update(begin = concat_101, begin_mask = key_cache_internal_tensor_assign_9_begin_mask_0, end = concat_102, end_mask = key_cache_internal_tensor_assign_9_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_9_squeeze_mask_0, stride = key_cache_internal_tensor_assign_9_stride_0, update = key_states_87_cast_fp16, x = coreml_update_state_126)[name = string("key_cache_internal_tensor_assign_9_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_9_cast_fp16, input = key_cache)[name = string("coreml_update_state_128_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_128 = read_state(input = key_cache)[name = string("coreml_update_state_128")];
            tensor<int32, [4]> value_states_51_perm_0 = const()[name = string("value_states_51_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_9_stride_0 = const()[name = string("value_cache_internal_tensor_assign_9_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_9_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_9_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_9_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_9_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_9_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_9_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_51_cast_fp16 = transpose(perm = value_states_51_perm_0, x = var_3562_cast_fp16)[name = string("transpose_232")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_9_cast_fp16 = slice_update(begin = concat_101, begin_mask = value_cache_internal_tensor_assign_9_begin_mask_0, end = concat_102, end_mask = value_cache_internal_tensor_assign_9_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_9_squeeze_mask_0, stride = value_cache_internal_tensor_assign_9_stride_0, update = value_states_51_cast_fp16, x = coreml_update_state_127)[name = string("value_cache_internal_tensor_assign_9_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_9_cast_fp16, input = value_cache)[name = string("coreml_update_state_129_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_129 = read_state(input = value_cache)[name = string("coreml_update_state_129")];
            tensor<int32, [4]> var_3656_begin_0 = const()[name = string("op_3656_begin_0"), val = tensor<int32, [4]>([8, 0, 0, 0])];
            tensor<int32, [4]> var_3656_end_0 = const()[name = string("op_3656_end_0"), val = tensor<int32, [4]>([9, 2, 2048, 128])];
            tensor<bool, [4]> var_3656_end_mask_0 = const()[name = string("op_3656_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3656_cast_fp16 = slice_by_index(begin = var_3656_begin_0, end = var_3656_end_0, end_mask = var_3656_end_mask_0, x = coreml_update_state_128)[name = string("op_3656_cast_fp16")];
            tensor<int32, [2]> tile_16 = const()[name = string("tile_16"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3659_axis_0 = const()[name = string("op_3659_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3659_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3659_cast_fp16_1 = split(axis = var_3659_axis_0, split_sizes = tile_16, x = var_3656_cast_fp16)[name = string("op_3659_cast_fp16")];
            tensor<int32, [4]> var_3666_begin_0 = const()[name = string("op_3666_begin_0"), val = tensor<int32, [4]>([8, 0, 0, 0])];
            tensor<int32, [4]> var_3666_end_0 = const()[name = string("op_3666_end_0"), val = tensor<int32, [4]>([9, 2, 2048, 128])];
            tensor<bool, [4]> var_3666_end_mask_0 = const()[name = string("op_3666_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3666_cast_fp16 = slice_by_index(begin = var_3666_begin_0, end = var_3666_end_0, end_mask = var_3666_end_mask_0, x = coreml_update_state_129)[name = string("op_3666_cast_fp16")];
            tensor<int32, [2]> tile_17 = const()[name = string("tile_17"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3669_axis_0 = const()[name = string("op_3669_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3669_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3669_cast_fp16_1 = split(axis = var_3669_axis_0, split_sizes = tile_17, x = var_3666_cast_fp16)[name = string("op_3669_cast_fp16")];
            tensor<int32, [2]> var_3672_split_sizes_0 = const()[name = string("op_3672_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3672_axis_0 = const()[name = string("op_3672_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_3672_0, tensor<fp16, [1, 8, 128, 8]> var_3672_1 = split(axis = var_3672_axis_0, split_sizes = var_3672_split_sizes_0, x = query_states_51_cast_fp16)[name = string("op_3672")];
            bool attn_weights_129_transpose_x_0 = const()[name = string("attn_weights_129_transpose_x_0"), val = bool(false)];
            bool attn_weights_129_transpose_y_0 = const()[name = string("attn_weights_129_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_129_cast_fp16 = matmul(transpose_x = attn_weights_129_transpose_x_0, transpose_y = attn_weights_129_transpose_y_0, x = var_3659_cast_fp16_0, y = var_3672_0)[name = string("attn_weights_129_cast_fp16")];
            fp16 var_3675_to_fp16 = const()[name = string("op_3675_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_131_cast_fp16 = mul(x = attn_weights_129_cast_fp16, y = var_3675_to_fp16)[name = string("attn_weights_131_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_133_cast_fp16 = add(x = attn_weights_131_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_133_cast_fp16")];
            int32 var_3679 = const()[name = string("op_3679"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_135_cast_fp16 = softmax(axis = var_3679, x = attn_weights_133_cast_fp16)[name = string("attn_weights_135_cast_fp16")];
            bool var_3685_transpose_x_1 = const()[name = string("op_3685_transpose_x_1"), val = bool(true)];
            bool var_3685_transpose_y_1 = const()[name = string("op_3685_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_3685_cast_fp16 = matmul(transpose_x = var_3685_transpose_x_1, transpose_y = var_3685_transpose_y_1, x = attn_weights_135_cast_fp16, y = var_3669_cast_fp16_0)[name = string("op_3685_cast_fp16")];
            bool attn_weights_137_transpose_x_0 = const()[name = string("attn_weights_137_transpose_x_0"), val = bool(false)];
            bool attn_weights_137_transpose_y_0 = const()[name = string("attn_weights_137_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_137_cast_fp16 = matmul(transpose_x = attn_weights_137_transpose_x_0, transpose_y = attn_weights_137_transpose_y_0, x = var_3659_cast_fp16_1, y = var_3672_1)[name = string("attn_weights_137_cast_fp16")];
            fp16 var_3687_to_fp16 = const()[name = string("op_3687_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_139_cast_fp16 = mul(x = attn_weights_137_cast_fp16, y = var_3687_to_fp16)[name = string("attn_weights_139_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_141_cast_fp16 = add(x = attn_weights_139_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_141_cast_fp16")];
            int32 var_3691 = const()[name = string("op_3691"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_143_cast_fp16 = softmax(axis = var_3691, x = attn_weights_141_cast_fp16)[name = string("attn_weights_143_cast_fp16")];
            bool attn_output_65_transpose_x_1 = const()[name = string("attn_output_65_transpose_x_1"), val = bool(true)];
            bool attn_output_65_transpose_y_1 = const()[name = string("attn_output_65_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_65_cast_fp16 = matmul(transpose_x = attn_output_65_transpose_x_1, transpose_y = attn_output_65_transpose_y_1, x = attn_weights_143_cast_fp16, y = var_3669_cast_fp16_1)[name = string("attn_output_65_cast_fp16")];
            int32 var_3699 = const()[name = string("op_3699"), val = int32(1)];
            bool attn_output_67_interleave_0 = const()[name = string("attn_output_67_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_67_cast_fp16 = concat(axis = var_3699, interleave = attn_output_67_interleave_0, values = (var_3685_cast_fp16, attn_output_65_cast_fp16))[name = string("attn_output_67_cast_fp16")];
            tensor<int32, [4]> var_3703_perm_0 = const()[name = string("op_3703_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_107x = const()[name = string("concat_107x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_3703_cast_fp16 = transpose(perm = var_3703_perm_0, x = attn_output_67_cast_fp16)[name = string("transpose_231")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_71_cast_fp16 = reshape(shape = concat_107x, x = var_3703_cast_fp16)[name = string("attn_output_71_cast_fp16")];
            tensor<int32, [2]> hidden_states_83_strides_0 = const()[name = string("hidden_states_83_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_83_pad_type_0 = const()[name = string("hidden_states_83_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_83_pad_0 = const()[name = string("hidden_states_83_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_83_dilations_0 = const()[name = string("hidden_states_83_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_83_groups_0 = const()[name = string("hidden_states_83_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_83_cast_fp16 = conv(dilations = hidden_states_83_dilations_0, groups = hidden_states_83_groups_0, pad = hidden_states_83_pad_0, pad_type = hidden_states_83_pad_type_0, strides = hidden_states_83_strides_0, weight = layers_8_self_attn_o_proj_weight_cast_fp16, x = attn_output_71_cast_fp16)[name = string("hidden_states_83_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_85_cast_fp16 = add(x = hidden_states_79_cast_fp16, y = hidden_states_83_cast_fp16)[name = string("hidden_states_85_cast_fp16")];
            fp16 const_90_promoted_to_fp16 = const()[name = string("const_90_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_3736_cast_fp16 = mul(x = hidden_states_85_cast_fp16, y = const_90_promoted_to_fp16)[name = string("op_3736_cast_fp16")];
            int32 var_3734 = const()[name = string("op_3734"), val = int32(1)];
            bool doubled_69_interleave_0 = const()[name = string("doubled_69_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_69_cast_fp16 = concat(axis = var_3734, interleave = doubled_69_interleave_0, values = (hidden_states_85_cast_fp16, var_3736_cast_fp16))[name = string("doubled_69_cast_fp16")];
            tensor<int32, [1]> out_35_axes_0 = const()[name = string("out_35_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_35_gamma_0_to_fp16 = const()[name = string("out_35_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1388061824)))];
            fp16 var_3746_to_fp16 = const()[name = string("op_3746_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_35_cast_fp16 = layer_norm(axes = out_35_axes_0, epsilon = var_3746_to_fp16, gamma = out_35_gamma_0_to_fp16, x = doubled_69_cast_fp16)[name = string("out_35_cast_fp16")];
            tensor<int32, [2]> var_3757_split_sizes_0 = const()[name = string("op_3757_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3757_axis_0 = const()[name = string("op_3757_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_3757_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_3757_cast_fp16_1 = split(axis = var_3757_axis_0, split_sizes = var_3757_split_sizes_0, x = out_35_cast_fp16)[name = string("op_3757_cast_fp16")];
            tensor<int32, [2]> input_17_strides_0 = const()[name = string("input_17_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_17_pad_type_0 = const()[name = string("input_17_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_17_pad_0 = const()[name = string("input_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_17_dilations_0 = const()[name = string("input_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_17_groups_0 = const()[name = string("input_17_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_17_cast_fp16 = conv(dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = layers_8_mlp_gate_proj_weight_cast_fp16, x = var_3757_cast_fp16_0)[name = string("input_17_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_3774_cast_fp16 = silu(x = input_17_cast_fp16)[name = string("op_3774_cast_fp16")];
            tensor<int32, [2]> var_3780_strides_0 = const()[name = string("op_3780_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_3780_pad_type_0 = const()[name = string("op_3780_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_3780_pad_0 = const()[name = string("op_3780_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_3780_dilations_0 = const()[name = string("op_3780_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3780_groups_0 = const()[name = string("op_3780_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_3780_cast_fp16 = conv(dilations = var_3780_dilations_0, groups = var_3780_groups_0, pad = var_3780_pad_0, pad_type = var_3780_pad_type_0, strides = var_3780_strides_0, weight = layers_8_mlp_up_proj_weight_cast_fp16, x = var_3757_cast_fp16_0)[name = string("op_3780_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_89_cast_fp16 = mul(x = var_3774_cast_fp16, y = var_3780_cast_fp16)[name = string("x_89_cast_fp16")];
            tensor<int32, [2]> hidden_states_87_strides_0 = const()[name = string("hidden_states_87_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_87_pad_type_0 = const()[name = string("hidden_states_87_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_87_pad_0 = const()[name = string("hidden_states_87_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_87_dilations_0 = const()[name = string("hidden_states_87_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_87_groups_0 = const()[name = string("hidden_states_87_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_87_cast_fp16 = conv(dilations = hidden_states_87_dilations_0, groups = hidden_states_87_groups_0, pad = hidden_states_87_pad_0, pad_type = hidden_states_87_pad_type_0, strides = hidden_states_87_strides_0, weight = layers_8_mlp_down_proj_weight_cast_fp16, x = x_89_cast_fp16)[name = string("hidden_states_87_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_89_cast_fp16 = add(x = hidden_states_85_cast_fp16, y = hidden_states_87_cast_fp16)[name = string("hidden_states_89_cast_fp16")];
            fp16 const_92_promoted_to_fp16 = const()[name = string("const_92_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_3798_cast_fp16 = mul(x = hidden_states_89_cast_fp16, y = const_92_promoted_to_fp16)[name = string("op_3798_cast_fp16")];
            int32 var_3796 = const()[name = string("op_3796"), val = int32(1)];
            bool doubled_73_interleave_0 = const()[name = string("doubled_73_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_73_cast_fp16 = concat(axis = var_3796, interleave = doubled_73_interleave_0, values = (hidden_states_89_cast_fp16, var_3798_cast_fp16))[name = string("doubled_73_cast_fp16")];
            tensor<int32, [1]> out_37_axes_0 = const()[name = string("out_37_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_37_gamma_0_to_fp16 = const()[name = string("out_37_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1388070080)))];
            fp16 var_3808_to_fp16 = const()[name = string("op_3808_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_37_cast_fp16 = layer_norm(axes = out_37_axes_0, epsilon = var_3808_to_fp16, gamma = out_37_gamma_0_to_fp16, x = doubled_73_cast_fp16)[name = string("out_37_cast_fp16")];
            tensor<int32, [2]> var_3819_split_sizes_0 = const()[name = string("op_3819_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3819_axis_0 = const()[name = string("op_3819_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_3819_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_3819_cast_fp16_1 = split(axis = var_3819_axis_0, split_sizes = var_3819_split_sizes_0, x = out_37_cast_fp16)[name = string("op_3819_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_9_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_9_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1388078336)))];
            tensor<int32, [2]> query_states_55_strides_0 = const()[name = string("query_states_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_55_pad_type_0 = const()[name = string("query_states_55_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_55_pad_0 = const()[name = string("query_states_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_55_dilations_0 = const()[name = string("query_states_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_55_groups_0 = const()[name = string("query_states_55_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_55_cast_fp16 = conv(dilations = query_states_55_dilations_0, groups = query_states_55_groups_0, pad = query_states_55_pad_0, pad_type = query_states_55_pad_type_0, strides = query_states_55_strides_0, weight = layers_9_self_attn_q_proj_weight_to_fp16, x = var_3819_cast_fp16_0)[name = string("query_states_55_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_9_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_9_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1396467008)))];
            tensor<int32, [2]> key_states_91_strides_0 = const()[name = string("key_states_91_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_91_pad_type_0 = const()[name = string("key_states_91_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_91_pad_0 = const()[name = string("key_states_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_91_dilations_0 = const()[name = string("key_states_91_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_91_groups_0 = const()[name = string("key_states_91_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_91_cast_fp16 = conv(dilations = key_states_91_dilations_0, groups = key_states_91_groups_0, pad = key_states_91_pad_0, pad_type = key_states_91_pad_type_0, strides = key_states_91_strides_0, weight = layers_9_self_attn_k_proj_weight_to_fp16, x = var_3819_cast_fp16_0)[name = string("key_states_91_cast_fp16")];
            tensor<int32, [2]> value_states_55_strides_0 = const()[name = string("value_states_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_55_pad_type_0 = const()[name = string("value_states_55_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_55_pad_0 = const()[name = string("value_states_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_55_dilations_0 = const()[name = string("value_states_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_55_groups_0 = const()[name = string("value_states_55_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_55_cast_fp16 = conv(dilations = value_states_55_dilations_0, groups = value_states_55_groups_0, pad = value_states_55_pad_0, pad_type = value_states_55_pad_type_0, strides = value_states_55_strides_0, weight = layers_9_self_attn_v_proj_weight_cast_fp16, x = var_3819_cast_fp16_0)[name = string("value_states_55_cast_fp16")];
            tensor<int32, [4]> concat_108x = const()[name = string("concat_108x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_91_cast_fp16 = reshape(shape = concat_108x, x = query_states_55_cast_fp16)[name = string("x_91_cast_fp16")];
            tensor<int32, [4]> concat_109x = const()[name = string("concat_109x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_3876_cast_fp16 = reshape(shape = concat_109x, x = key_states_91_cast_fp16)[name = string("op_3876_cast_fp16")];
            tensor<int32, [4]> concat_110x = const()[name = string("concat_110x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_3883_cast_fp16 = reshape(shape = concat_110x, x = value_states_55_cast_fp16)[name = string("op_3883_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_3887_cast_fp16 = mul(x = x_91_cast_fp16, y = var_859_cast_fp16)[name = string("op_3887_cast_fp16")];
            tensor<int32, [2]> var_3888_split_sizes_0 = const()[name = string("op_3888_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3888_axis_0 = const()[name = string("op_3888_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_3888_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_3888_cast_fp16_1 = split(axis = var_3888_axis_0, split_sizes = var_3888_split_sizes_0, x = x_91_cast_fp16)[name = string("op_3888_cast_fp16")];
            fp16 const_94_promoted_to_fp16 = const()[name = string("const_94_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_3890_cast_fp16 = mul(x = var_3888_cast_fp16_1, y = const_94_promoted_to_fp16)[name = string("op_3890_cast_fp16")];
            int32 var_3892 = const()[name = string("op_3892"), val = int32(-2)];
            bool var_3893_interleave_0 = const()[name = string("op_3893_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_3893_cast_fp16 = concat(axis = var_3892, interleave = var_3893_interleave_0, values = (var_3890_cast_fp16, var_3888_cast_fp16_0))[name = string("op_3893_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_3894_cast_fp16 = mul(x = var_3893_cast_fp16, y = var_866_cast_fp16)[name = string("op_3894_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_57_cast_fp16 = add(x = var_3887_cast_fp16, y = var_3894_cast_fp16)[name = string("query_states_57_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_3900_cast_fp16 = mul(x = var_3876_cast_fp16, y = var_859_cast_fp16)[name = string("op_3900_cast_fp16")];
            tensor<int32, [2]> var_3901_split_sizes_0 = const()[name = string("op_3901_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3901_axis_0 = const()[name = string("op_3901_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_3901_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_3901_cast_fp16_1 = split(axis = var_3901_axis_0, split_sizes = var_3901_split_sizes_0, x = var_3876_cast_fp16)[name = string("op_3901_cast_fp16")];
            fp16 const_95_promoted_to_fp16 = const()[name = string("const_95_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_3903_cast_fp16 = mul(x = var_3901_cast_fp16_1, y = const_95_promoted_to_fp16)[name = string("op_3903_cast_fp16")];
            int32 var_3905 = const()[name = string("op_3905"), val = int32(-2)];
            bool var_3906_interleave_0 = const()[name = string("op_3906_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_3906_cast_fp16 = concat(axis = var_3905, interleave = var_3906_interleave_0, values = (var_3903_cast_fp16, var_3901_cast_fp16_0))[name = string("op_3906_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_3907_cast_fp16 = mul(x = var_3906_cast_fp16, y = var_866_cast_fp16)[name = string("op_3907_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_95_cast_fp16 = add(x = var_3900_cast_fp16, y = var_3907_cast_fp16)[name = string("key_states_95_cast_fp16")];
            tensor<int32, [1]> expand_dims_108 = const()[name = string("expand_dims_108"), val = tensor<int32, [1]>([9])];
            tensor<int32, [1]> expand_dims_109 = const()[name = string("expand_dims_109"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_111 = const()[name = string("expand_dims_111"), val = tensor<int32, [1]>([0])];
            int32 concat_113_axis_0 = const()[name = string("concat_113_axis_0"), val = int32(0)];
            bool concat_113_interleave_0 = const()[name = string("concat_113_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_113 = concat(axis = concat_113_axis_0, interleave = concat_113_interleave_0, values = (expand_dims_108, expand_dims_109, position_id, expand_dims_111))[name = string("concat_113")];
            tensor<int32, [1]> expand_dims_112 = const()[name = string("expand_dims_112"), val = tensor<int32, [1]>([10])];
            tensor<int32, [1]> concat_114_values1_0 = const()[name = string("concat_114_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_114_values3_0 = const()[name = string("concat_114_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_114_axis_0 = const()[name = string("concat_114_axis_0"), val = int32(0)];
            bool concat_114_interleave_0 = const()[name = string("concat_114_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_114 = concat(axis = concat_114_axis_0, interleave = concat_114_interleave_0, values = (expand_dims_112, concat_114_values1_0, cache_position_end, concat_114_values3_0))[name = string("concat_114")];
            tensor<int32, [4]> key_states_97_perm_0 = const()[name = string("key_states_97_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_10_stride_0 = const()[name = string("key_cache_internal_tensor_assign_10_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_10_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_10_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_10_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_10_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_10_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_10_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_97_cast_fp16 = transpose(perm = key_states_97_perm_0, x = key_states_95_cast_fp16)[name = string("transpose_230")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_10_cast_fp16 = slice_update(begin = concat_113, begin_mask = key_cache_internal_tensor_assign_10_begin_mask_0, end = concat_114, end_mask = key_cache_internal_tensor_assign_10_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_10_squeeze_mask_0, stride = key_cache_internal_tensor_assign_10_stride_0, update = key_states_97_cast_fp16, x = coreml_update_state_128)[name = string("key_cache_internal_tensor_assign_10_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_10_cast_fp16, input = key_cache)[name = string("coreml_update_state_130_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_130 = read_state(input = key_cache)[name = string("coreml_update_state_130")];
            tensor<int32, [4]> value_states_57_perm_0 = const()[name = string("value_states_57_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_10_stride_0 = const()[name = string("value_cache_internal_tensor_assign_10_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_10_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_10_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_10_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_10_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_10_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_10_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_57_cast_fp16 = transpose(perm = value_states_57_perm_0, x = var_3883_cast_fp16)[name = string("transpose_229")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_10_cast_fp16 = slice_update(begin = concat_113, begin_mask = value_cache_internal_tensor_assign_10_begin_mask_0, end = concat_114, end_mask = value_cache_internal_tensor_assign_10_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_10_squeeze_mask_0, stride = value_cache_internal_tensor_assign_10_stride_0, update = value_states_57_cast_fp16, x = coreml_update_state_129)[name = string("value_cache_internal_tensor_assign_10_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_10_cast_fp16, input = value_cache)[name = string("coreml_update_state_131_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_131 = read_state(input = value_cache)[name = string("coreml_update_state_131")];
            tensor<int32, [4]> var_3977_begin_0 = const()[name = string("op_3977_begin_0"), val = tensor<int32, [4]>([9, 0, 0, 0])];
            tensor<int32, [4]> var_3977_end_0 = const()[name = string("op_3977_end_0"), val = tensor<int32, [4]>([10, 2, 2048, 128])];
            tensor<bool, [4]> var_3977_end_mask_0 = const()[name = string("op_3977_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3977_cast_fp16 = slice_by_index(begin = var_3977_begin_0, end = var_3977_end_0, end_mask = var_3977_end_mask_0, x = coreml_update_state_130)[name = string("op_3977_cast_fp16")];
            tensor<int32, [2]> tile_18 = const()[name = string("tile_18"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3980_axis_0 = const()[name = string("op_3980_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3980_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3980_cast_fp16_1 = split(axis = var_3980_axis_0, split_sizes = tile_18, x = var_3977_cast_fp16)[name = string("op_3980_cast_fp16")];
            tensor<int32, [4]> var_3987_begin_0 = const()[name = string("op_3987_begin_0"), val = tensor<int32, [4]>([9, 0, 0, 0])];
            tensor<int32, [4]> var_3987_end_0 = const()[name = string("op_3987_end_0"), val = tensor<int32, [4]>([10, 2, 2048, 128])];
            tensor<bool, [4]> var_3987_end_mask_0 = const()[name = string("op_3987_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3987_cast_fp16 = slice_by_index(begin = var_3987_begin_0, end = var_3987_end_0, end_mask = var_3987_end_mask_0, x = coreml_update_state_131)[name = string("op_3987_cast_fp16")];
            tensor<int32, [2]> tile_19 = const()[name = string("tile_19"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3990_axis_0 = const()[name = string("op_3990_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3990_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3990_cast_fp16_1 = split(axis = var_3990_axis_0, split_sizes = tile_19, x = var_3987_cast_fp16)[name = string("op_3990_cast_fp16")];
            tensor<int32, [2]> var_3993_split_sizes_0 = const()[name = string("op_3993_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3993_axis_0 = const()[name = string("op_3993_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_3993_0, tensor<fp16, [1, 8, 128, 8]> var_3993_1 = split(axis = var_3993_axis_0, split_sizes = var_3993_split_sizes_0, x = query_states_57_cast_fp16)[name = string("op_3993")];
            bool attn_weights_145_transpose_x_0 = const()[name = string("attn_weights_145_transpose_x_0"), val = bool(false)];
            bool attn_weights_145_transpose_y_0 = const()[name = string("attn_weights_145_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_145_cast_fp16 = matmul(transpose_x = attn_weights_145_transpose_x_0, transpose_y = attn_weights_145_transpose_y_0, x = var_3980_cast_fp16_0, y = var_3993_0)[name = string("attn_weights_145_cast_fp16")];
            fp16 var_3996_to_fp16 = const()[name = string("op_3996_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_147_cast_fp16 = mul(x = attn_weights_145_cast_fp16, y = var_3996_to_fp16)[name = string("attn_weights_147_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_149_cast_fp16 = add(x = attn_weights_147_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_149_cast_fp16")];
            int32 var_4000 = const()[name = string("op_4000"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_151_cast_fp16 = softmax(axis = var_4000, x = attn_weights_149_cast_fp16)[name = string("attn_weights_151_cast_fp16")];
            bool var_4006_transpose_x_1 = const()[name = string("op_4006_transpose_x_1"), val = bool(true)];
            bool var_4006_transpose_y_1 = const()[name = string("op_4006_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_4006_cast_fp16 = matmul(transpose_x = var_4006_transpose_x_1, transpose_y = var_4006_transpose_y_1, x = attn_weights_151_cast_fp16, y = var_3990_cast_fp16_0)[name = string("op_4006_cast_fp16")];
            bool attn_weights_153_transpose_x_0 = const()[name = string("attn_weights_153_transpose_x_0"), val = bool(false)];
            bool attn_weights_153_transpose_y_0 = const()[name = string("attn_weights_153_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_153_cast_fp16 = matmul(transpose_x = attn_weights_153_transpose_x_0, transpose_y = attn_weights_153_transpose_y_0, x = var_3980_cast_fp16_1, y = var_3993_1)[name = string("attn_weights_153_cast_fp16")];
            fp16 var_4008_to_fp16 = const()[name = string("op_4008_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_155_cast_fp16 = mul(x = attn_weights_153_cast_fp16, y = var_4008_to_fp16)[name = string("attn_weights_155_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_157_cast_fp16 = add(x = attn_weights_155_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_157_cast_fp16")];
            int32 var_4012 = const()[name = string("op_4012"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_159_cast_fp16 = softmax(axis = var_4012, x = attn_weights_157_cast_fp16)[name = string("attn_weights_159_cast_fp16")];
            bool attn_output_73_transpose_x_1 = const()[name = string("attn_output_73_transpose_x_1"), val = bool(true)];
            bool attn_output_73_transpose_y_1 = const()[name = string("attn_output_73_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_73_cast_fp16 = matmul(transpose_x = attn_output_73_transpose_x_1, transpose_y = attn_output_73_transpose_y_1, x = attn_weights_159_cast_fp16, y = var_3990_cast_fp16_1)[name = string("attn_output_73_cast_fp16")];
            int32 var_4020 = const()[name = string("op_4020"), val = int32(1)];
            bool attn_output_75_interleave_0 = const()[name = string("attn_output_75_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_75_cast_fp16 = concat(axis = var_4020, interleave = attn_output_75_interleave_0, values = (var_4006_cast_fp16, attn_output_73_cast_fp16))[name = string("attn_output_75_cast_fp16")];
            tensor<int32, [4]> var_4024_perm_0 = const()[name = string("op_4024_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_119x = const()[name = string("concat_119x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_4024_cast_fp16 = transpose(perm = var_4024_perm_0, x = attn_output_75_cast_fp16)[name = string("transpose_228")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_79_cast_fp16 = reshape(shape = concat_119x, x = var_4024_cast_fp16)[name = string("attn_output_79_cast_fp16")];
            tensor<int32, [2]> hidden_states_93_strides_0 = const()[name = string("hidden_states_93_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_93_pad_type_0 = const()[name = string("hidden_states_93_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_93_pad_0 = const()[name = string("hidden_states_93_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_93_dilations_0 = const()[name = string("hidden_states_93_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_93_groups_0 = const()[name = string("hidden_states_93_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_93_cast_fp16 = conv(dilations = hidden_states_93_dilations_0, groups = hidden_states_93_groups_0, pad = hidden_states_93_pad_0, pad_type = hidden_states_93_pad_type_0, strides = hidden_states_93_strides_0, weight = layers_9_self_attn_o_proj_weight_cast_fp16, x = attn_output_79_cast_fp16)[name = string("hidden_states_93_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_95_cast_fp16 = add(x = hidden_states_89_cast_fp16, y = hidden_states_93_cast_fp16)[name = string("hidden_states_95_cast_fp16")];
            fp16 const_100_promoted_to_fp16 = const()[name = string("const_100_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_4057_cast_fp16 = mul(x = hidden_states_95_cast_fp16, y = const_100_promoted_to_fp16)[name = string("op_4057_cast_fp16")];
            int32 var_4055 = const()[name = string("op_4055"), val = int32(1)];
            bool doubled_77_interleave_0 = const()[name = string("doubled_77_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_77_cast_fp16 = concat(axis = var_4055, interleave = doubled_77_interleave_0, values = (hidden_states_95_cast_fp16, var_4057_cast_fp16))[name = string("doubled_77_cast_fp16")];
            tensor<int32, [1]> out_39_axes_0 = const()[name = string("out_39_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_39_gamma_0_to_fp16 = const()[name = string("out_39_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1397515648)))];
            fp16 var_4067_to_fp16 = const()[name = string("op_4067_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_39_cast_fp16 = layer_norm(axes = out_39_axes_0, epsilon = var_4067_to_fp16, gamma = out_39_gamma_0_to_fp16, x = doubled_77_cast_fp16)[name = string("out_39_cast_fp16")];
            tensor<int32, [2]> var_4078_split_sizes_0 = const()[name = string("op_4078_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4078_axis_0 = const()[name = string("op_4078_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_4078_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_4078_cast_fp16_1 = split(axis = var_4078_axis_0, split_sizes = var_4078_split_sizes_0, x = out_39_cast_fp16)[name = string("op_4078_cast_fp16")];
            tensor<int32, [2]> input_19_strides_0 = const()[name = string("input_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_19_pad_type_0 = const()[name = string("input_19_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_19_pad_0 = const()[name = string("input_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_19_dilations_0 = const()[name = string("input_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_19_groups_0 = const()[name = string("input_19_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_19_cast_fp16 = conv(dilations = input_19_dilations_0, groups = input_19_groups_0, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = input_19_strides_0, weight = layers_9_mlp_gate_proj_weight_cast_fp16, x = var_4078_cast_fp16_0)[name = string("input_19_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_4095_cast_fp16 = silu(x = input_19_cast_fp16)[name = string("op_4095_cast_fp16")];
            tensor<int32, [2]> var_4101_strides_0 = const()[name = string("op_4101_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_4101_pad_type_0 = const()[name = string("op_4101_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_4101_pad_0 = const()[name = string("op_4101_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_4101_dilations_0 = const()[name = string("op_4101_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4101_groups_0 = const()[name = string("op_4101_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_4101_cast_fp16 = conv(dilations = var_4101_dilations_0, groups = var_4101_groups_0, pad = var_4101_pad_0, pad_type = var_4101_pad_type_0, strides = var_4101_strides_0, weight = layers_9_mlp_up_proj_weight_cast_fp16, x = var_4078_cast_fp16_0)[name = string("op_4101_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_99_cast_fp16 = mul(x = var_4095_cast_fp16, y = var_4101_cast_fp16)[name = string("x_99_cast_fp16")];
            tensor<int32, [2]> hidden_states_97_strides_0 = const()[name = string("hidden_states_97_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_97_pad_type_0 = const()[name = string("hidden_states_97_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_97_pad_0 = const()[name = string("hidden_states_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_97_dilations_0 = const()[name = string("hidden_states_97_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_97_groups_0 = const()[name = string("hidden_states_97_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_97_cast_fp16 = conv(dilations = hidden_states_97_dilations_0, groups = hidden_states_97_groups_0, pad = hidden_states_97_pad_0, pad_type = hidden_states_97_pad_type_0, strides = hidden_states_97_strides_0, weight = layers_9_mlp_down_proj_weight_cast_fp16, x = x_99_cast_fp16)[name = string("hidden_states_97_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_99_cast_fp16 = add(x = hidden_states_95_cast_fp16, y = hidden_states_97_cast_fp16)[name = string("hidden_states_99_cast_fp16")];
            fp16 const_102_promoted_to_fp16 = const()[name = string("const_102_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_4119_cast_fp16 = mul(x = hidden_states_99_cast_fp16, y = const_102_promoted_to_fp16)[name = string("op_4119_cast_fp16")];
            int32 var_4117 = const()[name = string("op_4117"), val = int32(1)];
            bool doubled_81_interleave_0 = const()[name = string("doubled_81_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_81_cast_fp16 = concat(axis = var_4117, interleave = doubled_81_interleave_0, values = (hidden_states_99_cast_fp16, var_4119_cast_fp16))[name = string("doubled_81_cast_fp16")];
            tensor<int32, [1]> out_41_axes_0 = const()[name = string("out_41_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_41_gamma_0_to_fp16 = const()[name = string("out_41_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1397523904)))];
            fp16 var_4129_to_fp16 = const()[name = string("op_4129_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_41_cast_fp16 = layer_norm(axes = out_41_axes_0, epsilon = var_4129_to_fp16, gamma = out_41_gamma_0_to_fp16, x = doubled_81_cast_fp16)[name = string("out_41_cast_fp16")];
            tensor<int32, [2]> var_4140_split_sizes_0 = const()[name = string("op_4140_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4140_axis_0 = const()[name = string("op_4140_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_4140_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_4140_cast_fp16_1 = split(axis = var_4140_axis_0, split_sizes = var_4140_split_sizes_0, x = out_41_cast_fp16)[name = string("op_4140_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_10_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_10_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1397532160)))];
            tensor<int32, [2]> query_states_61_strides_0 = const()[name = string("query_states_61_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_61_pad_type_0 = const()[name = string("query_states_61_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_61_pad_0 = const()[name = string("query_states_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_61_dilations_0 = const()[name = string("query_states_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_61_groups_0 = const()[name = string("query_states_61_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_61_cast_fp16 = conv(dilations = query_states_61_dilations_0, groups = query_states_61_groups_0, pad = query_states_61_pad_0, pad_type = query_states_61_pad_type_0, strides = query_states_61_strides_0, weight = layers_10_self_attn_q_proj_weight_to_fp16, x = var_4140_cast_fp16_0)[name = string("query_states_61_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_10_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_10_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1405920832)))];
            tensor<int32, [2]> key_states_101_strides_0 = const()[name = string("key_states_101_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_101_pad_type_0 = const()[name = string("key_states_101_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_101_pad_0 = const()[name = string("key_states_101_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_101_dilations_0 = const()[name = string("key_states_101_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_101_groups_0 = const()[name = string("key_states_101_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_101_cast_fp16 = conv(dilations = key_states_101_dilations_0, groups = key_states_101_groups_0, pad = key_states_101_pad_0, pad_type = key_states_101_pad_type_0, strides = key_states_101_strides_0, weight = layers_10_self_attn_k_proj_weight_to_fp16, x = var_4140_cast_fp16_0)[name = string("key_states_101_cast_fp16")];
            tensor<int32, [2]> value_states_61_strides_0 = const()[name = string("value_states_61_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_61_pad_type_0 = const()[name = string("value_states_61_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_61_pad_0 = const()[name = string("value_states_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_61_dilations_0 = const()[name = string("value_states_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_61_groups_0 = const()[name = string("value_states_61_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_61_cast_fp16 = conv(dilations = value_states_61_dilations_0, groups = value_states_61_groups_0, pad = value_states_61_pad_0, pad_type = value_states_61_pad_type_0, strides = value_states_61_strides_0, weight = layers_10_self_attn_v_proj_weight_cast_fp16, x = var_4140_cast_fp16_0)[name = string("value_states_61_cast_fp16")];
            tensor<int32, [4]> concat_120x = const()[name = string("concat_120x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_101_cast_fp16 = reshape(shape = concat_120x, x = query_states_61_cast_fp16)[name = string("x_101_cast_fp16")];
            tensor<int32, [4]> concat_121x = const()[name = string("concat_121x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_4197_cast_fp16 = reshape(shape = concat_121x, x = key_states_101_cast_fp16)[name = string("op_4197_cast_fp16")];
            tensor<int32, [4]> concat_122x = const()[name = string("concat_122x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_4204_cast_fp16 = reshape(shape = concat_122x, x = value_states_61_cast_fp16)[name = string("op_4204_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_4208_cast_fp16 = mul(x = x_101_cast_fp16, y = var_859_cast_fp16)[name = string("op_4208_cast_fp16")];
            tensor<int32, [2]> var_4209_split_sizes_0 = const()[name = string("op_4209_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4209_axis_0 = const()[name = string("op_4209_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_4209_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_4209_cast_fp16_1 = split(axis = var_4209_axis_0, split_sizes = var_4209_split_sizes_0, x = x_101_cast_fp16)[name = string("op_4209_cast_fp16")];
            fp16 const_104_promoted_to_fp16 = const()[name = string("const_104_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_4211_cast_fp16 = mul(x = var_4209_cast_fp16_1, y = const_104_promoted_to_fp16)[name = string("op_4211_cast_fp16")];
            int32 var_4213 = const()[name = string("op_4213"), val = int32(-2)];
            bool var_4214_interleave_0 = const()[name = string("op_4214_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_4214_cast_fp16 = concat(axis = var_4213, interleave = var_4214_interleave_0, values = (var_4211_cast_fp16, var_4209_cast_fp16_0))[name = string("op_4214_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_4215_cast_fp16 = mul(x = var_4214_cast_fp16, y = var_866_cast_fp16)[name = string("op_4215_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_63_cast_fp16 = add(x = var_4208_cast_fp16, y = var_4215_cast_fp16)[name = string("query_states_63_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_4221_cast_fp16 = mul(x = var_4197_cast_fp16, y = var_859_cast_fp16)[name = string("op_4221_cast_fp16")];
            tensor<int32, [2]> var_4222_split_sizes_0 = const()[name = string("op_4222_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4222_axis_0 = const()[name = string("op_4222_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_4222_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_4222_cast_fp16_1 = split(axis = var_4222_axis_0, split_sizes = var_4222_split_sizes_0, x = var_4197_cast_fp16)[name = string("op_4222_cast_fp16")];
            fp16 const_105_promoted_to_fp16 = const()[name = string("const_105_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_4224_cast_fp16 = mul(x = var_4222_cast_fp16_1, y = const_105_promoted_to_fp16)[name = string("op_4224_cast_fp16")];
            int32 var_4226 = const()[name = string("op_4226"), val = int32(-2)];
            bool var_4227_interleave_0 = const()[name = string("op_4227_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_4227_cast_fp16 = concat(axis = var_4226, interleave = var_4227_interleave_0, values = (var_4224_cast_fp16, var_4222_cast_fp16_0))[name = string("op_4227_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_4228_cast_fp16 = mul(x = var_4227_cast_fp16, y = var_866_cast_fp16)[name = string("op_4228_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_105_cast_fp16 = add(x = var_4221_cast_fp16, y = var_4228_cast_fp16)[name = string("key_states_105_cast_fp16")];
            tensor<int32, [1]> expand_dims_120 = const()[name = string("expand_dims_120"), val = tensor<int32, [1]>([10])];
            tensor<int32, [1]> expand_dims_121 = const()[name = string("expand_dims_121"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_123 = const()[name = string("expand_dims_123"), val = tensor<int32, [1]>([0])];
            int32 concat_125_axis_0 = const()[name = string("concat_125_axis_0"), val = int32(0)];
            bool concat_125_interleave_0 = const()[name = string("concat_125_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_125 = concat(axis = concat_125_axis_0, interleave = concat_125_interleave_0, values = (expand_dims_120, expand_dims_121, position_id, expand_dims_123))[name = string("concat_125")];
            tensor<int32, [1]> expand_dims_124 = const()[name = string("expand_dims_124"), val = tensor<int32, [1]>([11])];
            tensor<int32, [1]> concat_126_values1_0 = const()[name = string("concat_126_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_126_values3_0 = const()[name = string("concat_126_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_126_axis_0 = const()[name = string("concat_126_axis_0"), val = int32(0)];
            bool concat_126_interleave_0 = const()[name = string("concat_126_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_126 = concat(axis = concat_126_axis_0, interleave = concat_126_interleave_0, values = (expand_dims_124, concat_126_values1_0, cache_position_end, concat_126_values3_0))[name = string("concat_126")];
            tensor<int32, [4]> key_states_107_perm_0 = const()[name = string("key_states_107_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_11_stride_0 = const()[name = string("key_cache_internal_tensor_assign_11_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_11_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_11_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_11_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_11_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_11_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_11_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_107_cast_fp16 = transpose(perm = key_states_107_perm_0, x = key_states_105_cast_fp16)[name = string("transpose_227")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_11_cast_fp16 = slice_update(begin = concat_125, begin_mask = key_cache_internal_tensor_assign_11_begin_mask_0, end = concat_126, end_mask = key_cache_internal_tensor_assign_11_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_11_squeeze_mask_0, stride = key_cache_internal_tensor_assign_11_stride_0, update = key_states_107_cast_fp16, x = coreml_update_state_130)[name = string("key_cache_internal_tensor_assign_11_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_11_cast_fp16, input = key_cache)[name = string("coreml_update_state_132_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_132 = read_state(input = key_cache)[name = string("coreml_update_state_132")];
            tensor<int32, [4]> value_states_63_perm_0 = const()[name = string("value_states_63_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_11_stride_0 = const()[name = string("value_cache_internal_tensor_assign_11_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_11_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_11_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_11_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_11_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_11_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_11_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_63_cast_fp16 = transpose(perm = value_states_63_perm_0, x = var_4204_cast_fp16)[name = string("transpose_226")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_11_cast_fp16 = slice_update(begin = concat_125, begin_mask = value_cache_internal_tensor_assign_11_begin_mask_0, end = concat_126, end_mask = value_cache_internal_tensor_assign_11_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_11_squeeze_mask_0, stride = value_cache_internal_tensor_assign_11_stride_0, update = value_states_63_cast_fp16, x = coreml_update_state_131)[name = string("value_cache_internal_tensor_assign_11_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_11_cast_fp16, input = value_cache)[name = string("coreml_update_state_133_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_133 = read_state(input = value_cache)[name = string("coreml_update_state_133")];
            tensor<int32, [4]> var_4298_begin_0 = const()[name = string("op_4298_begin_0"), val = tensor<int32, [4]>([10, 0, 0, 0])];
            tensor<int32, [4]> var_4298_end_0 = const()[name = string("op_4298_end_0"), val = tensor<int32, [4]>([11, 2, 2048, 128])];
            tensor<bool, [4]> var_4298_end_mask_0 = const()[name = string("op_4298_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4298_cast_fp16 = slice_by_index(begin = var_4298_begin_0, end = var_4298_end_0, end_mask = var_4298_end_mask_0, x = coreml_update_state_132)[name = string("op_4298_cast_fp16")];
            tensor<int32, [2]> tile_20 = const()[name = string("tile_20"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4301_axis_0 = const()[name = string("op_4301_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4301_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4301_cast_fp16_1 = split(axis = var_4301_axis_0, split_sizes = tile_20, x = var_4298_cast_fp16)[name = string("op_4301_cast_fp16")];
            tensor<int32, [4]> var_4308_begin_0 = const()[name = string("op_4308_begin_0"), val = tensor<int32, [4]>([10, 0, 0, 0])];
            tensor<int32, [4]> var_4308_end_0 = const()[name = string("op_4308_end_0"), val = tensor<int32, [4]>([11, 2, 2048, 128])];
            tensor<bool, [4]> var_4308_end_mask_0 = const()[name = string("op_4308_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4308_cast_fp16 = slice_by_index(begin = var_4308_begin_0, end = var_4308_end_0, end_mask = var_4308_end_mask_0, x = coreml_update_state_133)[name = string("op_4308_cast_fp16")];
            tensor<int32, [2]> tile_21 = const()[name = string("tile_21"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4311_axis_0 = const()[name = string("op_4311_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4311_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4311_cast_fp16_1 = split(axis = var_4311_axis_0, split_sizes = tile_21, x = var_4308_cast_fp16)[name = string("op_4311_cast_fp16")];
            tensor<int32, [2]> var_4314_split_sizes_0 = const()[name = string("op_4314_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_4314_axis_0 = const()[name = string("op_4314_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_4314_0, tensor<fp16, [1, 8, 128, 8]> var_4314_1 = split(axis = var_4314_axis_0, split_sizes = var_4314_split_sizes_0, x = query_states_63_cast_fp16)[name = string("op_4314")];
            bool attn_weights_161_transpose_x_0 = const()[name = string("attn_weights_161_transpose_x_0"), val = bool(false)];
            bool attn_weights_161_transpose_y_0 = const()[name = string("attn_weights_161_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_161_cast_fp16 = matmul(transpose_x = attn_weights_161_transpose_x_0, transpose_y = attn_weights_161_transpose_y_0, x = var_4301_cast_fp16_0, y = var_4314_0)[name = string("attn_weights_161_cast_fp16")];
            fp16 var_4317_to_fp16 = const()[name = string("op_4317_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_163_cast_fp16 = mul(x = attn_weights_161_cast_fp16, y = var_4317_to_fp16)[name = string("attn_weights_163_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_165_cast_fp16 = add(x = attn_weights_163_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_165_cast_fp16")];
            int32 var_4321 = const()[name = string("op_4321"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_167_cast_fp16 = softmax(axis = var_4321, x = attn_weights_165_cast_fp16)[name = string("attn_weights_167_cast_fp16")];
            bool var_4327_transpose_x_1 = const()[name = string("op_4327_transpose_x_1"), val = bool(true)];
            bool var_4327_transpose_y_1 = const()[name = string("op_4327_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_4327_cast_fp16 = matmul(transpose_x = var_4327_transpose_x_1, transpose_y = var_4327_transpose_y_1, x = attn_weights_167_cast_fp16, y = var_4311_cast_fp16_0)[name = string("op_4327_cast_fp16")];
            bool attn_weights_169_transpose_x_0 = const()[name = string("attn_weights_169_transpose_x_0"), val = bool(false)];
            bool attn_weights_169_transpose_y_0 = const()[name = string("attn_weights_169_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_169_cast_fp16 = matmul(transpose_x = attn_weights_169_transpose_x_0, transpose_y = attn_weights_169_transpose_y_0, x = var_4301_cast_fp16_1, y = var_4314_1)[name = string("attn_weights_169_cast_fp16")];
            fp16 var_4329_to_fp16 = const()[name = string("op_4329_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_171_cast_fp16 = mul(x = attn_weights_169_cast_fp16, y = var_4329_to_fp16)[name = string("attn_weights_171_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_173_cast_fp16 = add(x = attn_weights_171_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_173_cast_fp16")];
            int32 var_4333 = const()[name = string("op_4333"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_175_cast_fp16 = softmax(axis = var_4333, x = attn_weights_173_cast_fp16)[name = string("attn_weights_175_cast_fp16")];
            bool attn_output_81_transpose_x_1 = const()[name = string("attn_output_81_transpose_x_1"), val = bool(true)];
            bool attn_output_81_transpose_y_1 = const()[name = string("attn_output_81_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_81_cast_fp16 = matmul(transpose_x = attn_output_81_transpose_x_1, transpose_y = attn_output_81_transpose_y_1, x = attn_weights_175_cast_fp16, y = var_4311_cast_fp16_1)[name = string("attn_output_81_cast_fp16")];
            int32 var_4341 = const()[name = string("op_4341"), val = int32(1)];
            bool attn_output_83_interleave_0 = const()[name = string("attn_output_83_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_83_cast_fp16 = concat(axis = var_4341, interleave = attn_output_83_interleave_0, values = (var_4327_cast_fp16, attn_output_81_cast_fp16))[name = string("attn_output_83_cast_fp16")];
            tensor<int32, [4]> var_4345_perm_0 = const()[name = string("op_4345_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_131x = const()[name = string("concat_131x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_4345_cast_fp16 = transpose(perm = var_4345_perm_0, x = attn_output_83_cast_fp16)[name = string("transpose_225")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_87_cast_fp16 = reshape(shape = concat_131x, x = var_4345_cast_fp16)[name = string("attn_output_87_cast_fp16")];
            tensor<int32, [2]> hidden_states_103_strides_0 = const()[name = string("hidden_states_103_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_103_pad_type_0 = const()[name = string("hidden_states_103_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_103_pad_0 = const()[name = string("hidden_states_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_103_dilations_0 = const()[name = string("hidden_states_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_103_groups_0 = const()[name = string("hidden_states_103_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_103_cast_fp16 = conv(dilations = hidden_states_103_dilations_0, groups = hidden_states_103_groups_0, pad = hidden_states_103_pad_0, pad_type = hidden_states_103_pad_type_0, strides = hidden_states_103_strides_0, weight = layers_10_self_attn_o_proj_weight_cast_fp16, x = attn_output_87_cast_fp16)[name = string("hidden_states_103_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_105_cast_fp16 = add(x = hidden_states_99_cast_fp16, y = hidden_states_103_cast_fp16)[name = string("hidden_states_105_cast_fp16")];
            fp16 const_110_promoted_to_fp16 = const()[name = string("const_110_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_4378_cast_fp16 = mul(x = hidden_states_105_cast_fp16, y = const_110_promoted_to_fp16)[name = string("op_4378_cast_fp16")];
            int32 var_4376 = const()[name = string("op_4376"), val = int32(1)];
            bool doubled_85_interleave_0 = const()[name = string("doubled_85_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_85_cast_fp16 = concat(axis = var_4376, interleave = doubled_85_interleave_0, values = (hidden_states_105_cast_fp16, var_4378_cast_fp16))[name = string("doubled_85_cast_fp16")];
            tensor<int32, [1]> out_43_axes_0 = const()[name = string("out_43_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_43_gamma_0_to_fp16 = const()[name = string("out_43_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406969472)))];
            fp16 var_4388_to_fp16 = const()[name = string("op_4388_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_43_cast_fp16 = layer_norm(axes = out_43_axes_0, epsilon = var_4388_to_fp16, gamma = out_43_gamma_0_to_fp16, x = doubled_85_cast_fp16)[name = string("out_43_cast_fp16")];
            tensor<int32, [2]> var_4399_split_sizes_0 = const()[name = string("op_4399_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4399_axis_0 = const()[name = string("op_4399_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_4399_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_4399_cast_fp16_1 = split(axis = var_4399_axis_0, split_sizes = var_4399_split_sizes_0, x = out_43_cast_fp16)[name = string("op_4399_cast_fp16")];
            tensor<int32, [2]> input_21_strides_0 = const()[name = string("input_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_21_pad_type_0 = const()[name = string("input_21_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_21_pad_0 = const()[name = string("input_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_21_dilations_0 = const()[name = string("input_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_21_groups_0 = const()[name = string("input_21_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_21_cast_fp16 = conv(dilations = input_21_dilations_0, groups = input_21_groups_0, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = input_21_strides_0, weight = layers_10_mlp_gate_proj_weight_cast_fp16, x = var_4399_cast_fp16_0)[name = string("input_21_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_4416_cast_fp16 = silu(x = input_21_cast_fp16)[name = string("op_4416_cast_fp16")];
            tensor<int32, [2]> var_4422_strides_0 = const()[name = string("op_4422_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_4422_pad_type_0 = const()[name = string("op_4422_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_4422_pad_0 = const()[name = string("op_4422_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_4422_dilations_0 = const()[name = string("op_4422_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4422_groups_0 = const()[name = string("op_4422_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_4422_cast_fp16 = conv(dilations = var_4422_dilations_0, groups = var_4422_groups_0, pad = var_4422_pad_0, pad_type = var_4422_pad_type_0, strides = var_4422_strides_0, weight = layers_10_mlp_up_proj_weight_cast_fp16, x = var_4399_cast_fp16_0)[name = string("op_4422_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_109_cast_fp16 = mul(x = var_4416_cast_fp16, y = var_4422_cast_fp16)[name = string("x_109_cast_fp16")];
            tensor<int32, [2]> hidden_states_107_strides_0 = const()[name = string("hidden_states_107_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_107_pad_type_0 = const()[name = string("hidden_states_107_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_107_pad_0 = const()[name = string("hidden_states_107_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_107_dilations_0 = const()[name = string("hidden_states_107_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_107_groups_0 = const()[name = string("hidden_states_107_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_107_cast_fp16 = conv(dilations = hidden_states_107_dilations_0, groups = hidden_states_107_groups_0, pad = hidden_states_107_pad_0, pad_type = hidden_states_107_pad_type_0, strides = hidden_states_107_strides_0, weight = layers_10_mlp_down_proj_weight_cast_fp16, x = x_109_cast_fp16)[name = string("hidden_states_107_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_109_cast_fp16 = add(x = hidden_states_105_cast_fp16, y = hidden_states_107_cast_fp16)[name = string("hidden_states_109_cast_fp16")];
            fp16 const_112_promoted_to_fp16 = const()[name = string("const_112_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_4440_cast_fp16 = mul(x = hidden_states_109_cast_fp16, y = const_112_promoted_to_fp16)[name = string("op_4440_cast_fp16")];
            int32 var_4438 = const()[name = string("op_4438"), val = int32(1)];
            bool doubled_89_interleave_0 = const()[name = string("doubled_89_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_89_cast_fp16 = concat(axis = var_4438, interleave = doubled_89_interleave_0, values = (hidden_states_109_cast_fp16, var_4440_cast_fp16))[name = string("doubled_89_cast_fp16")];
            tensor<int32, [1]> out_45_axes_0 = const()[name = string("out_45_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_45_gamma_0_to_fp16 = const()[name = string("out_45_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406977728)))];
            fp16 var_4450_to_fp16 = const()[name = string("op_4450_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_45_cast_fp16 = layer_norm(axes = out_45_axes_0, epsilon = var_4450_to_fp16, gamma = out_45_gamma_0_to_fp16, x = doubled_89_cast_fp16)[name = string("out_45_cast_fp16")];
            tensor<int32, [2]> var_4461_split_sizes_0 = const()[name = string("op_4461_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4461_axis_0 = const()[name = string("op_4461_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_4461_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_4461_cast_fp16_1 = split(axis = var_4461_axis_0, split_sizes = var_4461_split_sizes_0, x = out_45_cast_fp16)[name = string("op_4461_cast_fp16")];
            tensor<int32, [2]> query_states_67_strides_0 = const()[name = string("query_states_67_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_67_pad_type_0 = const()[name = string("query_states_67_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_67_pad_0 = const()[name = string("query_states_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_67_dilations_0 = const()[name = string("query_states_67_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_67_groups_0 = const()[name = string("query_states_67_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_67_cast_fp16 = conv(dilations = query_states_67_dilations_0, groups = query_states_67_groups_0, pad = query_states_67_pad_0, pad_type = query_states_67_pad_type_0, strides = query_states_67_strides_0, weight = layers_11_self_attn_q_proj_weight_cast_fp16, x = var_4461_cast_fp16_0)[name = string("query_states_67_cast_fp16")];
            tensor<int32, [2]> key_states_111_strides_0 = const()[name = string("key_states_111_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_111_pad_type_0 = const()[name = string("key_states_111_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_111_pad_0 = const()[name = string("key_states_111_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_111_dilations_0 = const()[name = string("key_states_111_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_111_groups_0 = const()[name = string("key_states_111_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_111_cast_fp16 = conv(dilations = key_states_111_dilations_0, groups = key_states_111_groups_0, pad = key_states_111_pad_0, pad_type = key_states_111_pad_type_0, strides = key_states_111_strides_0, weight = layers_11_self_attn_k_proj_weight_cast_fp16, x = var_4461_cast_fp16_0)[name = string("key_states_111_cast_fp16")];
            tensor<int32, [2]> value_states_67_strides_0 = const()[name = string("value_states_67_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_67_pad_type_0 = const()[name = string("value_states_67_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_67_pad_0 = const()[name = string("value_states_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_67_dilations_0 = const()[name = string("value_states_67_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_67_groups_0 = const()[name = string("value_states_67_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_67_cast_fp16 = conv(dilations = value_states_67_dilations_0, groups = value_states_67_groups_0, pad = value_states_67_pad_0, pad_type = value_states_67_pad_type_0, strides = value_states_67_strides_0, weight = layers_11_self_attn_v_proj_weight_cast_fp16, x = var_4461_cast_fp16_0)[name = string("value_states_67_cast_fp16")];
            tensor<int32, [4]> concat_132x = const()[name = string("concat_132x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_111_cast_fp16 = reshape(shape = concat_132x, x = query_states_67_cast_fp16)[name = string("x_111_cast_fp16")];
            tensor<int32, [4]> concat_133x = const()[name = string("concat_133x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_4518_cast_fp16 = reshape(shape = concat_133x, x = key_states_111_cast_fp16)[name = string("op_4518_cast_fp16")];
            tensor<int32, [4]> concat_134x = const()[name = string("concat_134x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_4525_cast_fp16 = reshape(shape = concat_134x, x = value_states_67_cast_fp16)[name = string("op_4525_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_4529_cast_fp16 = mul(x = x_111_cast_fp16, y = var_859_cast_fp16)[name = string("op_4529_cast_fp16")];
            tensor<int32, [2]> var_4530_split_sizes_0 = const()[name = string("op_4530_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4530_axis_0 = const()[name = string("op_4530_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_4530_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_4530_cast_fp16_1 = split(axis = var_4530_axis_0, split_sizes = var_4530_split_sizes_0, x = x_111_cast_fp16)[name = string("op_4530_cast_fp16")];
            fp16 const_114_promoted_to_fp16 = const()[name = string("const_114_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_4532_cast_fp16 = mul(x = var_4530_cast_fp16_1, y = const_114_promoted_to_fp16)[name = string("op_4532_cast_fp16")];
            int32 var_4534 = const()[name = string("op_4534"), val = int32(-2)];
            bool var_4535_interleave_0 = const()[name = string("op_4535_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_4535_cast_fp16 = concat(axis = var_4534, interleave = var_4535_interleave_0, values = (var_4532_cast_fp16, var_4530_cast_fp16_0))[name = string("op_4535_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_4536_cast_fp16 = mul(x = var_4535_cast_fp16, y = var_866_cast_fp16)[name = string("op_4536_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_69_cast_fp16 = add(x = var_4529_cast_fp16, y = var_4536_cast_fp16)[name = string("query_states_69_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_4542_cast_fp16 = mul(x = var_4518_cast_fp16, y = var_859_cast_fp16)[name = string("op_4542_cast_fp16")];
            tensor<int32, [2]> var_4543_split_sizes_0 = const()[name = string("op_4543_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4543_axis_0 = const()[name = string("op_4543_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_4543_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_4543_cast_fp16_1 = split(axis = var_4543_axis_0, split_sizes = var_4543_split_sizes_0, x = var_4518_cast_fp16)[name = string("op_4543_cast_fp16")];
            fp16 const_115_promoted_to_fp16 = const()[name = string("const_115_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_4545_cast_fp16 = mul(x = var_4543_cast_fp16_1, y = const_115_promoted_to_fp16)[name = string("op_4545_cast_fp16")];
            int32 var_4547 = const()[name = string("op_4547"), val = int32(-2)];
            bool var_4548_interleave_0 = const()[name = string("op_4548_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_4548_cast_fp16 = concat(axis = var_4547, interleave = var_4548_interleave_0, values = (var_4545_cast_fp16, var_4543_cast_fp16_0))[name = string("op_4548_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_4549_cast_fp16 = mul(x = var_4548_cast_fp16, y = var_866_cast_fp16)[name = string("op_4549_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_115_cast_fp16 = add(x = var_4542_cast_fp16, y = var_4549_cast_fp16)[name = string("key_states_115_cast_fp16")];
            tensor<int32, [1]> expand_dims_132 = const()[name = string("expand_dims_132"), val = tensor<int32, [1]>([11])];
            tensor<int32, [1]> expand_dims_133 = const()[name = string("expand_dims_133"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_135 = const()[name = string("expand_dims_135"), val = tensor<int32, [1]>([0])];
            int32 concat_137_axis_0 = const()[name = string("concat_137_axis_0"), val = int32(0)];
            bool concat_137_interleave_0 = const()[name = string("concat_137_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_137 = concat(axis = concat_137_axis_0, interleave = concat_137_interleave_0, values = (expand_dims_132, expand_dims_133, position_id, expand_dims_135))[name = string("concat_137")];
            tensor<int32, [1]> expand_dims_136 = const()[name = string("expand_dims_136"), val = tensor<int32, [1]>([12])];
            tensor<int32, [1]> concat_138_values1_0 = const()[name = string("concat_138_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_138_values3_0 = const()[name = string("concat_138_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_138_axis_0 = const()[name = string("concat_138_axis_0"), val = int32(0)];
            bool concat_138_interleave_0 = const()[name = string("concat_138_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_138 = concat(axis = concat_138_axis_0, interleave = concat_138_interleave_0, values = (expand_dims_136, concat_138_values1_0, cache_position_end, concat_138_values3_0))[name = string("concat_138")];
            tensor<int32, [4]> key_states_117_perm_0 = const()[name = string("key_states_117_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_12_stride_0 = const()[name = string("key_cache_internal_tensor_assign_12_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_12_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_12_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_12_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_12_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_12_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_12_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_117_cast_fp16 = transpose(perm = key_states_117_perm_0, x = key_states_115_cast_fp16)[name = string("transpose_224")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_12_cast_fp16 = slice_update(begin = concat_137, begin_mask = key_cache_internal_tensor_assign_12_begin_mask_0, end = concat_138, end_mask = key_cache_internal_tensor_assign_12_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_12_squeeze_mask_0, stride = key_cache_internal_tensor_assign_12_stride_0, update = key_states_117_cast_fp16, x = coreml_update_state_132)[name = string("key_cache_internal_tensor_assign_12_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_12_cast_fp16, input = key_cache)[name = string("coreml_update_state_134_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_134 = read_state(input = key_cache)[name = string("coreml_update_state_134")];
            tensor<int32, [4]> value_states_69_perm_0 = const()[name = string("value_states_69_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_12_stride_0 = const()[name = string("value_cache_internal_tensor_assign_12_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_12_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_12_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_12_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_12_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_12_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_12_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_69_cast_fp16 = transpose(perm = value_states_69_perm_0, x = var_4525_cast_fp16)[name = string("transpose_223")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_12_cast_fp16 = slice_update(begin = concat_137, begin_mask = value_cache_internal_tensor_assign_12_begin_mask_0, end = concat_138, end_mask = value_cache_internal_tensor_assign_12_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_12_squeeze_mask_0, stride = value_cache_internal_tensor_assign_12_stride_0, update = value_states_69_cast_fp16, x = coreml_update_state_133)[name = string("value_cache_internal_tensor_assign_12_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_12_cast_fp16, input = value_cache)[name = string("coreml_update_state_135_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_135 = read_state(input = value_cache)[name = string("coreml_update_state_135")];
            tensor<int32, [4]> var_4619_begin_0 = const()[name = string("op_4619_begin_0"), val = tensor<int32, [4]>([11, 0, 0, 0])];
            tensor<int32, [4]> var_4619_end_0 = const()[name = string("op_4619_end_0"), val = tensor<int32, [4]>([12, 2, 2048, 128])];
            tensor<bool, [4]> var_4619_end_mask_0 = const()[name = string("op_4619_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4619_cast_fp16 = slice_by_index(begin = var_4619_begin_0, end = var_4619_end_0, end_mask = var_4619_end_mask_0, x = coreml_update_state_134)[name = string("op_4619_cast_fp16")];
            tensor<int32, [2]> tile_22 = const()[name = string("tile_22"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4622_axis_0 = const()[name = string("op_4622_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4622_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4622_cast_fp16_1 = split(axis = var_4622_axis_0, split_sizes = tile_22, x = var_4619_cast_fp16)[name = string("op_4622_cast_fp16")];
            tensor<int32, [4]> var_4629_begin_0 = const()[name = string("op_4629_begin_0"), val = tensor<int32, [4]>([11, 0, 0, 0])];
            tensor<int32, [4]> var_4629_end_0 = const()[name = string("op_4629_end_0"), val = tensor<int32, [4]>([12, 2, 2048, 128])];
            tensor<bool, [4]> var_4629_end_mask_0 = const()[name = string("op_4629_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4629_cast_fp16 = slice_by_index(begin = var_4629_begin_0, end = var_4629_end_0, end_mask = var_4629_end_mask_0, x = coreml_update_state_135)[name = string("op_4629_cast_fp16")];
            tensor<int32, [2]> tile_23 = const()[name = string("tile_23"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4632_axis_0 = const()[name = string("op_4632_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4632_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4632_cast_fp16_1 = split(axis = var_4632_axis_0, split_sizes = tile_23, x = var_4629_cast_fp16)[name = string("op_4632_cast_fp16")];
            tensor<int32, [2]> var_4635_split_sizes_0 = const()[name = string("op_4635_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_4635_axis_0 = const()[name = string("op_4635_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_4635_0, tensor<fp16, [1, 8, 128, 8]> var_4635_1 = split(axis = var_4635_axis_0, split_sizes = var_4635_split_sizes_0, x = query_states_69_cast_fp16)[name = string("op_4635")];
            bool attn_weights_177_transpose_x_0 = const()[name = string("attn_weights_177_transpose_x_0"), val = bool(false)];
            bool attn_weights_177_transpose_y_0 = const()[name = string("attn_weights_177_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_177_cast_fp16 = matmul(transpose_x = attn_weights_177_transpose_x_0, transpose_y = attn_weights_177_transpose_y_0, x = var_4622_cast_fp16_0, y = var_4635_0)[name = string("attn_weights_177_cast_fp16")];
            fp16 var_4638_to_fp16 = const()[name = string("op_4638_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_179_cast_fp16 = mul(x = attn_weights_177_cast_fp16, y = var_4638_to_fp16)[name = string("attn_weights_179_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_181_cast_fp16 = add(x = attn_weights_179_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_181_cast_fp16")];
            int32 var_4642 = const()[name = string("op_4642"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_183_cast_fp16 = softmax(axis = var_4642, x = attn_weights_181_cast_fp16)[name = string("attn_weights_183_cast_fp16")];
            bool var_4648_transpose_x_1 = const()[name = string("op_4648_transpose_x_1"), val = bool(true)];
            bool var_4648_transpose_y_1 = const()[name = string("op_4648_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_4648_cast_fp16 = matmul(transpose_x = var_4648_transpose_x_1, transpose_y = var_4648_transpose_y_1, x = attn_weights_183_cast_fp16, y = var_4632_cast_fp16_0)[name = string("op_4648_cast_fp16")];
            bool attn_weights_185_transpose_x_0 = const()[name = string("attn_weights_185_transpose_x_0"), val = bool(false)];
            bool attn_weights_185_transpose_y_0 = const()[name = string("attn_weights_185_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_185_cast_fp16 = matmul(transpose_x = attn_weights_185_transpose_x_0, transpose_y = attn_weights_185_transpose_y_0, x = var_4622_cast_fp16_1, y = var_4635_1)[name = string("attn_weights_185_cast_fp16")];
            fp16 var_4650_to_fp16 = const()[name = string("op_4650_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_187_cast_fp16 = mul(x = attn_weights_185_cast_fp16, y = var_4650_to_fp16)[name = string("attn_weights_187_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_189_cast_fp16 = add(x = attn_weights_187_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_189_cast_fp16")];
            int32 var_4654 = const()[name = string("op_4654"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_191_cast_fp16 = softmax(axis = var_4654, x = attn_weights_189_cast_fp16)[name = string("attn_weights_191_cast_fp16")];
            bool attn_output_89_transpose_x_1 = const()[name = string("attn_output_89_transpose_x_1"), val = bool(true)];
            bool attn_output_89_transpose_y_1 = const()[name = string("attn_output_89_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_89_cast_fp16 = matmul(transpose_x = attn_output_89_transpose_x_1, transpose_y = attn_output_89_transpose_y_1, x = attn_weights_191_cast_fp16, y = var_4632_cast_fp16_1)[name = string("attn_output_89_cast_fp16")];
            int32 var_4662 = const()[name = string("op_4662"), val = int32(1)];
            bool attn_output_91_interleave_0 = const()[name = string("attn_output_91_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_91_cast_fp16 = concat(axis = var_4662, interleave = attn_output_91_interleave_0, values = (var_4648_cast_fp16, attn_output_89_cast_fp16))[name = string("attn_output_91_cast_fp16")];
            tensor<int32, [4]> var_4666_perm_0 = const()[name = string("op_4666_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_143x = const()[name = string("concat_143x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_4666_cast_fp16 = transpose(perm = var_4666_perm_0, x = attn_output_91_cast_fp16)[name = string("transpose_222")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_95_cast_fp16 = reshape(shape = concat_143x, x = var_4666_cast_fp16)[name = string("attn_output_95_cast_fp16")];
            tensor<int32, [2]> hidden_states_113_strides_0 = const()[name = string("hidden_states_113_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_113_pad_type_0 = const()[name = string("hidden_states_113_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_113_pad_0 = const()[name = string("hidden_states_113_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_113_dilations_0 = const()[name = string("hidden_states_113_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_113_groups_0 = const()[name = string("hidden_states_113_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_113_cast_fp16 = conv(dilations = hidden_states_113_dilations_0, groups = hidden_states_113_groups_0, pad = hidden_states_113_pad_0, pad_type = hidden_states_113_pad_type_0, strides = hidden_states_113_strides_0, weight = layers_11_self_attn_o_proj_weight_cast_fp16, x = attn_output_95_cast_fp16)[name = string("hidden_states_113_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_115_cast_fp16 = add(x = hidden_states_109_cast_fp16, y = hidden_states_113_cast_fp16)[name = string("hidden_states_115_cast_fp16")];
            fp16 const_120_promoted_to_fp16 = const()[name = string("const_120_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_4699_cast_fp16 = mul(x = hidden_states_115_cast_fp16, y = const_120_promoted_to_fp16)[name = string("op_4699_cast_fp16")];
            int32 var_4697 = const()[name = string("op_4697"), val = int32(1)];
            bool doubled_93_interleave_0 = const()[name = string("doubled_93_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_93_cast_fp16 = concat(axis = var_4697, interleave = doubled_93_interleave_0, values = (hidden_states_115_cast_fp16, var_4699_cast_fp16))[name = string("doubled_93_cast_fp16")];
            tensor<int32, [1]> out_47_axes_0 = const()[name = string("out_47_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_47_gamma_0_to_fp16 = const()[name = string("out_47_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406985984)))];
            fp16 var_4709_to_fp16 = const()[name = string("op_4709_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_47_cast_fp16 = layer_norm(axes = out_47_axes_0, epsilon = var_4709_to_fp16, gamma = out_47_gamma_0_to_fp16, x = doubled_93_cast_fp16)[name = string("out_47_cast_fp16")];
            tensor<int32, [2]> var_4720_split_sizes_0 = const()[name = string("op_4720_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4720_axis_0 = const()[name = string("op_4720_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_4720_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_4720_cast_fp16_1 = split(axis = var_4720_axis_0, split_sizes = var_4720_split_sizes_0, x = out_47_cast_fp16)[name = string("op_4720_cast_fp16")];
            tensor<int32, [2]> input_23_strides_0 = const()[name = string("input_23_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_23_pad_type_0 = const()[name = string("input_23_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_23_pad_0 = const()[name = string("input_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_23_dilations_0 = const()[name = string("input_23_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_23_groups_0 = const()[name = string("input_23_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_23_cast_fp16 = conv(dilations = input_23_dilations_0, groups = input_23_groups_0, pad = input_23_pad_0, pad_type = input_23_pad_type_0, strides = input_23_strides_0, weight = layers_11_mlp_gate_proj_weight_cast_fp16, x = var_4720_cast_fp16_0)[name = string("input_23_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_4737_cast_fp16 = silu(x = input_23_cast_fp16)[name = string("op_4737_cast_fp16")];
            tensor<int32, [2]> var_4743_strides_0 = const()[name = string("op_4743_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_4743_pad_type_0 = const()[name = string("op_4743_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_4743_pad_0 = const()[name = string("op_4743_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_4743_dilations_0 = const()[name = string("op_4743_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4743_groups_0 = const()[name = string("op_4743_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_4743_cast_fp16 = conv(dilations = var_4743_dilations_0, groups = var_4743_groups_0, pad = var_4743_pad_0, pad_type = var_4743_pad_type_0, strides = var_4743_strides_0, weight = layers_11_mlp_up_proj_weight_cast_fp16, x = var_4720_cast_fp16_0)[name = string("op_4743_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_119_cast_fp16 = mul(x = var_4737_cast_fp16, y = var_4743_cast_fp16)[name = string("x_119_cast_fp16")];
            tensor<int32, [2]> hidden_states_117_strides_0 = const()[name = string("hidden_states_117_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_117_pad_type_0 = const()[name = string("hidden_states_117_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_117_pad_0 = const()[name = string("hidden_states_117_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_117_dilations_0 = const()[name = string("hidden_states_117_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_117_groups_0 = const()[name = string("hidden_states_117_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_117_cast_fp16 = conv(dilations = hidden_states_117_dilations_0, groups = hidden_states_117_groups_0, pad = hidden_states_117_pad_0, pad_type = hidden_states_117_pad_type_0, strides = hidden_states_117_strides_0, weight = layers_11_mlp_down_proj_weight_cast_fp16, x = x_119_cast_fp16)[name = string("hidden_states_117_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_119_cast_fp16 = add(x = hidden_states_115_cast_fp16, y = hidden_states_117_cast_fp16)[name = string("hidden_states_119_cast_fp16")];
            fp16 const_122_promoted_to_fp16 = const()[name = string("const_122_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_4761_cast_fp16 = mul(x = hidden_states_119_cast_fp16, y = const_122_promoted_to_fp16)[name = string("op_4761_cast_fp16")];
            int32 var_4759 = const()[name = string("op_4759"), val = int32(1)];
            bool doubled_97_interleave_0 = const()[name = string("doubled_97_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_97_cast_fp16 = concat(axis = var_4759, interleave = doubled_97_interleave_0, values = (hidden_states_119_cast_fp16, var_4761_cast_fp16))[name = string("doubled_97_cast_fp16")];
            tensor<int32, [1]> out_49_axes_0 = const()[name = string("out_49_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_49_gamma_0_to_fp16 = const()[name = string("out_49_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406994240)))];
            fp16 var_4771_to_fp16 = const()[name = string("op_4771_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_49_cast_fp16 = layer_norm(axes = out_49_axes_0, epsilon = var_4771_to_fp16, gamma = out_49_gamma_0_to_fp16, x = doubled_97_cast_fp16)[name = string("out_49_cast_fp16")];
            tensor<int32, [2]> var_4782_split_sizes_0 = const()[name = string("op_4782_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4782_axis_0 = const()[name = string("op_4782_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_4782_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_4782_cast_fp16_1 = split(axis = var_4782_axis_0, split_sizes = var_4782_split_sizes_0, x = out_49_cast_fp16)[name = string("op_4782_cast_fp16")];
            tensor<int32, [2]> query_states_73_strides_0 = const()[name = string("query_states_73_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_73_pad_type_0 = const()[name = string("query_states_73_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_73_pad_0 = const()[name = string("query_states_73_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_73_dilations_0 = const()[name = string("query_states_73_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_73_groups_0 = const()[name = string("query_states_73_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_73_cast_fp16 = conv(dilations = query_states_73_dilations_0, groups = query_states_73_groups_0, pad = query_states_73_pad_0, pad_type = query_states_73_pad_type_0, strides = query_states_73_strides_0, weight = layers_12_self_attn_q_proj_weight_cast_fp16, x = var_4782_cast_fp16_0)[name = string("query_states_73_cast_fp16")];
            tensor<int32, [2]> key_states_121_strides_0 = const()[name = string("key_states_121_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_121_pad_type_0 = const()[name = string("key_states_121_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_121_pad_0 = const()[name = string("key_states_121_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_121_dilations_0 = const()[name = string("key_states_121_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_121_groups_0 = const()[name = string("key_states_121_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_121_cast_fp16 = conv(dilations = key_states_121_dilations_0, groups = key_states_121_groups_0, pad = key_states_121_pad_0, pad_type = key_states_121_pad_type_0, strides = key_states_121_strides_0, weight = layers_12_self_attn_k_proj_weight_cast_fp16, x = var_4782_cast_fp16_0)[name = string("key_states_121_cast_fp16")];
            tensor<int32, [2]> value_states_73_strides_0 = const()[name = string("value_states_73_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_73_pad_type_0 = const()[name = string("value_states_73_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_73_pad_0 = const()[name = string("value_states_73_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_73_dilations_0 = const()[name = string("value_states_73_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_73_groups_0 = const()[name = string("value_states_73_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_73_cast_fp16 = conv(dilations = value_states_73_dilations_0, groups = value_states_73_groups_0, pad = value_states_73_pad_0, pad_type = value_states_73_pad_type_0, strides = value_states_73_strides_0, weight = layers_12_self_attn_v_proj_weight_cast_fp16, x = var_4782_cast_fp16_0)[name = string("value_states_73_cast_fp16")];
            tensor<int32, [4]> concat_144x = const()[name = string("concat_144x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_121_cast_fp16 = reshape(shape = concat_144x, x = query_states_73_cast_fp16)[name = string("x_121_cast_fp16")];
            tensor<int32, [4]> concat_145x = const()[name = string("concat_145x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_4839_cast_fp16 = reshape(shape = concat_145x, x = key_states_121_cast_fp16)[name = string("op_4839_cast_fp16")];
            tensor<int32, [4]> concat_146x = const()[name = string("concat_146x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_4846_cast_fp16 = reshape(shape = concat_146x, x = value_states_73_cast_fp16)[name = string("op_4846_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_4850_cast_fp16 = mul(x = x_121_cast_fp16, y = var_859_cast_fp16)[name = string("op_4850_cast_fp16")];
            tensor<int32, [2]> var_4851_split_sizes_0 = const()[name = string("op_4851_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4851_axis_0 = const()[name = string("op_4851_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_4851_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_4851_cast_fp16_1 = split(axis = var_4851_axis_0, split_sizes = var_4851_split_sizes_0, x = x_121_cast_fp16)[name = string("op_4851_cast_fp16")];
            fp16 const_124_promoted_to_fp16 = const()[name = string("const_124_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_4853_cast_fp16 = mul(x = var_4851_cast_fp16_1, y = const_124_promoted_to_fp16)[name = string("op_4853_cast_fp16")];
            int32 var_4855 = const()[name = string("op_4855"), val = int32(-2)];
            bool var_4856_interleave_0 = const()[name = string("op_4856_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_4856_cast_fp16 = concat(axis = var_4855, interleave = var_4856_interleave_0, values = (var_4853_cast_fp16, var_4851_cast_fp16_0))[name = string("op_4856_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_4857_cast_fp16 = mul(x = var_4856_cast_fp16, y = var_866_cast_fp16)[name = string("op_4857_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_75_cast_fp16 = add(x = var_4850_cast_fp16, y = var_4857_cast_fp16)[name = string("query_states_75_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_4863_cast_fp16 = mul(x = var_4839_cast_fp16, y = var_859_cast_fp16)[name = string("op_4863_cast_fp16")];
            tensor<int32, [2]> var_4864_split_sizes_0 = const()[name = string("op_4864_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4864_axis_0 = const()[name = string("op_4864_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_4864_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_4864_cast_fp16_1 = split(axis = var_4864_axis_0, split_sizes = var_4864_split_sizes_0, x = var_4839_cast_fp16)[name = string("op_4864_cast_fp16")];
            fp16 const_125_promoted_to_fp16 = const()[name = string("const_125_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_4866_cast_fp16 = mul(x = var_4864_cast_fp16_1, y = const_125_promoted_to_fp16)[name = string("op_4866_cast_fp16")];
            int32 var_4868 = const()[name = string("op_4868"), val = int32(-2)];
            bool var_4869_interleave_0 = const()[name = string("op_4869_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_4869_cast_fp16 = concat(axis = var_4868, interleave = var_4869_interleave_0, values = (var_4866_cast_fp16, var_4864_cast_fp16_0))[name = string("op_4869_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_4870_cast_fp16 = mul(x = var_4869_cast_fp16, y = var_866_cast_fp16)[name = string("op_4870_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_125_cast_fp16 = add(x = var_4863_cast_fp16, y = var_4870_cast_fp16)[name = string("key_states_125_cast_fp16")];
            tensor<int32, [1]> expand_dims_144 = const()[name = string("expand_dims_144"), val = tensor<int32, [1]>([12])];
            tensor<int32, [1]> expand_dims_145 = const()[name = string("expand_dims_145"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_147 = const()[name = string("expand_dims_147"), val = tensor<int32, [1]>([0])];
            int32 concat_149_axis_0 = const()[name = string("concat_149_axis_0"), val = int32(0)];
            bool concat_149_interleave_0 = const()[name = string("concat_149_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_149 = concat(axis = concat_149_axis_0, interleave = concat_149_interleave_0, values = (expand_dims_144, expand_dims_145, position_id, expand_dims_147))[name = string("concat_149")];
            tensor<int32, [1]> expand_dims_148 = const()[name = string("expand_dims_148"), val = tensor<int32, [1]>([13])];
            tensor<int32, [1]> concat_150_values1_0 = const()[name = string("concat_150_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_150_values3_0 = const()[name = string("concat_150_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_150_axis_0 = const()[name = string("concat_150_axis_0"), val = int32(0)];
            bool concat_150_interleave_0 = const()[name = string("concat_150_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_150 = concat(axis = concat_150_axis_0, interleave = concat_150_interleave_0, values = (expand_dims_148, concat_150_values1_0, cache_position_end, concat_150_values3_0))[name = string("concat_150")];
            tensor<int32, [4]> key_states_127_perm_0 = const()[name = string("key_states_127_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_13_stride_0 = const()[name = string("key_cache_internal_tensor_assign_13_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_13_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_13_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_13_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_13_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_13_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_13_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_127_cast_fp16 = transpose(perm = key_states_127_perm_0, x = key_states_125_cast_fp16)[name = string("transpose_221")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_13_cast_fp16 = slice_update(begin = concat_149, begin_mask = key_cache_internal_tensor_assign_13_begin_mask_0, end = concat_150, end_mask = key_cache_internal_tensor_assign_13_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_13_squeeze_mask_0, stride = key_cache_internal_tensor_assign_13_stride_0, update = key_states_127_cast_fp16, x = coreml_update_state_134)[name = string("key_cache_internal_tensor_assign_13_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_13_cast_fp16, input = key_cache)[name = string("coreml_update_state_136_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_136 = read_state(input = key_cache)[name = string("coreml_update_state_136")];
            tensor<int32, [4]> value_states_75_perm_0 = const()[name = string("value_states_75_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_13_stride_0 = const()[name = string("value_cache_internal_tensor_assign_13_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_13_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_13_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_13_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_13_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_13_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_13_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_75_cast_fp16 = transpose(perm = value_states_75_perm_0, x = var_4846_cast_fp16)[name = string("transpose_220")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_13_cast_fp16 = slice_update(begin = concat_149, begin_mask = value_cache_internal_tensor_assign_13_begin_mask_0, end = concat_150, end_mask = value_cache_internal_tensor_assign_13_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_13_squeeze_mask_0, stride = value_cache_internal_tensor_assign_13_stride_0, update = value_states_75_cast_fp16, x = coreml_update_state_135)[name = string("value_cache_internal_tensor_assign_13_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_13_cast_fp16, input = value_cache)[name = string("coreml_update_state_137_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_137 = read_state(input = value_cache)[name = string("coreml_update_state_137")];
            tensor<int32, [4]> var_4940_begin_0 = const()[name = string("op_4940_begin_0"), val = tensor<int32, [4]>([12, 0, 0, 0])];
            tensor<int32, [4]> var_4940_end_0 = const()[name = string("op_4940_end_0"), val = tensor<int32, [4]>([13, 2, 2048, 128])];
            tensor<bool, [4]> var_4940_end_mask_0 = const()[name = string("op_4940_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4940_cast_fp16 = slice_by_index(begin = var_4940_begin_0, end = var_4940_end_0, end_mask = var_4940_end_mask_0, x = coreml_update_state_136)[name = string("op_4940_cast_fp16")];
            tensor<int32, [2]> tile_24 = const()[name = string("tile_24"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4943_axis_0 = const()[name = string("op_4943_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4943_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4943_cast_fp16_1 = split(axis = var_4943_axis_0, split_sizes = tile_24, x = var_4940_cast_fp16)[name = string("op_4943_cast_fp16")];
            tensor<int32, [4]> var_4950_begin_0 = const()[name = string("op_4950_begin_0"), val = tensor<int32, [4]>([12, 0, 0, 0])];
            tensor<int32, [4]> var_4950_end_0 = const()[name = string("op_4950_end_0"), val = tensor<int32, [4]>([13, 2, 2048, 128])];
            tensor<bool, [4]> var_4950_end_mask_0 = const()[name = string("op_4950_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4950_cast_fp16 = slice_by_index(begin = var_4950_begin_0, end = var_4950_end_0, end_mask = var_4950_end_mask_0, x = coreml_update_state_137)[name = string("op_4950_cast_fp16")];
            tensor<int32, [2]> tile_25 = const()[name = string("tile_25"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4953_axis_0 = const()[name = string("op_4953_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4953_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4953_cast_fp16_1 = split(axis = var_4953_axis_0, split_sizes = tile_25, x = var_4950_cast_fp16)[name = string("op_4953_cast_fp16")];
            tensor<int32, [2]> var_4956_split_sizes_0 = const()[name = string("op_4956_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_4956_axis_0 = const()[name = string("op_4956_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_4956_0, tensor<fp16, [1, 8, 128, 8]> var_4956_1 = split(axis = var_4956_axis_0, split_sizes = var_4956_split_sizes_0, x = query_states_75_cast_fp16)[name = string("op_4956")];
            bool attn_weights_193_transpose_x_0 = const()[name = string("attn_weights_193_transpose_x_0"), val = bool(false)];
            bool attn_weights_193_transpose_y_0 = const()[name = string("attn_weights_193_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_193_cast_fp16 = matmul(transpose_x = attn_weights_193_transpose_x_0, transpose_y = attn_weights_193_transpose_y_0, x = var_4943_cast_fp16_0, y = var_4956_0)[name = string("attn_weights_193_cast_fp16")];
            fp16 var_4959_to_fp16 = const()[name = string("op_4959_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_195_cast_fp16 = mul(x = attn_weights_193_cast_fp16, y = var_4959_to_fp16)[name = string("attn_weights_195_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_197_cast_fp16 = add(x = attn_weights_195_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_197_cast_fp16")];
            int32 var_4963 = const()[name = string("op_4963"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_199_cast_fp16 = softmax(axis = var_4963, x = attn_weights_197_cast_fp16)[name = string("attn_weights_199_cast_fp16")];
            bool var_4969_transpose_x_1 = const()[name = string("op_4969_transpose_x_1"), val = bool(true)];
            bool var_4969_transpose_y_1 = const()[name = string("op_4969_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_4969_cast_fp16 = matmul(transpose_x = var_4969_transpose_x_1, transpose_y = var_4969_transpose_y_1, x = attn_weights_199_cast_fp16, y = var_4953_cast_fp16_0)[name = string("op_4969_cast_fp16")];
            bool attn_weights_201_transpose_x_0 = const()[name = string("attn_weights_201_transpose_x_0"), val = bool(false)];
            bool attn_weights_201_transpose_y_0 = const()[name = string("attn_weights_201_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_201_cast_fp16 = matmul(transpose_x = attn_weights_201_transpose_x_0, transpose_y = attn_weights_201_transpose_y_0, x = var_4943_cast_fp16_1, y = var_4956_1)[name = string("attn_weights_201_cast_fp16")];
            fp16 var_4971_to_fp16 = const()[name = string("op_4971_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_203_cast_fp16 = mul(x = attn_weights_201_cast_fp16, y = var_4971_to_fp16)[name = string("attn_weights_203_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_205_cast_fp16 = add(x = attn_weights_203_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_205_cast_fp16")];
            int32 var_4975 = const()[name = string("op_4975"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_207_cast_fp16 = softmax(axis = var_4975, x = attn_weights_205_cast_fp16)[name = string("attn_weights_207_cast_fp16")];
            bool attn_output_97_transpose_x_1 = const()[name = string("attn_output_97_transpose_x_1"), val = bool(true)];
            bool attn_output_97_transpose_y_1 = const()[name = string("attn_output_97_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_97_cast_fp16 = matmul(transpose_x = attn_output_97_transpose_x_1, transpose_y = attn_output_97_transpose_y_1, x = attn_weights_207_cast_fp16, y = var_4953_cast_fp16_1)[name = string("attn_output_97_cast_fp16")];
            int32 var_4983 = const()[name = string("op_4983"), val = int32(1)];
            bool attn_output_99_interleave_0 = const()[name = string("attn_output_99_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_99_cast_fp16 = concat(axis = var_4983, interleave = attn_output_99_interleave_0, values = (var_4969_cast_fp16, attn_output_97_cast_fp16))[name = string("attn_output_99_cast_fp16")];
            tensor<int32, [4]> var_4987_perm_0 = const()[name = string("op_4987_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_155x = const()[name = string("concat_155x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_4987_cast_fp16 = transpose(perm = var_4987_perm_0, x = attn_output_99_cast_fp16)[name = string("transpose_219")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_103_cast_fp16 = reshape(shape = concat_155x, x = var_4987_cast_fp16)[name = string("attn_output_103_cast_fp16")];
            tensor<int32, [2]> hidden_states_123_strides_0 = const()[name = string("hidden_states_123_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_123_pad_type_0 = const()[name = string("hidden_states_123_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_123_pad_0 = const()[name = string("hidden_states_123_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_123_dilations_0 = const()[name = string("hidden_states_123_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_123_groups_0 = const()[name = string("hidden_states_123_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_123_cast_fp16 = conv(dilations = hidden_states_123_dilations_0, groups = hidden_states_123_groups_0, pad = hidden_states_123_pad_0, pad_type = hidden_states_123_pad_type_0, strides = hidden_states_123_strides_0, weight = layers_12_self_attn_o_proj_weight_cast_fp16, x = attn_output_103_cast_fp16)[name = string("hidden_states_123_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_125_cast_fp16 = add(x = hidden_states_119_cast_fp16, y = hidden_states_123_cast_fp16)[name = string("hidden_states_125_cast_fp16")];
            fp16 const_130_promoted_to_fp16 = const()[name = string("const_130_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_5020_cast_fp16 = mul(x = hidden_states_125_cast_fp16, y = const_130_promoted_to_fp16)[name = string("op_5020_cast_fp16")];
            int32 var_5018 = const()[name = string("op_5018"), val = int32(1)];
            bool doubled_101_interleave_0 = const()[name = string("doubled_101_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_101_cast_fp16 = concat(axis = var_5018, interleave = doubled_101_interleave_0, values = (hidden_states_125_cast_fp16, var_5020_cast_fp16))[name = string("doubled_101_cast_fp16")];
            tensor<int32, [1]> out_51_axes_0 = const()[name = string("out_51_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_51_gamma_0_to_fp16 = const()[name = string("out_51_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407002496)))];
            fp16 var_5030_to_fp16 = const()[name = string("op_5030_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_51_cast_fp16 = layer_norm(axes = out_51_axes_0, epsilon = var_5030_to_fp16, gamma = out_51_gamma_0_to_fp16, x = doubled_101_cast_fp16)[name = string("out_51_cast_fp16")];
            tensor<int32, [2]> var_5041_split_sizes_0 = const()[name = string("op_5041_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5041_axis_0 = const()[name = string("op_5041_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_5041_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_5041_cast_fp16_1 = split(axis = var_5041_axis_0, split_sizes = var_5041_split_sizes_0, x = out_51_cast_fp16)[name = string("op_5041_cast_fp16")];
            tensor<int32, [2]> input_25_strides_0 = const()[name = string("input_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_25_pad_type_0 = const()[name = string("input_25_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_25_pad_0 = const()[name = string("input_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_25_dilations_0 = const()[name = string("input_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_25_groups_0 = const()[name = string("input_25_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_25_cast_fp16 = conv(dilations = input_25_dilations_0, groups = input_25_groups_0, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = input_25_strides_0, weight = layers_12_mlp_gate_proj_weight_cast_fp16, x = var_5041_cast_fp16_0)[name = string("input_25_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_5058_cast_fp16 = silu(x = input_25_cast_fp16)[name = string("op_5058_cast_fp16")];
            tensor<int32, [2]> var_5064_strides_0 = const()[name = string("op_5064_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_5064_pad_type_0 = const()[name = string("op_5064_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_5064_pad_0 = const()[name = string("op_5064_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_5064_dilations_0 = const()[name = string("op_5064_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5064_groups_0 = const()[name = string("op_5064_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_5064_cast_fp16 = conv(dilations = var_5064_dilations_0, groups = var_5064_groups_0, pad = var_5064_pad_0, pad_type = var_5064_pad_type_0, strides = var_5064_strides_0, weight = layers_12_mlp_up_proj_weight_cast_fp16, x = var_5041_cast_fp16_0)[name = string("op_5064_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_129_cast_fp16 = mul(x = var_5058_cast_fp16, y = var_5064_cast_fp16)[name = string("x_129_cast_fp16")];
            tensor<int32, [2]> hidden_states_127_strides_0 = const()[name = string("hidden_states_127_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_127_pad_type_0 = const()[name = string("hidden_states_127_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_127_pad_0 = const()[name = string("hidden_states_127_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_127_dilations_0 = const()[name = string("hidden_states_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_127_groups_0 = const()[name = string("hidden_states_127_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_127_cast_fp16 = conv(dilations = hidden_states_127_dilations_0, groups = hidden_states_127_groups_0, pad = hidden_states_127_pad_0, pad_type = hidden_states_127_pad_type_0, strides = hidden_states_127_strides_0, weight = layers_12_mlp_down_proj_weight_cast_fp16, x = x_129_cast_fp16)[name = string("hidden_states_127_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_129_cast_fp16 = add(x = hidden_states_125_cast_fp16, y = hidden_states_127_cast_fp16)[name = string("hidden_states_129_cast_fp16")];
            fp16 const_132_promoted_to_fp16 = const()[name = string("const_132_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_5082_cast_fp16 = mul(x = hidden_states_129_cast_fp16, y = const_132_promoted_to_fp16)[name = string("op_5082_cast_fp16")];
            int32 var_5080 = const()[name = string("op_5080"), val = int32(1)];
            bool doubled_105_interleave_0 = const()[name = string("doubled_105_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_105_cast_fp16 = concat(axis = var_5080, interleave = doubled_105_interleave_0, values = (hidden_states_129_cast_fp16, var_5082_cast_fp16))[name = string("doubled_105_cast_fp16")];
            tensor<int32, [1]> out_53_axes_0 = const()[name = string("out_53_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_53_gamma_0_to_fp16 = const()[name = string("out_53_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407010752)))];
            fp16 var_5092_to_fp16 = const()[name = string("op_5092_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_53_cast_fp16 = layer_norm(axes = out_53_axes_0, epsilon = var_5092_to_fp16, gamma = out_53_gamma_0_to_fp16, x = doubled_105_cast_fp16)[name = string("out_53_cast_fp16")];
            tensor<int32, [2]> var_5103_split_sizes_0 = const()[name = string("op_5103_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5103_axis_0 = const()[name = string("op_5103_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_5103_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_5103_cast_fp16_1 = split(axis = var_5103_axis_0, split_sizes = var_5103_split_sizes_0, x = out_53_cast_fp16)[name = string("op_5103_cast_fp16")];
            tensor<int32, [2]> query_states_79_strides_0 = const()[name = string("query_states_79_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_79_pad_type_0 = const()[name = string("query_states_79_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_79_pad_0 = const()[name = string("query_states_79_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_79_dilations_0 = const()[name = string("query_states_79_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_79_groups_0 = const()[name = string("query_states_79_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_79_cast_fp16 = conv(dilations = query_states_79_dilations_0, groups = query_states_79_groups_0, pad = query_states_79_pad_0, pad_type = query_states_79_pad_type_0, strides = query_states_79_strides_0, weight = layers_13_self_attn_q_proj_weight_cast_fp16, x = var_5103_cast_fp16_0)[name = string("query_states_79_cast_fp16")];
            tensor<int32, [2]> key_states_131_strides_0 = const()[name = string("key_states_131_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_131_pad_type_0 = const()[name = string("key_states_131_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_131_pad_0 = const()[name = string("key_states_131_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_131_dilations_0 = const()[name = string("key_states_131_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_131_groups_0 = const()[name = string("key_states_131_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_131_cast_fp16 = conv(dilations = key_states_131_dilations_0, groups = key_states_131_groups_0, pad = key_states_131_pad_0, pad_type = key_states_131_pad_type_0, strides = key_states_131_strides_0, weight = layers_13_self_attn_k_proj_weight_cast_fp16, x = var_5103_cast_fp16_0)[name = string("key_states_131_cast_fp16")];
            tensor<int32, [2]> value_states_79_strides_0 = const()[name = string("value_states_79_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_79_pad_type_0 = const()[name = string("value_states_79_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_79_pad_0 = const()[name = string("value_states_79_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_79_dilations_0 = const()[name = string("value_states_79_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_79_groups_0 = const()[name = string("value_states_79_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_79_cast_fp16 = conv(dilations = value_states_79_dilations_0, groups = value_states_79_groups_0, pad = value_states_79_pad_0, pad_type = value_states_79_pad_type_0, strides = value_states_79_strides_0, weight = layers_13_self_attn_v_proj_weight_cast_fp16, x = var_5103_cast_fp16_0)[name = string("value_states_79_cast_fp16")];
            tensor<int32, [4]> concat_156x = const()[name = string("concat_156x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_131_cast_fp16 = reshape(shape = concat_156x, x = query_states_79_cast_fp16)[name = string("x_131_cast_fp16")];
            tensor<int32, [4]> concat_157x = const()[name = string("concat_157x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_5160_cast_fp16 = reshape(shape = concat_157x, x = key_states_131_cast_fp16)[name = string("op_5160_cast_fp16")];
            tensor<int32, [4]> concat_158x = const()[name = string("concat_158x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_5167_cast_fp16 = reshape(shape = concat_158x, x = value_states_79_cast_fp16)[name = string("op_5167_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_5171_cast_fp16 = mul(x = x_131_cast_fp16, y = var_859_cast_fp16)[name = string("op_5171_cast_fp16")];
            tensor<int32, [2]> var_5172_split_sizes_0 = const()[name = string("op_5172_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5172_axis_0 = const()[name = string("op_5172_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_5172_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_5172_cast_fp16_1 = split(axis = var_5172_axis_0, split_sizes = var_5172_split_sizes_0, x = x_131_cast_fp16)[name = string("op_5172_cast_fp16")];
            fp16 const_134_promoted_to_fp16 = const()[name = string("const_134_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_5174_cast_fp16 = mul(x = var_5172_cast_fp16_1, y = const_134_promoted_to_fp16)[name = string("op_5174_cast_fp16")];
            int32 var_5176 = const()[name = string("op_5176"), val = int32(-2)];
            bool var_5177_interleave_0 = const()[name = string("op_5177_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_5177_cast_fp16 = concat(axis = var_5176, interleave = var_5177_interleave_0, values = (var_5174_cast_fp16, var_5172_cast_fp16_0))[name = string("op_5177_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_5178_cast_fp16 = mul(x = var_5177_cast_fp16, y = var_866_cast_fp16)[name = string("op_5178_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_81_cast_fp16 = add(x = var_5171_cast_fp16, y = var_5178_cast_fp16)[name = string("query_states_81_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_5184_cast_fp16 = mul(x = var_5160_cast_fp16, y = var_859_cast_fp16)[name = string("op_5184_cast_fp16")];
            tensor<int32, [2]> var_5185_split_sizes_0 = const()[name = string("op_5185_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5185_axis_0 = const()[name = string("op_5185_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_5185_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_5185_cast_fp16_1 = split(axis = var_5185_axis_0, split_sizes = var_5185_split_sizes_0, x = var_5160_cast_fp16)[name = string("op_5185_cast_fp16")];
            fp16 const_135_promoted_to_fp16 = const()[name = string("const_135_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_5187_cast_fp16 = mul(x = var_5185_cast_fp16_1, y = const_135_promoted_to_fp16)[name = string("op_5187_cast_fp16")];
            int32 var_5189 = const()[name = string("op_5189"), val = int32(-2)];
            bool var_5190_interleave_0 = const()[name = string("op_5190_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_5190_cast_fp16 = concat(axis = var_5189, interleave = var_5190_interleave_0, values = (var_5187_cast_fp16, var_5185_cast_fp16_0))[name = string("op_5190_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_5191_cast_fp16 = mul(x = var_5190_cast_fp16, y = var_866_cast_fp16)[name = string("op_5191_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_135_cast_fp16 = add(x = var_5184_cast_fp16, y = var_5191_cast_fp16)[name = string("key_states_135_cast_fp16")];
            tensor<int32, [1]> expand_dims_156 = const()[name = string("expand_dims_156"), val = tensor<int32, [1]>([13])];
            tensor<int32, [1]> expand_dims_157 = const()[name = string("expand_dims_157"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_159 = const()[name = string("expand_dims_159"), val = tensor<int32, [1]>([0])];
            int32 concat_161_axis_0 = const()[name = string("concat_161_axis_0"), val = int32(0)];
            bool concat_161_interleave_0 = const()[name = string("concat_161_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_161 = concat(axis = concat_161_axis_0, interleave = concat_161_interleave_0, values = (expand_dims_156, expand_dims_157, position_id, expand_dims_159))[name = string("concat_161")];
            tensor<int32, [1]> expand_dims_160 = const()[name = string("expand_dims_160"), val = tensor<int32, [1]>([14])];
            tensor<int32, [1]> concat_162_values1_0 = const()[name = string("concat_162_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_162_values3_0 = const()[name = string("concat_162_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_162_axis_0 = const()[name = string("concat_162_axis_0"), val = int32(0)];
            bool concat_162_interleave_0 = const()[name = string("concat_162_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_162 = concat(axis = concat_162_axis_0, interleave = concat_162_interleave_0, values = (expand_dims_160, concat_162_values1_0, cache_position_end, concat_162_values3_0))[name = string("concat_162")];
            tensor<int32, [4]> key_states_137_perm_0 = const()[name = string("key_states_137_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_14_stride_0 = const()[name = string("key_cache_internal_tensor_assign_14_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_14_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_14_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_14_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_14_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_14_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_14_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_137_cast_fp16 = transpose(perm = key_states_137_perm_0, x = key_states_135_cast_fp16)[name = string("transpose_218")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_14_cast_fp16 = slice_update(begin = concat_161, begin_mask = key_cache_internal_tensor_assign_14_begin_mask_0, end = concat_162, end_mask = key_cache_internal_tensor_assign_14_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_14_squeeze_mask_0, stride = key_cache_internal_tensor_assign_14_stride_0, update = key_states_137_cast_fp16, x = coreml_update_state_136)[name = string("key_cache_internal_tensor_assign_14_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_14_cast_fp16, input = key_cache)[name = string("coreml_update_state_138_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_138 = read_state(input = key_cache)[name = string("coreml_update_state_138")];
            tensor<int32, [4]> value_states_81_perm_0 = const()[name = string("value_states_81_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_14_stride_0 = const()[name = string("value_cache_internal_tensor_assign_14_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_14_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_14_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_14_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_14_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_14_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_14_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_81_cast_fp16 = transpose(perm = value_states_81_perm_0, x = var_5167_cast_fp16)[name = string("transpose_217")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_14_cast_fp16 = slice_update(begin = concat_161, begin_mask = value_cache_internal_tensor_assign_14_begin_mask_0, end = concat_162, end_mask = value_cache_internal_tensor_assign_14_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_14_squeeze_mask_0, stride = value_cache_internal_tensor_assign_14_stride_0, update = value_states_81_cast_fp16, x = coreml_update_state_137)[name = string("value_cache_internal_tensor_assign_14_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_14_cast_fp16, input = value_cache)[name = string("coreml_update_state_139_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_139 = read_state(input = value_cache)[name = string("coreml_update_state_139")];
            tensor<int32, [4]> var_5261_begin_0 = const()[name = string("op_5261_begin_0"), val = tensor<int32, [4]>([13, 0, 0, 0])];
            tensor<int32, [4]> var_5261_end_0 = const()[name = string("op_5261_end_0"), val = tensor<int32, [4]>([14, 2, 2048, 128])];
            tensor<bool, [4]> var_5261_end_mask_0 = const()[name = string("op_5261_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5261_cast_fp16 = slice_by_index(begin = var_5261_begin_0, end = var_5261_end_0, end_mask = var_5261_end_mask_0, x = coreml_update_state_138)[name = string("op_5261_cast_fp16")];
            tensor<int32, [2]> tile_26 = const()[name = string("tile_26"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5264_axis_0 = const()[name = string("op_5264_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5264_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5264_cast_fp16_1 = split(axis = var_5264_axis_0, split_sizes = tile_26, x = var_5261_cast_fp16)[name = string("op_5264_cast_fp16")];
            tensor<int32, [4]> var_5271_begin_0 = const()[name = string("op_5271_begin_0"), val = tensor<int32, [4]>([13, 0, 0, 0])];
            tensor<int32, [4]> var_5271_end_0 = const()[name = string("op_5271_end_0"), val = tensor<int32, [4]>([14, 2, 2048, 128])];
            tensor<bool, [4]> var_5271_end_mask_0 = const()[name = string("op_5271_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5271_cast_fp16 = slice_by_index(begin = var_5271_begin_0, end = var_5271_end_0, end_mask = var_5271_end_mask_0, x = coreml_update_state_139)[name = string("op_5271_cast_fp16")];
            tensor<int32, [2]> tile_27 = const()[name = string("tile_27"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5274_axis_0 = const()[name = string("op_5274_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5274_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5274_cast_fp16_1 = split(axis = var_5274_axis_0, split_sizes = tile_27, x = var_5271_cast_fp16)[name = string("op_5274_cast_fp16")];
            tensor<int32, [2]> var_5277_split_sizes_0 = const()[name = string("op_5277_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_5277_axis_0 = const()[name = string("op_5277_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_5277_0, tensor<fp16, [1, 8, 128, 8]> var_5277_1 = split(axis = var_5277_axis_0, split_sizes = var_5277_split_sizes_0, x = query_states_81_cast_fp16)[name = string("op_5277")];
            bool attn_weights_209_transpose_x_0 = const()[name = string("attn_weights_209_transpose_x_0"), val = bool(false)];
            bool attn_weights_209_transpose_y_0 = const()[name = string("attn_weights_209_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_209_cast_fp16 = matmul(transpose_x = attn_weights_209_transpose_x_0, transpose_y = attn_weights_209_transpose_y_0, x = var_5264_cast_fp16_0, y = var_5277_0)[name = string("attn_weights_209_cast_fp16")];
            fp16 var_5280_to_fp16 = const()[name = string("op_5280_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_211_cast_fp16 = mul(x = attn_weights_209_cast_fp16, y = var_5280_to_fp16)[name = string("attn_weights_211_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_213_cast_fp16 = add(x = attn_weights_211_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_213_cast_fp16")];
            int32 var_5284 = const()[name = string("op_5284"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_215_cast_fp16 = softmax(axis = var_5284, x = attn_weights_213_cast_fp16)[name = string("attn_weights_215_cast_fp16")];
            bool var_5290_transpose_x_1 = const()[name = string("op_5290_transpose_x_1"), val = bool(true)];
            bool var_5290_transpose_y_1 = const()[name = string("op_5290_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_5290_cast_fp16 = matmul(transpose_x = var_5290_transpose_x_1, transpose_y = var_5290_transpose_y_1, x = attn_weights_215_cast_fp16, y = var_5274_cast_fp16_0)[name = string("op_5290_cast_fp16")];
            bool attn_weights_217_transpose_x_0 = const()[name = string("attn_weights_217_transpose_x_0"), val = bool(false)];
            bool attn_weights_217_transpose_y_0 = const()[name = string("attn_weights_217_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_217_cast_fp16 = matmul(transpose_x = attn_weights_217_transpose_x_0, transpose_y = attn_weights_217_transpose_y_0, x = var_5264_cast_fp16_1, y = var_5277_1)[name = string("attn_weights_217_cast_fp16")];
            fp16 var_5292_to_fp16 = const()[name = string("op_5292_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_219_cast_fp16 = mul(x = attn_weights_217_cast_fp16, y = var_5292_to_fp16)[name = string("attn_weights_219_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_221_cast_fp16 = add(x = attn_weights_219_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_221_cast_fp16")];
            int32 var_5296 = const()[name = string("op_5296"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_223_cast_fp16 = softmax(axis = var_5296, x = attn_weights_221_cast_fp16)[name = string("attn_weights_223_cast_fp16")];
            bool attn_output_105_transpose_x_1 = const()[name = string("attn_output_105_transpose_x_1"), val = bool(true)];
            bool attn_output_105_transpose_y_1 = const()[name = string("attn_output_105_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_105_cast_fp16 = matmul(transpose_x = attn_output_105_transpose_x_1, transpose_y = attn_output_105_transpose_y_1, x = attn_weights_223_cast_fp16, y = var_5274_cast_fp16_1)[name = string("attn_output_105_cast_fp16")];
            int32 var_5304 = const()[name = string("op_5304"), val = int32(1)];
            bool attn_output_107_interleave_0 = const()[name = string("attn_output_107_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_107_cast_fp16 = concat(axis = var_5304, interleave = attn_output_107_interleave_0, values = (var_5290_cast_fp16, attn_output_105_cast_fp16))[name = string("attn_output_107_cast_fp16")];
            tensor<int32, [4]> var_5308_perm_0 = const()[name = string("op_5308_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_167x = const()[name = string("concat_167x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_5308_cast_fp16 = transpose(perm = var_5308_perm_0, x = attn_output_107_cast_fp16)[name = string("transpose_216")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_111_cast_fp16 = reshape(shape = concat_167x, x = var_5308_cast_fp16)[name = string("attn_output_111_cast_fp16")];
            tensor<int32, [2]> hidden_states_133_strides_0 = const()[name = string("hidden_states_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_133_pad_type_0 = const()[name = string("hidden_states_133_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_133_pad_0 = const()[name = string("hidden_states_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_133_dilations_0 = const()[name = string("hidden_states_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_133_groups_0 = const()[name = string("hidden_states_133_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_133_cast_fp16 = conv(dilations = hidden_states_133_dilations_0, groups = hidden_states_133_groups_0, pad = hidden_states_133_pad_0, pad_type = hidden_states_133_pad_type_0, strides = hidden_states_133_strides_0, weight = layers_13_self_attn_o_proj_weight_cast_fp16, x = attn_output_111_cast_fp16)[name = string("hidden_states_133_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_135_cast_fp16 = add(x = hidden_states_129_cast_fp16, y = hidden_states_133_cast_fp16)[name = string("hidden_states_135_cast_fp16")];
            fp16 const_140_promoted_to_fp16 = const()[name = string("const_140_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_5341_cast_fp16 = mul(x = hidden_states_135_cast_fp16, y = const_140_promoted_to_fp16)[name = string("op_5341_cast_fp16")];
            int32 var_5339 = const()[name = string("op_5339"), val = int32(1)];
            bool doubled_109_interleave_0 = const()[name = string("doubled_109_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_109_cast_fp16 = concat(axis = var_5339, interleave = doubled_109_interleave_0, values = (hidden_states_135_cast_fp16, var_5341_cast_fp16))[name = string("doubled_109_cast_fp16")];
            tensor<int32, [1]> out_55_axes_0 = const()[name = string("out_55_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_55_gamma_0_to_fp16 = const()[name = string("out_55_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407019008)))];
            fp16 var_5351_to_fp16 = const()[name = string("op_5351_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_55_cast_fp16 = layer_norm(axes = out_55_axes_0, epsilon = var_5351_to_fp16, gamma = out_55_gamma_0_to_fp16, x = doubled_109_cast_fp16)[name = string("out_55_cast_fp16")];
            tensor<int32, [2]> var_5362_split_sizes_0 = const()[name = string("op_5362_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5362_axis_0 = const()[name = string("op_5362_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_5362_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_5362_cast_fp16_1 = split(axis = var_5362_axis_0, split_sizes = var_5362_split_sizes_0, x = out_55_cast_fp16)[name = string("op_5362_cast_fp16")];
            tensor<int32, [2]> input_27_strides_0 = const()[name = string("input_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_27_pad_type_0 = const()[name = string("input_27_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_27_pad_0 = const()[name = string("input_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_27_dilations_0 = const()[name = string("input_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_27_groups_0 = const()[name = string("input_27_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_27_cast_fp16 = conv(dilations = input_27_dilations_0, groups = input_27_groups_0, pad = input_27_pad_0, pad_type = input_27_pad_type_0, strides = input_27_strides_0, weight = layers_13_mlp_gate_proj_weight_cast_fp16, x = var_5362_cast_fp16_0)[name = string("input_27_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_5379_cast_fp16 = silu(x = input_27_cast_fp16)[name = string("op_5379_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_13_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_13_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407027264)))];
            tensor<int32, [2]> var_5385_strides_0 = const()[name = string("op_5385_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_5385_pad_type_0 = const()[name = string("op_5385_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_5385_pad_0 = const()[name = string("op_5385_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_5385_dilations_0 = const()[name = string("op_5385_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5385_groups_0 = const()[name = string("op_5385_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_5385_cast_fp16 = conv(dilations = var_5385_dilations_0, groups = var_5385_groups_0, pad = var_5385_pad_0, pad_type = var_5385_pad_type_0, strides = var_5385_strides_0, weight = layers_13_mlp_up_proj_weight_to_fp16, x = var_5362_cast_fp16_0)[name = string("op_5385_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_139_cast_fp16 = mul(x = var_5379_cast_fp16, y = var_5385_cast_fp16)[name = string("x_139_cast_fp16")];
            tensor<int32, [2]> hidden_states_137_strides_0 = const()[name = string("hidden_states_137_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_137_pad_type_0 = const()[name = string("hidden_states_137_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_137_pad_0 = const()[name = string("hidden_states_137_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_137_dilations_0 = const()[name = string("hidden_states_137_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_137_groups_0 = const()[name = string("hidden_states_137_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_137_cast_fp16 = conv(dilations = hidden_states_137_dilations_0, groups = hidden_states_137_groups_0, pad = hidden_states_137_pad_0, pad_type = hidden_states_137_pad_type_0, strides = hidden_states_137_strides_0, weight = layers_13_mlp_down_proj_weight_cast_fp16, x = x_139_cast_fp16)[name = string("hidden_states_137_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_139_cast_fp16 = add(x = hidden_states_135_cast_fp16, y = hidden_states_137_cast_fp16)[name = string("hidden_states_139_cast_fp16")];
            fp16 const_142_promoted_to_fp16 = const()[name = string("const_142_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_5403_cast_fp16 = mul(x = hidden_states_139_cast_fp16, y = const_142_promoted_to_fp16)[name = string("op_5403_cast_fp16")];
            int32 var_5401 = const()[name = string("op_5401"), val = int32(1)];
            bool doubled_113_interleave_0 = const()[name = string("doubled_113_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_113_cast_fp16 = concat(axis = var_5401, interleave = doubled_113_interleave_0, values = (hidden_states_139_cast_fp16, var_5403_cast_fp16))[name = string("doubled_113_cast_fp16")];
            tensor<int32, [1]> out_57_axes_0 = const()[name = string("out_57_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_57_gamma_0_to_fp16 = const()[name = string("out_57_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1432193152)))];
            fp16 var_5413_to_fp16 = const()[name = string("op_5413_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_57_cast_fp16 = layer_norm(axes = out_57_axes_0, epsilon = var_5413_to_fp16, gamma = out_57_gamma_0_to_fp16, x = doubled_113_cast_fp16)[name = string("out_57_cast_fp16")];
            tensor<int32, [2]> var_5424_split_sizes_0 = const()[name = string("op_5424_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5424_axis_0 = const()[name = string("op_5424_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_5424_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_5424_cast_fp16_1 = split(axis = var_5424_axis_0, split_sizes = var_5424_split_sizes_0, x = out_57_cast_fp16)[name = string("op_5424_cast_fp16")];
            tensor<int32, [2]> query_states_85_strides_0 = const()[name = string("query_states_85_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_85_pad_type_0 = const()[name = string("query_states_85_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_85_pad_0 = const()[name = string("query_states_85_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_85_dilations_0 = const()[name = string("query_states_85_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_85_groups_0 = const()[name = string("query_states_85_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_85_cast_fp16 = conv(dilations = query_states_85_dilations_0, groups = query_states_85_groups_0, pad = query_states_85_pad_0, pad_type = query_states_85_pad_type_0, strides = query_states_85_strides_0, weight = layers_14_self_attn_q_proj_weight_cast_fp16, x = var_5424_cast_fp16_0)[name = string("query_states_85_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_14_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_14_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1432201408)))];
            tensor<int32, [2]> key_states_141_strides_0 = const()[name = string("key_states_141_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_141_pad_type_0 = const()[name = string("key_states_141_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_141_pad_0 = const()[name = string("key_states_141_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_141_dilations_0 = const()[name = string("key_states_141_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_141_groups_0 = const()[name = string("key_states_141_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_141_cast_fp16 = conv(dilations = key_states_141_dilations_0, groups = key_states_141_groups_0, pad = key_states_141_pad_0, pad_type = key_states_141_pad_type_0, strides = key_states_141_strides_0, weight = layers_14_self_attn_k_proj_weight_to_fp16, x = var_5424_cast_fp16_0)[name = string("key_states_141_cast_fp16")];
            tensor<int32, [2]> value_states_85_strides_0 = const()[name = string("value_states_85_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_85_pad_type_0 = const()[name = string("value_states_85_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_85_pad_0 = const()[name = string("value_states_85_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_85_dilations_0 = const()[name = string("value_states_85_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_85_groups_0 = const()[name = string("value_states_85_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_85_cast_fp16 = conv(dilations = value_states_85_dilations_0, groups = value_states_85_groups_0, pad = value_states_85_pad_0, pad_type = value_states_85_pad_type_0, strides = value_states_85_strides_0, weight = layers_14_self_attn_v_proj_weight_cast_fp16, x = var_5424_cast_fp16_0)[name = string("value_states_85_cast_fp16")];
            tensor<int32, [4]> concat_168x = const()[name = string("concat_168x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_141_cast_fp16 = reshape(shape = concat_168x, x = query_states_85_cast_fp16)[name = string("x_141_cast_fp16")];
            tensor<int32, [4]> concat_169x = const()[name = string("concat_169x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_5481_cast_fp16 = reshape(shape = concat_169x, x = key_states_141_cast_fp16)[name = string("op_5481_cast_fp16")];
            tensor<int32, [4]> concat_170x = const()[name = string("concat_170x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_5488_cast_fp16 = reshape(shape = concat_170x, x = value_states_85_cast_fp16)[name = string("op_5488_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_5492_cast_fp16 = mul(x = x_141_cast_fp16, y = var_859_cast_fp16)[name = string("op_5492_cast_fp16")];
            tensor<int32, [2]> var_5493_split_sizes_0 = const()[name = string("op_5493_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5493_axis_0 = const()[name = string("op_5493_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_5493_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_5493_cast_fp16_1 = split(axis = var_5493_axis_0, split_sizes = var_5493_split_sizes_0, x = x_141_cast_fp16)[name = string("op_5493_cast_fp16")];
            fp16 const_144_promoted_to_fp16 = const()[name = string("const_144_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_5495_cast_fp16 = mul(x = var_5493_cast_fp16_1, y = const_144_promoted_to_fp16)[name = string("op_5495_cast_fp16")];
            int32 var_5497 = const()[name = string("op_5497"), val = int32(-2)];
            bool var_5498_interleave_0 = const()[name = string("op_5498_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_5498_cast_fp16 = concat(axis = var_5497, interleave = var_5498_interleave_0, values = (var_5495_cast_fp16, var_5493_cast_fp16_0))[name = string("op_5498_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_5499_cast_fp16 = mul(x = var_5498_cast_fp16, y = var_866_cast_fp16)[name = string("op_5499_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_87_cast_fp16 = add(x = var_5492_cast_fp16, y = var_5499_cast_fp16)[name = string("query_states_87_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_5505_cast_fp16 = mul(x = var_5481_cast_fp16, y = var_859_cast_fp16)[name = string("op_5505_cast_fp16")];
            tensor<int32, [2]> var_5506_split_sizes_0 = const()[name = string("op_5506_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5506_axis_0 = const()[name = string("op_5506_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_5506_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_5506_cast_fp16_1 = split(axis = var_5506_axis_0, split_sizes = var_5506_split_sizes_0, x = var_5481_cast_fp16)[name = string("op_5506_cast_fp16")];
            fp16 const_145_promoted_to_fp16 = const()[name = string("const_145_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_5508_cast_fp16 = mul(x = var_5506_cast_fp16_1, y = const_145_promoted_to_fp16)[name = string("op_5508_cast_fp16")];
            int32 var_5510 = const()[name = string("op_5510"), val = int32(-2)];
            bool var_5511_interleave_0 = const()[name = string("op_5511_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_5511_cast_fp16 = concat(axis = var_5510, interleave = var_5511_interleave_0, values = (var_5508_cast_fp16, var_5506_cast_fp16_0))[name = string("op_5511_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_5512_cast_fp16 = mul(x = var_5511_cast_fp16, y = var_866_cast_fp16)[name = string("op_5512_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_145_cast_fp16 = add(x = var_5505_cast_fp16, y = var_5512_cast_fp16)[name = string("key_states_145_cast_fp16")];
            tensor<int32, [1]> expand_dims_168 = const()[name = string("expand_dims_168"), val = tensor<int32, [1]>([14])];
            tensor<int32, [1]> expand_dims_169 = const()[name = string("expand_dims_169"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_171 = const()[name = string("expand_dims_171"), val = tensor<int32, [1]>([0])];
            int32 concat_173_axis_0 = const()[name = string("concat_173_axis_0"), val = int32(0)];
            bool concat_173_interleave_0 = const()[name = string("concat_173_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_173 = concat(axis = concat_173_axis_0, interleave = concat_173_interleave_0, values = (expand_dims_168, expand_dims_169, position_id, expand_dims_171))[name = string("concat_173")];
            tensor<int32, [1]> expand_dims_172 = const()[name = string("expand_dims_172"), val = tensor<int32, [1]>([15])];
            tensor<int32, [1]> concat_174_values1_0 = const()[name = string("concat_174_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_174_values3_0 = const()[name = string("concat_174_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_174_axis_0 = const()[name = string("concat_174_axis_0"), val = int32(0)];
            bool concat_174_interleave_0 = const()[name = string("concat_174_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_174 = concat(axis = concat_174_axis_0, interleave = concat_174_interleave_0, values = (expand_dims_172, concat_174_values1_0, cache_position_end, concat_174_values3_0))[name = string("concat_174")];
            tensor<int32, [4]> key_states_147_perm_0 = const()[name = string("key_states_147_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_15_stride_0 = const()[name = string("key_cache_internal_tensor_assign_15_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_15_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_15_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_15_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_15_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_15_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_15_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_147_cast_fp16 = transpose(perm = key_states_147_perm_0, x = key_states_145_cast_fp16)[name = string("transpose_215")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_15_cast_fp16 = slice_update(begin = concat_173, begin_mask = key_cache_internal_tensor_assign_15_begin_mask_0, end = concat_174, end_mask = key_cache_internal_tensor_assign_15_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_15_squeeze_mask_0, stride = key_cache_internal_tensor_assign_15_stride_0, update = key_states_147_cast_fp16, x = coreml_update_state_138)[name = string("key_cache_internal_tensor_assign_15_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_15_cast_fp16, input = key_cache)[name = string("coreml_update_state_140_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_140 = read_state(input = key_cache)[name = string("coreml_update_state_140")];
            tensor<int32, [4]> value_states_87_perm_0 = const()[name = string("value_states_87_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_15_stride_0 = const()[name = string("value_cache_internal_tensor_assign_15_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_15_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_15_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_15_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_15_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_15_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_15_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_87_cast_fp16 = transpose(perm = value_states_87_perm_0, x = var_5488_cast_fp16)[name = string("transpose_214")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_15_cast_fp16 = slice_update(begin = concat_173, begin_mask = value_cache_internal_tensor_assign_15_begin_mask_0, end = concat_174, end_mask = value_cache_internal_tensor_assign_15_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_15_squeeze_mask_0, stride = value_cache_internal_tensor_assign_15_stride_0, update = value_states_87_cast_fp16, x = coreml_update_state_139)[name = string("value_cache_internal_tensor_assign_15_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_15_cast_fp16, input = value_cache)[name = string("coreml_update_state_141_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_141 = read_state(input = value_cache)[name = string("coreml_update_state_141")];
            tensor<int32, [4]> var_5582_begin_0 = const()[name = string("op_5582_begin_0"), val = tensor<int32, [4]>([14, 0, 0, 0])];
            tensor<int32, [4]> var_5582_end_0 = const()[name = string("op_5582_end_0"), val = tensor<int32, [4]>([15, 2, 2048, 128])];
            tensor<bool, [4]> var_5582_end_mask_0 = const()[name = string("op_5582_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5582_cast_fp16 = slice_by_index(begin = var_5582_begin_0, end = var_5582_end_0, end_mask = var_5582_end_mask_0, x = coreml_update_state_140)[name = string("op_5582_cast_fp16")];
            tensor<int32, [2]> tile_28 = const()[name = string("tile_28"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5585_axis_0 = const()[name = string("op_5585_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5585_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5585_cast_fp16_1 = split(axis = var_5585_axis_0, split_sizes = tile_28, x = var_5582_cast_fp16)[name = string("op_5585_cast_fp16")];
            tensor<int32, [4]> var_5592_begin_0 = const()[name = string("op_5592_begin_0"), val = tensor<int32, [4]>([14, 0, 0, 0])];
            tensor<int32, [4]> var_5592_end_0 = const()[name = string("op_5592_end_0"), val = tensor<int32, [4]>([15, 2, 2048, 128])];
            tensor<bool, [4]> var_5592_end_mask_0 = const()[name = string("op_5592_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5592_cast_fp16 = slice_by_index(begin = var_5592_begin_0, end = var_5592_end_0, end_mask = var_5592_end_mask_0, x = coreml_update_state_141)[name = string("op_5592_cast_fp16")];
            tensor<int32, [2]> tile_29 = const()[name = string("tile_29"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5595_axis_0 = const()[name = string("op_5595_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5595_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5595_cast_fp16_1 = split(axis = var_5595_axis_0, split_sizes = tile_29, x = var_5592_cast_fp16)[name = string("op_5595_cast_fp16")];
            tensor<int32, [2]> var_5598_split_sizes_0 = const()[name = string("op_5598_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_5598_axis_0 = const()[name = string("op_5598_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_5598_0, tensor<fp16, [1, 8, 128, 8]> var_5598_1 = split(axis = var_5598_axis_0, split_sizes = var_5598_split_sizes_0, x = query_states_87_cast_fp16)[name = string("op_5598")];
            bool attn_weights_225_transpose_x_0 = const()[name = string("attn_weights_225_transpose_x_0"), val = bool(false)];
            bool attn_weights_225_transpose_y_0 = const()[name = string("attn_weights_225_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_225_cast_fp16 = matmul(transpose_x = attn_weights_225_transpose_x_0, transpose_y = attn_weights_225_transpose_y_0, x = var_5585_cast_fp16_0, y = var_5598_0)[name = string("attn_weights_225_cast_fp16")];
            fp16 var_5601_to_fp16 = const()[name = string("op_5601_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_227_cast_fp16 = mul(x = attn_weights_225_cast_fp16, y = var_5601_to_fp16)[name = string("attn_weights_227_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_229_cast_fp16 = add(x = attn_weights_227_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_229_cast_fp16")];
            int32 var_5605 = const()[name = string("op_5605"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_231_cast_fp16 = softmax(axis = var_5605, x = attn_weights_229_cast_fp16)[name = string("attn_weights_231_cast_fp16")];
            bool var_5611_transpose_x_1 = const()[name = string("op_5611_transpose_x_1"), val = bool(true)];
            bool var_5611_transpose_y_1 = const()[name = string("op_5611_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_5611_cast_fp16 = matmul(transpose_x = var_5611_transpose_x_1, transpose_y = var_5611_transpose_y_1, x = attn_weights_231_cast_fp16, y = var_5595_cast_fp16_0)[name = string("op_5611_cast_fp16")];
            bool attn_weights_233_transpose_x_0 = const()[name = string("attn_weights_233_transpose_x_0"), val = bool(false)];
            bool attn_weights_233_transpose_y_0 = const()[name = string("attn_weights_233_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_233_cast_fp16 = matmul(transpose_x = attn_weights_233_transpose_x_0, transpose_y = attn_weights_233_transpose_y_0, x = var_5585_cast_fp16_1, y = var_5598_1)[name = string("attn_weights_233_cast_fp16")];
            fp16 var_5613_to_fp16 = const()[name = string("op_5613_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_235_cast_fp16 = mul(x = attn_weights_233_cast_fp16, y = var_5613_to_fp16)[name = string("attn_weights_235_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_237_cast_fp16 = add(x = attn_weights_235_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_237_cast_fp16")];
            int32 var_5617 = const()[name = string("op_5617"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_239_cast_fp16 = softmax(axis = var_5617, x = attn_weights_237_cast_fp16)[name = string("attn_weights_239_cast_fp16")];
            bool attn_output_113_transpose_x_1 = const()[name = string("attn_output_113_transpose_x_1"), val = bool(true)];
            bool attn_output_113_transpose_y_1 = const()[name = string("attn_output_113_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_113_cast_fp16 = matmul(transpose_x = attn_output_113_transpose_x_1, transpose_y = attn_output_113_transpose_y_1, x = attn_weights_239_cast_fp16, y = var_5595_cast_fp16_1)[name = string("attn_output_113_cast_fp16")];
            int32 var_5625 = const()[name = string("op_5625"), val = int32(1)];
            bool attn_output_115_interleave_0 = const()[name = string("attn_output_115_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_115_cast_fp16 = concat(axis = var_5625, interleave = attn_output_115_interleave_0, values = (var_5611_cast_fp16, attn_output_113_cast_fp16))[name = string("attn_output_115_cast_fp16")];
            tensor<int32, [4]> var_5629_perm_0 = const()[name = string("op_5629_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_179x = const()[name = string("concat_179x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_5629_cast_fp16 = transpose(perm = var_5629_perm_0, x = attn_output_115_cast_fp16)[name = string("transpose_213")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_119_cast_fp16 = reshape(shape = concat_179x, x = var_5629_cast_fp16)[name = string("attn_output_119_cast_fp16")];
            tensor<int32, [2]> hidden_states_143_strides_0 = const()[name = string("hidden_states_143_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_143_pad_type_0 = const()[name = string("hidden_states_143_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_143_pad_0 = const()[name = string("hidden_states_143_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_143_dilations_0 = const()[name = string("hidden_states_143_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_143_groups_0 = const()[name = string("hidden_states_143_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_143_cast_fp16 = conv(dilations = hidden_states_143_dilations_0, groups = hidden_states_143_groups_0, pad = hidden_states_143_pad_0, pad_type = hidden_states_143_pad_type_0, strides = hidden_states_143_strides_0, weight = layers_14_self_attn_o_proj_weight_cast_fp16, x = attn_output_119_cast_fp16)[name = string("hidden_states_143_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_145_cast_fp16 = add(x = hidden_states_139_cast_fp16, y = hidden_states_143_cast_fp16)[name = string("hidden_states_145_cast_fp16")];
            fp16 const_150_promoted_to_fp16 = const()[name = string("const_150_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_5662_cast_fp16 = mul(x = hidden_states_145_cast_fp16, y = const_150_promoted_to_fp16)[name = string("op_5662_cast_fp16")];
            int32 var_5660 = const()[name = string("op_5660"), val = int32(1)];
            bool doubled_117_interleave_0 = const()[name = string("doubled_117_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_117_cast_fp16 = concat(axis = var_5660, interleave = doubled_117_interleave_0, values = (hidden_states_145_cast_fp16, var_5662_cast_fp16))[name = string("doubled_117_cast_fp16")];
            tensor<int32, [1]> out_59_axes_0 = const()[name = string("out_59_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_59_gamma_0_to_fp16 = const()[name = string("out_59_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433250048)))];
            fp16 var_5672_to_fp16 = const()[name = string("op_5672_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_59_cast_fp16 = layer_norm(axes = out_59_axes_0, epsilon = var_5672_to_fp16, gamma = out_59_gamma_0_to_fp16, x = doubled_117_cast_fp16)[name = string("out_59_cast_fp16")];
            tensor<int32, [2]> var_5683_split_sizes_0 = const()[name = string("op_5683_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5683_axis_0 = const()[name = string("op_5683_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_5683_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_5683_cast_fp16_1 = split(axis = var_5683_axis_0, split_sizes = var_5683_split_sizes_0, x = out_59_cast_fp16)[name = string("op_5683_cast_fp16")];
            tensor<int32, [2]> input_29_strides_0 = const()[name = string("input_29_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_29_pad_type_0 = const()[name = string("input_29_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_29_pad_0 = const()[name = string("input_29_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_29_dilations_0 = const()[name = string("input_29_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_29_groups_0 = const()[name = string("input_29_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_29_cast_fp16 = conv(dilations = input_29_dilations_0, groups = input_29_groups_0, pad = input_29_pad_0, pad_type = input_29_pad_type_0, strides = input_29_strides_0, weight = layers_14_mlp_gate_proj_weight_cast_fp16, x = var_5683_cast_fp16_0)[name = string("input_29_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_5700_cast_fp16 = silu(x = input_29_cast_fp16)[name = string("op_5700_cast_fp16")];
            tensor<int32, [2]> var_5706_strides_0 = const()[name = string("op_5706_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_5706_pad_type_0 = const()[name = string("op_5706_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_5706_pad_0 = const()[name = string("op_5706_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_5706_dilations_0 = const()[name = string("op_5706_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5706_groups_0 = const()[name = string("op_5706_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_5706_cast_fp16 = conv(dilations = var_5706_dilations_0, groups = var_5706_groups_0, pad = var_5706_pad_0, pad_type = var_5706_pad_type_0, strides = var_5706_strides_0, weight = layers_14_mlp_up_proj_weight_cast_fp16, x = var_5683_cast_fp16_0)[name = string("op_5706_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_149_cast_fp16 = mul(x = var_5700_cast_fp16, y = var_5706_cast_fp16)[name = string("x_149_cast_fp16")];
            tensor<int32, [2]> hidden_states_147_strides_0 = const()[name = string("hidden_states_147_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_147_pad_type_0 = const()[name = string("hidden_states_147_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_147_pad_0 = const()[name = string("hidden_states_147_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_147_dilations_0 = const()[name = string("hidden_states_147_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_147_groups_0 = const()[name = string("hidden_states_147_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_147_cast_fp16 = conv(dilations = hidden_states_147_dilations_0, groups = hidden_states_147_groups_0, pad = hidden_states_147_pad_0, pad_type = hidden_states_147_pad_type_0, strides = hidden_states_147_strides_0, weight = layers_14_mlp_down_proj_weight_cast_fp16, x = x_149_cast_fp16)[name = string("hidden_states_147_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_149_cast_fp16 = add(x = hidden_states_145_cast_fp16, y = hidden_states_147_cast_fp16)[name = string("hidden_states_149_cast_fp16")];
            fp16 const_152_promoted_to_fp16 = const()[name = string("const_152_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_5724_cast_fp16 = mul(x = hidden_states_149_cast_fp16, y = const_152_promoted_to_fp16)[name = string("op_5724_cast_fp16")];
            int32 var_5722 = const()[name = string("op_5722"), val = int32(1)];
            bool doubled_121_interleave_0 = const()[name = string("doubled_121_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_121_cast_fp16 = concat(axis = var_5722, interleave = doubled_121_interleave_0, values = (hidden_states_149_cast_fp16, var_5724_cast_fp16))[name = string("doubled_121_cast_fp16")];
            tensor<int32, [1]> out_61_axes_0 = const()[name = string("out_61_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_61_gamma_0_to_fp16 = const()[name = string("out_61_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433258304)))];
            fp16 var_5734_to_fp16 = const()[name = string("op_5734_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_61_cast_fp16 = layer_norm(axes = out_61_axes_0, epsilon = var_5734_to_fp16, gamma = out_61_gamma_0_to_fp16, x = doubled_121_cast_fp16)[name = string("out_61_cast_fp16")];
            tensor<int32, [2]> var_5745_split_sizes_0 = const()[name = string("op_5745_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5745_axis_0 = const()[name = string("op_5745_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_5745_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_5745_cast_fp16_1 = split(axis = var_5745_axis_0, split_sizes = var_5745_split_sizes_0, x = out_61_cast_fp16)[name = string("op_5745_cast_fp16")];
            tensor<int32, [2]> query_states_91_strides_0 = const()[name = string("query_states_91_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_91_pad_type_0 = const()[name = string("query_states_91_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_91_pad_0 = const()[name = string("query_states_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_91_dilations_0 = const()[name = string("query_states_91_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_91_groups_0 = const()[name = string("query_states_91_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_91_cast_fp16 = conv(dilations = query_states_91_dilations_0, groups = query_states_91_groups_0, pad = query_states_91_pad_0, pad_type = query_states_91_pad_type_0, strides = query_states_91_strides_0, weight = layers_15_self_attn_q_proj_weight_cast_fp16, x = var_5745_cast_fp16_0)[name = string("query_states_91_cast_fp16")];
            tensor<int32, [2]> key_states_151_strides_0 = const()[name = string("key_states_151_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_151_pad_type_0 = const()[name = string("key_states_151_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_151_pad_0 = const()[name = string("key_states_151_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_151_dilations_0 = const()[name = string("key_states_151_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_151_groups_0 = const()[name = string("key_states_151_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_151_cast_fp16 = conv(dilations = key_states_151_dilations_0, groups = key_states_151_groups_0, pad = key_states_151_pad_0, pad_type = key_states_151_pad_type_0, strides = key_states_151_strides_0, weight = layers_15_self_attn_k_proj_weight_cast_fp16, x = var_5745_cast_fp16_0)[name = string("key_states_151_cast_fp16")];
            tensor<int32, [2]> value_states_91_strides_0 = const()[name = string("value_states_91_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_91_pad_type_0 = const()[name = string("value_states_91_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_91_pad_0 = const()[name = string("value_states_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_91_dilations_0 = const()[name = string("value_states_91_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_91_groups_0 = const()[name = string("value_states_91_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_91_cast_fp16 = conv(dilations = value_states_91_dilations_0, groups = value_states_91_groups_0, pad = value_states_91_pad_0, pad_type = value_states_91_pad_type_0, strides = value_states_91_strides_0, weight = layers_15_self_attn_v_proj_weight_cast_fp16, x = var_5745_cast_fp16_0)[name = string("value_states_91_cast_fp16")];
            tensor<int32, [4]> concat_180x = const()[name = string("concat_180x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_151_cast_fp16 = reshape(shape = concat_180x, x = query_states_91_cast_fp16)[name = string("x_151_cast_fp16")];
            tensor<int32, [4]> concat_181x = const()[name = string("concat_181x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_5802_cast_fp16 = reshape(shape = concat_181x, x = key_states_151_cast_fp16)[name = string("op_5802_cast_fp16")];
            tensor<int32, [4]> concat_182x = const()[name = string("concat_182x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_5809_cast_fp16 = reshape(shape = concat_182x, x = value_states_91_cast_fp16)[name = string("op_5809_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_5813_cast_fp16 = mul(x = x_151_cast_fp16, y = var_859_cast_fp16)[name = string("op_5813_cast_fp16")];
            tensor<int32, [2]> var_5814_split_sizes_0 = const()[name = string("op_5814_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5814_axis_0 = const()[name = string("op_5814_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_5814_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_5814_cast_fp16_1 = split(axis = var_5814_axis_0, split_sizes = var_5814_split_sizes_0, x = x_151_cast_fp16)[name = string("op_5814_cast_fp16")];
            fp16 const_154_promoted_to_fp16 = const()[name = string("const_154_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_5816_cast_fp16 = mul(x = var_5814_cast_fp16_1, y = const_154_promoted_to_fp16)[name = string("op_5816_cast_fp16")];
            int32 var_5818 = const()[name = string("op_5818"), val = int32(-2)];
            bool var_5819_interleave_0 = const()[name = string("op_5819_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_5819_cast_fp16 = concat(axis = var_5818, interleave = var_5819_interleave_0, values = (var_5816_cast_fp16, var_5814_cast_fp16_0))[name = string("op_5819_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_5820_cast_fp16 = mul(x = var_5819_cast_fp16, y = var_866_cast_fp16)[name = string("op_5820_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_93_cast_fp16 = add(x = var_5813_cast_fp16, y = var_5820_cast_fp16)[name = string("query_states_93_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_5826_cast_fp16 = mul(x = var_5802_cast_fp16, y = var_859_cast_fp16)[name = string("op_5826_cast_fp16")];
            tensor<int32, [2]> var_5827_split_sizes_0 = const()[name = string("op_5827_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5827_axis_0 = const()[name = string("op_5827_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_5827_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_5827_cast_fp16_1 = split(axis = var_5827_axis_0, split_sizes = var_5827_split_sizes_0, x = var_5802_cast_fp16)[name = string("op_5827_cast_fp16")];
            fp16 const_155_promoted_to_fp16 = const()[name = string("const_155_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_5829_cast_fp16 = mul(x = var_5827_cast_fp16_1, y = const_155_promoted_to_fp16)[name = string("op_5829_cast_fp16")];
            int32 var_5831 = const()[name = string("op_5831"), val = int32(-2)];
            bool var_5832_interleave_0 = const()[name = string("op_5832_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_5832_cast_fp16 = concat(axis = var_5831, interleave = var_5832_interleave_0, values = (var_5829_cast_fp16, var_5827_cast_fp16_0))[name = string("op_5832_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_5833_cast_fp16 = mul(x = var_5832_cast_fp16, y = var_866_cast_fp16)[name = string("op_5833_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_155_cast_fp16 = add(x = var_5826_cast_fp16, y = var_5833_cast_fp16)[name = string("key_states_155_cast_fp16")];
            tensor<int32, [1]> expand_dims_180 = const()[name = string("expand_dims_180"), val = tensor<int32, [1]>([15])];
            tensor<int32, [1]> expand_dims_181 = const()[name = string("expand_dims_181"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_183 = const()[name = string("expand_dims_183"), val = tensor<int32, [1]>([0])];
            int32 concat_185_axis_0 = const()[name = string("concat_185_axis_0"), val = int32(0)];
            bool concat_185_interleave_0 = const()[name = string("concat_185_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_185 = concat(axis = concat_185_axis_0, interleave = concat_185_interleave_0, values = (expand_dims_180, expand_dims_181, position_id, expand_dims_183))[name = string("concat_185")];
            tensor<int32, [1]> expand_dims_184 = const()[name = string("expand_dims_184"), val = tensor<int32, [1]>([16])];
            tensor<int32, [1]> concat_186_values1_0 = const()[name = string("concat_186_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_186_values3_0 = const()[name = string("concat_186_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_186_axis_0 = const()[name = string("concat_186_axis_0"), val = int32(0)];
            bool concat_186_interleave_0 = const()[name = string("concat_186_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_186 = concat(axis = concat_186_axis_0, interleave = concat_186_interleave_0, values = (expand_dims_184, concat_186_values1_0, cache_position_end, concat_186_values3_0))[name = string("concat_186")];
            tensor<int32, [4]> key_states_157_perm_0 = const()[name = string("key_states_157_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_16_stride_0 = const()[name = string("key_cache_internal_tensor_assign_16_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_16_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_16_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_16_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_16_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_16_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_16_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_157_cast_fp16 = transpose(perm = key_states_157_perm_0, x = key_states_155_cast_fp16)[name = string("transpose_212")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_16_cast_fp16 = slice_update(begin = concat_185, begin_mask = key_cache_internal_tensor_assign_16_begin_mask_0, end = concat_186, end_mask = key_cache_internal_tensor_assign_16_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_16_squeeze_mask_0, stride = key_cache_internal_tensor_assign_16_stride_0, update = key_states_157_cast_fp16, x = coreml_update_state_140)[name = string("key_cache_internal_tensor_assign_16_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_16_cast_fp16, input = key_cache)[name = string("coreml_update_state_142_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_142 = read_state(input = key_cache)[name = string("coreml_update_state_142")];
            tensor<int32, [4]> value_states_93_perm_0 = const()[name = string("value_states_93_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_16_stride_0 = const()[name = string("value_cache_internal_tensor_assign_16_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_16_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_16_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_16_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_16_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_16_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_16_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_93_cast_fp16 = transpose(perm = value_states_93_perm_0, x = var_5809_cast_fp16)[name = string("transpose_211")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_16_cast_fp16 = slice_update(begin = concat_185, begin_mask = value_cache_internal_tensor_assign_16_begin_mask_0, end = concat_186, end_mask = value_cache_internal_tensor_assign_16_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_16_squeeze_mask_0, stride = value_cache_internal_tensor_assign_16_stride_0, update = value_states_93_cast_fp16, x = coreml_update_state_141)[name = string("value_cache_internal_tensor_assign_16_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_16_cast_fp16, input = value_cache)[name = string("coreml_update_state_143_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_143 = read_state(input = value_cache)[name = string("coreml_update_state_143")];
            tensor<int32, [4]> var_5903_begin_0 = const()[name = string("op_5903_begin_0"), val = tensor<int32, [4]>([15, 0, 0, 0])];
            tensor<int32, [4]> var_5903_end_0 = const()[name = string("op_5903_end_0"), val = tensor<int32, [4]>([16, 2, 2048, 128])];
            tensor<bool, [4]> var_5903_end_mask_0 = const()[name = string("op_5903_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5903_cast_fp16 = slice_by_index(begin = var_5903_begin_0, end = var_5903_end_0, end_mask = var_5903_end_mask_0, x = coreml_update_state_142)[name = string("op_5903_cast_fp16")];
            tensor<int32, [2]> tile_30 = const()[name = string("tile_30"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5906_axis_0 = const()[name = string("op_5906_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5906_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5906_cast_fp16_1 = split(axis = var_5906_axis_0, split_sizes = tile_30, x = var_5903_cast_fp16)[name = string("op_5906_cast_fp16")];
            tensor<int32, [4]> var_5913_begin_0 = const()[name = string("op_5913_begin_0"), val = tensor<int32, [4]>([15, 0, 0, 0])];
            tensor<int32, [4]> var_5913_end_0 = const()[name = string("op_5913_end_0"), val = tensor<int32, [4]>([16, 2, 2048, 128])];
            tensor<bool, [4]> var_5913_end_mask_0 = const()[name = string("op_5913_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5913_cast_fp16 = slice_by_index(begin = var_5913_begin_0, end = var_5913_end_0, end_mask = var_5913_end_mask_0, x = coreml_update_state_143)[name = string("op_5913_cast_fp16")];
            tensor<int32, [2]> tile_31 = const()[name = string("tile_31"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5916_axis_0 = const()[name = string("op_5916_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5916_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5916_cast_fp16_1 = split(axis = var_5916_axis_0, split_sizes = tile_31, x = var_5913_cast_fp16)[name = string("op_5916_cast_fp16")];
            tensor<int32, [2]> var_5919_split_sizes_0 = const()[name = string("op_5919_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_5919_axis_0 = const()[name = string("op_5919_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_5919_0, tensor<fp16, [1, 8, 128, 8]> var_5919_1 = split(axis = var_5919_axis_0, split_sizes = var_5919_split_sizes_0, x = query_states_93_cast_fp16)[name = string("op_5919")];
            bool attn_weights_241_transpose_x_0 = const()[name = string("attn_weights_241_transpose_x_0"), val = bool(false)];
            bool attn_weights_241_transpose_y_0 = const()[name = string("attn_weights_241_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_241_cast_fp16 = matmul(transpose_x = attn_weights_241_transpose_x_0, transpose_y = attn_weights_241_transpose_y_0, x = var_5906_cast_fp16_0, y = var_5919_0)[name = string("attn_weights_241_cast_fp16")];
            fp16 var_5922_to_fp16 = const()[name = string("op_5922_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_243_cast_fp16 = mul(x = attn_weights_241_cast_fp16, y = var_5922_to_fp16)[name = string("attn_weights_243_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_245_cast_fp16 = add(x = attn_weights_243_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_245_cast_fp16")];
            int32 var_5926 = const()[name = string("op_5926"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_247_cast_fp16 = softmax(axis = var_5926, x = attn_weights_245_cast_fp16)[name = string("attn_weights_247_cast_fp16")];
            bool var_5932_transpose_x_1 = const()[name = string("op_5932_transpose_x_1"), val = bool(true)];
            bool var_5932_transpose_y_1 = const()[name = string("op_5932_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_5932_cast_fp16 = matmul(transpose_x = var_5932_transpose_x_1, transpose_y = var_5932_transpose_y_1, x = attn_weights_247_cast_fp16, y = var_5916_cast_fp16_0)[name = string("op_5932_cast_fp16")];
            bool attn_weights_249_transpose_x_0 = const()[name = string("attn_weights_249_transpose_x_0"), val = bool(false)];
            bool attn_weights_249_transpose_y_0 = const()[name = string("attn_weights_249_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_249_cast_fp16 = matmul(transpose_x = attn_weights_249_transpose_x_0, transpose_y = attn_weights_249_transpose_y_0, x = var_5906_cast_fp16_1, y = var_5919_1)[name = string("attn_weights_249_cast_fp16")];
            fp16 var_5934_to_fp16 = const()[name = string("op_5934_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_251_cast_fp16 = mul(x = attn_weights_249_cast_fp16, y = var_5934_to_fp16)[name = string("attn_weights_251_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_253_cast_fp16 = add(x = attn_weights_251_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_253_cast_fp16")];
            int32 var_5938 = const()[name = string("op_5938"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_255_cast_fp16 = softmax(axis = var_5938, x = attn_weights_253_cast_fp16)[name = string("attn_weights_255_cast_fp16")];
            bool attn_output_121_transpose_x_1 = const()[name = string("attn_output_121_transpose_x_1"), val = bool(true)];
            bool attn_output_121_transpose_y_1 = const()[name = string("attn_output_121_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_121_cast_fp16 = matmul(transpose_x = attn_output_121_transpose_x_1, transpose_y = attn_output_121_transpose_y_1, x = attn_weights_255_cast_fp16, y = var_5916_cast_fp16_1)[name = string("attn_output_121_cast_fp16")];
            int32 var_5946 = const()[name = string("op_5946"), val = int32(1)];
            bool attn_output_123_interleave_0 = const()[name = string("attn_output_123_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_123_cast_fp16 = concat(axis = var_5946, interleave = attn_output_123_interleave_0, values = (var_5932_cast_fp16, attn_output_121_cast_fp16))[name = string("attn_output_123_cast_fp16")];
            tensor<int32, [4]> var_5950_perm_0 = const()[name = string("op_5950_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_191x = const()[name = string("concat_191x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_5950_cast_fp16 = transpose(perm = var_5950_perm_0, x = attn_output_123_cast_fp16)[name = string("transpose_210")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_127_cast_fp16 = reshape(shape = concat_191x, x = var_5950_cast_fp16)[name = string("attn_output_127_cast_fp16")];
            tensor<int32, [2]> hidden_states_153_strides_0 = const()[name = string("hidden_states_153_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_153_pad_type_0 = const()[name = string("hidden_states_153_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_153_pad_0 = const()[name = string("hidden_states_153_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_153_dilations_0 = const()[name = string("hidden_states_153_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_153_groups_0 = const()[name = string("hidden_states_153_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_153_cast_fp16 = conv(dilations = hidden_states_153_dilations_0, groups = hidden_states_153_groups_0, pad = hidden_states_153_pad_0, pad_type = hidden_states_153_pad_type_0, strides = hidden_states_153_strides_0, weight = layers_15_self_attn_o_proj_weight_cast_fp16, x = attn_output_127_cast_fp16)[name = string("hidden_states_153_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_155_cast_fp16 = add(x = hidden_states_149_cast_fp16, y = hidden_states_153_cast_fp16)[name = string("hidden_states_155_cast_fp16")];
            fp16 const_160_promoted_to_fp16 = const()[name = string("const_160_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_5983_cast_fp16 = mul(x = hidden_states_155_cast_fp16, y = const_160_promoted_to_fp16)[name = string("op_5983_cast_fp16")];
            int32 var_5981 = const()[name = string("op_5981"), val = int32(1)];
            bool doubled_125_interleave_0 = const()[name = string("doubled_125_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_125_cast_fp16 = concat(axis = var_5981, interleave = doubled_125_interleave_0, values = (hidden_states_155_cast_fp16, var_5983_cast_fp16))[name = string("doubled_125_cast_fp16")];
            tensor<int32, [1]> out_63_axes_0 = const()[name = string("out_63_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_63_gamma_0_to_fp16 = const()[name = string("out_63_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433266560)))];
            fp16 var_5993_to_fp16 = const()[name = string("op_5993_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_63_cast_fp16 = layer_norm(axes = out_63_axes_0, epsilon = var_5993_to_fp16, gamma = out_63_gamma_0_to_fp16, x = doubled_125_cast_fp16)[name = string("out_63_cast_fp16")];
            tensor<int32, [2]> var_6004_split_sizes_0 = const()[name = string("op_6004_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6004_axis_0 = const()[name = string("op_6004_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_6004_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_6004_cast_fp16_1 = split(axis = var_6004_axis_0, split_sizes = var_6004_split_sizes_0, x = out_63_cast_fp16)[name = string("op_6004_cast_fp16")];
            tensor<int32, [2]> input_31_strides_0 = const()[name = string("input_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_31_pad_type_0 = const()[name = string("input_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_31_pad_0 = const()[name = string("input_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_31_dilations_0 = const()[name = string("input_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_31_groups_0 = const()[name = string("input_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_31_cast_fp16 = conv(dilations = input_31_dilations_0, groups = input_31_groups_0, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = input_31_strides_0, weight = layers_15_mlp_gate_proj_weight_cast_fp16, x = var_6004_cast_fp16_0)[name = string("input_31_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_6021_cast_fp16 = silu(x = input_31_cast_fp16)[name = string("op_6021_cast_fp16")];
            tensor<int32, [2]> var_6027_strides_0 = const()[name = string("op_6027_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6027_pad_type_0 = const()[name = string("op_6027_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6027_pad_0 = const()[name = string("op_6027_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6027_dilations_0 = const()[name = string("op_6027_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6027_groups_0 = const()[name = string("op_6027_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_6027_cast_fp16 = conv(dilations = var_6027_dilations_0, groups = var_6027_groups_0, pad = var_6027_pad_0, pad_type = var_6027_pad_type_0, strides = var_6027_strides_0, weight = layers_15_mlp_up_proj_weight_cast_fp16, x = var_6004_cast_fp16_0)[name = string("op_6027_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_159_cast_fp16 = mul(x = var_6021_cast_fp16, y = var_6027_cast_fp16)[name = string("x_159_cast_fp16")];
            tensor<int32, [2]> hidden_states_157_strides_0 = const()[name = string("hidden_states_157_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_157_pad_type_0 = const()[name = string("hidden_states_157_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_157_pad_0 = const()[name = string("hidden_states_157_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_157_dilations_0 = const()[name = string("hidden_states_157_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_157_groups_0 = const()[name = string("hidden_states_157_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_157_cast_fp16 = conv(dilations = hidden_states_157_dilations_0, groups = hidden_states_157_groups_0, pad = hidden_states_157_pad_0, pad_type = hidden_states_157_pad_type_0, strides = hidden_states_157_strides_0, weight = layers_15_mlp_down_proj_weight_cast_fp16, x = x_159_cast_fp16)[name = string("hidden_states_157_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_159_cast_fp16 = add(x = hidden_states_155_cast_fp16, y = hidden_states_157_cast_fp16)[name = string("hidden_states_159_cast_fp16")];
            fp16 const_162_promoted_to_fp16 = const()[name = string("const_162_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_6045_cast_fp16 = mul(x = hidden_states_159_cast_fp16, y = const_162_promoted_to_fp16)[name = string("op_6045_cast_fp16")];
            int32 var_6043 = const()[name = string("op_6043"), val = int32(1)];
            bool doubled_129_interleave_0 = const()[name = string("doubled_129_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_129_cast_fp16 = concat(axis = var_6043, interleave = doubled_129_interleave_0, values = (hidden_states_159_cast_fp16, var_6045_cast_fp16))[name = string("doubled_129_cast_fp16")];
            tensor<int32, [1]> out_65_axes_0 = const()[name = string("out_65_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_65_gamma_0_to_fp16 = const()[name = string("out_65_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433274816)))];
            fp16 var_6055_to_fp16 = const()[name = string("op_6055_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_65_cast_fp16 = layer_norm(axes = out_65_axes_0, epsilon = var_6055_to_fp16, gamma = out_65_gamma_0_to_fp16, x = doubled_129_cast_fp16)[name = string("out_65_cast_fp16")];
            tensor<int32, [2]> var_6066_split_sizes_0 = const()[name = string("op_6066_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6066_axis_0 = const()[name = string("op_6066_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_6066_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_6066_cast_fp16_1 = split(axis = var_6066_axis_0, split_sizes = var_6066_split_sizes_0, x = out_65_cast_fp16)[name = string("op_6066_cast_fp16")];
            tensor<int32, [2]> query_states_97_strides_0 = const()[name = string("query_states_97_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_97_pad_type_0 = const()[name = string("query_states_97_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_97_pad_0 = const()[name = string("query_states_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_97_dilations_0 = const()[name = string("query_states_97_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_97_groups_0 = const()[name = string("query_states_97_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_97_cast_fp16 = conv(dilations = query_states_97_dilations_0, groups = query_states_97_groups_0, pad = query_states_97_pad_0, pad_type = query_states_97_pad_type_0, strides = query_states_97_strides_0, weight = layers_16_self_attn_q_proj_weight_cast_fp16, x = var_6066_cast_fp16_0)[name = string("query_states_97_cast_fp16")];
            tensor<int32, [2]> key_states_161_strides_0 = const()[name = string("key_states_161_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_161_pad_type_0 = const()[name = string("key_states_161_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_161_pad_0 = const()[name = string("key_states_161_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_161_dilations_0 = const()[name = string("key_states_161_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_161_groups_0 = const()[name = string("key_states_161_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_161_cast_fp16 = conv(dilations = key_states_161_dilations_0, groups = key_states_161_groups_0, pad = key_states_161_pad_0, pad_type = key_states_161_pad_type_0, strides = key_states_161_strides_0, weight = layers_16_self_attn_k_proj_weight_cast_fp16, x = var_6066_cast_fp16_0)[name = string("key_states_161_cast_fp16")];
            tensor<int32, [2]> value_states_97_strides_0 = const()[name = string("value_states_97_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_97_pad_type_0 = const()[name = string("value_states_97_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_97_pad_0 = const()[name = string("value_states_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_97_dilations_0 = const()[name = string("value_states_97_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_97_groups_0 = const()[name = string("value_states_97_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_97_cast_fp16 = conv(dilations = value_states_97_dilations_0, groups = value_states_97_groups_0, pad = value_states_97_pad_0, pad_type = value_states_97_pad_type_0, strides = value_states_97_strides_0, weight = layers_16_self_attn_v_proj_weight_cast_fp16, x = var_6066_cast_fp16_0)[name = string("value_states_97_cast_fp16")];
            tensor<int32, [4]> concat_192x = const()[name = string("concat_192x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_161_cast_fp16 = reshape(shape = concat_192x, x = query_states_97_cast_fp16)[name = string("x_161_cast_fp16")];
            tensor<int32, [4]> concat_193x = const()[name = string("concat_193x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_6123_cast_fp16 = reshape(shape = concat_193x, x = key_states_161_cast_fp16)[name = string("op_6123_cast_fp16")];
            tensor<int32, [4]> concat_194x = const()[name = string("concat_194x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_6130_cast_fp16 = reshape(shape = concat_194x, x = value_states_97_cast_fp16)[name = string("op_6130_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_6134_cast_fp16 = mul(x = x_161_cast_fp16, y = var_859_cast_fp16)[name = string("op_6134_cast_fp16")];
            tensor<int32, [2]> var_6135_split_sizes_0 = const()[name = string("op_6135_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6135_axis_0 = const()[name = string("op_6135_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_6135_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_6135_cast_fp16_1 = split(axis = var_6135_axis_0, split_sizes = var_6135_split_sizes_0, x = x_161_cast_fp16)[name = string("op_6135_cast_fp16")];
            fp16 const_164_promoted_to_fp16 = const()[name = string("const_164_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_6137_cast_fp16 = mul(x = var_6135_cast_fp16_1, y = const_164_promoted_to_fp16)[name = string("op_6137_cast_fp16")];
            int32 var_6139 = const()[name = string("op_6139"), val = int32(-2)];
            bool var_6140_interleave_0 = const()[name = string("op_6140_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_6140_cast_fp16 = concat(axis = var_6139, interleave = var_6140_interleave_0, values = (var_6137_cast_fp16, var_6135_cast_fp16_0))[name = string("op_6140_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_6141_cast_fp16 = mul(x = var_6140_cast_fp16, y = var_866_cast_fp16)[name = string("op_6141_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_99_cast_fp16 = add(x = var_6134_cast_fp16, y = var_6141_cast_fp16)[name = string("query_states_99_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_6147_cast_fp16 = mul(x = var_6123_cast_fp16, y = var_859_cast_fp16)[name = string("op_6147_cast_fp16")];
            tensor<int32, [2]> var_6148_split_sizes_0 = const()[name = string("op_6148_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6148_axis_0 = const()[name = string("op_6148_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_6148_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_6148_cast_fp16_1 = split(axis = var_6148_axis_0, split_sizes = var_6148_split_sizes_0, x = var_6123_cast_fp16)[name = string("op_6148_cast_fp16")];
            fp16 const_165_promoted_to_fp16 = const()[name = string("const_165_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_6150_cast_fp16 = mul(x = var_6148_cast_fp16_1, y = const_165_promoted_to_fp16)[name = string("op_6150_cast_fp16")];
            int32 var_6152 = const()[name = string("op_6152"), val = int32(-2)];
            bool var_6153_interleave_0 = const()[name = string("op_6153_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_6153_cast_fp16 = concat(axis = var_6152, interleave = var_6153_interleave_0, values = (var_6150_cast_fp16, var_6148_cast_fp16_0))[name = string("op_6153_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_6154_cast_fp16 = mul(x = var_6153_cast_fp16, y = var_866_cast_fp16)[name = string("op_6154_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_165_cast_fp16 = add(x = var_6147_cast_fp16, y = var_6154_cast_fp16)[name = string("key_states_165_cast_fp16")];
            tensor<int32, [1]> expand_dims_192 = const()[name = string("expand_dims_192"), val = tensor<int32, [1]>([16])];
            tensor<int32, [1]> expand_dims_193 = const()[name = string("expand_dims_193"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_195 = const()[name = string("expand_dims_195"), val = tensor<int32, [1]>([0])];
            int32 concat_197_axis_0 = const()[name = string("concat_197_axis_0"), val = int32(0)];
            bool concat_197_interleave_0 = const()[name = string("concat_197_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_197 = concat(axis = concat_197_axis_0, interleave = concat_197_interleave_0, values = (expand_dims_192, expand_dims_193, position_id, expand_dims_195))[name = string("concat_197")];
            tensor<int32, [1]> expand_dims_196 = const()[name = string("expand_dims_196"), val = tensor<int32, [1]>([17])];
            tensor<int32, [1]> concat_198_values1_0 = const()[name = string("concat_198_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_198_values3_0 = const()[name = string("concat_198_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_198_axis_0 = const()[name = string("concat_198_axis_0"), val = int32(0)];
            bool concat_198_interleave_0 = const()[name = string("concat_198_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_198 = concat(axis = concat_198_axis_0, interleave = concat_198_interleave_0, values = (expand_dims_196, concat_198_values1_0, cache_position_end, concat_198_values3_0))[name = string("concat_198")];
            tensor<int32, [4]> key_states_167_perm_0 = const()[name = string("key_states_167_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_17_stride_0 = const()[name = string("key_cache_internal_tensor_assign_17_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_17_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_17_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_17_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_17_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_17_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_17_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_167_cast_fp16 = transpose(perm = key_states_167_perm_0, x = key_states_165_cast_fp16)[name = string("transpose_209")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_17_cast_fp16 = slice_update(begin = concat_197, begin_mask = key_cache_internal_tensor_assign_17_begin_mask_0, end = concat_198, end_mask = key_cache_internal_tensor_assign_17_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_17_squeeze_mask_0, stride = key_cache_internal_tensor_assign_17_stride_0, update = key_states_167_cast_fp16, x = coreml_update_state_142)[name = string("key_cache_internal_tensor_assign_17_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_17_cast_fp16, input = key_cache)[name = string("coreml_update_state_144_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_144 = read_state(input = key_cache)[name = string("coreml_update_state_144")];
            tensor<int32, [4]> value_states_99_perm_0 = const()[name = string("value_states_99_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_17_stride_0 = const()[name = string("value_cache_internal_tensor_assign_17_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_17_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_17_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_17_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_17_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_17_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_17_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_99_cast_fp16 = transpose(perm = value_states_99_perm_0, x = var_6130_cast_fp16)[name = string("transpose_208")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_17_cast_fp16 = slice_update(begin = concat_197, begin_mask = value_cache_internal_tensor_assign_17_begin_mask_0, end = concat_198, end_mask = value_cache_internal_tensor_assign_17_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_17_squeeze_mask_0, stride = value_cache_internal_tensor_assign_17_stride_0, update = value_states_99_cast_fp16, x = coreml_update_state_143)[name = string("value_cache_internal_tensor_assign_17_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_17_cast_fp16, input = value_cache)[name = string("coreml_update_state_145_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_145 = read_state(input = value_cache)[name = string("coreml_update_state_145")];
            tensor<int32, [4]> var_6224_begin_0 = const()[name = string("op_6224_begin_0"), val = tensor<int32, [4]>([16, 0, 0, 0])];
            tensor<int32, [4]> var_6224_end_0 = const()[name = string("op_6224_end_0"), val = tensor<int32, [4]>([17, 2, 2048, 128])];
            tensor<bool, [4]> var_6224_end_mask_0 = const()[name = string("op_6224_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6224_cast_fp16 = slice_by_index(begin = var_6224_begin_0, end = var_6224_end_0, end_mask = var_6224_end_mask_0, x = coreml_update_state_144)[name = string("op_6224_cast_fp16")];
            tensor<int32, [2]> tile_32 = const()[name = string("tile_32"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6227_axis_0 = const()[name = string("op_6227_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6227_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6227_cast_fp16_1 = split(axis = var_6227_axis_0, split_sizes = tile_32, x = var_6224_cast_fp16)[name = string("op_6227_cast_fp16")];
            tensor<int32, [4]> var_6234_begin_0 = const()[name = string("op_6234_begin_0"), val = tensor<int32, [4]>([16, 0, 0, 0])];
            tensor<int32, [4]> var_6234_end_0 = const()[name = string("op_6234_end_0"), val = tensor<int32, [4]>([17, 2, 2048, 128])];
            tensor<bool, [4]> var_6234_end_mask_0 = const()[name = string("op_6234_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6234_cast_fp16 = slice_by_index(begin = var_6234_begin_0, end = var_6234_end_0, end_mask = var_6234_end_mask_0, x = coreml_update_state_145)[name = string("op_6234_cast_fp16")];
            tensor<int32, [2]> tile_33 = const()[name = string("tile_33"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6237_axis_0 = const()[name = string("op_6237_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6237_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6237_cast_fp16_1 = split(axis = var_6237_axis_0, split_sizes = tile_33, x = var_6234_cast_fp16)[name = string("op_6237_cast_fp16")];
            tensor<int32, [2]> var_6240_split_sizes_0 = const()[name = string("op_6240_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_6240_axis_0 = const()[name = string("op_6240_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_6240_0, tensor<fp16, [1, 8, 128, 8]> var_6240_1 = split(axis = var_6240_axis_0, split_sizes = var_6240_split_sizes_0, x = query_states_99_cast_fp16)[name = string("op_6240")];
            bool attn_weights_257_transpose_x_0 = const()[name = string("attn_weights_257_transpose_x_0"), val = bool(false)];
            bool attn_weights_257_transpose_y_0 = const()[name = string("attn_weights_257_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_257_cast_fp16 = matmul(transpose_x = attn_weights_257_transpose_x_0, transpose_y = attn_weights_257_transpose_y_0, x = var_6227_cast_fp16_0, y = var_6240_0)[name = string("attn_weights_257_cast_fp16")];
            fp16 var_6243_to_fp16 = const()[name = string("op_6243_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_259_cast_fp16 = mul(x = attn_weights_257_cast_fp16, y = var_6243_to_fp16)[name = string("attn_weights_259_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_261_cast_fp16 = add(x = attn_weights_259_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_261_cast_fp16")];
            int32 var_6247 = const()[name = string("op_6247"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_263_cast_fp16 = softmax(axis = var_6247, x = attn_weights_261_cast_fp16)[name = string("attn_weights_263_cast_fp16")];
            bool var_6253_transpose_x_1 = const()[name = string("op_6253_transpose_x_1"), val = bool(true)];
            bool var_6253_transpose_y_1 = const()[name = string("op_6253_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_6253_cast_fp16 = matmul(transpose_x = var_6253_transpose_x_1, transpose_y = var_6253_transpose_y_1, x = attn_weights_263_cast_fp16, y = var_6237_cast_fp16_0)[name = string("op_6253_cast_fp16")];
            bool attn_weights_265_transpose_x_0 = const()[name = string("attn_weights_265_transpose_x_0"), val = bool(false)];
            bool attn_weights_265_transpose_y_0 = const()[name = string("attn_weights_265_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_265_cast_fp16 = matmul(transpose_x = attn_weights_265_transpose_x_0, transpose_y = attn_weights_265_transpose_y_0, x = var_6227_cast_fp16_1, y = var_6240_1)[name = string("attn_weights_265_cast_fp16")];
            fp16 var_6255_to_fp16 = const()[name = string("op_6255_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_267_cast_fp16 = mul(x = attn_weights_265_cast_fp16, y = var_6255_to_fp16)[name = string("attn_weights_267_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_269_cast_fp16 = add(x = attn_weights_267_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_269_cast_fp16")];
            int32 var_6259 = const()[name = string("op_6259"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_271_cast_fp16 = softmax(axis = var_6259, x = attn_weights_269_cast_fp16)[name = string("attn_weights_271_cast_fp16")];
            bool attn_output_129_transpose_x_1 = const()[name = string("attn_output_129_transpose_x_1"), val = bool(true)];
            bool attn_output_129_transpose_y_1 = const()[name = string("attn_output_129_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_129_cast_fp16 = matmul(transpose_x = attn_output_129_transpose_x_1, transpose_y = attn_output_129_transpose_y_1, x = attn_weights_271_cast_fp16, y = var_6237_cast_fp16_1)[name = string("attn_output_129_cast_fp16")];
            int32 var_6267 = const()[name = string("op_6267"), val = int32(1)];
            bool attn_output_131_interleave_0 = const()[name = string("attn_output_131_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_131_cast_fp16 = concat(axis = var_6267, interleave = attn_output_131_interleave_0, values = (var_6253_cast_fp16, attn_output_129_cast_fp16))[name = string("attn_output_131_cast_fp16")];
            tensor<int32, [4]> var_6271_perm_0 = const()[name = string("op_6271_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_203x = const()[name = string("concat_203x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_6271_cast_fp16 = transpose(perm = var_6271_perm_0, x = attn_output_131_cast_fp16)[name = string("transpose_207")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_135_cast_fp16 = reshape(shape = concat_203x, x = var_6271_cast_fp16)[name = string("attn_output_135_cast_fp16")];
            tensor<int32, [2]> hidden_states_163_strides_0 = const()[name = string("hidden_states_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_163_pad_type_0 = const()[name = string("hidden_states_163_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_163_pad_0 = const()[name = string("hidden_states_163_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_163_dilations_0 = const()[name = string("hidden_states_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_163_groups_0 = const()[name = string("hidden_states_163_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_163_cast_fp16 = conv(dilations = hidden_states_163_dilations_0, groups = hidden_states_163_groups_0, pad = hidden_states_163_pad_0, pad_type = hidden_states_163_pad_type_0, strides = hidden_states_163_strides_0, weight = layers_16_self_attn_o_proj_weight_cast_fp16, x = attn_output_135_cast_fp16)[name = string("hidden_states_163_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_165_cast_fp16 = add(x = hidden_states_159_cast_fp16, y = hidden_states_163_cast_fp16)[name = string("hidden_states_165_cast_fp16")];
            fp16 const_170_promoted_to_fp16 = const()[name = string("const_170_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_6304_cast_fp16 = mul(x = hidden_states_165_cast_fp16, y = const_170_promoted_to_fp16)[name = string("op_6304_cast_fp16")];
            int32 var_6302 = const()[name = string("op_6302"), val = int32(1)];
            bool doubled_133_interleave_0 = const()[name = string("doubled_133_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_133_cast_fp16 = concat(axis = var_6302, interleave = doubled_133_interleave_0, values = (hidden_states_165_cast_fp16, var_6304_cast_fp16))[name = string("doubled_133_cast_fp16")];
            tensor<int32, [1]> out_67_axes_0 = const()[name = string("out_67_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_67_gamma_0_to_fp16 = const()[name = string("out_67_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433283072)))];
            fp16 var_6314_to_fp16 = const()[name = string("op_6314_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_67_cast_fp16 = layer_norm(axes = out_67_axes_0, epsilon = var_6314_to_fp16, gamma = out_67_gamma_0_to_fp16, x = doubled_133_cast_fp16)[name = string("out_67_cast_fp16")];
            tensor<int32, [2]> var_6325_split_sizes_0 = const()[name = string("op_6325_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6325_axis_0 = const()[name = string("op_6325_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_6325_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_6325_cast_fp16_1 = split(axis = var_6325_axis_0, split_sizes = var_6325_split_sizes_0, x = out_67_cast_fp16)[name = string("op_6325_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_16_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_16_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433291328)))];
            tensor<int32, [2]> input_33_strides_0 = const()[name = string("input_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_33_pad_type_0 = const()[name = string("input_33_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_33_pad_0 = const()[name = string("input_33_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_33_dilations_0 = const()[name = string("input_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_33_groups_0 = const()[name = string("input_33_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_33_cast_fp16 = conv(dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = layers_16_mlp_gate_proj_weight_to_fp16, x = var_6325_cast_fp16_0)[name = string("input_33_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_6342_cast_fp16 = silu(x = input_33_cast_fp16)[name = string("op_6342_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_16_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_16_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1458457216)))];
            tensor<int32, [2]> var_6348_strides_0 = const()[name = string("op_6348_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6348_pad_type_0 = const()[name = string("op_6348_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6348_pad_0 = const()[name = string("op_6348_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6348_dilations_0 = const()[name = string("op_6348_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6348_groups_0 = const()[name = string("op_6348_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_6348_cast_fp16 = conv(dilations = var_6348_dilations_0, groups = var_6348_groups_0, pad = var_6348_pad_0, pad_type = var_6348_pad_type_0, strides = var_6348_strides_0, weight = layers_16_mlp_up_proj_weight_to_fp16, x = var_6325_cast_fp16_0)[name = string("op_6348_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_169_cast_fp16 = mul(x = var_6342_cast_fp16, y = var_6348_cast_fp16)[name = string("x_169_cast_fp16")];
            tensor<int32, [2]> hidden_states_167_strides_0 = const()[name = string("hidden_states_167_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_167_pad_type_0 = const()[name = string("hidden_states_167_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_167_pad_0 = const()[name = string("hidden_states_167_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_167_dilations_0 = const()[name = string("hidden_states_167_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_167_groups_0 = const()[name = string("hidden_states_167_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_167_cast_fp16 = conv(dilations = hidden_states_167_dilations_0, groups = hidden_states_167_groups_0, pad = hidden_states_167_pad_0, pad_type = hidden_states_167_pad_type_0, strides = hidden_states_167_strides_0, weight = layers_16_mlp_down_proj_weight_cast_fp16, x = x_169_cast_fp16)[name = string("hidden_states_167_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_169_cast_fp16 = add(x = hidden_states_165_cast_fp16, y = hidden_states_167_cast_fp16)[name = string("hidden_states_169_cast_fp16")];
            fp16 const_172_promoted_to_fp16 = const()[name = string("const_172_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_6366_cast_fp16 = mul(x = hidden_states_169_cast_fp16, y = const_172_promoted_to_fp16)[name = string("op_6366_cast_fp16")];
            int32 var_6364 = const()[name = string("op_6364"), val = int32(1)];
            bool doubled_137_interleave_0 = const()[name = string("doubled_137_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_137_cast_fp16 = concat(axis = var_6364, interleave = doubled_137_interleave_0, values = (hidden_states_169_cast_fp16, var_6366_cast_fp16))[name = string("doubled_137_cast_fp16")];
            tensor<int32, [1]> out_69_axes_0 = const()[name = string("out_69_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_69_gamma_0_to_fp16 = const()[name = string("out_69_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483623104)))];
            fp16 var_6376_to_fp16 = const()[name = string("op_6376_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_69_cast_fp16 = layer_norm(axes = out_69_axes_0, epsilon = var_6376_to_fp16, gamma = out_69_gamma_0_to_fp16, x = doubled_137_cast_fp16)[name = string("out_69_cast_fp16")];
            tensor<int32, [2]> var_6387_split_sizes_0 = const()[name = string("op_6387_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6387_axis_0 = const()[name = string("op_6387_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_6387_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_6387_cast_fp16_1 = split(axis = var_6387_axis_0, split_sizes = var_6387_split_sizes_0, x = out_69_cast_fp16)[name = string("op_6387_cast_fp16")];
            tensor<int32, [2]> query_states_103_strides_0 = const()[name = string("query_states_103_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_103_pad_type_0 = const()[name = string("query_states_103_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_103_pad_0 = const()[name = string("query_states_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_103_dilations_0 = const()[name = string("query_states_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_103_groups_0 = const()[name = string("query_states_103_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_103_cast_fp16 = conv(dilations = query_states_103_dilations_0, groups = query_states_103_groups_0, pad = query_states_103_pad_0, pad_type = query_states_103_pad_type_0, strides = query_states_103_strides_0, weight = layers_17_self_attn_q_proj_weight_cast_fp16, x = var_6387_cast_fp16_0)[name = string("query_states_103_cast_fp16")];
            tensor<int32, [2]> key_states_171_strides_0 = const()[name = string("key_states_171_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_171_pad_type_0 = const()[name = string("key_states_171_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_171_pad_0 = const()[name = string("key_states_171_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_171_dilations_0 = const()[name = string("key_states_171_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_171_groups_0 = const()[name = string("key_states_171_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_171_cast_fp16 = conv(dilations = key_states_171_dilations_0, groups = key_states_171_groups_0, pad = key_states_171_pad_0, pad_type = key_states_171_pad_type_0, strides = key_states_171_strides_0, weight = layers_17_self_attn_k_proj_weight_cast_fp16, x = var_6387_cast_fp16_0)[name = string("key_states_171_cast_fp16")];
            tensor<int32, [2]> value_states_103_strides_0 = const()[name = string("value_states_103_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_103_pad_type_0 = const()[name = string("value_states_103_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_103_pad_0 = const()[name = string("value_states_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_103_dilations_0 = const()[name = string("value_states_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_103_groups_0 = const()[name = string("value_states_103_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_103_cast_fp16 = conv(dilations = value_states_103_dilations_0, groups = value_states_103_groups_0, pad = value_states_103_pad_0, pad_type = value_states_103_pad_type_0, strides = value_states_103_strides_0, weight = layers_17_self_attn_v_proj_weight_cast_fp16, x = var_6387_cast_fp16_0)[name = string("value_states_103_cast_fp16")];
            tensor<int32, [4]> concat_204x = const()[name = string("concat_204x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_171_cast_fp16 = reshape(shape = concat_204x, x = query_states_103_cast_fp16)[name = string("x_171_cast_fp16")];
            tensor<int32, [4]> concat_205x = const()[name = string("concat_205x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_6444_cast_fp16 = reshape(shape = concat_205x, x = key_states_171_cast_fp16)[name = string("op_6444_cast_fp16")];
            tensor<int32, [4]> concat_206x = const()[name = string("concat_206x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_6451_cast_fp16 = reshape(shape = concat_206x, x = value_states_103_cast_fp16)[name = string("op_6451_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_6455_cast_fp16 = mul(x = x_171_cast_fp16, y = var_859_cast_fp16)[name = string("op_6455_cast_fp16")];
            tensor<int32, [2]> var_6456_split_sizes_0 = const()[name = string("op_6456_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6456_axis_0 = const()[name = string("op_6456_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_6456_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_6456_cast_fp16_1 = split(axis = var_6456_axis_0, split_sizes = var_6456_split_sizes_0, x = x_171_cast_fp16)[name = string("op_6456_cast_fp16")];
            fp16 const_174_promoted_to_fp16 = const()[name = string("const_174_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_6458_cast_fp16 = mul(x = var_6456_cast_fp16_1, y = const_174_promoted_to_fp16)[name = string("op_6458_cast_fp16")];
            int32 var_6460 = const()[name = string("op_6460"), val = int32(-2)];
            bool var_6461_interleave_0 = const()[name = string("op_6461_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_6461_cast_fp16 = concat(axis = var_6460, interleave = var_6461_interleave_0, values = (var_6458_cast_fp16, var_6456_cast_fp16_0))[name = string("op_6461_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_6462_cast_fp16 = mul(x = var_6461_cast_fp16, y = var_866_cast_fp16)[name = string("op_6462_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_105_cast_fp16 = add(x = var_6455_cast_fp16, y = var_6462_cast_fp16)[name = string("query_states_105_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_6468_cast_fp16 = mul(x = var_6444_cast_fp16, y = var_859_cast_fp16)[name = string("op_6468_cast_fp16")];
            tensor<int32, [2]> var_6469_split_sizes_0 = const()[name = string("op_6469_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6469_axis_0 = const()[name = string("op_6469_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_6469_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_6469_cast_fp16_1 = split(axis = var_6469_axis_0, split_sizes = var_6469_split_sizes_0, x = var_6444_cast_fp16)[name = string("op_6469_cast_fp16")];
            fp16 const_175_promoted_to_fp16 = const()[name = string("const_175_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_6471_cast_fp16 = mul(x = var_6469_cast_fp16_1, y = const_175_promoted_to_fp16)[name = string("op_6471_cast_fp16")];
            int32 var_6473 = const()[name = string("op_6473"), val = int32(-2)];
            bool var_6474_interleave_0 = const()[name = string("op_6474_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_6474_cast_fp16 = concat(axis = var_6473, interleave = var_6474_interleave_0, values = (var_6471_cast_fp16, var_6469_cast_fp16_0))[name = string("op_6474_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_6475_cast_fp16 = mul(x = var_6474_cast_fp16, y = var_866_cast_fp16)[name = string("op_6475_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_175_cast_fp16 = add(x = var_6468_cast_fp16, y = var_6475_cast_fp16)[name = string("key_states_175_cast_fp16")];
            tensor<int32, [1]> expand_dims_204 = const()[name = string("expand_dims_204"), val = tensor<int32, [1]>([17])];
            tensor<int32, [1]> expand_dims_205 = const()[name = string("expand_dims_205"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_207 = const()[name = string("expand_dims_207"), val = tensor<int32, [1]>([0])];
            int32 concat_209_axis_0 = const()[name = string("concat_209_axis_0"), val = int32(0)];
            bool concat_209_interleave_0 = const()[name = string("concat_209_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_209 = concat(axis = concat_209_axis_0, interleave = concat_209_interleave_0, values = (expand_dims_204, expand_dims_205, position_id, expand_dims_207))[name = string("concat_209")];
            tensor<int32, [1]> expand_dims_208 = const()[name = string("expand_dims_208"), val = tensor<int32, [1]>([18])];
            tensor<int32, [1]> concat_210_values1_0 = const()[name = string("concat_210_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_210_values3_0 = const()[name = string("concat_210_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_210_axis_0 = const()[name = string("concat_210_axis_0"), val = int32(0)];
            bool concat_210_interleave_0 = const()[name = string("concat_210_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_210 = concat(axis = concat_210_axis_0, interleave = concat_210_interleave_0, values = (expand_dims_208, concat_210_values1_0, cache_position_end, concat_210_values3_0))[name = string("concat_210")];
            tensor<int32, [4]> key_states_177_perm_0 = const()[name = string("key_states_177_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_18_stride_0 = const()[name = string("key_cache_internal_tensor_assign_18_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_18_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_18_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_18_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_18_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_18_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_18_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_177_cast_fp16 = transpose(perm = key_states_177_perm_0, x = key_states_175_cast_fp16)[name = string("transpose_206")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_18_cast_fp16 = slice_update(begin = concat_209, begin_mask = key_cache_internal_tensor_assign_18_begin_mask_0, end = concat_210, end_mask = key_cache_internal_tensor_assign_18_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_18_squeeze_mask_0, stride = key_cache_internal_tensor_assign_18_stride_0, update = key_states_177_cast_fp16, x = coreml_update_state_144)[name = string("key_cache_internal_tensor_assign_18_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_18_cast_fp16, input = key_cache)[name = string("coreml_update_state_146_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_146 = read_state(input = key_cache)[name = string("coreml_update_state_146")];
            tensor<int32, [4]> value_states_105_perm_0 = const()[name = string("value_states_105_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_18_stride_0 = const()[name = string("value_cache_internal_tensor_assign_18_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_18_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_18_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_18_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_18_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_18_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_18_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_105_cast_fp16 = transpose(perm = value_states_105_perm_0, x = var_6451_cast_fp16)[name = string("transpose_205")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_18_cast_fp16 = slice_update(begin = concat_209, begin_mask = value_cache_internal_tensor_assign_18_begin_mask_0, end = concat_210, end_mask = value_cache_internal_tensor_assign_18_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_18_squeeze_mask_0, stride = value_cache_internal_tensor_assign_18_stride_0, update = value_states_105_cast_fp16, x = coreml_update_state_145)[name = string("value_cache_internal_tensor_assign_18_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_18_cast_fp16, input = value_cache)[name = string("coreml_update_state_147_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_147 = read_state(input = value_cache)[name = string("coreml_update_state_147")];
            tensor<int32, [4]> var_6545_begin_0 = const()[name = string("op_6545_begin_0"), val = tensor<int32, [4]>([17, 0, 0, 0])];
            tensor<int32, [4]> var_6545_end_0 = const()[name = string("op_6545_end_0"), val = tensor<int32, [4]>([18, 2, 2048, 128])];
            tensor<bool, [4]> var_6545_end_mask_0 = const()[name = string("op_6545_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6545_cast_fp16 = slice_by_index(begin = var_6545_begin_0, end = var_6545_end_0, end_mask = var_6545_end_mask_0, x = coreml_update_state_146)[name = string("op_6545_cast_fp16")];
            tensor<int32, [2]> tile_34 = const()[name = string("tile_34"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6548_axis_0 = const()[name = string("op_6548_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6548_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6548_cast_fp16_1 = split(axis = var_6548_axis_0, split_sizes = tile_34, x = var_6545_cast_fp16)[name = string("op_6548_cast_fp16")];
            tensor<int32, [4]> var_6555_begin_0 = const()[name = string("op_6555_begin_0"), val = tensor<int32, [4]>([17, 0, 0, 0])];
            tensor<int32, [4]> var_6555_end_0 = const()[name = string("op_6555_end_0"), val = tensor<int32, [4]>([18, 2, 2048, 128])];
            tensor<bool, [4]> var_6555_end_mask_0 = const()[name = string("op_6555_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6555_cast_fp16 = slice_by_index(begin = var_6555_begin_0, end = var_6555_end_0, end_mask = var_6555_end_mask_0, x = coreml_update_state_147)[name = string("op_6555_cast_fp16")];
            tensor<int32, [2]> tile_35 = const()[name = string("tile_35"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6558_axis_0 = const()[name = string("op_6558_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6558_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6558_cast_fp16_1 = split(axis = var_6558_axis_0, split_sizes = tile_35, x = var_6555_cast_fp16)[name = string("op_6558_cast_fp16")];
            tensor<int32, [2]> var_6561_split_sizes_0 = const()[name = string("op_6561_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_6561_axis_0 = const()[name = string("op_6561_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_6561_0, tensor<fp16, [1, 8, 128, 8]> var_6561_1 = split(axis = var_6561_axis_0, split_sizes = var_6561_split_sizes_0, x = query_states_105_cast_fp16)[name = string("op_6561")];
            bool attn_weights_273_transpose_x_0 = const()[name = string("attn_weights_273_transpose_x_0"), val = bool(false)];
            bool attn_weights_273_transpose_y_0 = const()[name = string("attn_weights_273_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_273_cast_fp16 = matmul(transpose_x = attn_weights_273_transpose_x_0, transpose_y = attn_weights_273_transpose_y_0, x = var_6548_cast_fp16_0, y = var_6561_0)[name = string("attn_weights_273_cast_fp16")];
            fp16 var_6564_to_fp16 = const()[name = string("op_6564_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_275_cast_fp16 = mul(x = attn_weights_273_cast_fp16, y = var_6564_to_fp16)[name = string("attn_weights_275_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_277_cast_fp16 = add(x = attn_weights_275_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_277_cast_fp16")];
            int32 var_6568 = const()[name = string("op_6568"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_279_cast_fp16 = softmax(axis = var_6568, x = attn_weights_277_cast_fp16)[name = string("attn_weights_279_cast_fp16")];
            bool var_6574_transpose_x_1 = const()[name = string("op_6574_transpose_x_1"), val = bool(true)];
            bool var_6574_transpose_y_1 = const()[name = string("op_6574_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_6574_cast_fp16 = matmul(transpose_x = var_6574_transpose_x_1, transpose_y = var_6574_transpose_y_1, x = attn_weights_279_cast_fp16, y = var_6558_cast_fp16_0)[name = string("op_6574_cast_fp16")];
            bool attn_weights_281_transpose_x_0 = const()[name = string("attn_weights_281_transpose_x_0"), val = bool(false)];
            bool attn_weights_281_transpose_y_0 = const()[name = string("attn_weights_281_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_281_cast_fp16 = matmul(transpose_x = attn_weights_281_transpose_x_0, transpose_y = attn_weights_281_transpose_y_0, x = var_6548_cast_fp16_1, y = var_6561_1)[name = string("attn_weights_281_cast_fp16")];
            fp16 var_6576_to_fp16 = const()[name = string("op_6576_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_283_cast_fp16 = mul(x = attn_weights_281_cast_fp16, y = var_6576_to_fp16)[name = string("attn_weights_283_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_285_cast_fp16 = add(x = attn_weights_283_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_285_cast_fp16")];
            int32 var_6580 = const()[name = string("op_6580"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_287_cast_fp16 = softmax(axis = var_6580, x = attn_weights_285_cast_fp16)[name = string("attn_weights_287_cast_fp16")];
            bool attn_output_137_transpose_x_1 = const()[name = string("attn_output_137_transpose_x_1"), val = bool(true)];
            bool attn_output_137_transpose_y_1 = const()[name = string("attn_output_137_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_137_cast_fp16 = matmul(transpose_x = attn_output_137_transpose_x_1, transpose_y = attn_output_137_transpose_y_1, x = attn_weights_287_cast_fp16, y = var_6558_cast_fp16_1)[name = string("attn_output_137_cast_fp16")];
            int32 var_6588 = const()[name = string("op_6588"), val = int32(1)];
            bool attn_output_139_interleave_0 = const()[name = string("attn_output_139_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_139_cast_fp16 = concat(axis = var_6588, interleave = attn_output_139_interleave_0, values = (var_6574_cast_fp16, attn_output_137_cast_fp16))[name = string("attn_output_139_cast_fp16")];
            tensor<int32, [4]> var_6592_perm_0 = const()[name = string("op_6592_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_215x = const()[name = string("concat_215x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_6592_cast_fp16 = transpose(perm = var_6592_perm_0, x = attn_output_139_cast_fp16)[name = string("transpose_204")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_143_cast_fp16 = reshape(shape = concat_215x, x = var_6592_cast_fp16)[name = string("attn_output_143_cast_fp16")];
            tensor<int32, [2]> hidden_states_173_strides_0 = const()[name = string("hidden_states_173_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_173_pad_type_0 = const()[name = string("hidden_states_173_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_173_pad_0 = const()[name = string("hidden_states_173_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_173_dilations_0 = const()[name = string("hidden_states_173_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_173_groups_0 = const()[name = string("hidden_states_173_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_173_cast_fp16 = conv(dilations = hidden_states_173_dilations_0, groups = hidden_states_173_groups_0, pad = hidden_states_173_pad_0, pad_type = hidden_states_173_pad_type_0, strides = hidden_states_173_strides_0, weight = layers_17_self_attn_o_proj_weight_cast_fp16, x = attn_output_143_cast_fp16)[name = string("hidden_states_173_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_175_cast_fp16 = add(x = hidden_states_169_cast_fp16, y = hidden_states_173_cast_fp16)[name = string("hidden_states_175_cast_fp16")];
            fp16 const_180_promoted_to_fp16 = const()[name = string("const_180_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_6625_cast_fp16 = mul(x = hidden_states_175_cast_fp16, y = const_180_promoted_to_fp16)[name = string("op_6625_cast_fp16")];
            int32 var_6623 = const()[name = string("op_6623"), val = int32(1)];
            bool doubled_141_interleave_0 = const()[name = string("doubled_141_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_141_cast_fp16 = concat(axis = var_6623, interleave = doubled_141_interleave_0, values = (hidden_states_175_cast_fp16, var_6625_cast_fp16))[name = string("doubled_141_cast_fp16")];
            tensor<int32, [1]> out_71_axes_0 = const()[name = string("out_71_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_71_gamma_0_to_fp16 = const()[name = string("out_71_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483631360)))];
            fp16 var_6635_to_fp16 = const()[name = string("op_6635_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_71_cast_fp16 = layer_norm(axes = out_71_axes_0, epsilon = var_6635_to_fp16, gamma = out_71_gamma_0_to_fp16, x = doubled_141_cast_fp16)[name = string("out_71_cast_fp16")];
            tensor<int32, [2]> var_6646_split_sizes_0 = const()[name = string("op_6646_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6646_axis_0 = const()[name = string("op_6646_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_6646_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_6646_cast_fp16_1 = split(axis = var_6646_axis_0, split_sizes = var_6646_split_sizes_0, x = out_71_cast_fp16)[name = string("op_6646_cast_fp16")];
            tensor<int32, [2]> input_35_strides_0 = const()[name = string("input_35_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_35_pad_type_0 = const()[name = string("input_35_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_35_pad_0 = const()[name = string("input_35_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_35_dilations_0 = const()[name = string("input_35_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_35_groups_0 = const()[name = string("input_35_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_35_cast_fp16 = conv(dilations = input_35_dilations_0, groups = input_35_groups_0, pad = input_35_pad_0, pad_type = input_35_pad_type_0, strides = input_35_strides_0, weight = layers_17_mlp_gate_proj_weight_cast_fp16, x = var_6646_cast_fp16_0)[name = string("input_35_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_6663_cast_fp16 = silu(x = input_35_cast_fp16)[name = string("op_6663_cast_fp16")];
            tensor<int32, [2]> var_6669_strides_0 = const()[name = string("op_6669_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6669_pad_type_0 = const()[name = string("op_6669_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6669_pad_0 = const()[name = string("op_6669_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6669_dilations_0 = const()[name = string("op_6669_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6669_groups_0 = const()[name = string("op_6669_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_6669_cast_fp16 = conv(dilations = var_6669_dilations_0, groups = var_6669_groups_0, pad = var_6669_pad_0, pad_type = var_6669_pad_type_0, strides = var_6669_strides_0, weight = layers_17_mlp_up_proj_weight_cast_fp16, x = var_6646_cast_fp16_0)[name = string("op_6669_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_179_cast_fp16 = mul(x = var_6663_cast_fp16, y = var_6669_cast_fp16)[name = string("x_179_cast_fp16")];
            tensor<int32, [2]> hidden_states_177_strides_0 = const()[name = string("hidden_states_177_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_177_pad_type_0 = const()[name = string("hidden_states_177_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_177_pad_0 = const()[name = string("hidden_states_177_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_177_dilations_0 = const()[name = string("hidden_states_177_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_177_groups_0 = const()[name = string("hidden_states_177_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_177_cast_fp16 = conv(dilations = hidden_states_177_dilations_0, groups = hidden_states_177_groups_0, pad = hidden_states_177_pad_0, pad_type = hidden_states_177_pad_type_0, strides = hidden_states_177_strides_0, weight = layers_17_mlp_down_proj_weight_cast_fp16, x = x_179_cast_fp16)[name = string("hidden_states_177_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_179_cast_fp16 = add(x = hidden_states_175_cast_fp16, y = hidden_states_177_cast_fp16)[name = string("hidden_states_179_cast_fp16")];
            fp16 const_182_promoted_to_fp16 = const()[name = string("const_182_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_6687_cast_fp16 = mul(x = hidden_states_179_cast_fp16, y = const_182_promoted_to_fp16)[name = string("op_6687_cast_fp16")];
            int32 var_6685 = const()[name = string("op_6685"), val = int32(1)];
            bool doubled_145_interleave_0 = const()[name = string("doubled_145_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_145_cast_fp16 = concat(axis = var_6685, interleave = doubled_145_interleave_0, values = (hidden_states_179_cast_fp16, var_6687_cast_fp16))[name = string("doubled_145_cast_fp16")];
            tensor<int32, [1]> out_73_axes_0 = const()[name = string("out_73_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_73_gamma_0_to_fp16 = const()[name = string("out_73_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483639616)))];
            fp16 var_6697_to_fp16 = const()[name = string("op_6697_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_73_cast_fp16 = layer_norm(axes = out_73_axes_0, epsilon = var_6697_to_fp16, gamma = out_73_gamma_0_to_fp16, x = doubled_145_cast_fp16)[name = string("out_73_cast_fp16")];
            tensor<int32, [2]> var_6708_split_sizes_0 = const()[name = string("op_6708_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6708_axis_0 = const()[name = string("op_6708_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_6708_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_6708_cast_fp16_1 = split(axis = var_6708_axis_0, split_sizes = var_6708_split_sizes_0, x = out_73_cast_fp16)[name = string("op_6708_cast_fp16")];
            tensor<int32, [2]> query_states_109_strides_0 = const()[name = string("query_states_109_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_109_pad_type_0 = const()[name = string("query_states_109_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_109_pad_0 = const()[name = string("query_states_109_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_109_dilations_0 = const()[name = string("query_states_109_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_109_groups_0 = const()[name = string("query_states_109_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_109_cast_fp16 = conv(dilations = query_states_109_dilations_0, groups = query_states_109_groups_0, pad = query_states_109_pad_0, pad_type = query_states_109_pad_type_0, strides = query_states_109_strides_0, weight = layers_18_self_attn_q_proj_weight_cast_fp16, x = var_6708_cast_fp16_0)[name = string("query_states_109_cast_fp16")];
            tensor<int32, [2]> key_states_181_strides_0 = const()[name = string("key_states_181_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_181_pad_type_0 = const()[name = string("key_states_181_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_181_pad_0 = const()[name = string("key_states_181_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_181_dilations_0 = const()[name = string("key_states_181_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_181_groups_0 = const()[name = string("key_states_181_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_181_cast_fp16 = conv(dilations = key_states_181_dilations_0, groups = key_states_181_groups_0, pad = key_states_181_pad_0, pad_type = key_states_181_pad_type_0, strides = key_states_181_strides_0, weight = layers_18_self_attn_k_proj_weight_cast_fp16, x = var_6708_cast_fp16_0)[name = string("key_states_181_cast_fp16")];
            tensor<int32, [2]> value_states_109_strides_0 = const()[name = string("value_states_109_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_109_pad_type_0 = const()[name = string("value_states_109_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_109_pad_0 = const()[name = string("value_states_109_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_109_dilations_0 = const()[name = string("value_states_109_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_109_groups_0 = const()[name = string("value_states_109_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_109_cast_fp16 = conv(dilations = value_states_109_dilations_0, groups = value_states_109_groups_0, pad = value_states_109_pad_0, pad_type = value_states_109_pad_type_0, strides = value_states_109_strides_0, weight = layers_18_self_attn_v_proj_weight_cast_fp16, x = var_6708_cast_fp16_0)[name = string("value_states_109_cast_fp16")];
            tensor<int32, [4]> concat_216x = const()[name = string("concat_216x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_181_cast_fp16 = reshape(shape = concat_216x, x = query_states_109_cast_fp16)[name = string("x_181_cast_fp16")];
            tensor<int32, [4]> concat_217x = const()[name = string("concat_217x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_6765_cast_fp16 = reshape(shape = concat_217x, x = key_states_181_cast_fp16)[name = string("op_6765_cast_fp16")];
            tensor<int32, [4]> concat_218x = const()[name = string("concat_218x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_6772_cast_fp16 = reshape(shape = concat_218x, x = value_states_109_cast_fp16)[name = string("op_6772_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_6776_cast_fp16 = mul(x = x_181_cast_fp16, y = var_859_cast_fp16)[name = string("op_6776_cast_fp16")];
            tensor<int32, [2]> var_6777_split_sizes_0 = const()[name = string("op_6777_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6777_axis_0 = const()[name = string("op_6777_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_6777_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_6777_cast_fp16_1 = split(axis = var_6777_axis_0, split_sizes = var_6777_split_sizes_0, x = x_181_cast_fp16)[name = string("op_6777_cast_fp16")];
            fp16 const_184_promoted_to_fp16 = const()[name = string("const_184_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_6779_cast_fp16 = mul(x = var_6777_cast_fp16_1, y = const_184_promoted_to_fp16)[name = string("op_6779_cast_fp16")];
            int32 var_6781 = const()[name = string("op_6781"), val = int32(-2)];
            bool var_6782_interleave_0 = const()[name = string("op_6782_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_6782_cast_fp16 = concat(axis = var_6781, interleave = var_6782_interleave_0, values = (var_6779_cast_fp16, var_6777_cast_fp16_0))[name = string("op_6782_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_6783_cast_fp16 = mul(x = var_6782_cast_fp16, y = var_866_cast_fp16)[name = string("op_6783_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_111_cast_fp16 = add(x = var_6776_cast_fp16, y = var_6783_cast_fp16)[name = string("query_states_111_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_6789_cast_fp16 = mul(x = var_6765_cast_fp16, y = var_859_cast_fp16)[name = string("op_6789_cast_fp16")];
            tensor<int32, [2]> var_6790_split_sizes_0 = const()[name = string("op_6790_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6790_axis_0 = const()[name = string("op_6790_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_6790_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_6790_cast_fp16_1 = split(axis = var_6790_axis_0, split_sizes = var_6790_split_sizes_0, x = var_6765_cast_fp16)[name = string("op_6790_cast_fp16")];
            fp16 const_185_promoted_to_fp16 = const()[name = string("const_185_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_6792_cast_fp16 = mul(x = var_6790_cast_fp16_1, y = const_185_promoted_to_fp16)[name = string("op_6792_cast_fp16")];
            int32 var_6794 = const()[name = string("op_6794"), val = int32(-2)];
            bool var_6795_interleave_0 = const()[name = string("op_6795_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_6795_cast_fp16 = concat(axis = var_6794, interleave = var_6795_interleave_0, values = (var_6792_cast_fp16, var_6790_cast_fp16_0))[name = string("op_6795_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_6796_cast_fp16 = mul(x = var_6795_cast_fp16, y = var_866_cast_fp16)[name = string("op_6796_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_185_cast_fp16 = add(x = var_6789_cast_fp16, y = var_6796_cast_fp16)[name = string("key_states_185_cast_fp16")];
            tensor<int32, [1]> expand_dims_216 = const()[name = string("expand_dims_216"), val = tensor<int32, [1]>([18])];
            tensor<int32, [1]> expand_dims_217 = const()[name = string("expand_dims_217"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_219 = const()[name = string("expand_dims_219"), val = tensor<int32, [1]>([0])];
            int32 concat_221_axis_0 = const()[name = string("concat_221_axis_0"), val = int32(0)];
            bool concat_221_interleave_0 = const()[name = string("concat_221_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_221 = concat(axis = concat_221_axis_0, interleave = concat_221_interleave_0, values = (expand_dims_216, expand_dims_217, position_id, expand_dims_219))[name = string("concat_221")];
            tensor<int32, [1]> expand_dims_220 = const()[name = string("expand_dims_220"), val = tensor<int32, [1]>([19])];
            tensor<int32, [1]> concat_222_values1_0 = const()[name = string("concat_222_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_222_values3_0 = const()[name = string("concat_222_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_222_axis_0 = const()[name = string("concat_222_axis_0"), val = int32(0)];
            bool concat_222_interleave_0 = const()[name = string("concat_222_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_222 = concat(axis = concat_222_axis_0, interleave = concat_222_interleave_0, values = (expand_dims_220, concat_222_values1_0, cache_position_end, concat_222_values3_0))[name = string("concat_222")];
            tensor<int32, [4]> key_states_187_perm_0 = const()[name = string("key_states_187_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_19_stride_0 = const()[name = string("key_cache_internal_tensor_assign_19_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_19_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_19_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_19_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_19_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_19_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_19_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_187_cast_fp16 = transpose(perm = key_states_187_perm_0, x = key_states_185_cast_fp16)[name = string("transpose_203")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_19_cast_fp16 = slice_update(begin = concat_221, begin_mask = key_cache_internal_tensor_assign_19_begin_mask_0, end = concat_222, end_mask = key_cache_internal_tensor_assign_19_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_19_squeeze_mask_0, stride = key_cache_internal_tensor_assign_19_stride_0, update = key_states_187_cast_fp16, x = coreml_update_state_146)[name = string("key_cache_internal_tensor_assign_19_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_19_cast_fp16, input = key_cache)[name = string("coreml_update_state_148_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_148 = read_state(input = key_cache)[name = string("coreml_update_state_148")];
            tensor<int32, [4]> value_states_111_perm_0 = const()[name = string("value_states_111_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_19_stride_0 = const()[name = string("value_cache_internal_tensor_assign_19_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_19_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_19_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_19_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_19_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_19_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_19_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_111_cast_fp16 = transpose(perm = value_states_111_perm_0, x = var_6772_cast_fp16)[name = string("transpose_202")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_19_cast_fp16 = slice_update(begin = concat_221, begin_mask = value_cache_internal_tensor_assign_19_begin_mask_0, end = concat_222, end_mask = value_cache_internal_tensor_assign_19_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_19_squeeze_mask_0, stride = value_cache_internal_tensor_assign_19_stride_0, update = value_states_111_cast_fp16, x = coreml_update_state_147)[name = string("value_cache_internal_tensor_assign_19_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_19_cast_fp16, input = value_cache)[name = string("coreml_update_state_149_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_149 = read_state(input = value_cache)[name = string("coreml_update_state_149")];
            tensor<int32, [4]> var_6866_begin_0 = const()[name = string("op_6866_begin_0"), val = tensor<int32, [4]>([18, 0, 0, 0])];
            tensor<int32, [4]> var_6866_end_0 = const()[name = string("op_6866_end_0"), val = tensor<int32, [4]>([19, 2, 2048, 128])];
            tensor<bool, [4]> var_6866_end_mask_0 = const()[name = string("op_6866_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6866_cast_fp16 = slice_by_index(begin = var_6866_begin_0, end = var_6866_end_0, end_mask = var_6866_end_mask_0, x = coreml_update_state_148)[name = string("op_6866_cast_fp16")];
            tensor<int32, [2]> tile_36 = const()[name = string("tile_36"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6869_axis_0 = const()[name = string("op_6869_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6869_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6869_cast_fp16_1 = split(axis = var_6869_axis_0, split_sizes = tile_36, x = var_6866_cast_fp16)[name = string("op_6869_cast_fp16")];
            tensor<int32, [4]> var_6876_begin_0 = const()[name = string("op_6876_begin_0"), val = tensor<int32, [4]>([18, 0, 0, 0])];
            tensor<int32, [4]> var_6876_end_0 = const()[name = string("op_6876_end_0"), val = tensor<int32, [4]>([19, 2, 2048, 128])];
            tensor<bool, [4]> var_6876_end_mask_0 = const()[name = string("op_6876_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6876_cast_fp16 = slice_by_index(begin = var_6876_begin_0, end = var_6876_end_0, end_mask = var_6876_end_mask_0, x = coreml_update_state_149)[name = string("op_6876_cast_fp16")];
            tensor<int32, [2]> tile_37 = const()[name = string("tile_37"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6879_axis_0 = const()[name = string("op_6879_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6879_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6879_cast_fp16_1 = split(axis = var_6879_axis_0, split_sizes = tile_37, x = var_6876_cast_fp16)[name = string("op_6879_cast_fp16")];
            tensor<int32, [2]> var_6882_split_sizes_0 = const()[name = string("op_6882_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_6882_axis_0 = const()[name = string("op_6882_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_6882_0, tensor<fp16, [1, 8, 128, 8]> var_6882_1 = split(axis = var_6882_axis_0, split_sizes = var_6882_split_sizes_0, x = query_states_111_cast_fp16)[name = string("op_6882")];
            bool attn_weights_289_transpose_x_0 = const()[name = string("attn_weights_289_transpose_x_0"), val = bool(false)];
            bool attn_weights_289_transpose_y_0 = const()[name = string("attn_weights_289_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_289_cast_fp16 = matmul(transpose_x = attn_weights_289_transpose_x_0, transpose_y = attn_weights_289_transpose_y_0, x = var_6869_cast_fp16_0, y = var_6882_0)[name = string("attn_weights_289_cast_fp16")];
            fp16 var_6885_to_fp16 = const()[name = string("op_6885_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_291_cast_fp16 = mul(x = attn_weights_289_cast_fp16, y = var_6885_to_fp16)[name = string("attn_weights_291_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_293_cast_fp16 = add(x = attn_weights_291_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_293_cast_fp16")];
            int32 var_6889 = const()[name = string("op_6889"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_295_cast_fp16 = softmax(axis = var_6889, x = attn_weights_293_cast_fp16)[name = string("attn_weights_295_cast_fp16")];
            bool var_6895_transpose_x_1 = const()[name = string("op_6895_transpose_x_1"), val = bool(true)];
            bool var_6895_transpose_y_1 = const()[name = string("op_6895_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_6895_cast_fp16 = matmul(transpose_x = var_6895_transpose_x_1, transpose_y = var_6895_transpose_y_1, x = attn_weights_295_cast_fp16, y = var_6879_cast_fp16_0)[name = string("op_6895_cast_fp16")];
            bool attn_weights_297_transpose_x_0 = const()[name = string("attn_weights_297_transpose_x_0"), val = bool(false)];
            bool attn_weights_297_transpose_y_0 = const()[name = string("attn_weights_297_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_297_cast_fp16 = matmul(transpose_x = attn_weights_297_transpose_x_0, transpose_y = attn_weights_297_transpose_y_0, x = var_6869_cast_fp16_1, y = var_6882_1)[name = string("attn_weights_297_cast_fp16")];
            fp16 var_6897_to_fp16 = const()[name = string("op_6897_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_299_cast_fp16 = mul(x = attn_weights_297_cast_fp16, y = var_6897_to_fp16)[name = string("attn_weights_299_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_301_cast_fp16 = add(x = attn_weights_299_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_301_cast_fp16")];
            int32 var_6901 = const()[name = string("op_6901"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_303_cast_fp16 = softmax(axis = var_6901, x = attn_weights_301_cast_fp16)[name = string("attn_weights_303_cast_fp16")];
            bool attn_output_145_transpose_x_1 = const()[name = string("attn_output_145_transpose_x_1"), val = bool(true)];
            bool attn_output_145_transpose_y_1 = const()[name = string("attn_output_145_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_145_cast_fp16 = matmul(transpose_x = attn_output_145_transpose_x_1, transpose_y = attn_output_145_transpose_y_1, x = attn_weights_303_cast_fp16, y = var_6879_cast_fp16_1)[name = string("attn_output_145_cast_fp16")];
            int32 var_6909 = const()[name = string("op_6909"), val = int32(1)];
            bool attn_output_147_interleave_0 = const()[name = string("attn_output_147_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_147_cast_fp16 = concat(axis = var_6909, interleave = attn_output_147_interleave_0, values = (var_6895_cast_fp16, attn_output_145_cast_fp16))[name = string("attn_output_147_cast_fp16")];
            tensor<int32, [4]> var_6913_perm_0 = const()[name = string("op_6913_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_227x = const()[name = string("concat_227x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_6913_cast_fp16 = transpose(perm = var_6913_perm_0, x = attn_output_147_cast_fp16)[name = string("transpose_201")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_151_cast_fp16 = reshape(shape = concat_227x, x = var_6913_cast_fp16)[name = string("attn_output_151_cast_fp16")];
            tensor<int32, [2]> hidden_states_183_strides_0 = const()[name = string("hidden_states_183_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_183_pad_type_0 = const()[name = string("hidden_states_183_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_183_pad_0 = const()[name = string("hidden_states_183_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_183_dilations_0 = const()[name = string("hidden_states_183_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_183_groups_0 = const()[name = string("hidden_states_183_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_183_cast_fp16 = conv(dilations = hidden_states_183_dilations_0, groups = hidden_states_183_groups_0, pad = hidden_states_183_pad_0, pad_type = hidden_states_183_pad_type_0, strides = hidden_states_183_strides_0, weight = layers_18_self_attn_o_proj_weight_cast_fp16, x = attn_output_151_cast_fp16)[name = string("hidden_states_183_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_185_cast_fp16 = add(x = hidden_states_179_cast_fp16, y = hidden_states_183_cast_fp16)[name = string("hidden_states_185_cast_fp16")];
            fp16 const_190_promoted_to_fp16 = const()[name = string("const_190_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_6946_cast_fp16 = mul(x = hidden_states_185_cast_fp16, y = const_190_promoted_to_fp16)[name = string("op_6946_cast_fp16")];
            int32 var_6944 = const()[name = string("op_6944"), val = int32(1)];
            bool doubled_149_interleave_0 = const()[name = string("doubled_149_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_149_cast_fp16 = concat(axis = var_6944, interleave = doubled_149_interleave_0, values = (hidden_states_185_cast_fp16, var_6946_cast_fp16))[name = string("doubled_149_cast_fp16")];
            tensor<int32, [1]> out_75_axes_0 = const()[name = string("out_75_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_75_gamma_0_to_fp16 = const()[name = string("out_75_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483647872)))];
            fp16 var_6956_to_fp16 = const()[name = string("op_6956_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_75_cast_fp16 = layer_norm(axes = out_75_axes_0, epsilon = var_6956_to_fp16, gamma = out_75_gamma_0_to_fp16, x = doubled_149_cast_fp16)[name = string("out_75_cast_fp16")];
            tensor<int32, [2]> var_6967_split_sizes_0 = const()[name = string("op_6967_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6967_axis_0 = const()[name = string("op_6967_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_6967_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_6967_cast_fp16_1 = split(axis = var_6967_axis_0, split_sizes = var_6967_split_sizes_0, x = out_75_cast_fp16)[name = string("op_6967_cast_fp16")];
            tensor<int32, [2]> input_37_strides_0 = const()[name = string("input_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_37_pad_type_0 = const()[name = string("input_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_37_pad_0 = const()[name = string("input_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_37_dilations_0 = const()[name = string("input_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_37_groups_0 = const()[name = string("input_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_37_cast_fp16 = conv(dilations = input_37_dilations_0, groups = input_37_groups_0, pad = input_37_pad_0, pad_type = input_37_pad_type_0, strides = input_37_strides_0, weight = layers_18_mlp_gate_proj_weight_cast_fp16, x = var_6967_cast_fp16_0)[name = string("input_37_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_6984_cast_fp16 = silu(x = input_37_cast_fp16)[name = string("op_6984_cast_fp16")];
            tensor<int32, [2]> var_6990_strides_0 = const()[name = string("op_6990_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6990_pad_type_0 = const()[name = string("op_6990_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6990_pad_0 = const()[name = string("op_6990_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6990_dilations_0 = const()[name = string("op_6990_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6990_groups_0 = const()[name = string("op_6990_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_6990_cast_fp16 = conv(dilations = var_6990_dilations_0, groups = var_6990_groups_0, pad = var_6990_pad_0, pad_type = var_6990_pad_type_0, strides = var_6990_strides_0, weight = layers_18_mlp_up_proj_weight_cast_fp16, x = var_6967_cast_fp16_0)[name = string("op_6990_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_189_cast_fp16 = mul(x = var_6984_cast_fp16, y = var_6990_cast_fp16)[name = string("x_189_cast_fp16")];
            tensor<int32, [2]> hidden_states_187_strides_0 = const()[name = string("hidden_states_187_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_187_pad_type_0 = const()[name = string("hidden_states_187_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_187_pad_0 = const()[name = string("hidden_states_187_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_187_dilations_0 = const()[name = string("hidden_states_187_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_187_groups_0 = const()[name = string("hidden_states_187_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_187_cast_fp16 = conv(dilations = hidden_states_187_dilations_0, groups = hidden_states_187_groups_0, pad = hidden_states_187_pad_0, pad_type = hidden_states_187_pad_type_0, strides = hidden_states_187_strides_0, weight = layers_18_mlp_down_proj_weight_cast_fp16, x = x_189_cast_fp16)[name = string("hidden_states_187_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_189_cast_fp16 = add(x = hidden_states_185_cast_fp16, y = hidden_states_187_cast_fp16)[name = string("hidden_states_189_cast_fp16")];
            fp16 const_192_promoted_to_fp16 = const()[name = string("const_192_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_7008_cast_fp16 = mul(x = hidden_states_189_cast_fp16, y = const_192_promoted_to_fp16)[name = string("op_7008_cast_fp16")];
            int32 var_7006 = const()[name = string("op_7006"), val = int32(1)];
            bool doubled_153_interleave_0 = const()[name = string("doubled_153_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_153_cast_fp16 = concat(axis = var_7006, interleave = doubled_153_interleave_0, values = (hidden_states_189_cast_fp16, var_7008_cast_fp16))[name = string("doubled_153_cast_fp16")];
            tensor<int32, [1]> out_77_axes_0 = const()[name = string("out_77_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_77_gamma_0_to_fp16 = const()[name = string("out_77_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483656128)))];
            fp16 var_7018_to_fp16 = const()[name = string("op_7018_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_77_cast_fp16 = layer_norm(axes = out_77_axes_0, epsilon = var_7018_to_fp16, gamma = out_77_gamma_0_to_fp16, x = doubled_153_cast_fp16)[name = string("out_77_cast_fp16")];
            tensor<int32, [2]> var_7029_split_sizes_0 = const()[name = string("op_7029_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7029_axis_0 = const()[name = string("op_7029_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_7029_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_7029_cast_fp16_1 = split(axis = var_7029_axis_0, split_sizes = var_7029_split_sizes_0, x = out_77_cast_fp16)[name = string("op_7029_cast_fp16")];
            tensor<int32, [2]> query_states_115_strides_0 = const()[name = string("query_states_115_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_115_pad_type_0 = const()[name = string("query_states_115_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_115_pad_0 = const()[name = string("query_states_115_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_115_dilations_0 = const()[name = string("query_states_115_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_115_groups_0 = const()[name = string("query_states_115_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_115_cast_fp16 = conv(dilations = query_states_115_dilations_0, groups = query_states_115_groups_0, pad = query_states_115_pad_0, pad_type = query_states_115_pad_type_0, strides = query_states_115_strides_0, weight = layers_19_self_attn_q_proj_weight_cast_fp16, x = var_7029_cast_fp16_0)[name = string("query_states_115_cast_fp16")];
            tensor<int32, [2]> key_states_191_strides_0 = const()[name = string("key_states_191_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_191_pad_type_0 = const()[name = string("key_states_191_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_191_pad_0 = const()[name = string("key_states_191_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_191_dilations_0 = const()[name = string("key_states_191_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_191_groups_0 = const()[name = string("key_states_191_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_191_cast_fp16 = conv(dilations = key_states_191_dilations_0, groups = key_states_191_groups_0, pad = key_states_191_pad_0, pad_type = key_states_191_pad_type_0, strides = key_states_191_strides_0, weight = layers_19_self_attn_k_proj_weight_cast_fp16, x = var_7029_cast_fp16_0)[name = string("key_states_191_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_19_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_19_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483664384)))];
            tensor<int32, [2]> value_states_115_strides_0 = const()[name = string("value_states_115_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_115_pad_type_0 = const()[name = string("value_states_115_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_115_pad_0 = const()[name = string("value_states_115_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_115_dilations_0 = const()[name = string("value_states_115_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_115_groups_0 = const()[name = string("value_states_115_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_115_cast_fp16 = conv(dilations = value_states_115_dilations_0, groups = value_states_115_groups_0, pad = value_states_115_pad_0, pad_type = value_states_115_pad_type_0, strides = value_states_115_strides_0, weight = layers_19_self_attn_v_proj_weight_to_fp16, x = var_7029_cast_fp16_0)[name = string("value_states_115_cast_fp16")];
            tensor<int32, [4]> concat_228x = const()[name = string("concat_228x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_191_cast_fp16 = reshape(shape = concat_228x, x = query_states_115_cast_fp16)[name = string("x_191_cast_fp16")];
            tensor<int32, [4]> concat_229x = const()[name = string("concat_229x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_7086_cast_fp16 = reshape(shape = concat_229x, x = key_states_191_cast_fp16)[name = string("op_7086_cast_fp16")];
            tensor<int32, [4]> concat_230x = const()[name = string("concat_230x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_7093_cast_fp16 = reshape(shape = concat_230x, x = value_states_115_cast_fp16)[name = string("op_7093_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_7097_cast_fp16 = mul(x = x_191_cast_fp16, y = var_859_cast_fp16)[name = string("op_7097_cast_fp16")];
            tensor<int32, [2]> var_7098_split_sizes_0 = const()[name = string("op_7098_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7098_axis_0 = const()[name = string("op_7098_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_7098_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_7098_cast_fp16_1 = split(axis = var_7098_axis_0, split_sizes = var_7098_split_sizes_0, x = x_191_cast_fp16)[name = string("op_7098_cast_fp16")];
            fp16 const_194_promoted_to_fp16 = const()[name = string("const_194_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_7100_cast_fp16 = mul(x = var_7098_cast_fp16_1, y = const_194_promoted_to_fp16)[name = string("op_7100_cast_fp16")];
            int32 var_7102 = const()[name = string("op_7102"), val = int32(-2)];
            bool var_7103_interleave_0 = const()[name = string("op_7103_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_7103_cast_fp16 = concat(axis = var_7102, interleave = var_7103_interleave_0, values = (var_7100_cast_fp16, var_7098_cast_fp16_0))[name = string("op_7103_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_7104_cast_fp16 = mul(x = var_7103_cast_fp16, y = var_866_cast_fp16)[name = string("op_7104_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_117_cast_fp16 = add(x = var_7097_cast_fp16, y = var_7104_cast_fp16)[name = string("query_states_117_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_7110_cast_fp16 = mul(x = var_7086_cast_fp16, y = var_859_cast_fp16)[name = string("op_7110_cast_fp16")];
            tensor<int32, [2]> var_7111_split_sizes_0 = const()[name = string("op_7111_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7111_axis_0 = const()[name = string("op_7111_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_7111_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_7111_cast_fp16_1 = split(axis = var_7111_axis_0, split_sizes = var_7111_split_sizes_0, x = var_7086_cast_fp16)[name = string("op_7111_cast_fp16")];
            fp16 const_195_promoted_to_fp16 = const()[name = string("const_195_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_7113_cast_fp16 = mul(x = var_7111_cast_fp16_1, y = const_195_promoted_to_fp16)[name = string("op_7113_cast_fp16")];
            int32 var_7115 = const()[name = string("op_7115"), val = int32(-2)];
            bool var_7116_interleave_0 = const()[name = string("op_7116_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_7116_cast_fp16 = concat(axis = var_7115, interleave = var_7116_interleave_0, values = (var_7113_cast_fp16, var_7111_cast_fp16_0))[name = string("op_7116_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_7117_cast_fp16 = mul(x = var_7116_cast_fp16, y = var_866_cast_fp16)[name = string("op_7117_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_195_cast_fp16 = add(x = var_7110_cast_fp16, y = var_7117_cast_fp16)[name = string("key_states_195_cast_fp16")];
            tensor<int32, [1]> expand_dims_228 = const()[name = string("expand_dims_228"), val = tensor<int32, [1]>([19])];
            tensor<int32, [1]> expand_dims_229 = const()[name = string("expand_dims_229"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_231 = const()[name = string("expand_dims_231"), val = tensor<int32, [1]>([0])];
            int32 concat_233_axis_0 = const()[name = string("concat_233_axis_0"), val = int32(0)];
            bool concat_233_interleave_0 = const()[name = string("concat_233_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_233 = concat(axis = concat_233_axis_0, interleave = concat_233_interleave_0, values = (expand_dims_228, expand_dims_229, position_id, expand_dims_231))[name = string("concat_233")];
            tensor<int32, [1]> expand_dims_232 = const()[name = string("expand_dims_232"), val = tensor<int32, [1]>([20])];
            tensor<int32, [1]> concat_234_values1_0 = const()[name = string("concat_234_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_234_values3_0 = const()[name = string("concat_234_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_234_axis_0 = const()[name = string("concat_234_axis_0"), val = int32(0)];
            bool concat_234_interleave_0 = const()[name = string("concat_234_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_234 = concat(axis = concat_234_axis_0, interleave = concat_234_interleave_0, values = (expand_dims_232, concat_234_values1_0, cache_position_end, concat_234_values3_0))[name = string("concat_234")];
            tensor<int32, [4]> key_states_197_perm_0 = const()[name = string("key_states_197_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_20_stride_0 = const()[name = string("key_cache_internal_tensor_assign_20_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_20_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_20_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_20_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_20_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_20_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_20_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_197_cast_fp16 = transpose(perm = key_states_197_perm_0, x = key_states_195_cast_fp16)[name = string("transpose_200")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_20_cast_fp16 = slice_update(begin = concat_233, begin_mask = key_cache_internal_tensor_assign_20_begin_mask_0, end = concat_234, end_mask = key_cache_internal_tensor_assign_20_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_20_squeeze_mask_0, stride = key_cache_internal_tensor_assign_20_stride_0, update = key_states_197_cast_fp16, x = coreml_update_state_148)[name = string("key_cache_internal_tensor_assign_20_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_20_cast_fp16, input = key_cache)[name = string("coreml_update_state_150_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_150 = read_state(input = key_cache)[name = string("coreml_update_state_150")];
            tensor<int32, [4]> value_states_117_perm_0 = const()[name = string("value_states_117_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_20_stride_0 = const()[name = string("value_cache_internal_tensor_assign_20_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_20_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_20_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_20_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_20_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_20_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_20_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_117_cast_fp16 = transpose(perm = value_states_117_perm_0, x = var_7093_cast_fp16)[name = string("transpose_199")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_20_cast_fp16 = slice_update(begin = concat_233, begin_mask = value_cache_internal_tensor_assign_20_begin_mask_0, end = concat_234, end_mask = value_cache_internal_tensor_assign_20_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_20_squeeze_mask_0, stride = value_cache_internal_tensor_assign_20_stride_0, update = value_states_117_cast_fp16, x = coreml_update_state_149)[name = string("value_cache_internal_tensor_assign_20_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_20_cast_fp16, input = value_cache)[name = string("coreml_update_state_151_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_151 = read_state(input = value_cache)[name = string("coreml_update_state_151")];
            tensor<int32, [4]> var_7187_begin_0 = const()[name = string("op_7187_begin_0"), val = tensor<int32, [4]>([19, 0, 0, 0])];
            tensor<int32, [4]> var_7187_end_0 = const()[name = string("op_7187_end_0"), val = tensor<int32, [4]>([20, 2, 2048, 128])];
            tensor<bool, [4]> var_7187_end_mask_0 = const()[name = string("op_7187_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7187_cast_fp16 = slice_by_index(begin = var_7187_begin_0, end = var_7187_end_0, end_mask = var_7187_end_mask_0, x = coreml_update_state_150)[name = string("op_7187_cast_fp16")];
            tensor<int32, [2]> tile_38 = const()[name = string("tile_38"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7190_axis_0 = const()[name = string("op_7190_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7190_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7190_cast_fp16_1 = split(axis = var_7190_axis_0, split_sizes = tile_38, x = var_7187_cast_fp16)[name = string("op_7190_cast_fp16")];
            tensor<int32, [4]> var_7197_begin_0 = const()[name = string("op_7197_begin_0"), val = tensor<int32, [4]>([19, 0, 0, 0])];
            tensor<int32, [4]> var_7197_end_0 = const()[name = string("op_7197_end_0"), val = tensor<int32, [4]>([20, 2, 2048, 128])];
            tensor<bool, [4]> var_7197_end_mask_0 = const()[name = string("op_7197_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7197_cast_fp16 = slice_by_index(begin = var_7197_begin_0, end = var_7197_end_0, end_mask = var_7197_end_mask_0, x = coreml_update_state_151)[name = string("op_7197_cast_fp16")];
            tensor<int32, [2]> tile_39 = const()[name = string("tile_39"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7200_axis_0 = const()[name = string("op_7200_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7200_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7200_cast_fp16_1 = split(axis = var_7200_axis_0, split_sizes = tile_39, x = var_7197_cast_fp16)[name = string("op_7200_cast_fp16")];
            tensor<int32, [2]> var_7203_split_sizes_0 = const()[name = string("op_7203_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_7203_axis_0 = const()[name = string("op_7203_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_7203_0, tensor<fp16, [1, 8, 128, 8]> var_7203_1 = split(axis = var_7203_axis_0, split_sizes = var_7203_split_sizes_0, x = query_states_117_cast_fp16)[name = string("op_7203")];
            bool attn_weights_305_transpose_x_0 = const()[name = string("attn_weights_305_transpose_x_0"), val = bool(false)];
            bool attn_weights_305_transpose_y_0 = const()[name = string("attn_weights_305_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_305_cast_fp16 = matmul(transpose_x = attn_weights_305_transpose_x_0, transpose_y = attn_weights_305_transpose_y_0, x = var_7190_cast_fp16_0, y = var_7203_0)[name = string("attn_weights_305_cast_fp16")];
            fp16 var_7206_to_fp16 = const()[name = string("op_7206_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_307_cast_fp16 = mul(x = attn_weights_305_cast_fp16, y = var_7206_to_fp16)[name = string("attn_weights_307_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_309_cast_fp16 = add(x = attn_weights_307_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_309_cast_fp16")];
            int32 var_7210 = const()[name = string("op_7210"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_311_cast_fp16 = softmax(axis = var_7210, x = attn_weights_309_cast_fp16)[name = string("attn_weights_311_cast_fp16")];
            bool var_7216_transpose_x_1 = const()[name = string("op_7216_transpose_x_1"), val = bool(true)];
            bool var_7216_transpose_y_1 = const()[name = string("op_7216_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_7216_cast_fp16 = matmul(transpose_x = var_7216_transpose_x_1, transpose_y = var_7216_transpose_y_1, x = attn_weights_311_cast_fp16, y = var_7200_cast_fp16_0)[name = string("op_7216_cast_fp16")];
            bool attn_weights_313_transpose_x_0 = const()[name = string("attn_weights_313_transpose_x_0"), val = bool(false)];
            bool attn_weights_313_transpose_y_0 = const()[name = string("attn_weights_313_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_313_cast_fp16 = matmul(transpose_x = attn_weights_313_transpose_x_0, transpose_y = attn_weights_313_transpose_y_0, x = var_7190_cast_fp16_1, y = var_7203_1)[name = string("attn_weights_313_cast_fp16")];
            fp16 var_7218_to_fp16 = const()[name = string("op_7218_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_315_cast_fp16 = mul(x = attn_weights_313_cast_fp16, y = var_7218_to_fp16)[name = string("attn_weights_315_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_317_cast_fp16 = add(x = attn_weights_315_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_317_cast_fp16")];
            int32 var_7222 = const()[name = string("op_7222"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_319_cast_fp16 = softmax(axis = var_7222, x = attn_weights_317_cast_fp16)[name = string("attn_weights_319_cast_fp16")];
            bool attn_output_153_transpose_x_1 = const()[name = string("attn_output_153_transpose_x_1"), val = bool(true)];
            bool attn_output_153_transpose_y_1 = const()[name = string("attn_output_153_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_153_cast_fp16 = matmul(transpose_x = attn_output_153_transpose_x_1, transpose_y = attn_output_153_transpose_y_1, x = attn_weights_319_cast_fp16, y = var_7200_cast_fp16_1)[name = string("attn_output_153_cast_fp16")];
            int32 var_7230 = const()[name = string("op_7230"), val = int32(1)];
            bool attn_output_155_interleave_0 = const()[name = string("attn_output_155_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_155_cast_fp16 = concat(axis = var_7230, interleave = attn_output_155_interleave_0, values = (var_7216_cast_fp16, attn_output_153_cast_fp16))[name = string("attn_output_155_cast_fp16")];
            tensor<int32, [4]> var_7234_perm_0 = const()[name = string("op_7234_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_239x = const()[name = string("concat_239x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_7234_cast_fp16 = transpose(perm = var_7234_perm_0, x = attn_output_155_cast_fp16)[name = string("transpose_198")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_159_cast_fp16 = reshape(shape = concat_239x, x = var_7234_cast_fp16)[name = string("attn_output_159_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_19_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_19_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1484713024)))];
            tensor<int32, [2]> hidden_states_193_strides_0 = const()[name = string("hidden_states_193_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_193_pad_type_0 = const()[name = string("hidden_states_193_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_193_pad_0 = const()[name = string("hidden_states_193_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_193_dilations_0 = const()[name = string("hidden_states_193_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_193_groups_0 = const()[name = string("hidden_states_193_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_193_cast_fp16 = conv(dilations = hidden_states_193_dilations_0, groups = hidden_states_193_groups_0, pad = hidden_states_193_pad_0, pad_type = hidden_states_193_pad_type_0, strides = hidden_states_193_strides_0, weight = layers_19_self_attn_o_proj_weight_to_fp16, x = attn_output_159_cast_fp16)[name = string("hidden_states_193_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_195_cast_fp16 = add(x = hidden_states_189_cast_fp16, y = hidden_states_193_cast_fp16)[name = string("hidden_states_195_cast_fp16")];
            fp16 const_200_promoted_to_fp16 = const()[name = string("const_200_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_7267_cast_fp16 = mul(x = hidden_states_195_cast_fp16, y = const_200_promoted_to_fp16)[name = string("op_7267_cast_fp16")];
            int32 var_7265 = const()[name = string("op_7265"), val = int32(1)];
            bool doubled_157_interleave_0 = const()[name = string("doubled_157_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_157_cast_fp16 = concat(axis = var_7265, interleave = doubled_157_interleave_0, values = (hidden_states_195_cast_fp16, var_7267_cast_fp16))[name = string("doubled_157_cast_fp16")];
            tensor<int32, [1]> out_79_axes_0 = const()[name = string("out_79_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_79_gamma_0_to_fp16 = const()[name = string("out_79_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1493101696)))];
            fp16 var_7277_to_fp16 = const()[name = string("op_7277_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_79_cast_fp16 = layer_norm(axes = out_79_axes_0, epsilon = var_7277_to_fp16, gamma = out_79_gamma_0_to_fp16, x = doubled_157_cast_fp16)[name = string("out_79_cast_fp16")];
            tensor<int32, [2]> var_7288_split_sizes_0 = const()[name = string("op_7288_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7288_axis_0 = const()[name = string("op_7288_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_7288_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_7288_cast_fp16_1 = split(axis = var_7288_axis_0, split_sizes = var_7288_split_sizes_0, x = out_79_cast_fp16)[name = string("op_7288_cast_fp16")];
            tensor<int32, [2]> input_39_strides_0 = const()[name = string("input_39_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_39_pad_type_0 = const()[name = string("input_39_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_39_pad_0 = const()[name = string("input_39_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_39_dilations_0 = const()[name = string("input_39_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_39_groups_0 = const()[name = string("input_39_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_39_cast_fp16 = conv(dilations = input_39_dilations_0, groups = input_39_groups_0, pad = input_39_pad_0, pad_type = input_39_pad_type_0, strides = input_39_strides_0, weight = layers_19_mlp_gate_proj_weight_cast_fp16, x = var_7288_cast_fp16_0)[name = string("input_39_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_7305_cast_fp16 = silu(x = input_39_cast_fp16)[name = string("op_7305_cast_fp16")];
            tensor<int32, [2]> var_7311_strides_0 = const()[name = string("op_7311_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_7311_pad_type_0 = const()[name = string("op_7311_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_7311_pad_0 = const()[name = string("op_7311_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_7311_dilations_0 = const()[name = string("op_7311_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7311_groups_0 = const()[name = string("op_7311_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_7311_cast_fp16 = conv(dilations = var_7311_dilations_0, groups = var_7311_groups_0, pad = var_7311_pad_0, pad_type = var_7311_pad_type_0, strides = var_7311_strides_0, weight = layers_19_mlp_up_proj_weight_cast_fp16, x = var_7288_cast_fp16_0)[name = string("op_7311_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_199_cast_fp16 = mul(x = var_7305_cast_fp16, y = var_7311_cast_fp16)[name = string("x_199_cast_fp16")];
            tensor<int32, [2]> hidden_states_197_strides_0 = const()[name = string("hidden_states_197_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_197_pad_type_0 = const()[name = string("hidden_states_197_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_197_pad_0 = const()[name = string("hidden_states_197_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_197_dilations_0 = const()[name = string("hidden_states_197_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_197_groups_0 = const()[name = string("hidden_states_197_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_197_cast_fp16 = conv(dilations = hidden_states_197_dilations_0, groups = hidden_states_197_groups_0, pad = hidden_states_197_pad_0, pad_type = hidden_states_197_pad_type_0, strides = hidden_states_197_strides_0, weight = layers_19_mlp_down_proj_weight_cast_fp16, x = x_199_cast_fp16)[name = string("hidden_states_197_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_199_cast_fp16 = add(x = hidden_states_195_cast_fp16, y = hidden_states_197_cast_fp16)[name = string("hidden_states_199_cast_fp16")];
            fp16 const_202_promoted_to_fp16 = const()[name = string("const_202_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_7329_cast_fp16 = mul(x = hidden_states_199_cast_fp16, y = const_202_promoted_to_fp16)[name = string("op_7329_cast_fp16")];
            int32 var_7327 = const()[name = string("op_7327"), val = int32(1)];
            bool doubled_161_interleave_0 = const()[name = string("doubled_161_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_161_cast_fp16 = concat(axis = var_7327, interleave = doubled_161_interleave_0, values = (hidden_states_199_cast_fp16, var_7329_cast_fp16))[name = string("doubled_161_cast_fp16")];
            tensor<int32, [1]> out_81_axes_0 = const()[name = string("out_81_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_81_gamma_0_to_fp16 = const()[name = string("out_81_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1493109952)))];
            fp16 var_7339_to_fp16 = const()[name = string("op_7339_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_81_cast_fp16 = layer_norm(axes = out_81_axes_0, epsilon = var_7339_to_fp16, gamma = out_81_gamma_0_to_fp16, x = doubled_161_cast_fp16)[name = string("out_81_cast_fp16")];
            tensor<int32, [2]> var_7350_split_sizes_0 = const()[name = string("op_7350_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7350_axis_0 = const()[name = string("op_7350_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_7350_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_7350_cast_fp16_1 = split(axis = var_7350_axis_0, split_sizes = var_7350_split_sizes_0, x = out_81_cast_fp16)[name = string("op_7350_cast_fp16")];
            tensor<int32, [2]> query_states_121_strides_0 = const()[name = string("query_states_121_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_121_pad_type_0 = const()[name = string("query_states_121_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_121_pad_0 = const()[name = string("query_states_121_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_121_dilations_0 = const()[name = string("query_states_121_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_121_groups_0 = const()[name = string("query_states_121_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_121_cast_fp16 = conv(dilations = query_states_121_dilations_0, groups = query_states_121_groups_0, pad = query_states_121_pad_0, pad_type = query_states_121_pad_type_0, strides = query_states_121_strides_0, weight = layers_20_self_attn_q_proj_weight_cast_fp16, x = var_7350_cast_fp16_0)[name = string("query_states_121_cast_fp16")];
            tensor<int32, [2]> key_states_201_strides_0 = const()[name = string("key_states_201_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_201_pad_type_0 = const()[name = string("key_states_201_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_201_pad_0 = const()[name = string("key_states_201_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_201_dilations_0 = const()[name = string("key_states_201_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_201_groups_0 = const()[name = string("key_states_201_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_201_cast_fp16 = conv(dilations = key_states_201_dilations_0, groups = key_states_201_groups_0, pad = key_states_201_pad_0, pad_type = key_states_201_pad_type_0, strides = key_states_201_strides_0, weight = layers_20_self_attn_k_proj_weight_cast_fp16, x = var_7350_cast_fp16_0)[name = string("key_states_201_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_20_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_20_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1493118208)))];
            tensor<int32, [2]> value_states_121_strides_0 = const()[name = string("value_states_121_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_121_pad_type_0 = const()[name = string("value_states_121_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_121_pad_0 = const()[name = string("value_states_121_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_121_dilations_0 = const()[name = string("value_states_121_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_121_groups_0 = const()[name = string("value_states_121_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_121_cast_fp16 = conv(dilations = value_states_121_dilations_0, groups = value_states_121_groups_0, pad = value_states_121_pad_0, pad_type = value_states_121_pad_type_0, strides = value_states_121_strides_0, weight = layers_20_self_attn_v_proj_weight_to_fp16, x = var_7350_cast_fp16_0)[name = string("value_states_121_cast_fp16")];
            tensor<int32, [4]> concat_240x = const()[name = string("concat_240x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_201_cast_fp16 = reshape(shape = concat_240x, x = query_states_121_cast_fp16)[name = string("x_201_cast_fp16")];
            tensor<int32, [4]> concat_241x = const()[name = string("concat_241x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_7407_cast_fp16 = reshape(shape = concat_241x, x = key_states_201_cast_fp16)[name = string("op_7407_cast_fp16")];
            tensor<int32, [4]> concat_242x = const()[name = string("concat_242x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_7414_cast_fp16 = reshape(shape = concat_242x, x = value_states_121_cast_fp16)[name = string("op_7414_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_7418_cast_fp16 = mul(x = x_201_cast_fp16, y = var_859_cast_fp16)[name = string("op_7418_cast_fp16")];
            tensor<int32, [2]> var_7419_split_sizes_0 = const()[name = string("op_7419_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7419_axis_0 = const()[name = string("op_7419_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_7419_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_7419_cast_fp16_1 = split(axis = var_7419_axis_0, split_sizes = var_7419_split_sizes_0, x = x_201_cast_fp16)[name = string("op_7419_cast_fp16")];
            fp16 const_204_promoted_to_fp16 = const()[name = string("const_204_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_7421_cast_fp16 = mul(x = var_7419_cast_fp16_1, y = const_204_promoted_to_fp16)[name = string("op_7421_cast_fp16")];
            int32 var_7423 = const()[name = string("op_7423"), val = int32(-2)];
            bool var_7424_interleave_0 = const()[name = string("op_7424_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_7424_cast_fp16 = concat(axis = var_7423, interleave = var_7424_interleave_0, values = (var_7421_cast_fp16, var_7419_cast_fp16_0))[name = string("op_7424_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_7425_cast_fp16 = mul(x = var_7424_cast_fp16, y = var_866_cast_fp16)[name = string("op_7425_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_123_cast_fp16 = add(x = var_7418_cast_fp16, y = var_7425_cast_fp16)[name = string("query_states_123_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_7431_cast_fp16 = mul(x = var_7407_cast_fp16, y = var_859_cast_fp16)[name = string("op_7431_cast_fp16")];
            tensor<int32, [2]> var_7432_split_sizes_0 = const()[name = string("op_7432_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7432_axis_0 = const()[name = string("op_7432_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_7432_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_7432_cast_fp16_1 = split(axis = var_7432_axis_0, split_sizes = var_7432_split_sizes_0, x = var_7407_cast_fp16)[name = string("op_7432_cast_fp16")];
            fp16 const_205_promoted_to_fp16 = const()[name = string("const_205_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_7434_cast_fp16 = mul(x = var_7432_cast_fp16_1, y = const_205_promoted_to_fp16)[name = string("op_7434_cast_fp16")];
            int32 var_7436 = const()[name = string("op_7436"), val = int32(-2)];
            bool var_7437_interleave_0 = const()[name = string("op_7437_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_7437_cast_fp16 = concat(axis = var_7436, interleave = var_7437_interleave_0, values = (var_7434_cast_fp16, var_7432_cast_fp16_0))[name = string("op_7437_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_7438_cast_fp16 = mul(x = var_7437_cast_fp16, y = var_866_cast_fp16)[name = string("op_7438_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_205_cast_fp16 = add(x = var_7431_cast_fp16, y = var_7438_cast_fp16)[name = string("key_states_205_cast_fp16")];
            tensor<int32, [1]> expand_dims_240 = const()[name = string("expand_dims_240"), val = tensor<int32, [1]>([20])];
            tensor<int32, [1]> expand_dims_241 = const()[name = string("expand_dims_241"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_243 = const()[name = string("expand_dims_243"), val = tensor<int32, [1]>([0])];
            int32 concat_245_axis_0 = const()[name = string("concat_245_axis_0"), val = int32(0)];
            bool concat_245_interleave_0 = const()[name = string("concat_245_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_245 = concat(axis = concat_245_axis_0, interleave = concat_245_interleave_0, values = (expand_dims_240, expand_dims_241, position_id, expand_dims_243))[name = string("concat_245")];
            tensor<int32, [1]> expand_dims_244 = const()[name = string("expand_dims_244"), val = tensor<int32, [1]>([21])];
            tensor<int32, [1]> concat_246_values1_0 = const()[name = string("concat_246_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_246_values3_0 = const()[name = string("concat_246_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_246_axis_0 = const()[name = string("concat_246_axis_0"), val = int32(0)];
            bool concat_246_interleave_0 = const()[name = string("concat_246_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_246 = concat(axis = concat_246_axis_0, interleave = concat_246_interleave_0, values = (expand_dims_244, concat_246_values1_0, cache_position_end, concat_246_values3_0))[name = string("concat_246")];
            tensor<int32, [4]> key_states_207_perm_0 = const()[name = string("key_states_207_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_21_stride_0 = const()[name = string("key_cache_internal_tensor_assign_21_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_21_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_21_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_21_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_21_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_21_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_21_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_207_cast_fp16 = transpose(perm = key_states_207_perm_0, x = key_states_205_cast_fp16)[name = string("transpose_197")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_21_cast_fp16 = slice_update(begin = concat_245, begin_mask = key_cache_internal_tensor_assign_21_begin_mask_0, end = concat_246, end_mask = key_cache_internal_tensor_assign_21_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_21_squeeze_mask_0, stride = key_cache_internal_tensor_assign_21_stride_0, update = key_states_207_cast_fp16, x = coreml_update_state_150)[name = string("key_cache_internal_tensor_assign_21_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_21_cast_fp16, input = key_cache)[name = string("coreml_update_state_152_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_152 = read_state(input = key_cache)[name = string("coreml_update_state_152")];
            tensor<int32, [4]> value_states_123_perm_0 = const()[name = string("value_states_123_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_21_stride_0 = const()[name = string("value_cache_internal_tensor_assign_21_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_21_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_21_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_21_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_21_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_21_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_21_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_123_cast_fp16 = transpose(perm = value_states_123_perm_0, x = var_7414_cast_fp16)[name = string("transpose_196")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_21_cast_fp16 = slice_update(begin = concat_245, begin_mask = value_cache_internal_tensor_assign_21_begin_mask_0, end = concat_246, end_mask = value_cache_internal_tensor_assign_21_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_21_squeeze_mask_0, stride = value_cache_internal_tensor_assign_21_stride_0, update = value_states_123_cast_fp16, x = coreml_update_state_151)[name = string("value_cache_internal_tensor_assign_21_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_21_cast_fp16, input = value_cache)[name = string("coreml_update_state_153_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_153 = read_state(input = value_cache)[name = string("coreml_update_state_153")];
            tensor<int32, [4]> var_7508_begin_0 = const()[name = string("op_7508_begin_0"), val = tensor<int32, [4]>([20, 0, 0, 0])];
            tensor<int32, [4]> var_7508_end_0 = const()[name = string("op_7508_end_0"), val = tensor<int32, [4]>([21, 2, 2048, 128])];
            tensor<bool, [4]> var_7508_end_mask_0 = const()[name = string("op_7508_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7508_cast_fp16 = slice_by_index(begin = var_7508_begin_0, end = var_7508_end_0, end_mask = var_7508_end_mask_0, x = coreml_update_state_152)[name = string("op_7508_cast_fp16")];
            tensor<int32, [2]> tile_40 = const()[name = string("tile_40"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7511_axis_0 = const()[name = string("op_7511_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7511_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7511_cast_fp16_1 = split(axis = var_7511_axis_0, split_sizes = tile_40, x = var_7508_cast_fp16)[name = string("op_7511_cast_fp16")];
            tensor<int32, [4]> var_7518_begin_0 = const()[name = string("op_7518_begin_0"), val = tensor<int32, [4]>([20, 0, 0, 0])];
            tensor<int32, [4]> var_7518_end_0 = const()[name = string("op_7518_end_0"), val = tensor<int32, [4]>([21, 2, 2048, 128])];
            tensor<bool, [4]> var_7518_end_mask_0 = const()[name = string("op_7518_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7518_cast_fp16 = slice_by_index(begin = var_7518_begin_0, end = var_7518_end_0, end_mask = var_7518_end_mask_0, x = coreml_update_state_153)[name = string("op_7518_cast_fp16")];
            tensor<int32, [2]> tile_41 = const()[name = string("tile_41"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7521_axis_0 = const()[name = string("op_7521_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7521_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7521_cast_fp16_1 = split(axis = var_7521_axis_0, split_sizes = tile_41, x = var_7518_cast_fp16)[name = string("op_7521_cast_fp16")];
            tensor<int32, [2]> var_7524_split_sizes_0 = const()[name = string("op_7524_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_7524_axis_0 = const()[name = string("op_7524_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_7524_0, tensor<fp16, [1, 8, 128, 8]> var_7524_1 = split(axis = var_7524_axis_0, split_sizes = var_7524_split_sizes_0, x = query_states_123_cast_fp16)[name = string("op_7524")];
            bool attn_weights_321_transpose_x_0 = const()[name = string("attn_weights_321_transpose_x_0"), val = bool(false)];
            bool attn_weights_321_transpose_y_0 = const()[name = string("attn_weights_321_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_321_cast_fp16 = matmul(transpose_x = attn_weights_321_transpose_x_0, transpose_y = attn_weights_321_transpose_y_0, x = var_7511_cast_fp16_0, y = var_7524_0)[name = string("attn_weights_321_cast_fp16")];
            fp16 var_7527_to_fp16 = const()[name = string("op_7527_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_323_cast_fp16 = mul(x = attn_weights_321_cast_fp16, y = var_7527_to_fp16)[name = string("attn_weights_323_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_325_cast_fp16 = add(x = attn_weights_323_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_325_cast_fp16")];
            int32 var_7531 = const()[name = string("op_7531"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_327_cast_fp16 = softmax(axis = var_7531, x = attn_weights_325_cast_fp16)[name = string("attn_weights_327_cast_fp16")];
            bool var_7537_transpose_x_1 = const()[name = string("op_7537_transpose_x_1"), val = bool(true)];
            bool var_7537_transpose_y_1 = const()[name = string("op_7537_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_7537_cast_fp16 = matmul(transpose_x = var_7537_transpose_x_1, transpose_y = var_7537_transpose_y_1, x = attn_weights_327_cast_fp16, y = var_7521_cast_fp16_0)[name = string("op_7537_cast_fp16")];
            bool attn_weights_329_transpose_x_0 = const()[name = string("attn_weights_329_transpose_x_0"), val = bool(false)];
            bool attn_weights_329_transpose_y_0 = const()[name = string("attn_weights_329_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_329_cast_fp16 = matmul(transpose_x = attn_weights_329_transpose_x_0, transpose_y = attn_weights_329_transpose_y_0, x = var_7511_cast_fp16_1, y = var_7524_1)[name = string("attn_weights_329_cast_fp16")];
            fp16 var_7539_to_fp16 = const()[name = string("op_7539_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_331_cast_fp16 = mul(x = attn_weights_329_cast_fp16, y = var_7539_to_fp16)[name = string("attn_weights_331_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_333_cast_fp16 = add(x = attn_weights_331_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_333_cast_fp16")];
            int32 var_7543 = const()[name = string("op_7543"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_335_cast_fp16 = softmax(axis = var_7543, x = attn_weights_333_cast_fp16)[name = string("attn_weights_335_cast_fp16")];
            bool attn_output_161_transpose_x_1 = const()[name = string("attn_output_161_transpose_x_1"), val = bool(true)];
            bool attn_output_161_transpose_y_1 = const()[name = string("attn_output_161_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_161_cast_fp16 = matmul(transpose_x = attn_output_161_transpose_x_1, transpose_y = attn_output_161_transpose_y_1, x = attn_weights_335_cast_fp16, y = var_7521_cast_fp16_1)[name = string("attn_output_161_cast_fp16")];
            int32 var_7551 = const()[name = string("op_7551"), val = int32(1)];
            bool attn_output_163_interleave_0 = const()[name = string("attn_output_163_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_163_cast_fp16 = concat(axis = var_7551, interleave = attn_output_163_interleave_0, values = (var_7537_cast_fp16, attn_output_161_cast_fp16))[name = string("attn_output_163_cast_fp16")];
            tensor<int32, [4]> var_7555_perm_0 = const()[name = string("op_7555_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_251x = const()[name = string("concat_251x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_7555_cast_fp16 = transpose(perm = var_7555_perm_0, x = attn_output_163_cast_fp16)[name = string("transpose_195")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_167_cast_fp16 = reshape(shape = concat_251x, x = var_7555_cast_fp16)[name = string("attn_output_167_cast_fp16")];
            tensor<int32, [2]> hidden_states_203_strides_0 = const()[name = string("hidden_states_203_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_203_pad_type_0 = const()[name = string("hidden_states_203_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_203_pad_0 = const()[name = string("hidden_states_203_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_203_dilations_0 = const()[name = string("hidden_states_203_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_203_groups_0 = const()[name = string("hidden_states_203_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_203_cast_fp16 = conv(dilations = hidden_states_203_dilations_0, groups = hidden_states_203_groups_0, pad = hidden_states_203_pad_0, pad_type = hidden_states_203_pad_type_0, strides = hidden_states_203_strides_0, weight = layers_20_self_attn_o_proj_weight_cast_fp16, x = attn_output_167_cast_fp16)[name = string("hidden_states_203_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_205_cast_fp16 = add(x = hidden_states_199_cast_fp16, y = hidden_states_203_cast_fp16)[name = string("hidden_states_205_cast_fp16")];
            fp16 const_210_promoted_to_fp16 = const()[name = string("const_210_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_7588_cast_fp16 = mul(x = hidden_states_205_cast_fp16, y = const_210_promoted_to_fp16)[name = string("op_7588_cast_fp16")];
            int32 var_7586 = const()[name = string("op_7586"), val = int32(1)];
            bool doubled_165_interleave_0 = const()[name = string("doubled_165_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_165_cast_fp16 = concat(axis = var_7586, interleave = doubled_165_interleave_0, values = (hidden_states_205_cast_fp16, var_7588_cast_fp16))[name = string("doubled_165_cast_fp16")];
            tensor<int32, [1]> out_83_axes_0 = const()[name = string("out_83_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_83_gamma_0_to_fp16 = const()[name = string("out_83_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1494166848)))];
            fp16 var_7598_to_fp16 = const()[name = string("op_7598_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_83_cast_fp16 = layer_norm(axes = out_83_axes_0, epsilon = var_7598_to_fp16, gamma = out_83_gamma_0_to_fp16, x = doubled_165_cast_fp16)[name = string("out_83_cast_fp16")];
            tensor<int32, [2]> var_7609_split_sizes_0 = const()[name = string("op_7609_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7609_axis_0 = const()[name = string("op_7609_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_7609_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_7609_cast_fp16_1 = split(axis = var_7609_axis_0, split_sizes = var_7609_split_sizes_0, x = out_83_cast_fp16)[name = string("op_7609_cast_fp16")];
            tensor<int32, [2]> input_41_strides_0 = const()[name = string("input_41_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_41_pad_type_0 = const()[name = string("input_41_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_41_pad_0 = const()[name = string("input_41_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_41_dilations_0 = const()[name = string("input_41_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_41_groups_0 = const()[name = string("input_41_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_41_cast_fp16 = conv(dilations = input_41_dilations_0, groups = input_41_groups_0, pad = input_41_pad_0, pad_type = input_41_pad_type_0, strides = input_41_strides_0, weight = layers_20_mlp_gate_proj_weight_cast_fp16, x = var_7609_cast_fp16_0)[name = string("input_41_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_7626_cast_fp16 = silu(x = input_41_cast_fp16)[name = string("op_7626_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_20_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_20_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1494175104)))];
            tensor<int32, [2]> var_7632_strides_0 = const()[name = string("op_7632_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_7632_pad_type_0 = const()[name = string("op_7632_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_7632_pad_0 = const()[name = string("op_7632_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_7632_dilations_0 = const()[name = string("op_7632_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7632_groups_0 = const()[name = string("op_7632_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_7632_cast_fp16 = conv(dilations = var_7632_dilations_0, groups = var_7632_groups_0, pad = var_7632_pad_0, pad_type = var_7632_pad_type_0, strides = var_7632_strides_0, weight = layers_20_mlp_up_proj_weight_to_fp16, x = var_7609_cast_fp16_0)[name = string("op_7632_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_209_cast_fp16 = mul(x = var_7626_cast_fp16, y = var_7632_cast_fp16)[name = string("x_209_cast_fp16")];
            tensor<int32, [2]> hidden_states_207_strides_0 = const()[name = string("hidden_states_207_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_207_pad_type_0 = const()[name = string("hidden_states_207_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_207_pad_0 = const()[name = string("hidden_states_207_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_207_dilations_0 = const()[name = string("hidden_states_207_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_207_groups_0 = const()[name = string("hidden_states_207_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_207_cast_fp16 = conv(dilations = hidden_states_207_dilations_0, groups = hidden_states_207_groups_0, pad = hidden_states_207_pad_0, pad_type = hidden_states_207_pad_type_0, strides = hidden_states_207_strides_0, weight = layers_20_mlp_down_proj_weight_cast_fp16, x = x_209_cast_fp16)[name = string("hidden_states_207_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_209_cast_fp16 = add(x = hidden_states_205_cast_fp16, y = hidden_states_207_cast_fp16)[name = string("hidden_states_209_cast_fp16")];
            fp16 const_212_promoted_to_fp16 = const()[name = string("const_212_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_7650_cast_fp16 = mul(x = hidden_states_209_cast_fp16, y = const_212_promoted_to_fp16)[name = string("op_7650_cast_fp16")];
            int32 var_7648 = const()[name = string("op_7648"), val = int32(1)];
            bool doubled_169_interleave_0 = const()[name = string("doubled_169_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_169_cast_fp16 = concat(axis = var_7648, interleave = doubled_169_interleave_0, values = (hidden_states_209_cast_fp16, var_7650_cast_fp16))[name = string("doubled_169_cast_fp16")];
            tensor<int32, [1]> out_85_axes_0 = const()[name = string("out_85_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_85_gamma_0_to_fp16 = const()[name = string("out_85_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1519340992)))];
            fp16 var_7660_to_fp16 = const()[name = string("op_7660_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_85_cast_fp16 = layer_norm(axes = out_85_axes_0, epsilon = var_7660_to_fp16, gamma = out_85_gamma_0_to_fp16, x = doubled_169_cast_fp16)[name = string("out_85_cast_fp16")];
            tensor<int32, [2]> var_7671_split_sizes_0 = const()[name = string("op_7671_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7671_axis_0 = const()[name = string("op_7671_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_7671_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_7671_cast_fp16_1 = split(axis = var_7671_axis_0, split_sizes = var_7671_split_sizes_0, x = out_85_cast_fp16)[name = string("op_7671_cast_fp16")];
            tensor<int32, [2]> query_states_127_strides_0 = const()[name = string("query_states_127_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_127_pad_type_0 = const()[name = string("query_states_127_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_127_pad_0 = const()[name = string("query_states_127_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_127_dilations_0 = const()[name = string("query_states_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_127_groups_0 = const()[name = string("query_states_127_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_127_cast_fp16 = conv(dilations = query_states_127_dilations_0, groups = query_states_127_groups_0, pad = query_states_127_pad_0, pad_type = query_states_127_pad_type_0, strides = query_states_127_strides_0, weight = layers_21_self_attn_q_proj_weight_cast_fp16, x = var_7671_cast_fp16_0)[name = string("query_states_127_cast_fp16")];
            tensor<int32, [2]> key_states_211_strides_0 = const()[name = string("key_states_211_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_211_pad_type_0 = const()[name = string("key_states_211_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_211_pad_0 = const()[name = string("key_states_211_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_211_dilations_0 = const()[name = string("key_states_211_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_211_groups_0 = const()[name = string("key_states_211_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_211_cast_fp16 = conv(dilations = key_states_211_dilations_0, groups = key_states_211_groups_0, pad = key_states_211_pad_0, pad_type = key_states_211_pad_type_0, strides = key_states_211_strides_0, weight = layers_21_self_attn_k_proj_weight_cast_fp16, x = var_7671_cast_fp16_0)[name = string("key_states_211_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_21_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_21_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1519349248)))];
            tensor<int32, [2]> value_states_127_strides_0 = const()[name = string("value_states_127_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_127_pad_type_0 = const()[name = string("value_states_127_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_127_pad_0 = const()[name = string("value_states_127_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_127_dilations_0 = const()[name = string("value_states_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_127_groups_0 = const()[name = string("value_states_127_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_127_cast_fp16 = conv(dilations = value_states_127_dilations_0, groups = value_states_127_groups_0, pad = value_states_127_pad_0, pad_type = value_states_127_pad_type_0, strides = value_states_127_strides_0, weight = layers_21_self_attn_v_proj_weight_to_fp16, x = var_7671_cast_fp16_0)[name = string("value_states_127_cast_fp16")];
            tensor<int32, [4]> concat_252x = const()[name = string("concat_252x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_211_cast_fp16 = reshape(shape = concat_252x, x = query_states_127_cast_fp16)[name = string("x_211_cast_fp16")];
            tensor<int32, [4]> concat_253x = const()[name = string("concat_253x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_7728_cast_fp16 = reshape(shape = concat_253x, x = key_states_211_cast_fp16)[name = string("op_7728_cast_fp16")];
            tensor<int32, [4]> concat_254x = const()[name = string("concat_254x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_7735_cast_fp16 = reshape(shape = concat_254x, x = value_states_127_cast_fp16)[name = string("op_7735_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_7739_cast_fp16 = mul(x = x_211_cast_fp16, y = var_859_cast_fp16)[name = string("op_7739_cast_fp16")];
            tensor<int32, [2]> var_7740_split_sizes_0 = const()[name = string("op_7740_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7740_axis_0 = const()[name = string("op_7740_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_7740_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_7740_cast_fp16_1 = split(axis = var_7740_axis_0, split_sizes = var_7740_split_sizes_0, x = x_211_cast_fp16)[name = string("op_7740_cast_fp16")];
            fp16 const_214_promoted_to_fp16 = const()[name = string("const_214_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_7742_cast_fp16 = mul(x = var_7740_cast_fp16_1, y = const_214_promoted_to_fp16)[name = string("op_7742_cast_fp16")];
            int32 var_7744 = const()[name = string("op_7744"), val = int32(-2)];
            bool var_7745_interleave_0 = const()[name = string("op_7745_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_7745_cast_fp16 = concat(axis = var_7744, interleave = var_7745_interleave_0, values = (var_7742_cast_fp16, var_7740_cast_fp16_0))[name = string("op_7745_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_7746_cast_fp16 = mul(x = var_7745_cast_fp16, y = var_866_cast_fp16)[name = string("op_7746_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_129_cast_fp16 = add(x = var_7739_cast_fp16, y = var_7746_cast_fp16)[name = string("query_states_129_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_7752_cast_fp16 = mul(x = var_7728_cast_fp16, y = var_859_cast_fp16)[name = string("op_7752_cast_fp16")];
            tensor<int32, [2]> var_7753_split_sizes_0 = const()[name = string("op_7753_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7753_axis_0 = const()[name = string("op_7753_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_7753_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_7753_cast_fp16_1 = split(axis = var_7753_axis_0, split_sizes = var_7753_split_sizes_0, x = var_7728_cast_fp16)[name = string("op_7753_cast_fp16")];
            fp16 const_215_promoted_to_fp16 = const()[name = string("const_215_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_7755_cast_fp16 = mul(x = var_7753_cast_fp16_1, y = const_215_promoted_to_fp16)[name = string("op_7755_cast_fp16")];
            int32 var_7757 = const()[name = string("op_7757"), val = int32(-2)];
            bool var_7758_interleave_0 = const()[name = string("op_7758_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_7758_cast_fp16 = concat(axis = var_7757, interleave = var_7758_interleave_0, values = (var_7755_cast_fp16, var_7753_cast_fp16_0))[name = string("op_7758_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_7759_cast_fp16 = mul(x = var_7758_cast_fp16, y = var_866_cast_fp16)[name = string("op_7759_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_215_cast_fp16 = add(x = var_7752_cast_fp16, y = var_7759_cast_fp16)[name = string("key_states_215_cast_fp16")];
            tensor<int32, [1]> expand_dims_252 = const()[name = string("expand_dims_252"), val = tensor<int32, [1]>([21])];
            tensor<int32, [1]> expand_dims_253 = const()[name = string("expand_dims_253"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_255 = const()[name = string("expand_dims_255"), val = tensor<int32, [1]>([0])];
            int32 concat_257_axis_0 = const()[name = string("concat_257_axis_0"), val = int32(0)];
            bool concat_257_interleave_0 = const()[name = string("concat_257_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_257 = concat(axis = concat_257_axis_0, interleave = concat_257_interleave_0, values = (expand_dims_252, expand_dims_253, position_id, expand_dims_255))[name = string("concat_257")];
            tensor<int32, [1]> expand_dims_256 = const()[name = string("expand_dims_256"), val = tensor<int32, [1]>([22])];
            tensor<int32, [1]> concat_258_values1_0 = const()[name = string("concat_258_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_258_values3_0 = const()[name = string("concat_258_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_258_axis_0 = const()[name = string("concat_258_axis_0"), val = int32(0)];
            bool concat_258_interleave_0 = const()[name = string("concat_258_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_258 = concat(axis = concat_258_axis_0, interleave = concat_258_interleave_0, values = (expand_dims_256, concat_258_values1_0, cache_position_end, concat_258_values3_0))[name = string("concat_258")];
            tensor<int32, [4]> key_states_217_perm_0 = const()[name = string("key_states_217_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_22_stride_0 = const()[name = string("key_cache_internal_tensor_assign_22_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_22_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_22_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_22_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_22_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_22_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_22_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_217_cast_fp16 = transpose(perm = key_states_217_perm_0, x = key_states_215_cast_fp16)[name = string("transpose_194")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_22_cast_fp16 = slice_update(begin = concat_257, begin_mask = key_cache_internal_tensor_assign_22_begin_mask_0, end = concat_258, end_mask = key_cache_internal_tensor_assign_22_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_22_squeeze_mask_0, stride = key_cache_internal_tensor_assign_22_stride_0, update = key_states_217_cast_fp16, x = coreml_update_state_152)[name = string("key_cache_internal_tensor_assign_22_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_22_cast_fp16, input = key_cache)[name = string("coreml_update_state_154_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_154 = read_state(input = key_cache)[name = string("coreml_update_state_154")];
            tensor<int32, [4]> value_states_129_perm_0 = const()[name = string("value_states_129_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_22_stride_0 = const()[name = string("value_cache_internal_tensor_assign_22_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_22_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_22_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_22_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_22_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_22_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_22_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_129_cast_fp16 = transpose(perm = value_states_129_perm_0, x = var_7735_cast_fp16)[name = string("transpose_193")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_22_cast_fp16 = slice_update(begin = concat_257, begin_mask = value_cache_internal_tensor_assign_22_begin_mask_0, end = concat_258, end_mask = value_cache_internal_tensor_assign_22_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_22_squeeze_mask_0, stride = value_cache_internal_tensor_assign_22_stride_0, update = value_states_129_cast_fp16, x = coreml_update_state_153)[name = string("value_cache_internal_tensor_assign_22_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_22_cast_fp16, input = value_cache)[name = string("coreml_update_state_155_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_155 = read_state(input = value_cache)[name = string("coreml_update_state_155")];
            tensor<int32, [4]> var_7829_begin_0 = const()[name = string("op_7829_begin_0"), val = tensor<int32, [4]>([21, 0, 0, 0])];
            tensor<int32, [4]> var_7829_end_0 = const()[name = string("op_7829_end_0"), val = tensor<int32, [4]>([22, 2, 2048, 128])];
            tensor<bool, [4]> var_7829_end_mask_0 = const()[name = string("op_7829_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7829_cast_fp16 = slice_by_index(begin = var_7829_begin_0, end = var_7829_end_0, end_mask = var_7829_end_mask_0, x = coreml_update_state_154)[name = string("op_7829_cast_fp16")];
            tensor<int32, [2]> tile_42 = const()[name = string("tile_42"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7832_axis_0 = const()[name = string("op_7832_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7832_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7832_cast_fp16_1 = split(axis = var_7832_axis_0, split_sizes = tile_42, x = var_7829_cast_fp16)[name = string("op_7832_cast_fp16")];
            tensor<int32, [4]> var_7839_begin_0 = const()[name = string("op_7839_begin_0"), val = tensor<int32, [4]>([21, 0, 0, 0])];
            tensor<int32, [4]> var_7839_end_0 = const()[name = string("op_7839_end_0"), val = tensor<int32, [4]>([22, 2, 2048, 128])];
            tensor<bool, [4]> var_7839_end_mask_0 = const()[name = string("op_7839_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7839_cast_fp16 = slice_by_index(begin = var_7839_begin_0, end = var_7839_end_0, end_mask = var_7839_end_mask_0, x = coreml_update_state_155)[name = string("op_7839_cast_fp16")];
            tensor<int32, [2]> tile_43 = const()[name = string("tile_43"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7842_axis_0 = const()[name = string("op_7842_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7842_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7842_cast_fp16_1 = split(axis = var_7842_axis_0, split_sizes = tile_43, x = var_7839_cast_fp16)[name = string("op_7842_cast_fp16")];
            tensor<int32, [2]> var_7845_split_sizes_0 = const()[name = string("op_7845_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_7845_axis_0 = const()[name = string("op_7845_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_7845_0, tensor<fp16, [1, 8, 128, 8]> var_7845_1 = split(axis = var_7845_axis_0, split_sizes = var_7845_split_sizes_0, x = query_states_129_cast_fp16)[name = string("op_7845")];
            bool attn_weights_337_transpose_x_0 = const()[name = string("attn_weights_337_transpose_x_0"), val = bool(false)];
            bool attn_weights_337_transpose_y_0 = const()[name = string("attn_weights_337_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_337_cast_fp16 = matmul(transpose_x = attn_weights_337_transpose_x_0, transpose_y = attn_weights_337_transpose_y_0, x = var_7832_cast_fp16_0, y = var_7845_0)[name = string("attn_weights_337_cast_fp16")];
            fp16 var_7848_to_fp16 = const()[name = string("op_7848_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_339_cast_fp16 = mul(x = attn_weights_337_cast_fp16, y = var_7848_to_fp16)[name = string("attn_weights_339_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_341_cast_fp16 = add(x = attn_weights_339_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_341_cast_fp16")];
            int32 var_7852 = const()[name = string("op_7852"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_343_cast_fp16 = softmax(axis = var_7852, x = attn_weights_341_cast_fp16)[name = string("attn_weights_343_cast_fp16")];
            bool var_7858_transpose_x_1 = const()[name = string("op_7858_transpose_x_1"), val = bool(true)];
            bool var_7858_transpose_y_1 = const()[name = string("op_7858_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_7858_cast_fp16 = matmul(transpose_x = var_7858_transpose_x_1, transpose_y = var_7858_transpose_y_1, x = attn_weights_343_cast_fp16, y = var_7842_cast_fp16_0)[name = string("op_7858_cast_fp16")];
            bool attn_weights_345_transpose_x_0 = const()[name = string("attn_weights_345_transpose_x_0"), val = bool(false)];
            bool attn_weights_345_transpose_y_0 = const()[name = string("attn_weights_345_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_345_cast_fp16 = matmul(transpose_x = attn_weights_345_transpose_x_0, transpose_y = attn_weights_345_transpose_y_0, x = var_7832_cast_fp16_1, y = var_7845_1)[name = string("attn_weights_345_cast_fp16")];
            fp16 var_7860_to_fp16 = const()[name = string("op_7860_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_347_cast_fp16 = mul(x = attn_weights_345_cast_fp16, y = var_7860_to_fp16)[name = string("attn_weights_347_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_349_cast_fp16 = add(x = attn_weights_347_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_349_cast_fp16")];
            int32 var_7864 = const()[name = string("op_7864"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_351_cast_fp16 = softmax(axis = var_7864, x = attn_weights_349_cast_fp16)[name = string("attn_weights_351_cast_fp16")];
            bool attn_output_169_transpose_x_1 = const()[name = string("attn_output_169_transpose_x_1"), val = bool(true)];
            bool attn_output_169_transpose_y_1 = const()[name = string("attn_output_169_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_169_cast_fp16 = matmul(transpose_x = attn_output_169_transpose_x_1, transpose_y = attn_output_169_transpose_y_1, x = attn_weights_351_cast_fp16, y = var_7842_cast_fp16_1)[name = string("attn_output_169_cast_fp16")];
            int32 var_7872 = const()[name = string("op_7872"), val = int32(1)];
            bool attn_output_171_interleave_0 = const()[name = string("attn_output_171_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_171_cast_fp16 = concat(axis = var_7872, interleave = attn_output_171_interleave_0, values = (var_7858_cast_fp16, attn_output_169_cast_fp16))[name = string("attn_output_171_cast_fp16")];
            tensor<int32, [4]> var_7876_perm_0 = const()[name = string("op_7876_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_263x = const()[name = string("concat_263x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_7876_cast_fp16 = transpose(perm = var_7876_perm_0, x = attn_output_171_cast_fp16)[name = string("transpose_192")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_175_cast_fp16 = reshape(shape = concat_263x, x = var_7876_cast_fp16)[name = string("attn_output_175_cast_fp16")];
            tensor<int32, [2]> hidden_states_213_strides_0 = const()[name = string("hidden_states_213_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_213_pad_type_0 = const()[name = string("hidden_states_213_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_213_pad_0 = const()[name = string("hidden_states_213_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_213_dilations_0 = const()[name = string("hidden_states_213_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_213_groups_0 = const()[name = string("hidden_states_213_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_213_cast_fp16 = conv(dilations = hidden_states_213_dilations_0, groups = hidden_states_213_groups_0, pad = hidden_states_213_pad_0, pad_type = hidden_states_213_pad_type_0, strides = hidden_states_213_strides_0, weight = layers_21_self_attn_o_proj_weight_cast_fp16, x = attn_output_175_cast_fp16)[name = string("hidden_states_213_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_215_cast_fp16 = add(x = hidden_states_209_cast_fp16, y = hidden_states_213_cast_fp16)[name = string("hidden_states_215_cast_fp16")];
            fp16 const_220_promoted_to_fp16 = const()[name = string("const_220_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_7909_cast_fp16 = mul(x = hidden_states_215_cast_fp16, y = const_220_promoted_to_fp16)[name = string("op_7909_cast_fp16")];
            int32 var_7907 = const()[name = string("op_7907"), val = int32(1)];
            bool doubled_173_interleave_0 = const()[name = string("doubled_173_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_173_cast_fp16 = concat(axis = var_7907, interleave = doubled_173_interleave_0, values = (hidden_states_215_cast_fp16, var_7909_cast_fp16))[name = string("doubled_173_cast_fp16")];
            tensor<int32, [1]> out_87_axes_0 = const()[name = string("out_87_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_87_gamma_0_to_fp16 = const()[name = string("out_87_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1520397888)))];
            fp16 var_7919_to_fp16 = const()[name = string("op_7919_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_87_cast_fp16 = layer_norm(axes = out_87_axes_0, epsilon = var_7919_to_fp16, gamma = out_87_gamma_0_to_fp16, x = doubled_173_cast_fp16)[name = string("out_87_cast_fp16")];
            tensor<int32, [2]> var_7930_split_sizes_0 = const()[name = string("op_7930_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7930_axis_0 = const()[name = string("op_7930_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_7930_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_7930_cast_fp16_1 = split(axis = var_7930_axis_0, split_sizes = var_7930_split_sizes_0, x = out_87_cast_fp16)[name = string("op_7930_cast_fp16")];
            tensor<int32, [2]> input_43_strides_0 = const()[name = string("input_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_43_pad_type_0 = const()[name = string("input_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_43_pad_0 = const()[name = string("input_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_43_dilations_0 = const()[name = string("input_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_43_groups_0 = const()[name = string("input_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_43_cast_fp16 = conv(dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = layers_21_mlp_gate_proj_weight_cast_fp16, x = var_7930_cast_fp16_0)[name = string("input_43_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_7947_cast_fp16 = silu(x = input_43_cast_fp16)[name = string("op_7947_cast_fp16")];
            tensor<int32, [2]> var_7953_strides_0 = const()[name = string("op_7953_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_7953_pad_type_0 = const()[name = string("op_7953_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_7953_pad_0 = const()[name = string("op_7953_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_7953_dilations_0 = const()[name = string("op_7953_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7953_groups_0 = const()[name = string("op_7953_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_7953_cast_fp16 = conv(dilations = var_7953_dilations_0, groups = var_7953_groups_0, pad = var_7953_pad_0, pad_type = var_7953_pad_type_0, strides = var_7953_strides_0, weight = layers_21_mlp_up_proj_weight_cast_fp16, x = var_7930_cast_fp16_0)[name = string("op_7953_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_219_cast_fp16 = mul(x = var_7947_cast_fp16, y = var_7953_cast_fp16)[name = string("x_219_cast_fp16")];
            tensor<int32, [2]> hidden_states_217_strides_0 = const()[name = string("hidden_states_217_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_217_pad_type_0 = const()[name = string("hidden_states_217_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_217_pad_0 = const()[name = string("hidden_states_217_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_217_dilations_0 = const()[name = string("hidden_states_217_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_217_groups_0 = const()[name = string("hidden_states_217_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_217_cast_fp16 = conv(dilations = hidden_states_217_dilations_0, groups = hidden_states_217_groups_0, pad = hidden_states_217_pad_0, pad_type = hidden_states_217_pad_type_0, strides = hidden_states_217_strides_0, weight = layers_21_mlp_down_proj_weight_cast_fp16, x = x_219_cast_fp16)[name = string("hidden_states_217_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_219_cast_fp16 = add(x = hidden_states_215_cast_fp16, y = hidden_states_217_cast_fp16)[name = string("hidden_states_219_cast_fp16")];
            fp16 const_222_promoted_to_fp16 = const()[name = string("const_222_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_7971_cast_fp16 = mul(x = hidden_states_219_cast_fp16, y = const_222_promoted_to_fp16)[name = string("op_7971_cast_fp16")];
            int32 var_7969 = const()[name = string("op_7969"), val = int32(1)];
            bool doubled_177_interleave_0 = const()[name = string("doubled_177_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_177_cast_fp16 = concat(axis = var_7969, interleave = doubled_177_interleave_0, values = (hidden_states_219_cast_fp16, var_7971_cast_fp16))[name = string("doubled_177_cast_fp16")];
            tensor<int32, [1]> out_89_axes_0 = const()[name = string("out_89_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_89_gamma_0_to_fp16 = const()[name = string("out_89_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1520406144)))];
            fp16 var_7981_to_fp16 = const()[name = string("op_7981_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_89_cast_fp16 = layer_norm(axes = out_89_axes_0, epsilon = var_7981_to_fp16, gamma = out_89_gamma_0_to_fp16, x = doubled_177_cast_fp16)[name = string("out_89_cast_fp16")];
            tensor<int32, [2]> var_7992_split_sizes_0 = const()[name = string("op_7992_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7992_axis_0 = const()[name = string("op_7992_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_7992_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_7992_cast_fp16_1 = split(axis = var_7992_axis_0, split_sizes = var_7992_split_sizes_0, x = out_89_cast_fp16)[name = string("op_7992_cast_fp16")];
            tensor<int32, [2]> query_states_133_strides_0 = const()[name = string("query_states_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_133_pad_type_0 = const()[name = string("query_states_133_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_133_pad_0 = const()[name = string("query_states_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_133_dilations_0 = const()[name = string("query_states_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_133_groups_0 = const()[name = string("query_states_133_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_133_cast_fp16 = conv(dilations = query_states_133_dilations_0, groups = query_states_133_groups_0, pad = query_states_133_pad_0, pad_type = query_states_133_pad_type_0, strides = query_states_133_strides_0, weight = layers_22_self_attn_q_proj_weight_cast_fp16, x = var_7992_cast_fp16_0)[name = string("query_states_133_cast_fp16")];
            tensor<int32, [2]> key_states_221_strides_0 = const()[name = string("key_states_221_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_221_pad_type_0 = const()[name = string("key_states_221_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_221_pad_0 = const()[name = string("key_states_221_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_221_dilations_0 = const()[name = string("key_states_221_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_221_groups_0 = const()[name = string("key_states_221_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_221_cast_fp16 = conv(dilations = key_states_221_dilations_0, groups = key_states_221_groups_0, pad = key_states_221_pad_0, pad_type = key_states_221_pad_type_0, strides = key_states_221_strides_0, weight = layers_22_self_attn_k_proj_weight_cast_fp16, x = var_7992_cast_fp16_0)[name = string("key_states_221_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_22_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_22_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1520414400)))];
            tensor<int32, [2]> value_states_133_strides_0 = const()[name = string("value_states_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_133_pad_type_0 = const()[name = string("value_states_133_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_133_pad_0 = const()[name = string("value_states_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_133_dilations_0 = const()[name = string("value_states_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_133_groups_0 = const()[name = string("value_states_133_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_133_cast_fp16 = conv(dilations = value_states_133_dilations_0, groups = value_states_133_groups_0, pad = value_states_133_pad_0, pad_type = value_states_133_pad_type_0, strides = value_states_133_strides_0, weight = layers_22_self_attn_v_proj_weight_to_fp16, x = var_7992_cast_fp16_0)[name = string("value_states_133_cast_fp16")];
            tensor<int32, [4]> concat_264x = const()[name = string("concat_264x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_221_cast_fp16 = reshape(shape = concat_264x, x = query_states_133_cast_fp16)[name = string("x_221_cast_fp16")];
            tensor<int32, [4]> concat_265x = const()[name = string("concat_265x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_8049_cast_fp16 = reshape(shape = concat_265x, x = key_states_221_cast_fp16)[name = string("op_8049_cast_fp16")];
            tensor<int32, [4]> concat_266x = const()[name = string("concat_266x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_8056_cast_fp16 = reshape(shape = concat_266x, x = value_states_133_cast_fp16)[name = string("op_8056_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_8060_cast_fp16 = mul(x = x_221_cast_fp16, y = var_859_cast_fp16)[name = string("op_8060_cast_fp16")];
            tensor<int32, [2]> var_8061_split_sizes_0 = const()[name = string("op_8061_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8061_axis_0 = const()[name = string("op_8061_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_8061_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_8061_cast_fp16_1 = split(axis = var_8061_axis_0, split_sizes = var_8061_split_sizes_0, x = x_221_cast_fp16)[name = string("op_8061_cast_fp16")];
            fp16 const_224_promoted_to_fp16 = const()[name = string("const_224_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_8063_cast_fp16 = mul(x = var_8061_cast_fp16_1, y = const_224_promoted_to_fp16)[name = string("op_8063_cast_fp16")];
            int32 var_8065 = const()[name = string("op_8065"), val = int32(-2)];
            bool var_8066_interleave_0 = const()[name = string("op_8066_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_8066_cast_fp16 = concat(axis = var_8065, interleave = var_8066_interleave_0, values = (var_8063_cast_fp16, var_8061_cast_fp16_0))[name = string("op_8066_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_8067_cast_fp16 = mul(x = var_8066_cast_fp16, y = var_866_cast_fp16)[name = string("op_8067_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_135_cast_fp16 = add(x = var_8060_cast_fp16, y = var_8067_cast_fp16)[name = string("query_states_135_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_8073_cast_fp16 = mul(x = var_8049_cast_fp16, y = var_859_cast_fp16)[name = string("op_8073_cast_fp16")];
            tensor<int32, [2]> var_8074_split_sizes_0 = const()[name = string("op_8074_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8074_axis_0 = const()[name = string("op_8074_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_8074_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_8074_cast_fp16_1 = split(axis = var_8074_axis_0, split_sizes = var_8074_split_sizes_0, x = var_8049_cast_fp16)[name = string("op_8074_cast_fp16")];
            fp16 const_225_promoted_to_fp16 = const()[name = string("const_225_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_8076_cast_fp16 = mul(x = var_8074_cast_fp16_1, y = const_225_promoted_to_fp16)[name = string("op_8076_cast_fp16")];
            int32 var_8078 = const()[name = string("op_8078"), val = int32(-2)];
            bool var_8079_interleave_0 = const()[name = string("op_8079_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_8079_cast_fp16 = concat(axis = var_8078, interleave = var_8079_interleave_0, values = (var_8076_cast_fp16, var_8074_cast_fp16_0))[name = string("op_8079_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_8080_cast_fp16 = mul(x = var_8079_cast_fp16, y = var_866_cast_fp16)[name = string("op_8080_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_225_cast_fp16 = add(x = var_8073_cast_fp16, y = var_8080_cast_fp16)[name = string("key_states_225_cast_fp16")];
            tensor<int32, [1]> expand_dims_264 = const()[name = string("expand_dims_264"), val = tensor<int32, [1]>([22])];
            tensor<int32, [1]> expand_dims_265 = const()[name = string("expand_dims_265"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_267 = const()[name = string("expand_dims_267"), val = tensor<int32, [1]>([0])];
            int32 concat_269_axis_0 = const()[name = string("concat_269_axis_0"), val = int32(0)];
            bool concat_269_interleave_0 = const()[name = string("concat_269_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_269 = concat(axis = concat_269_axis_0, interleave = concat_269_interleave_0, values = (expand_dims_264, expand_dims_265, position_id, expand_dims_267))[name = string("concat_269")];
            tensor<int32, [1]> expand_dims_268 = const()[name = string("expand_dims_268"), val = tensor<int32, [1]>([23])];
            tensor<int32, [1]> concat_270_values1_0 = const()[name = string("concat_270_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_270_values3_0 = const()[name = string("concat_270_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_270_axis_0 = const()[name = string("concat_270_axis_0"), val = int32(0)];
            bool concat_270_interleave_0 = const()[name = string("concat_270_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_270 = concat(axis = concat_270_axis_0, interleave = concat_270_interleave_0, values = (expand_dims_268, concat_270_values1_0, cache_position_end, concat_270_values3_0))[name = string("concat_270")];
            tensor<int32, [4]> key_states_227_perm_0 = const()[name = string("key_states_227_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_23_stride_0 = const()[name = string("key_cache_internal_tensor_assign_23_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_23_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_23_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_23_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_23_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_23_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_23_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_227_cast_fp16 = transpose(perm = key_states_227_perm_0, x = key_states_225_cast_fp16)[name = string("transpose_191")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_23_cast_fp16 = slice_update(begin = concat_269, begin_mask = key_cache_internal_tensor_assign_23_begin_mask_0, end = concat_270, end_mask = key_cache_internal_tensor_assign_23_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_23_squeeze_mask_0, stride = key_cache_internal_tensor_assign_23_stride_0, update = key_states_227_cast_fp16, x = coreml_update_state_154)[name = string("key_cache_internal_tensor_assign_23_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_23_cast_fp16, input = key_cache)[name = string("coreml_update_state_156_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_156 = read_state(input = key_cache)[name = string("coreml_update_state_156")];
            tensor<int32, [4]> value_states_135_perm_0 = const()[name = string("value_states_135_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_23_stride_0 = const()[name = string("value_cache_internal_tensor_assign_23_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_23_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_23_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_23_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_23_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_23_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_23_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_135_cast_fp16 = transpose(perm = value_states_135_perm_0, x = var_8056_cast_fp16)[name = string("transpose_190")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_23_cast_fp16 = slice_update(begin = concat_269, begin_mask = value_cache_internal_tensor_assign_23_begin_mask_0, end = concat_270, end_mask = value_cache_internal_tensor_assign_23_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_23_squeeze_mask_0, stride = value_cache_internal_tensor_assign_23_stride_0, update = value_states_135_cast_fp16, x = coreml_update_state_155)[name = string("value_cache_internal_tensor_assign_23_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_23_cast_fp16, input = value_cache)[name = string("coreml_update_state_157_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_157 = read_state(input = value_cache)[name = string("coreml_update_state_157")];
            tensor<int32, [4]> var_8150_begin_0 = const()[name = string("op_8150_begin_0"), val = tensor<int32, [4]>([22, 0, 0, 0])];
            tensor<int32, [4]> var_8150_end_0 = const()[name = string("op_8150_end_0"), val = tensor<int32, [4]>([23, 2, 2048, 128])];
            tensor<bool, [4]> var_8150_end_mask_0 = const()[name = string("op_8150_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8150_cast_fp16 = slice_by_index(begin = var_8150_begin_0, end = var_8150_end_0, end_mask = var_8150_end_mask_0, x = coreml_update_state_156)[name = string("op_8150_cast_fp16")];
            tensor<int32, [2]> tile_44 = const()[name = string("tile_44"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8153_axis_0 = const()[name = string("op_8153_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8153_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8153_cast_fp16_1 = split(axis = var_8153_axis_0, split_sizes = tile_44, x = var_8150_cast_fp16)[name = string("op_8153_cast_fp16")];
            tensor<int32, [4]> var_8160_begin_0 = const()[name = string("op_8160_begin_0"), val = tensor<int32, [4]>([22, 0, 0, 0])];
            tensor<int32, [4]> var_8160_end_0 = const()[name = string("op_8160_end_0"), val = tensor<int32, [4]>([23, 2, 2048, 128])];
            tensor<bool, [4]> var_8160_end_mask_0 = const()[name = string("op_8160_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8160_cast_fp16 = slice_by_index(begin = var_8160_begin_0, end = var_8160_end_0, end_mask = var_8160_end_mask_0, x = coreml_update_state_157)[name = string("op_8160_cast_fp16")];
            tensor<int32, [2]> tile_45 = const()[name = string("tile_45"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8163_axis_0 = const()[name = string("op_8163_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8163_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8163_cast_fp16_1 = split(axis = var_8163_axis_0, split_sizes = tile_45, x = var_8160_cast_fp16)[name = string("op_8163_cast_fp16")];
            tensor<int32, [2]> var_8166_split_sizes_0 = const()[name = string("op_8166_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_8166_axis_0 = const()[name = string("op_8166_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_8166_0, tensor<fp16, [1, 8, 128, 8]> var_8166_1 = split(axis = var_8166_axis_0, split_sizes = var_8166_split_sizes_0, x = query_states_135_cast_fp16)[name = string("op_8166")];
            bool attn_weights_353_transpose_x_0 = const()[name = string("attn_weights_353_transpose_x_0"), val = bool(false)];
            bool attn_weights_353_transpose_y_0 = const()[name = string("attn_weights_353_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_353_cast_fp16 = matmul(transpose_x = attn_weights_353_transpose_x_0, transpose_y = attn_weights_353_transpose_y_0, x = var_8153_cast_fp16_0, y = var_8166_0)[name = string("attn_weights_353_cast_fp16")];
            fp16 var_8169_to_fp16 = const()[name = string("op_8169_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_355_cast_fp16 = mul(x = attn_weights_353_cast_fp16, y = var_8169_to_fp16)[name = string("attn_weights_355_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_357_cast_fp16 = add(x = attn_weights_355_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_357_cast_fp16")];
            int32 var_8173 = const()[name = string("op_8173"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_359_cast_fp16 = softmax(axis = var_8173, x = attn_weights_357_cast_fp16)[name = string("attn_weights_359_cast_fp16")];
            bool var_8179_transpose_x_1 = const()[name = string("op_8179_transpose_x_1"), val = bool(true)];
            bool var_8179_transpose_y_1 = const()[name = string("op_8179_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_8179_cast_fp16 = matmul(transpose_x = var_8179_transpose_x_1, transpose_y = var_8179_transpose_y_1, x = attn_weights_359_cast_fp16, y = var_8163_cast_fp16_0)[name = string("op_8179_cast_fp16")];
            bool attn_weights_361_transpose_x_0 = const()[name = string("attn_weights_361_transpose_x_0"), val = bool(false)];
            bool attn_weights_361_transpose_y_0 = const()[name = string("attn_weights_361_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_361_cast_fp16 = matmul(transpose_x = attn_weights_361_transpose_x_0, transpose_y = attn_weights_361_transpose_y_0, x = var_8153_cast_fp16_1, y = var_8166_1)[name = string("attn_weights_361_cast_fp16")];
            fp16 var_8181_to_fp16 = const()[name = string("op_8181_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_363_cast_fp16 = mul(x = attn_weights_361_cast_fp16, y = var_8181_to_fp16)[name = string("attn_weights_363_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_365_cast_fp16 = add(x = attn_weights_363_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_365_cast_fp16")];
            int32 var_8185 = const()[name = string("op_8185"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_367_cast_fp16 = softmax(axis = var_8185, x = attn_weights_365_cast_fp16)[name = string("attn_weights_367_cast_fp16")];
            bool attn_output_177_transpose_x_1 = const()[name = string("attn_output_177_transpose_x_1"), val = bool(true)];
            bool attn_output_177_transpose_y_1 = const()[name = string("attn_output_177_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_177_cast_fp16 = matmul(transpose_x = attn_output_177_transpose_x_1, transpose_y = attn_output_177_transpose_y_1, x = attn_weights_367_cast_fp16, y = var_8163_cast_fp16_1)[name = string("attn_output_177_cast_fp16")];
            int32 var_8193 = const()[name = string("op_8193"), val = int32(1)];
            bool attn_output_179_interleave_0 = const()[name = string("attn_output_179_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_179_cast_fp16 = concat(axis = var_8193, interleave = attn_output_179_interleave_0, values = (var_8179_cast_fp16, attn_output_177_cast_fp16))[name = string("attn_output_179_cast_fp16")];
            tensor<int32, [4]> var_8197_perm_0 = const()[name = string("op_8197_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_275x = const()[name = string("concat_275x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_8197_cast_fp16 = transpose(perm = var_8197_perm_0, x = attn_output_179_cast_fp16)[name = string("transpose_189")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_183_cast_fp16 = reshape(shape = concat_275x, x = var_8197_cast_fp16)[name = string("attn_output_183_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_22_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_22_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1521463040)))];
            tensor<int32, [2]> hidden_states_223_strides_0 = const()[name = string("hidden_states_223_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_223_pad_type_0 = const()[name = string("hidden_states_223_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_223_pad_0 = const()[name = string("hidden_states_223_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_223_dilations_0 = const()[name = string("hidden_states_223_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_223_groups_0 = const()[name = string("hidden_states_223_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_223_cast_fp16 = conv(dilations = hidden_states_223_dilations_0, groups = hidden_states_223_groups_0, pad = hidden_states_223_pad_0, pad_type = hidden_states_223_pad_type_0, strides = hidden_states_223_strides_0, weight = layers_22_self_attn_o_proj_weight_to_fp16, x = attn_output_183_cast_fp16)[name = string("hidden_states_223_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_225_cast_fp16 = add(x = hidden_states_219_cast_fp16, y = hidden_states_223_cast_fp16)[name = string("hidden_states_225_cast_fp16")];
            fp16 const_230_promoted_to_fp16 = const()[name = string("const_230_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_8230_cast_fp16 = mul(x = hidden_states_225_cast_fp16, y = const_230_promoted_to_fp16)[name = string("op_8230_cast_fp16")];
            int32 var_8228 = const()[name = string("op_8228"), val = int32(1)];
            bool doubled_181_interleave_0 = const()[name = string("doubled_181_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_181_cast_fp16 = concat(axis = var_8228, interleave = doubled_181_interleave_0, values = (hidden_states_225_cast_fp16, var_8230_cast_fp16))[name = string("doubled_181_cast_fp16")];
            tensor<int32, [1]> out_91_axes_0 = const()[name = string("out_91_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_91_gamma_0_to_fp16 = const()[name = string("out_91_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1529851712)))];
            fp16 var_8240_to_fp16 = const()[name = string("op_8240_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_91_cast_fp16 = layer_norm(axes = out_91_axes_0, epsilon = var_8240_to_fp16, gamma = out_91_gamma_0_to_fp16, x = doubled_181_cast_fp16)[name = string("out_91_cast_fp16")];
            tensor<int32, [2]> var_8251_split_sizes_0 = const()[name = string("op_8251_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8251_axis_0 = const()[name = string("op_8251_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_8251_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_8251_cast_fp16_1 = split(axis = var_8251_axis_0, split_sizes = var_8251_split_sizes_0, x = out_91_cast_fp16)[name = string("op_8251_cast_fp16")];
            tensor<int32, [2]> input_45_strides_0 = const()[name = string("input_45_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_45_pad_type_0 = const()[name = string("input_45_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_45_pad_0 = const()[name = string("input_45_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_45_dilations_0 = const()[name = string("input_45_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_45_groups_0 = const()[name = string("input_45_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_45_cast_fp16 = conv(dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = layers_22_mlp_gate_proj_weight_cast_fp16, x = var_8251_cast_fp16_0)[name = string("input_45_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_8268_cast_fp16 = silu(x = input_45_cast_fp16)[name = string("op_8268_cast_fp16")];
            tensor<int32, [2]> var_8274_strides_0 = const()[name = string("op_8274_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_8274_pad_type_0 = const()[name = string("op_8274_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_8274_pad_0 = const()[name = string("op_8274_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_8274_dilations_0 = const()[name = string("op_8274_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8274_groups_0 = const()[name = string("op_8274_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_8274_cast_fp16 = conv(dilations = var_8274_dilations_0, groups = var_8274_groups_0, pad = var_8274_pad_0, pad_type = var_8274_pad_type_0, strides = var_8274_strides_0, weight = layers_22_mlp_up_proj_weight_cast_fp16, x = var_8251_cast_fp16_0)[name = string("op_8274_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_229_cast_fp16 = mul(x = var_8268_cast_fp16, y = var_8274_cast_fp16)[name = string("x_229_cast_fp16")];
            tensor<int32, [2]> hidden_states_227_strides_0 = const()[name = string("hidden_states_227_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_227_pad_type_0 = const()[name = string("hidden_states_227_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_227_pad_0 = const()[name = string("hidden_states_227_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_227_dilations_0 = const()[name = string("hidden_states_227_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_227_groups_0 = const()[name = string("hidden_states_227_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_227_cast_fp16 = conv(dilations = hidden_states_227_dilations_0, groups = hidden_states_227_groups_0, pad = hidden_states_227_pad_0, pad_type = hidden_states_227_pad_type_0, strides = hidden_states_227_strides_0, weight = layers_22_mlp_down_proj_weight_cast_fp16, x = x_229_cast_fp16)[name = string("hidden_states_227_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_229_cast_fp16 = add(x = hidden_states_225_cast_fp16, y = hidden_states_227_cast_fp16)[name = string("hidden_states_229_cast_fp16")];
            fp16 const_232_promoted_to_fp16 = const()[name = string("const_232_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_8292_cast_fp16 = mul(x = hidden_states_229_cast_fp16, y = const_232_promoted_to_fp16)[name = string("op_8292_cast_fp16")];
            int32 var_8290 = const()[name = string("op_8290"), val = int32(1)];
            bool doubled_185_interleave_0 = const()[name = string("doubled_185_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_185_cast_fp16 = concat(axis = var_8290, interleave = doubled_185_interleave_0, values = (hidden_states_229_cast_fp16, var_8292_cast_fp16))[name = string("doubled_185_cast_fp16")];
            tensor<int32, [1]> out_93_axes_0 = const()[name = string("out_93_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_93_gamma_0_to_fp16 = const()[name = string("out_93_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1529859968)))];
            fp16 var_8302_to_fp16 = const()[name = string("op_8302_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_93_cast_fp16 = layer_norm(axes = out_93_axes_0, epsilon = var_8302_to_fp16, gamma = out_93_gamma_0_to_fp16, x = doubled_185_cast_fp16)[name = string("out_93_cast_fp16")];
            tensor<int32, [2]> var_8313_split_sizes_0 = const()[name = string("op_8313_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8313_axis_0 = const()[name = string("op_8313_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_8313_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_8313_cast_fp16_1 = split(axis = var_8313_axis_0, split_sizes = var_8313_split_sizes_0, x = out_93_cast_fp16)[name = string("op_8313_cast_fp16")];
            tensor<int32, [2]> query_states_139_strides_0 = const()[name = string("query_states_139_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_139_pad_type_0 = const()[name = string("query_states_139_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_139_pad_0 = const()[name = string("query_states_139_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_139_dilations_0 = const()[name = string("query_states_139_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_139_groups_0 = const()[name = string("query_states_139_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_139_cast_fp16 = conv(dilations = query_states_139_dilations_0, groups = query_states_139_groups_0, pad = query_states_139_pad_0, pad_type = query_states_139_pad_type_0, strides = query_states_139_strides_0, weight = layers_23_self_attn_q_proj_weight_cast_fp16, x = var_8313_cast_fp16_0)[name = string("query_states_139_cast_fp16")];
            tensor<int32, [2]> key_states_231_strides_0 = const()[name = string("key_states_231_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_231_pad_type_0 = const()[name = string("key_states_231_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_231_pad_0 = const()[name = string("key_states_231_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_231_dilations_0 = const()[name = string("key_states_231_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_231_groups_0 = const()[name = string("key_states_231_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_231_cast_fp16 = conv(dilations = key_states_231_dilations_0, groups = key_states_231_groups_0, pad = key_states_231_pad_0, pad_type = key_states_231_pad_type_0, strides = key_states_231_strides_0, weight = layers_23_self_attn_k_proj_weight_cast_fp16, x = var_8313_cast_fp16_0)[name = string("key_states_231_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_23_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_23_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1529868224)))];
            tensor<int32, [2]> value_states_139_strides_0 = const()[name = string("value_states_139_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_139_pad_type_0 = const()[name = string("value_states_139_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_139_pad_0 = const()[name = string("value_states_139_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_139_dilations_0 = const()[name = string("value_states_139_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_139_groups_0 = const()[name = string("value_states_139_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_139_cast_fp16 = conv(dilations = value_states_139_dilations_0, groups = value_states_139_groups_0, pad = value_states_139_pad_0, pad_type = value_states_139_pad_type_0, strides = value_states_139_strides_0, weight = layers_23_self_attn_v_proj_weight_to_fp16, x = var_8313_cast_fp16_0)[name = string("value_states_139_cast_fp16")];
            tensor<int32, [4]> concat_276x = const()[name = string("concat_276x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_231_cast_fp16 = reshape(shape = concat_276x, x = query_states_139_cast_fp16)[name = string("x_231_cast_fp16")];
            tensor<int32, [4]> concat_277x = const()[name = string("concat_277x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_8370_cast_fp16 = reshape(shape = concat_277x, x = key_states_231_cast_fp16)[name = string("op_8370_cast_fp16")];
            tensor<int32, [4]> concat_278x = const()[name = string("concat_278x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_8377_cast_fp16 = reshape(shape = concat_278x, x = value_states_139_cast_fp16)[name = string("op_8377_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_8381_cast_fp16 = mul(x = x_231_cast_fp16, y = var_859_cast_fp16)[name = string("op_8381_cast_fp16")];
            tensor<int32, [2]> var_8382_split_sizes_0 = const()[name = string("op_8382_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8382_axis_0 = const()[name = string("op_8382_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_8382_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_8382_cast_fp16_1 = split(axis = var_8382_axis_0, split_sizes = var_8382_split_sizes_0, x = x_231_cast_fp16)[name = string("op_8382_cast_fp16")];
            fp16 const_234_promoted_to_fp16 = const()[name = string("const_234_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_8384_cast_fp16 = mul(x = var_8382_cast_fp16_1, y = const_234_promoted_to_fp16)[name = string("op_8384_cast_fp16")];
            int32 var_8386 = const()[name = string("op_8386"), val = int32(-2)];
            bool var_8387_interleave_0 = const()[name = string("op_8387_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_8387_cast_fp16 = concat(axis = var_8386, interleave = var_8387_interleave_0, values = (var_8384_cast_fp16, var_8382_cast_fp16_0))[name = string("op_8387_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_8388_cast_fp16 = mul(x = var_8387_cast_fp16, y = var_866_cast_fp16)[name = string("op_8388_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_141_cast_fp16 = add(x = var_8381_cast_fp16, y = var_8388_cast_fp16)[name = string("query_states_141_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_8394_cast_fp16 = mul(x = var_8370_cast_fp16, y = var_859_cast_fp16)[name = string("op_8394_cast_fp16")];
            tensor<int32, [2]> var_8395_split_sizes_0 = const()[name = string("op_8395_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8395_axis_0 = const()[name = string("op_8395_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_8395_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_8395_cast_fp16_1 = split(axis = var_8395_axis_0, split_sizes = var_8395_split_sizes_0, x = var_8370_cast_fp16)[name = string("op_8395_cast_fp16")];
            fp16 const_235_promoted_to_fp16 = const()[name = string("const_235_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_8397_cast_fp16 = mul(x = var_8395_cast_fp16_1, y = const_235_promoted_to_fp16)[name = string("op_8397_cast_fp16")];
            int32 var_8399 = const()[name = string("op_8399"), val = int32(-2)];
            bool var_8400_interleave_0 = const()[name = string("op_8400_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_8400_cast_fp16 = concat(axis = var_8399, interleave = var_8400_interleave_0, values = (var_8397_cast_fp16, var_8395_cast_fp16_0))[name = string("op_8400_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_8401_cast_fp16 = mul(x = var_8400_cast_fp16, y = var_866_cast_fp16)[name = string("op_8401_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_235_cast_fp16 = add(x = var_8394_cast_fp16, y = var_8401_cast_fp16)[name = string("key_states_235_cast_fp16")];
            tensor<int32, [1]> expand_dims_276 = const()[name = string("expand_dims_276"), val = tensor<int32, [1]>([23])];
            tensor<int32, [1]> expand_dims_277 = const()[name = string("expand_dims_277"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_279 = const()[name = string("expand_dims_279"), val = tensor<int32, [1]>([0])];
            int32 concat_281_axis_0 = const()[name = string("concat_281_axis_0"), val = int32(0)];
            bool concat_281_interleave_0 = const()[name = string("concat_281_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_281 = concat(axis = concat_281_axis_0, interleave = concat_281_interleave_0, values = (expand_dims_276, expand_dims_277, position_id, expand_dims_279))[name = string("concat_281")];
            tensor<int32, [1]> expand_dims_280 = const()[name = string("expand_dims_280"), val = tensor<int32, [1]>([24])];
            tensor<int32, [1]> concat_282_values1_0 = const()[name = string("concat_282_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_282_values3_0 = const()[name = string("concat_282_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_282_axis_0 = const()[name = string("concat_282_axis_0"), val = int32(0)];
            bool concat_282_interleave_0 = const()[name = string("concat_282_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_282 = concat(axis = concat_282_axis_0, interleave = concat_282_interleave_0, values = (expand_dims_280, concat_282_values1_0, cache_position_end, concat_282_values3_0))[name = string("concat_282")];
            tensor<int32, [4]> key_states_237_perm_0 = const()[name = string("key_states_237_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_24_stride_0 = const()[name = string("key_cache_internal_tensor_assign_24_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_24_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_24_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_24_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_24_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_24_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_24_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_237_cast_fp16 = transpose(perm = key_states_237_perm_0, x = key_states_235_cast_fp16)[name = string("transpose_188")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_24_cast_fp16 = slice_update(begin = concat_281, begin_mask = key_cache_internal_tensor_assign_24_begin_mask_0, end = concat_282, end_mask = key_cache_internal_tensor_assign_24_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_24_squeeze_mask_0, stride = key_cache_internal_tensor_assign_24_stride_0, update = key_states_237_cast_fp16, x = coreml_update_state_156)[name = string("key_cache_internal_tensor_assign_24_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_24_cast_fp16, input = key_cache)[name = string("coreml_update_state_158_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_158 = read_state(input = key_cache)[name = string("coreml_update_state_158")];
            tensor<int32, [4]> value_states_141_perm_0 = const()[name = string("value_states_141_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_24_stride_0 = const()[name = string("value_cache_internal_tensor_assign_24_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_24_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_24_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_24_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_24_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_24_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_24_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_141_cast_fp16 = transpose(perm = value_states_141_perm_0, x = var_8377_cast_fp16)[name = string("transpose_187")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_24_cast_fp16 = slice_update(begin = concat_281, begin_mask = value_cache_internal_tensor_assign_24_begin_mask_0, end = concat_282, end_mask = value_cache_internal_tensor_assign_24_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_24_squeeze_mask_0, stride = value_cache_internal_tensor_assign_24_stride_0, update = value_states_141_cast_fp16, x = coreml_update_state_157)[name = string("value_cache_internal_tensor_assign_24_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_24_cast_fp16, input = value_cache)[name = string("coreml_update_state_159_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_159 = read_state(input = value_cache)[name = string("coreml_update_state_159")];
            tensor<int32, [4]> var_8471_begin_0 = const()[name = string("op_8471_begin_0"), val = tensor<int32, [4]>([23, 0, 0, 0])];
            tensor<int32, [4]> var_8471_end_0 = const()[name = string("op_8471_end_0"), val = tensor<int32, [4]>([24, 2, 2048, 128])];
            tensor<bool, [4]> var_8471_end_mask_0 = const()[name = string("op_8471_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8471_cast_fp16 = slice_by_index(begin = var_8471_begin_0, end = var_8471_end_0, end_mask = var_8471_end_mask_0, x = coreml_update_state_158)[name = string("op_8471_cast_fp16")];
            tensor<int32, [2]> tile_46 = const()[name = string("tile_46"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8474_axis_0 = const()[name = string("op_8474_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8474_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8474_cast_fp16_1 = split(axis = var_8474_axis_0, split_sizes = tile_46, x = var_8471_cast_fp16)[name = string("op_8474_cast_fp16")];
            tensor<int32, [4]> var_8481_begin_0 = const()[name = string("op_8481_begin_0"), val = tensor<int32, [4]>([23, 0, 0, 0])];
            tensor<int32, [4]> var_8481_end_0 = const()[name = string("op_8481_end_0"), val = tensor<int32, [4]>([24, 2, 2048, 128])];
            tensor<bool, [4]> var_8481_end_mask_0 = const()[name = string("op_8481_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8481_cast_fp16 = slice_by_index(begin = var_8481_begin_0, end = var_8481_end_0, end_mask = var_8481_end_mask_0, x = coreml_update_state_159)[name = string("op_8481_cast_fp16")];
            tensor<int32, [2]> tile_47 = const()[name = string("tile_47"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8484_axis_0 = const()[name = string("op_8484_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8484_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8484_cast_fp16_1 = split(axis = var_8484_axis_0, split_sizes = tile_47, x = var_8481_cast_fp16)[name = string("op_8484_cast_fp16")];
            tensor<int32, [2]> var_8487_split_sizes_0 = const()[name = string("op_8487_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_8487_axis_0 = const()[name = string("op_8487_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_8487_0, tensor<fp16, [1, 8, 128, 8]> var_8487_1 = split(axis = var_8487_axis_0, split_sizes = var_8487_split_sizes_0, x = query_states_141_cast_fp16)[name = string("op_8487")];
            bool attn_weights_369_transpose_x_0 = const()[name = string("attn_weights_369_transpose_x_0"), val = bool(false)];
            bool attn_weights_369_transpose_y_0 = const()[name = string("attn_weights_369_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_369_cast_fp16 = matmul(transpose_x = attn_weights_369_transpose_x_0, transpose_y = attn_weights_369_transpose_y_0, x = var_8474_cast_fp16_0, y = var_8487_0)[name = string("attn_weights_369_cast_fp16")];
            fp16 var_8490_to_fp16 = const()[name = string("op_8490_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_371_cast_fp16 = mul(x = attn_weights_369_cast_fp16, y = var_8490_to_fp16)[name = string("attn_weights_371_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_373_cast_fp16 = add(x = attn_weights_371_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_373_cast_fp16")];
            int32 var_8494 = const()[name = string("op_8494"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_375_cast_fp16 = softmax(axis = var_8494, x = attn_weights_373_cast_fp16)[name = string("attn_weights_375_cast_fp16")];
            bool var_8500_transpose_x_1 = const()[name = string("op_8500_transpose_x_1"), val = bool(true)];
            bool var_8500_transpose_y_1 = const()[name = string("op_8500_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_8500_cast_fp16 = matmul(transpose_x = var_8500_transpose_x_1, transpose_y = var_8500_transpose_y_1, x = attn_weights_375_cast_fp16, y = var_8484_cast_fp16_0)[name = string("op_8500_cast_fp16")];
            bool attn_weights_377_transpose_x_0 = const()[name = string("attn_weights_377_transpose_x_0"), val = bool(false)];
            bool attn_weights_377_transpose_y_0 = const()[name = string("attn_weights_377_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_377_cast_fp16 = matmul(transpose_x = attn_weights_377_transpose_x_0, transpose_y = attn_weights_377_transpose_y_0, x = var_8474_cast_fp16_1, y = var_8487_1)[name = string("attn_weights_377_cast_fp16")];
            fp16 var_8502_to_fp16 = const()[name = string("op_8502_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_379_cast_fp16 = mul(x = attn_weights_377_cast_fp16, y = var_8502_to_fp16)[name = string("attn_weights_379_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_381_cast_fp16 = add(x = attn_weights_379_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_381_cast_fp16")];
            int32 var_8506 = const()[name = string("op_8506"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_383_cast_fp16 = softmax(axis = var_8506, x = attn_weights_381_cast_fp16)[name = string("attn_weights_383_cast_fp16")];
            bool attn_output_185_transpose_x_1 = const()[name = string("attn_output_185_transpose_x_1"), val = bool(true)];
            bool attn_output_185_transpose_y_1 = const()[name = string("attn_output_185_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_185_cast_fp16 = matmul(transpose_x = attn_output_185_transpose_x_1, transpose_y = attn_output_185_transpose_y_1, x = attn_weights_383_cast_fp16, y = var_8484_cast_fp16_1)[name = string("attn_output_185_cast_fp16")];
            int32 var_8514 = const()[name = string("op_8514"), val = int32(1)];
            bool attn_output_187_interleave_0 = const()[name = string("attn_output_187_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_187_cast_fp16 = concat(axis = var_8514, interleave = attn_output_187_interleave_0, values = (var_8500_cast_fp16, attn_output_185_cast_fp16))[name = string("attn_output_187_cast_fp16")];
            tensor<int32, [4]> var_8518_perm_0 = const()[name = string("op_8518_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_287x = const()[name = string("concat_287x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_8518_cast_fp16 = transpose(perm = var_8518_perm_0, x = attn_output_187_cast_fp16)[name = string("transpose_186")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_191_cast_fp16 = reshape(shape = concat_287x, x = var_8518_cast_fp16)[name = string("attn_output_191_cast_fp16")];
            tensor<int32, [2]> hidden_states_233_strides_0 = const()[name = string("hidden_states_233_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_233_pad_type_0 = const()[name = string("hidden_states_233_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_233_pad_0 = const()[name = string("hidden_states_233_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_233_dilations_0 = const()[name = string("hidden_states_233_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_233_groups_0 = const()[name = string("hidden_states_233_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_233_cast_fp16 = conv(dilations = hidden_states_233_dilations_0, groups = hidden_states_233_groups_0, pad = hidden_states_233_pad_0, pad_type = hidden_states_233_pad_type_0, strides = hidden_states_233_strides_0, weight = layers_23_self_attn_o_proj_weight_cast_fp16, x = attn_output_191_cast_fp16)[name = string("hidden_states_233_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_235_cast_fp16 = add(x = hidden_states_229_cast_fp16, y = hidden_states_233_cast_fp16)[name = string("hidden_states_235_cast_fp16")];
            fp16 const_240_promoted_to_fp16 = const()[name = string("const_240_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_8551_cast_fp16 = mul(x = hidden_states_235_cast_fp16, y = const_240_promoted_to_fp16)[name = string("op_8551_cast_fp16")];
            int32 var_8549 = const()[name = string("op_8549"), val = int32(1)];
            bool doubled_189_interleave_0 = const()[name = string("doubled_189_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_189_cast_fp16 = concat(axis = var_8549, interleave = doubled_189_interleave_0, values = (hidden_states_235_cast_fp16, var_8551_cast_fp16))[name = string("doubled_189_cast_fp16")];
            tensor<int32, [1]> out_95_axes_0 = const()[name = string("out_95_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_95_gamma_0_to_fp16 = const()[name = string("out_95_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1530916864)))];
            fp16 var_8561_to_fp16 = const()[name = string("op_8561_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_95_cast_fp16 = layer_norm(axes = out_95_axes_0, epsilon = var_8561_to_fp16, gamma = out_95_gamma_0_to_fp16, x = doubled_189_cast_fp16)[name = string("out_95_cast_fp16")];
            tensor<int32, [2]> var_8572_split_sizes_0 = const()[name = string("op_8572_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8572_axis_0 = const()[name = string("op_8572_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_8572_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_8572_cast_fp16_1 = split(axis = var_8572_axis_0, split_sizes = var_8572_split_sizes_0, x = out_95_cast_fp16)[name = string("op_8572_cast_fp16")];
            tensor<int32, [2]> input_47_strides_0 = const()[name = string("input_47_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_47_pad_type_0 = const()[name = string("input_47_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_47_dilations_0 = const()[name = string("input_47_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_47_groups_0 = const()[name = string("input_47_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_47_cast_fp16 = conv(dilations = input_47_dilations_0, groups = input_47_groups_0, pad = input_47_pad_0, pad_type = input_47_pad_type_0, strides = input_47_strides_0, weight = layers_23_mlp_gate_proj_weight_cast_fp16, x = var_8572_cast_fp16_0)[name = string("input_47_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_8589_cast_fp16 = silu(x = input_47_cast_fp16)[name = string("op_8589_cast_fp16")];
            tensor<int32, [2]> var_8595_strides_0 = const()[name = string("op_8595_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_8595_pad_type_0 = const()[name = string("op_8595_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_8595_pad_0 = const()[name = string("op_8595_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_8595_dilations_0 = const()[name = string("op_8595_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8595_groups_0 = const()[name = string("op_8595_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_8595_cast_fp16 = conv(dilations = var_8595_dilations_0, groups = var_8595_groups_0, pad = var_8595_pad_0, pad_type = var_8595_pad_type_0, strides = var_8595_strides_0, weight = layers_23_mlp_up_proj_weight_cast_fp16, x = var_8572_cast_fp16_0)[name = string("op_8595_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_239_cast_fp16 = mul(x = var_8589_cast_fp16, y = var_8595_cast_fp16)[name = string("x_239_cast_fp16")];
            tensor<int32, [2]> hidden_states_237_strides_0 = const()[name = string("hidden_states_237_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_237_pad_type_0 = const()[name = string("hidden_states_237_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_237_pad_0 = const()[name = string("hidden_states_237_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_237_dilations_0 = const()[name = string("hidden_states_237_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_237_groups_0 = const()[name = string("hidden_states_237_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_237_cast_fp16 = conv(dilations = hidden_states_237_dilations_0, groups = hidden_states_237_groups_0, pad = hidden_states_237_pad_0, pad_type = hidden_states_237_pad_type_0, strides = hidden_states_237_strides_0, weight = layers_23_mlp_down_proj_weight_cast_fp16, x = x_239_cast_fp16)[name = string("hidden_states_237_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_239_cast_fp16 = add(x = hidden_states_235_cast_fp16, y = hidden_states_237_cast_fp16)[name = string("hidden_states_239_cast_fp16")];
            fp16 const_242_promoted_to_fp16 = const()[name = string("const_242_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_8613_cast_fp16 = mul(x = hidden_states_239_cast_fp16, y = const_242_promoted_to_fp16)[name = string("op_8613_cast_fp16")];
            int32 var_8611 = const()[name = string("op_8611"), val = int32(1)];
            bool doubled_193_interleave_0 = const()[name = string("doubled_193_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_193_cast_fp16 = concat(axis = var_8611, interleave = doubled_193_interleave_0, values = (hidden_states_239_cast_fp16, var_8613_cast_fp16))[name = string("doubled_193_cast_fp16")];
            tensor<int32, [1]> out_97_axes_0 = const()[name = string("out_97_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_97_gamma_0_to_fp16 = const()[name = string("out_97_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1530925120)))];
            fp16 var_8623_to_fp16 = const()[name = string("op_8623_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_97_cast_fp16 = layer_norm(axes = out_97_axes_0, epsilon = var_8623_to_fp16, gamma = out_97_gamma_0_to_fp16, x = doubled_193_cast_fp16)[name = string("out_97_cast_fp16")];
            tensor<int32, [2]> var_8634_split_sizes_0 = const()[name = string("op_8634_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8634_axis_0 = const()[name = string("op_8634_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_8634_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_8634_cast_fp16_1 = split(axis = var_8634_axis_0, split_sizes = var_8634_split_sizes_0, x = out_97_cast_fp16)[name = string("op_8634_cast_fp16")];
            tensor<int32, [2]> query_states_145_strides_0 = const()[name = string("query_states_145_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_145_pad_type_0 = const()[name = string("query_states_145_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_145_pad_0 = const()[name = string("query_states_145_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_145_dilations_0 = const()[name = string("query_states_145_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_145_groups_0 = const()[name = string("query_states_145_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_145_cast_fp16 = conv(dilations = query_states_145_dilations_0, groups = query_states_145_groups_0, pad = query_states_145_pad_0, pad_type = query_states_145_pad_type_0, strides = query_states_145_strides_0, weight = layers_24_self_attn_q_proj_weight_cast_fp16, x = var_8634_cast_fp16_0)[name = string("query_states_145_cast_fp16")];
            tensor<int32, [2]> key_states_241_strides_0 = const()[name = string("key_states_241_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_241_pad_type_0 = const()[name = string("key_states_241_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_241_pad_0 = const()[name = string("key_states_241_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_241_dilations_0 = const()[name = string("key_states_241_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_241_groups_0 = const()[name = string("key_states_241_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_241_cast_fp16 = conv(dilations = key_states_241_dilations_0, groups = key_states_241_groups_0, pad = key_states_241_pad_0, pad_type = key_states_241_pad_type_0, strides = key_states_241_strides_0, weight = layers_24_self_attn_k_proj_weight_cast_fp16, x = var_8634_cast_fp16_0)[name = string("key_states_241_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_24_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_24_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1530933376)))];
            tensor<int32, [2]> value_states_145_strides_0 = const()[name = string("value_states_145_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_145_pad_type_0 = const()[name = string("value_states_145_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_145_pad_0 = const()[name = string("value_states_145_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_145_dilations_0 = const()[name = string("value_states_145_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_145_groups_0 = const()[name = string("value_states_145_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_145_cast_fp16 = conv(dilations = value_states_145_dilations_0, groups = value_states_145_groups_0, pad = value_states_145_pad_0, pad_type = value_states_145_pad_type_0, strides = value_states_145_strides_0, weight = layers_24_self_attn_v_proj_weight_to_fp16, x = var_8634_cast_fp16_0)[name = string("value_states_145_cast_fp16")];
            tensor<int32, [4]> concat_288x = const()[name = string("concat_288x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_241_cast_fp16 = reshape(shape = concat_288x, x = query_states_145_cast_fp16)[name = string("x_241_cast_fp16")];
            tensor<int32, [4]> concat_289x = const()[name = string("concat_289x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_8691_cast_fp16 = reshape(shape = concat_289x, x = key_states_241_cast_fp16)[name = string("op_8691_cast_fp16")];
            tensor<int32, [4]> concat_290x = const()[name = string("concat_290x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_8698_cast_fp16 = reshape(shape = concat_290x, x = value_states_145_cast_fp16)[name = string("op_8698_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_8702_cast_fp16 = mul(x = x_241_cast_fp16, y = var_859_cast_fp16)[name = string("op_8702_cast_fp16")];
            tensor<int32, [2]> var_8703_split_sizes_0 = const()[name = string("op_8703_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8703_axis_0 = const()[name = string("op_8703_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_8703_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_8703_cast_fp16_1 = split(axis = var_8703_axis_0, split_sizes = var_8703_split_sizes_0, x = x_241_cast_fp16)[name = string("op_8703_cast_fp16")];
            fp16 const_244_promoted_to_fp16 = const()[name = string("const_244_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_8705_cast_fp16 = mul(x = var_8703_cast_fp16_1, y = const_244_promoted_to_fp16)[name = string("op_8705_cast_fp16")];
            int32 var_8707 = const()[name = string("op_8707"), val = int32(-2)];
            bool var_8708_interleave_0 = const()[name = string("op_8708_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_8708_cast_fp16 = concat(axis = var_8707, interleave = var_8708_interleave_0, values = (var_8705_cast_fp16, var_8703_cast_fp16_0))[name = string("op_8708_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_8709_cast_fp16 = mul(x = var_8708_cast_fp16, y = var_866_cast_fp16)[name = string("op_8709_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_147_cast_fp16 = add(x = var_8702_cast_fp16, y = var_8709_cast_fp16)[name = string("query_states_147_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_8715_cast_fp16 = mul(x = var_8691_cast_fp16, y = var_859_cast_fp16)[name = string("op_8715_cast_fp16")];
            tensor<int32, [2]> var_8716_split_sizes_0 = const()[name = string("op_8716_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8716_axis_0 = const()[name = string("op_8716_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_8716_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_8716_cast_fp16_1 = split(axis = var_8716_axis_0, split_sizes = var_8716_split_sizes_0, x = var_8691_cast_fp16)[name = string("op_8716_cast_fp16")];
            fp16 const_245_promoted_to_fp16 = const()[name = string("const_245_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_8718_cast_fp16 = mul(x = var_8716_cast_fp16_1, y = const_245_promoted_to_fp16)[name = string("op_8718_cast_fp16")];
            int32 var_8720 = const()[name = string("op_8720"), val = int32(-2)];
            bool var_8721_interleave_0 = const()[name = string("op_8721_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_8721_cast_fp16 = concat(axis = var_8720, interleave = var_8721_interleave_0, values = (var_8718_cast_fp16, var_8716_cast_fp16_0))[name = string("op_8721_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_8722_cast_fp16 = mul(x = var_8721_cast_fp16, y = var_866_cast_fp16)[name = string("op_8722_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_245_cast_fp16 = add(x = var_8715_cast_fp16, y = var_8722_cast_fp16)[name = string("key_states_245_cast_fp16")];
            tensor<int32, [1]> expand_dims_288 = const()[name = string("expand_dims_288"), val = tensor<int32, [1]>([24])];
            tensor<int32, [1]> expand_dims_289 = const()[name = string("expand_dims_289"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_291 = const()[name = string("expand_dims_291"), val = tensor<int32, [1]>([0])];
            int32 concat_293_axis_0 = const()[name = string("concat_293_axis_0"), val = int32(0)];
            bool concat_293_interleave_0 = const()[name = string("concat_293_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_293 = concat(axis = concat_293_axis_0, interleave = concat_293_interleave_0, values = (expand_dims_288, expand_dims_289, position_id, expand_dims_291))[name = string("concat_293")];
            tensor<int32, [1]> expand_dims_292 = const()[name = string("expand_dims_292"), val = tensor<int32, [1]>([25])];
            tensor<int32, [1]> concat_294_values1_0 = const()[name = string("concat_294_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_294_values3_0 = const()[name = string("concat_294_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_294_axis_0 = const()[name = string("concat_294_axis_0"), val = int32(0)];
            bool concat_294_interleave_0 = const()[name = string("concat_294_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_294 = concat(axis = concat_294_axis_0, interleave = concat_294_interleave_0, values = (expand_dims_292, concat_294_values1_0, cache_position_end, concat_294_values3_0))[name = string("concat_294")];
            tensor<int32, [4]> key_states_247_perm_0 = const()[name = string("key_states_247_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_25_stride_0 = const()[name = string("key_cache_internal_tensor_assign_25_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_25_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_25_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_25_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_25_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_25_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_25_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_247_cast_fp16 = transpose(perm = key_states_247_perm_0, x = key_states_245_cast_fp16)[name = string("transpose_185")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_25_cast_fp16 = slice_update(begin = concat_293, begin_mask = key_cache_internal_tensor_assign_25_begin_mask_0, end = concat_294, end_mask = key_cache_internal_tensor_assign_25_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_25_squeeze_mask_0, stride = key_cache_internal_tensor_assign_25_stride_0, update = key_states_247_cast_fp16, x = coreml_update_state_158)[name = string("key_cache_internal_tensor_assign_25_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_25_cast_fp16, input = key_cache)[name = string("coreml_update_state_160_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_160 = read_state(input = key_cache)[name = string("coreml_update_state_160")];
            tensor<int32, [4]> value_states_147_perm_0 = const()[name = string("value_states_147_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_25_stride_0 = const()[name = string("value_cache_internal_tensor_assign_25_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_25_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_25_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_25_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_25_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_25_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_25_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_147_cast_fp16 = transpose(perm = value_states_147_perm_0, x = var_8698_cast_fp16)[name = string("transpose_184")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_25_cast_fp16 = slice_update(begin = concat_293, begin_mask = value_cache_internal_tensor_assign_25_begin_mask_0, end = concat_294, end_mask = value_cache_internal_tensor_assign_25_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_25_squeeze_mask_0, stride = value_cache_internal_tensor_assign_25_stride_0, update = value_states_147_cast_fp16, x = coreml_update_state_159)[name = string("value_cache_internal_tensor_assign_25_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_25_cast_fp16, input = value_cache)[name = string("coreml_update_state_161_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_161 = read_state(input = value_cache)[name = string("coreml_update_state_161")];
            tensor<int32, [4]> var_8792_begin_0 = const()[name = string("op_8792_begin_0"), val = tensor<int32, [4]>([24, 0, 0, 0])];
            tensor<int32, [4]> var_8792_end_0 = const()[name = string("op_8792_end_0"), val = tensor<int32, [4]>([25, 2, 2048, 128])];
            tensor<bool, [4]> var_8792_end_mask_0 = const()[name = string("op_8792_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8792_cast_fp16 = slice_by_index(begin = var_8792_begin_0, end = var_8792_end_0, end_mask = var_8792_end_mask_0, x = coreml_update_state_160)[name = string("op_8792_cast_fp16")];
            tensor<int32, [2]> tile_48 = const()[name = string("tile_48"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8795_axis_0 = const()[name = string("op_8795_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8795_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8795_cast_fp16_1 = split(axis = var_8795_axis_0, split_sizes = tile_48, x = var_8792_cast_fp16)[name = string("op_8795_cast_fp16")];
            tensor<int32, [4]> var_8802_begin_0 = const()[name = string("op_8802_begin_0"), val = tensor<int32, [4]>([24, 0, 0, 0])];
            tensor<int32, [4]> var_8802_end_0 = const()[name = string("op_8802_end_0"), val = tensor<int32, [4]>([25, 2, 2048, 128])];
            tensor<bool, [4]> var_8802_end_mask_0 = const()[name = string("op_8802_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8802_cast_fp16 = slice_by_index(begin = var_8802_begin_0, end = var_8802_end_0, end_mask = var_8802_end_mask_0, x = coreml_update_state_161)[name = string("op_8802_cast_fp16")];
            tensor<int32, [2]> tile_49 = const()[name = string("tile_49"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8805_axis_0 = const()[name = string("op_8805_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8805_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8805_cast_fp16_1 = split(axis = var_8805_axis_0, split_sizes = tile_49, x = var_8802_cast_fp16)[name = string("op_8805_cast_fp16")];
            tensor<int32, [2]> var_8808_split_sizes_0 = const()[name = string("op_8808_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_8808_axis_0 = const()[name = string("op_8808_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_8808_0, tensor<fp16, [1, 8, 128, 8]> var_8808_1 = split(axis = var_8808_axis_0, split_sizes = var_8808_split_sizes_0, x = query_states_147_cast_fp16)[name = string("op_8808")];
            bool attn_weights_385_transpose_x_0 = const()[name = string("attn_weights_385_transpose_x_0"), val = bool(false)];
            bool attn_weights_385_transpose_y_0 = const()[name = string("attn_weights_385_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_385_cast_fp16 = matmul(transpose_x = attn_weights_385_transpose_x_0, transpose_y = attn_weights_385_transpose_y_0, x = var_8795_cast_fp16_0, y = var_8808_0)[name = string("attn_weights_385_cast_fp16")];
            fp16 var_8811_to_fp16 = const()[name = string("op_8811_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_387_cast_fp16 = mul(x = attn_weights_385_cast_fp16, y = var_8811_to_fp16)[name = string("attn_weights_387_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_389_cast_fp16 = add(x = attn_weights_387_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_389_cast_fp16")];
            int32 var_8815 = const()[name = string("op_8815"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_391_cast_fp16 = softmax(axis = var_8815, x = attn_weights_389_cast_fp16)[name = string("attn_weights_391_cast_fp16")];
            bool var_8821_transpose_x_1 = const()[name = string("op_8821_transpose_x_1"), val = bool(true)];
            bool var_8821_transpose_y_1 = const()[name = string("op_8821_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_8821_cast_fp16 = matmul(transpose_x = var_8821_transpose_x_1, transpose_y = var_8821_transpose_y_1, x = attn_weights_391_cast_fp16, y = var_8805_cast_fp16_0)[name = string("op_8821_cast_fp16")];
            bool attn_weights_393_transpose_x_0 = const()[name = string("attn_weights_393_transpose_x_0"), val = bool(false)];
            bool attn_weights_393_transpose_y_0 = const()[name = string("attn_weights_393_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_393_cast_fp16 = matmul(transpose_x = attn_weights_393_transpose_x_0, transpose_y = attn_weights_393_transpose_y_0, x = var_8795_cast_fp16_1, y = var_8808_1)[name = string("attn_weights_393_cast_fp16")];
            fp16 var_8823_to_fp16 = const()[name = string("op_8823_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_395_cast_fp16 = mul(x = attn_weights_393_cast_fp16, y = var_8823_to_fp16)[name = string("attn_weights_395_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_397_cast_fp16 = add(x = attn_weights_395_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_397_cast_fp16")];
            int32 var_8827 = const()[name = string("op_8827"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_399_cast_fp16 = softmax(axis = var_8827, x = attn_weights_397_cast_fp16)[name = string("attn_weights_399_cast_fp16")];
            bool attn_output_193_transpose_x_1 = const()[name = string("attn_output_193_transpose_x_1"), val = bool(true)];
            bool attn_output_193_transpose_y_1 = const()[name = string("attn_output_193_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_193_cast_fp16 = matmul(transpose_x = attn_output_193_transpose_x_1, transpose_y = attn_output_193_transpose_y_1, x = attn_weights_399_cast_fp16, y = var_8805_cast_fp16_1)[name = string("attn_output_193_cast_fp16")];
            int32 var_8835 = const()[name = string("op_8835"), val = int32(1)];
            bool attn_output_195_interleave_0 = const()[name = string("attn_output_195_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_195_cast_fp16 = concat(axis = var_8835, interleave = attn_output_195_interleave_0, values = (var_8821_cast_fp16, attn_output_193_cast_fp16))[name = string("attn_output_195_cast_fp16")];
            tensor<int32, [4]> var_8839_perm_0 = const()[name = string("op_8839_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_299x = const()[name = string("concat_299x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_8839_cast_fp16 = transpose(perm = var_8839_perm_0, x = attn_output_195_cast_fp16)[name = string("transpose_183")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_199_cast_fp16 = reshape(shape = concat_299x, x = var_8839_cast_fp16)[name = string("attn_output_199_cast_fp16")];
            tensor<int32, [2]> hidden_states_243_strides_0 = const()[name = string("hidden_states_243_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_243_pad_type_0 = const()[name = string("hidden_states_243_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_243_pad_0 = const()[name = string("hidden_states_243_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_243_dilations_0 = const()[name = string("hidden_states_243_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_243_groups_0 = const()[name = string("hidden_states_243_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_243_cast_fp16 = conv(dilations = hidden_states_243_dilations_0, groups = hidden_states_243_groups_0, pad = hidden_states_243_pad_0, pad_type = hidden_states_243_pad_type_0, strides = hidden_states_243_strides_0, weight = layers_24_self_attn_o_proj_weight_cast_fp16, x = attn_output_199_cast_fp16)[name = string("hidden_states_243_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_245_cast_fp16 = add(x = hidden_states_239_cast_fp16, y = hidden_states_243_cast_fp16)[name = string("hidden_states_245_cast_fp16")];
            fp16 const_250_promoted_to_fp16 = const()[name = string("const_250_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_8872_cast_fp16 = mul(x = hidden_states_245_cast_fp16, y = const_250_promoted_to_fp16)[name = string("op_8872_cast_fp16")];
            int32 var_8870 = const()[name = string("op_8870"), val = int32(1)];
            bool doubled_197_interleave_0 = const()[name = string("doubled_197_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_197_cast_fp16 = concat(axis = var_8870, interleave = doubled_197_interleave_0, values = (hidden_states_245_cast_fp16, var_8872_cast_fp16))[name = string("doubled_197_cast_fp16")];
            tensor<int32, [1]> out_99_axes_0 = const()[name = string("out_99_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_99_gamma_0_to_fp16 = const()[name = string("out_99_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1531982016)))];
            fp16 var_8882_to_fp16 = const()[name = string("op_8882_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_99_cast_fp16 = layer_norm(axes = out_99_axes_0, epsilon = var_8882_to_fp16, gamma = out_99_gamma_0_to_fp16, x = doubled_197_cast_fp16)[name = string("out_99_cast_fp16")];
            tensor<int32, [2]> var_8893_split_sizes_0 = const()[name = string("op_8893_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8893_axis_0 = const()[name = string("op_8893_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_8893_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_8893_cast_fp16_1 = split(axis = var_8893_axis_0, split_sizes = var_8893_split_sizes_0, x = out_99_cast_fp16)[name = string("op_8893_cast_fp16")];
            tensor<int32, [2]> input_49_strides_0 = const()[name = string("input_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_49_pad_type_0 = const()[name = string("input_49_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_49_pad_0 = const()[name = string("input_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_49_dilations_0 = const()[name = string("input_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_49_groups_0 = const()[name = string("input_49_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_49_cast_fp16 = conv(dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = layers_24_mlp_gate_proj_weight_cast_fp16, x = var_8893_cast_fp16_0)[name = string("input_49_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_8910_cast_fp16 = silu(x = input_49_cast_fp16)[name = string("op_8910_cast_fp16")];
            tensor<int32, [2]> var_8916_strides_0 = const()[name = string("op_8916_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_8916_pad_type_0 = const()[name = string("op_8916_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_8916_pad_0 = const()[name = string("op_8916_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_8916_dilations_0 = const()[name = string("op_8916_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8916_groups_0 = const()[name = string("op_8916_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_8916_cast_fp16 = conv(dilations = var_8916_dilations_0, groups = var_8916_groups_0, pad = var_8916_pad_0, pad_type = var_8916_pad_type_0, strides = var_8916_strides_0, weight = layers_24_mlp_up_proj_weight_cast_fp16, x = var_8893_cast_fp16_0)[name = string("op_8916_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_249_cast_fp16 = mul(x = var_8910_cast_fp16, y = var_8916_cast_fp16)[name = string("x_249_cast_fp16")];
            tensor<int32, [2]> hidden_states_247_strides_0 = const()[name = string("hidden_states_247_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_247_pad_type_0 = const()[name = string("hidden_states_247_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_247_pad_0 = const()[name = string("hidden_states_247_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_247_dilations_0 = const()[name = string("hidden_states_247_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_247_groups_0 = const()[name = string("hidden_states_247_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_247_cast_fp16 = conv(dilations = hidden_states_247_dilations_0, groups = hidden_states_247_groups_0, pad = hidden_states_247_pad_0, pad_type = hidden_states_247_pad_type_0, strides = hidden_states_247_strides_0, weight = layers_24_mlp_down_proj_weight_cast_fp16, x = x_249_cast_fp16)[name = string("hidden_states_247_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_249_cast_fp16 = add(x = hidden_states_245_cast_fp16, y = hidden_states_247_cast_fp16)[name = string("hidden_states_249_cast_fp16")];
            fp16 const_252_promoted_to_fp16 = const()[name = string("const_252_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_8934_cast_fp16 = mul(x = hidden_states_249_cast_fp16, y = const_252_promoted_to_fp16)[name = string("op_8934_cast_fp16")];
            int32 var_8932 = const()[name = string("op_8932"), val = int32(1)];
            bool doubled_201_interleave_0 = const()[name = string("doubled_201_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_201_cast_fp16 = concat(axis = var_8932, interleave = doubled_201_interleave_0, values = (hidden_states_249_cast_fp16, var_8934_cast_fp16))[name = string("doubled_201_cast_fp16")];
            tensor<int32, [1]> out_101_axes_0 = const()[name = string("out_101_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_101_gamma_0_to_fp16 = const()[name = string("out_101_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1531990272)))];
            fp16 var_8944_to_fp16 = const()[name = string("op_8944_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_101_cast_fp16 = layer_norm(axes = out_101_axes_0, epsilon = var_8944_to_fp16, gamma = out_101_gamma_0_to_fp16, x = doubled_201_cast_fp16)[name = string("out_101_cast_fp16")];
            tensor<int32, [2]> var_8955_split_sizes_0 = const()[name = string("op_8955_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8955_axis_0 = const()[name = string("op_8955_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_8955_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_8955_cast_fp16_1 = split(axis = var_8955_axis_0, split_sizes = var_8955_split_sizes_0, x = out_101_cast_fp16)[name = string("op_8955_cast_fp16")];
            tensor<int32, [2]> query_states_151_strides_0 = const()[name = string("query_states_151_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_151_pad_type_0 = const()[name = string("query_states_151_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_151_pad_0 = const()[name = string("query_states_151_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_151_dilations_0 = const()[name = string("query_states_151_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_151_groups_0 = const()[name = string("query_states_151_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_151_cast_fp16 = conv(dilations = query_states_151_dilations_0, groups = query_states_151_groups_0, pad = query_states_151_pad_0, pad_type = query_states_151_pad_type_0, strides = query_states_151_strides_0, weight = layers_25_self_attn_q_proj_weight_cast_fp16, x = var_8955_cast_fp16_0)[name = string("query_states_151_cast_fp16")];
            tensor<int32, [2]> key_states_251_strides_0 = const()[name = string("key_states_251_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_251_pad_type_0 = const()[name = string("key_states_251_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_251_pad_0 = const()[name = string("key_states_251_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_251_dilations_0 = const()[name = string("key_states_251_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_251_groups_0 = const()[name = string("key_states_251_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_251_cast_fp16 = conv(dilations = key_states_251_dilations_0, groups = key_states_251_groups_0, pad = key_states_251_pad_0, pad_type = key_states_251_pad_type_0, strides = key_states_251_strides_0, weight = layers_25_self_attn_k_proj_weight_cast_fp16, x = var_8955_cast_fp16_0)[name = string("key_states_251_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_25_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_25_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1531998528)))];
            tensor<int32, [2]> value_states_151_strides_0 = const()[name = string("value_states_151_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_151_pad_type_0 = const()[name = string("value_states_151_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_151_pad_0 = const()[name = string("value_states_151_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_151_dilations_0 = const()[name = string("value_states_151_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_151_groups_0 = const()[name = string("value_states_151_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_151_cast_fp16 = conv(dilations = value_states_151_dilations_0, groups = value_states_151_groups_0, pad = value_states_151_pad_0, pad_type = value_states_151_pad_type_0, strides = value_states_151_strides_0, weight = layers_25_self_attn_v_proj_weight_to_fp16, x = var_8955_cast_fp16_0)[name = string("value_states_151_cast_fp16")];
            tensor<int32, [4]> concat_300x = const()[name = string("concat_300x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_251_cast_fp16 = reshape(shape = concat_300x, x = query_states_151_cast_fp16)[name = string("x_251_cast_fp16")];
            tensor<int32, [4]> concat_301x = const()[name = string("concat_301x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_9012_cast_fp16 = reshape(shape = concat_301x, x = key_states_251_cast_fp16)[name = string("op_9012_cast_fp16")];
            tensor<int32, [4]> concat_302x = const()[name = string("concat_302x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_9019_cast_fp16 = reshape(shape = concat_302x, x = value_states_151_cast_fp16)[name = string("op_9019_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_9023_cast_fp16 = mul(x = x_251_cast_fp16, y = var_859_cast_fp16)[name = string("op_9023_cast_fp16")];
            tensor<int32, [2]> var_9024_split_sizes_0 = const()[name = string("op_9024_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9024_axis_0 = const()[name = string("op_9024_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_9024_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_9024_cast_fp16_1 = split(axis = var_9024_axis_0, split_sizes = var_9024_split_sizes_0, x = x_251_cast_fp16)[name = string("op_9024_cast_fp16")];
            fp16 const_254_promoted_to_fp16 = const()[name = string("const_254_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_9026_cast_fp16 = mul(x = var_9024_cast_fp16_1, y = const_254_promoted_to_fp16)[name = string("op_9026_cast_fp16")];
            int32 var_9028 = const()[name = string("op_9028"), val = int32(-2)];
            bool var_9029_interleave_0 = const()[name = string("op_9029_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_9029_cast_fp16 = concat(axis = var_9028, interleave = var_9029_interleave_0, values = (var_9026_cast_fp16, var_9024_cast_fp16_0))[name = string("op_9029_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_9030_cast_fp16 = mul(x = var_9029_cast_fp16, y = var_866_cast_fp16)[name = string("op_9030_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_153_cast_fp16 = add(x = var_9023_cast_fp16, y = var_9030_cast_fp16)[name = string("query_states_153_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_9036_cast_fp16 = mul(x = var_9012_cast_fp16, y = var_859_cast_fp16)[name = string("op_9036_cast_fp16")];
            tensor<int32, [2]> var_9037_split_sizes_0 = const()[name = string("op_9037_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9037_axis_0 = const()[name = string("op_9037_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_9037_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_9037_cast_fp16_1 = split(axis = var_9037_axis_0, split_sizes = var_9037_split_sizes_0, x = var_9012_cast_fp16)[name = string("op_9037_cast_fp16")];
            fp16 const_255_promoted_to_fp16 = const()[name = string("const_255_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_9039_cast_fp16 = mul(x = var_9037_cast_fp16_1, y = const_255_promoted_to_fp16)[name = string("op_9039_cast_fp16")];
            int32 var_9041 = const()[name = string("op_9041"), val = int32(-2)];
            bool var_9042_interleave_0 = const()[name = string("op_9042_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_9042_cast_fp16 = concat(axis = var_9041, interleave = var_9042_interleave_0, values = (var_9039_cast_fp16, var_9037_cast_fp16_0))[name = string("op_9042_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_9043_cast_fp16 = mul(x = var_9042_cast_fp16, y = var_866_cast_fp16)[name = string("op_9043_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_255_cast_fp16 = add(x = var_9036_cast_fp16, y = var_9043_cast_fp16)[name = string("key_states_255_cast_fp16")];
            tensor<int32, [1]> expand_dims_300 = const()[name = string("expand_dims_300"), val = tensor<int32, [1]>([25])];
            tensor<int32, [1]> expand_dims_301 = const()[name = string("expand_dims_301"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_303 = const()[name = string("expand_dims_303"), val = tensor<int32, [1]>([0])];
            int32 concat_305_axis_0 = const()[name = string("concat_305_axis_0"), val = int32(0)];
            bool concat_305_interleave_0 = const()[name = string("concat_305_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_305 = concat(axis = concat_305_axis_0, interleave = concat_305_interleave_0, values = (expand_dims_300, expand_dims_301, position_id, expand_dims_303))[name = string("concat_305")];
            tensor<int32, [1]> expand_dims_304 = const()[name = string("expand_dims_304"), val = tensor<int32, [1]>([26])];
            tensor<int32, [1]> concat_306_values1_0 = const()[name = string("concat_306_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_306_values3_0 = const()[name = string("concat_306_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_306_axis_0 = const()[name = string("concat_306_axis_0"), val = int32(0)];
            bool concat_306_interleave_0 = const()[name = string("concat_306_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_306 = concat(axis = concat_306_axis_0, interleave = concat_306_interleave_0, values = (expand_dims_304, concat_306_values1_0, cache_position_end, concat_306_values3_0))[name = string("concat_306")];
            tensor<int32, [4]> key_states_257_perm_0 = const()[name = string("key_states_257_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_26_stride_0 = const()[name = string("key_cache_internal_tensor_assign_26_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_26_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_26_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_26_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_26_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_26_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_26_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_257_cast_fp16 = transpose(perm = key_states_257_perm_0, x = key_states_255_cast_fp16)[name = string("transpose_182")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_26_cast_fp16 = slice_update(begin = concat_305, begin_mask = key_cache_internal_tensor_assign_26_begin_mask_0, end = concat_306, end_mask = key_cache_internal_tensor_assign_26_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_26_squeeze_mask_0, stride = key_cache_internal_tensor_assign_26_stride_0, update = key_states_257_cast_fp16, x = coreml_update_state_160)[name = string("key_cache_internal_tensor_assign_26_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_26_cast_fp16, input = key_cache)[name = string("coreml_update_state_162_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_162 = read_state(input = key_cache)[name = string("coreml_update_state_162")];
            tensor<int32, [4]> value_states_153_perm_0 = const()[name = string("value_states_153_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_26_stride_0 = const()[name = string("value_cache_internal_tensor_assign_26_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_26_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_26_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_26_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_26_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_26_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_26_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_153_cast_fp16 = transpose(perm = value_states_153_perm_0, x = var_9019_cast_fp16)[name = string("transpose_181")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_26_cast_fp16 = slice_update(begin = concat_305, begin_mask = value_cache_internal_tensor_assign_26_begin_mask_0, end = concat_306, end_mask = value_cache_internal_tensor_assign_26_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_26_squeeze_mask_0, stride = value_cache_internal_tensor_assign_26_stride_0, update = value_states_153_cast_fp16, x = coreml_update_state_161)[name = string("value_cache_internal_tensor_assign_26_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_26_cast_fp16, input = value_cache)[name = string("coreml_update_state_163_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_163 = read_state(input = value_cache)[name = string("coreml_update_state_163")];
            tensor<int32, [4]> var_9113_begin_0 = const()[name = string("op_9113_begin_0"), val = tensor<int32, [4]>([25, 0, 0, 0])];
            tensor<int32, [4]> var_9113_end_0 = const()[name = string("op_9113_end_0"), val = tensor<int32, [4]>([26, 2, 2048, 128])];
            tensor<bool, [4]> var_9113_end_mask_0 = const()[name = string("op_9113_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9113_cast_fp16 = slice_by_index(begin = var_9113_begin_0, end = var_9113_end_0, end_mask = var_9113_end_mask_0, x = coreml_update_state_162)[name = string("op_9113_cast_fp16")];
            tensor<int32, [2]> tile_50 = const()[name = string("tile_50"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9116_axis_0 = const()[name = string("op_9116_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9116_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9116_cast_fp16_1 = split(axis = var_9116_axis_0, split_sizes = tile_50, x = var_9113_cast_fp16)[name = string("op_9116_cast_fp16")];
            tensor<int32, [4]> var_9123_begin_0 = const()[name = string("op_9123_begin_0"), val = tensor<int32, [4]>([25, 0, 0, 0])];
            tensor<int32, [4]> var_9123_end_0 = const()[name = string("op_9123_end_0"), val = tensor<int32, [4]>([26, 2, 2048, 128])];
            tensor<bool, [4]> var_9123_end_mask_0 = const()[name = string("op_9123_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9123_cast_fp16 = slice_by_index(begin = var_9123_begin_0, end = var_9123_end_0, end_mask = var_9123_end_mask_0, x = coreml_update_state_163)[name = string("op_9123_cast_fp16")];
            tensor<int32, [2]> tile_51 = const()[name = string("tile_51"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9126_axis_0 = const()[name = string("op_9126_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9126_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9126_cast_fp16_1 = split(axis = var_9126_axis_0, split_sizes = tile_51, x = var_9123_cast_fp16)[name = string("op_9126_cast_fp16")];
            tensor<int32, [2]> var_9129_split_sizes_0 = const()[name = string("op_9129_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_9129_axis_0 = const()[name = string("op_9129_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_9129_0, tensor<fp16, [1, 8, 128, 8]> var_9129_1 = split(axis = var_9129_axis_0, split_sizes = var_9129_split_sizes_0, x = query_states_153_cast_fp16)[name = string("op_9129")];
            bool attn_weights_401_transpose_x_0 = const()[name = string("attn_weights_401_transpose_x_0"), val = bool(false)];
            bool attn_weights_401_transpose_y_0 = const()[name = string("attn_weights_401_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_401_cast_fp16 = matmul(transpose_x = attn_weights_401_transpose_x_0, transpose_y = attn_weights_401_transpose_y_0, x = var_9116_cast_fp16_0, y = var_9129_0)[name = string("attn_weights_401_cast_fp16")];
            fp16 var_9132_to_fp16 = const()[name = string("op_9132_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_403_cast_fp16 = mul(x = attn_weights_401_cast_fp16, y = var_9132_to_fp16)[name = string("attn_weights_403_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_405_cast_fp16 = add(x = attn_weights_403_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_405_cast_fp16")];
            int32 var_9136 = const()[name = string("op_9136"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_407_cast_fp16 = softmax(axis = var_9136, x = attn_weights_405_cast_fp16)[name = string("attn_weights_407_cast_fp16")];
            bool var_9142_transpose_x_1 = const()[name = string("op_9142_transpose_x_1"), val = bool(true)];
            bool var_9142_transpose_y_1 = const()[name = string("op_9142_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_9142_cast_fp16 = matmul(transpose_x = var_9142_transpose_x_1, transpose_y = var_9142_transpose_y_1, x = attn_weights_407_cast_fp16, y = var_9126_cast_fp16_0)[name = string("op_9142_cast_fp16")];
            bool attn_weights_409_transpose_x_0 = const()[name = string("attn_weights_409_transpose_x_0"), val = bool(false)];
            bool attn_weights_409_transpose_y_0 = const()[name = string("attn_weights_409_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_409_cast_fp16 = matmul(transpose_x = attn_weights_409_transpose_x_0, transpose_y = attn_weights_409_transpose_y_0, x = var_9116_cast_fp16_1, y = var_9129_1)[name = string("attn_weights_409_cast_fp16")];
            fp16 var_9144_to_fp16 = const()[name = string("op_9144_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_411_cast_fp16 = mul(x = attn_weights_409_cast_fp16, y = var_9144_to_fp16)[name = string("attn_weights_411_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_413_cast_fp16 = add(x = attn_weights_411_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_413_cast_fp16")];
            int32 var_9148 = const()[name = string("op_9148"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_415_cast_fp16 = softmax(axis = var_9148, x = attn_weights_413_cast_fp16)[name = string("attn_weights_415_cast_fp16")];
            bool attn_output_201_transpose_x_1 = const()[name = string("attn_output_201_transpose_x_1"), val = bool(true)];
            bool attn_output_201_transpose_y_1 = const()[name = string("attn_output_201_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_201_cast_fp16 = matmul(transpose_x = attn_output_201_transpose_x_1, transpose_y = attn_output_201_transpose_y_1, x = attn_weights_415_cast_fp16, y = var_9126_cast_fp16_1)[name = string("attn_output_201_cast_fp16")];
            int32 var_9156 = const()[name = string("op_9156"), val = int32(1)];
            bool attn_output_203_interleave_0 = const()[name = string("attn_output_203_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_203_cast_fp16 = concat(axis = var_9156, interleave = attn_output_203_interleave_0, values = (var_9142_cast_fp16, attn_output_201_cast_fp16))[name = string("attn_output_203_cast_fp16")];
            tensor<int32, [4]> var_9160_perm_0 = const()[name = string("op_9160_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_311x = const()[name = string("concat_311x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_9160_cast_fp16 = transpose(perm = var_9160_perm_0, x = attn_output_203_cast_fp16)[name = string("transpose_180")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_207_cast_fp16 = reshape(shape = concat_311x, x = var_9160_cast_fp16)[name = string("attn_output_207_cast_fp16")];
            tensor<int32, [2]> hidden_states_253_strides_0 = const()[name = string("hidden_states_253_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_253_pad_type_0 = const()[name = string("hidden_states_253_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_253_pad_0 = const()[name = string("hidden_states_253_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_253_dilations_0 = const()[name = string("hidden_states_253_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_253_groups_0 = const()[name = string("hidden_states_253_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_253_cast_fp16 = conv(dilations = hidden_states_253_dilations_0, groups = hidden_states_253_groups_0, pad = hidden_states_253_pad_0, pad_type = hidden_states_253_pad_type_0, strides = hidden_states_253_strides_0, weight = layers_25_self_attn_o_proj_weight_cast_fp16, x = attn_output_207_cast_fp16)[name = string("hidden_states_253_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_255_cast_fp16 = add(x = hidden_states_249_cast_fp16, y = hidden_states_253_cast_fp16)[name = string("hidden_states_255_cast_fp16")];
            fp16 const_260_promoted_to_fp16 = const()[name = string("const_260_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_9193_cast_fp16 = mul(x = hidden_states_255_cast_fp16, y = const_260_promoted_to_fp16)[name = string("op_9193_cast_fp16")];
            int32 var_9191 = const()[name = string("op_9191"), val = int32(1)];
            bool doubled_205_interleave_0 = const()[name = string("doubled_205_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_205_cast_fp16 = concat(axis = var_9191, interleave = doubled_205_interleave_0, values = (hidden_states_255_cast_fp16, var_9193_cast_fp16))[name = string("doubled_205_cast_fp16")];
            tensor<int32, [1]> out_103_axes_0 = const()[name = string("out_103_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_103_gamma_0_to_fp16 = const()[name = string("out_103_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1533047168)))];
            fp16 var_9203_to_fp16 = const()[name = string("op_9203_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_103_cast_fp16 = layer_norm(axes = out_103_axes_0, epsilon = var_9203_to_fp16, gamma = out_103_gamma_0_to_fp16, x = doubled_205_cast_fp16)[name = string("out_103_cast_fp16")];
            tensor<int32, [2]> var_9214_split_sizes_0 = const()[name = string("op_9214_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9214_axis_0 = const()[name = string("op_9214_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_9214_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_9214_cast_fp16_1 = split(axis = var_9214_axis_0, split_sizes = var_9214_split_sizes_0, x = out_103_cast_fp16)[name = string("op_9214_cast_fp16")];
            tensor<int32, [2]> input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_51_cast_fp16 = conv(dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = layers_25_mlp_gate_proj_weight_cast_fp16, x = var_9214_cast_fp16_0)[name = string("input_51_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_9231_cast_fp16 = silu(x = input_51_cast_fp16)[name = string("op_9231_cast_fp16")];
            tensor<int32, [2]> var_9237_strides_0 = const()[name = string("op_9237_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_9237_pad_type_0 = const()[name = string("op_9237_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_9237_pad_0 = const()[name = string("op_9237_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_9237_dilations_0 = const()[name = string("op_9237_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9237_groups_0 = const()[name = string("op_9237_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_9237_cast_fp16 = conv(dilations = var_9237_dilations_0, groups = var_9237_groups_0, pad = var_9237_pad_0, pad_type = var_9237_pad_type_0, strides = var_9237_strides_0, weight = layers_25_mlp_up_proj_weight_cast_fp16, x = var_9214_cast_fp16_0)[name = string("op_9237_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_259_cast_fp16 = mul(x = var_9231_cast_fp16, y = var_9237_cast_fp16)[name = string("x_259_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_25_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_25_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1533055424)))];
            tensor<int32, [2]> hidden_states_257_strides_0 = const()[name = string("hidden_states_257_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_257_pad_type_0 = const()[name = string("hidden_states_257_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_257_pad_0 = const()[name = string("hidden_states_257_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_257_dilations_0 = const()[name = string("hidden_states_257_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_257_groups_0 = const()[name = string("hidden_states_257_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_257_cast_fp16 = conv(dilations = hidden_states_257_dilations_0, groups = hidden_states_257_groups_0, pad = hidden_states_257_pad_0, pad_type = hidden_states_257_pad_type_0, strides = hidden_states_257_strides_0, weight = layers_25_mlp_down_proj_weight_to_fp16, x = x_259_cast_fp16)[name = string("hidden_states_257_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_259_cast_fp16 = add(x = hidden_states_255_cast_fp16, y = hidden_states_257_cast_fp16)[name = string("hidden_states_259_cast_fp16")];
            fp16 const_262_promoted_to_fp16 = const()[name = string("const_262_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_9255_cast_fp16 = mul(x = hidden_states_259_cast_fp16, y = const_262_promoted_to_fp16)[name = string("op_9255_cast_fp16")];
            int32 var_9253 = const()[name = string("op_9253"), val = int32(1)];
            bool doubled_209_interleave_0 = const()[name = string("doubled_209_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_209_cast_fp16 = concat(axis = var_9253, interleave = doubled_209_interleave_0, values = (hidden_states_259_cast_fp16, var_9255_cast_fp16))[name = string("doubled_209_cast_fp16")];
            tensor<int32, [1]> out_105_axes_0 = const()[name = string("out_105_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_105_gamma_0_to_fp16 = const()[name = string("out_105_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1558221312)))];
            fp16 var_9265_to_fp16 = const()[name = string("op_9265_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_105_cast_fp16 = layer_norm(axes = out_105_axes_0, epsilon = var_9265_to_fp16, gamma = out_105_gamma_0_to_fp16, x = doubled_209_cast_fp16)[name = string("out_105_cast_fp16")];
            tensor<int32, [2]> var_9276_split_sizes_0 = const()[name = string("op_9276_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9276_axis_0 = const()[name = string("op_9276_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_9276_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_9276_cast_fp16_1 = split(axis = var_9276_axis_0, split_sizes = var_9276_split_sizes_0, x = out_105_cast_fp16)[name = string("op_9276_cast_fp16")];
            tensor<int32, [2]> query_states_157_strides_0 = const()[name = string("query_states_157_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_157_pad_type_0 = const()[name = string("query_states_157_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_157_pad_0 = const()[name = string("query_states_157_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_157_dilations_0 = const()[name = string("query_states_157_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_157_groups_0 = const()[name = string("query_states_157_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_157_cast_fp16 = conv(dilations = query_states_157_dilations_0, groups = query_states_157_groups_0, pad = query_states_157_pad_0, pad_type = query_states_157_pad_type_0, strides = query_states_157_strides_0, weight = layers_26_self_attn_q_proj_weight_cast_fp16, x = var_9276_cast_fp16_0)[name = string("query_states_157_cast_fp16")];
            tensor<int32, [2]> key_states_261_strides_0 = const()[name = string("key_states_261_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_261_pad_type_0 = const()[name = string("key_states_261_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_261_pad_0 = const()[name = string("key_states_261_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_261_dilations_0 = const()[name = string("key_states_261_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_261_groups_0 = const()[name = string("key_states_261_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_261_cast_fp16 = conv(dilations = key_states_261_dilations_0, groups = key_states_261_groups_0, pad = key_states_261_pad_0, pad_type = key_states_261_pad_type_0, strides = key_states_261_strides_0, weight = layers_26_self_attn_k_proj_weight_cast_fp16, x = var_9276_cast_fp16_0)[name = string("key_states_261_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_26_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_26_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1558229568)))];
            tensor<int32, [2]> value_states_157_strides_0 = const()[name = string("value_states_157_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_157_pad_type_0 = const()[name = string("value_states_157_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_157_pad_0 = const()[name = string("value_states_157_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_157_dilations_0 = const()[name = string("value_states_157_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_157_groups_0 = const()[name = string("value_states_157_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_157_cast_fp16 = conv(dilations = value_states_157_dilations_0, groups = value_states_157_groups_0, pad = value_states_157_pad_0, pad_type = value_states_157_pad_type_0, strides = value_states_157_strides_0, weight = layers_26_self_attn_v_proj_weight_to_fp16, x = var_9276_cast_fp16_0)[name = string("value_states_157_cast_fp16")];
            tensor<int32, [4]> concat_312x = const()[name = string("concat_312x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_261_cast_fp16 = reshape(shape = concat_312x, x = query_states_157_cast_fp16)[name = string("x_261_cast_fp16")];
            tensor<int32, [4]> concat_313x = const()[name = string("concat_313x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_9333_cast_fp16 = reshape(shape = concat_313x, x = key_states_261_cast_fp16)[name = string("op_9333_cast_fp16")];
            tensor<int32, [4]> concat_314x = const()[name = string("concat_314x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_9340_cast_fp16 = reshape(shape = concat_314x, x = value_states_157_cast_fp16)[name = string("op_9340_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_9344_cast_fp16 = mul(x = x_261_cast_fp16, y = var_859_cast_fp16)[name = string("op_9344_cast_fp16")];
            tensor<int32, [2]> var_9345_split_sizes_0 = const()[name = string("op_9345_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9345_axis_0 = const()[name = string("op_9345_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_9345_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_9345_cast_fp16_1 = split(axis = var_9345_axis_0, split_sizes = var_9345_split_sizes_0, x = x_261_cast_fp16)[name = string("op_9345_cast_fp16")];
            fp16 const_264_promoted_to_fp16 = const()[name = string("const_264_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_9347_cast_fp16 = mul(x = var_9345_cast_fp16_1, y = const_264_promoted_to_fp16)[name = string("op_9347_cast_fp16")];
            int32 var_9349 = const()[name = string("op_9349"), val = int32(-2)];
            bool var_9350_interleave_0 = const()[name = string("op_9350_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_9350_cast_fp16 = concat(axis = var_9349, interleave = var_9350_interleave_0, values = (var_9347_cast_fp16, var_9345_cast_fp16_0))[name = string("op_9350_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_9351_cast_fp16 = mul(x = var_9350_cast_fp16, y = var_866_cast_fp16)[name = string("op_9351_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_159_cast_fp16 = add(x = var_9344_cast_fp16, y = var_9351_cast_fp16)[name = string("query_states_159_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_9357_cast_fp16 = mul(x = var_9333_cast_fp16, y = var_859_cast_fp16)[name = string("op_9357_cast_fp16")];
            tensor<int32, [2]> var_9358_split_sizes_0 = const()[name = string("op_9358_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9358_axis_0 = const()[name = string("op_9358_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_9358_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_9358_cast_fp16_1 = split(axis = var_9358_axis_0, split_sizes = var_9358_split_sizes_0, x = var_9333_cast_fp16)[name = string("op_9358_cast_fp16")];
            fp16 const_265_promoted_to_fp16 = const()[name = string("const_265_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_9360_cast_fp16 = mul(x = var_9358_cast_fp16_1, y = const_265_promoted_to_fp16)[name = string("op_9360_cast_fp16")];
            int32 var_9362 = const()[name = string("op_9362"), val = int32(-2)];
            bool var_9363_interleave_0 = const()[name = string("op_9363_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_9363_cast_fp16 = concat(axis = var_9362, interleave = var_9363_interleave_0, values = (var_9360_cast_fp16, var_9358_cast_fp16_0))[name = string("op_9363_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_9364_cast_fp16 = mul(x = var_9363_cast_fp16, y = var_866_cast_fp16)[name = string("op_9364_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_265_cast_fp16 = add(x = var_9357_cast_fp16, y = var_9364_cast_fp16)[name = string("key_states_265_cast_fp16")];
            tensor<int32, [1]> expand_dims_312 = const()[name = string("expand_dims_312"), val = tensor<int32, [1]>([26])];
            tensor<int32, [1]> expand_dims_313 = const()[name = string("expand_dims_313"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_315 = const()[name = string("expand_dims_315"), val = tensor<int32, [1]>([0])];
            int32 concat_317_axis_0 = const()[name = string("concat_317_axis_0"), val = int32(0)];
            bool concat_317_interleave_0 = const()[name = string("concat_317_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_317 = concat(axis = concat_317_axis_0, interleave = concat_317_interleave_0, values = (expand_dims_312, expand_dims_313, position_id, expand_dims_315))[name = string("concat_317")];
            tensor<int32, [1]> expand_dims_316 = const()[name = string("expand_dims_316"), val = tensor<int32, [1]>([27])];
            tensor<int32, [1]> concat_318_values1_0 = const()[name = string("concat_318_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_318_values3_0 = const()[name = string("concat_318_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_318_axis_0 = const()[name = string("concat_318_axis_0"), val = int32(0)];
            bool concat_318_interleave_0 = const()[name = string("concat_318_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_318 = concat(axis = concat_318_axis_0, interleave = concat_318_interleave_0, values = (expand_dims_316, concat_318_values1_0, cache_position_end, concat_318_values3_0))[name = string("concat_318")];
            tensor<int32, [4]> key_states_267_perm_0 = const()[name = string("key_states_267_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_27_stride_0 = const()[name = string("key_cache_internal_tensor_assign_27_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_27_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_27_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_27_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_27_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_27_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_27_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_267_cast_fp16 = transpose(perm = key_states_267_perm_0, x = key_states_265_cast_fp16)[name = string("transpose_179")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_27_cast_fp16 = slice_update(begin = concat_317, begin_mask = key_cache_internal_tensor_assign_27_begin_mask_0, end = concat_318, end_mask = key_cache_internal_tensor_assign_27_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_27_squeeze_mask_0, stride = key_cache_internal_tensor_assign_27_stride_0, update = key_states_267_cast_fp16, x = coreml_update_state_162)[name = string("key_cache_internal_tensor_assign_27_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_27_cast_fp16, input = key_cache)[name = string("coreml_update_state_164_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_164 = read_state(input = key_cache)[name = string("coreml_update_state_164")];
            tensor<int32, [4]> value_states_159_perm_0 = const()[name = string("value_states_159_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_27_stride_0 = const()[name = string("value_cache_internal_tensor_assign_27_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_27_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_27_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_27_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_27_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_27_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_27_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_159_cast_fp16 = transpose(perm = value_states_159_perm_0, x = var_9340_cast_fp16)[name = string("transpose_178")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_27_cast_fp16 = slice_update(begin = concat_317, begin_mask = value_cache_internal_tensor_assign_27_begin_mask_0, end = concat_318, end_mask = value_cache_internal_tensor_assign_27_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_27_squeeze_mask_0, stride = value_cache_internal_tensor_assign_27_stride_0, update = value_states_159_cast_fp16, x = coreml_update_state_163)[name = string("value_cache_internal_tensor_assign_27_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_27_cast_fp16, input = value_cache)[name = string("coreml_update_state_165_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_165 = read_state(input = value_cache)[name = string("coreml_update_state_165")];
            tensor<int32, [4]> var_9434_begin_0 = const()[name = string("op_9434_begin_0"), val = tensor<int32, [4]>([26, 0, 0, 0])];
            tensor<int32, [4]> var_9434_end_0 = const()[name = string("op_9434_end_0"), val = tensor<int32, [4]>([27, 2, 2048, 128])];
            tensor<bool, [4]> var_9434_end_mask_0 = const()[name = string("op_9434_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9434_cast_fp16 = slice_by_index(begin = var_9434_begin_0, end = var_9434_end_0, end_mask = var_9434_end_mask_0, x = coreml_update_state_164)[name = string("op_9434_cast_fp16")];
            tensor<int32, [2]> tile_52 = const()[name = string("tile_52"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9437_axis_0 = const()[name = string("op_9437_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9437_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9437_cast_fp16_1 = split(axis = var_9437_axis_0, split_sizes = tile_52, x = var_9434_cast_fp16)[name = string("op_9437_cast_fp16")];
            tensor<int32, [4]> var_9444_begin_0 = const()[name = string("op_9444_begin_0"), val = tensor<int32, [4]>([26, 0, 0, 0])];
            tensor<int32, [4]> var_9444_end_0 = const()[name = string("op_9444_end_0"), val = tensor<int32, [4]>([27, 2, 2048, 128])];
            tensor<bool, [4]> var_9444_end_mask_0 = const()[name = string("op_9444_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9444_cast_fp16 = slice_by_index(begin = var_9444_begin_0, end = var_9444_end_0, end_mask = var_9444_end_mask_0, x = coreml_update_state_165)[name = string("op_9444_cast_fp16")];
            tensor<int32, [2]> tile_53 = const()[name = string("tile_53"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9447_axis_0 = const()[name = string("op_9447_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9447_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9447_cast_fp16_1 = split(axis = var_9447_axis_0, split_sizes = tile_53, x = var_9444_cast_fp16)[name = string("op_9447_cast_fp16")];
            tensor<int32, [2]> var_9450_split_sizes_0 = const()[name = string("op_9450_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_9450_axis_0 = const()[name = string("op_9450_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_9450_0, tensor<fp16, [1, 8, 128, 8]> var_9450_1 = split(axis = var_9450_axis_0, split_sizes = var_9450_split_sizes_0, x = query_states_159_cast_fp16)[name = string("op_9450")];
            bool attn_weights_417_transpose_x_0 = const()[name = string("attn_weights_417_transpose_x_0"), val = bool(false)];
            bool attn_weights_417_transpose_y_0 = const()[name = string("attn_weights_417_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_417_cast_fp16 = matmul(transpose_x = attn_weights_417_transpose_x_0, transpose_y = attn_weights_417_transpose_y_0, x = var_9437_cast_fp16_0, y = var_9450_0)[name = string("attn_weights_417_cast_fp16")];
            fp16 var_9453_to_fp16 = const()[name = string("op_9453_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_419_cast_fp16 = mul(x = attn_weights_417_cast_fp16, y = var_9453_to_fp16)[name = string("attn_weights_419_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_421_cast_fp16 = add(x = attn_weights_419_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_421_cast_fp16")];
            int32 var_9457 = const()[name = string("op_9457"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_423_cast_fp16 = softmax(axis = var_9457, x = attn_weights_421_cast_fp16)[name = string("attn_weights_423_cast_fp16")];
            bool var_9463_transpose_x_1 = const()[name = string("op_9463_transpose_x_1"), val = bool(true)];
            bool var_9463_transpose_y_1 = const()[name = string("op_9463_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_9463_cast_fp16 = matmul(transpose_x = var_9463_transpose_x_1, transpose_y = var_9463_transpose_y_1, x = attn_weights_423_cast_fp16, y = var_9447_cast_fp16_0)[name = string("op_9463_cast_fp16")];
            bool attn_weights_425_transpose_x_0 = const()[name = string("attn_weights_425_transpose_x_0"), val = bool(false)];
            bool attn_weights_425_transpose_y_0 = const()[name = string("attn_weights_425_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_425_cast_fp16 = matmul(transpose_x = attn_weights_425_transpose_x_0, transpose_y = attn_weights_425_transpose_y_0, x = var_9437_cast_fp16_1, y = var_9450_1)[name = string("attn_weights_425_cast_fp16")];
            fp16 var_9465_to_fp16 = const()[name = string("op_9465_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_427_cast_fp16 = mul(x = attn_weights_425_cast_fp16, y = var_9465_to_fp16)[name = string("attn_weights_427_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_429_cast_fp16 = add(x = attn_weights_427_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_429_cast_fp16")];
            int32 var_9469 = const()[name = string("op_9469"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_431_cast_fp16 = softmax(axis = var_9469, x = attn_weights_429_cast_fp16)[name = string("attn_weights_431_cast_fp16")];
            bool attn_output_209_transpose_x_1 = const()[name = string("attn_output_209_transpose_x_1"), val = bool(true)];
            bool attn_output_209_transpose_y_1 = const()[name = string("attn_output_209_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_209_cast_fp16 = matmul(transpose_x = attn_output_209_transpose_x_1, transpose_y = attn_output_209_transpose_y_1, x = attn_weights_431_cast_fp16, y = var_9447_cast_fp16_1)[name = string("attn_output_209_cast_fp16")];
            int32 var_9477 = const()[name = string("op_9477"), val = int32(1)];
            bool attn_output_211_interleave_0 = const()[name = string("attn_output_211_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_211_cast_fp16 = concat(axis = var_9477, interleave = attn_output_211_interleave_0, values = (var_9463_cast_fp16, attn_output_209_cast_fp16))[name = string("attn_output_211_cast_fp16")];
            tensor<int32, [4]> var_9481_perm_0 = const()[name = string("op_9481_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_323x = const()[name = string("concat_323x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_9481_cast_fp16 = transpose(perm = var_9481_perm_0, x = attn_output_211_cast_fp16)[name = string("transpose_177")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_215_cast_fp16 = reshape(shape = concat_323x, x = var_9481_cast_fp16)[name = string("attn_output_215_cast_fp16")];
            tensor<int32, [2]> hidden_states_263_strides_0 = const()[name = string("hidden_states_263_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_263_pad_type_0 = const()[name = string("hidden_states_263_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_263_pad_0 = const()[name = string("hidden_states_263_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_263_dilations_0 = const()[name = string("hidden_states_263_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_263_groups_0 = const()[name = string("hidden_states_263_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_263_cast_fp16 = conv(dilations = hidden_states_263_dilations_0, groups = hidden_states_263_groups_0, pad = hidden_states_263_pad_0, pad_type = hidden_states_263_pad_type_0, strides = hidden_states_263_strides_0, weight = layers_26_self_attn_o_proj_weight_cast_fp16, x = attn_output_215_cast_fp16)[name = string("hidden_states_263_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_265_cast_fp16 = add(x = hidden_states_259_cast_fp16, y = hidden_states_263_cast_fp16)[name = string("hidden_states_265_cast_fp16")];
            fp16 const_270_promoted_to_fp16 = const()[name = string("const_270_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_9514_cast_fp16 = mul(x = hidden_states_265_cast_fp16, y = const_270_promoted_to_fp16)[name = string("op_9514_cast_fp16")];
            int32 var_9512 = const()[name = string("op_9512"), val = int32(1)];
            bool doubled_213_interleave_0 = const()[name = string("doubled_213_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_213_cast_fp16 = concat(axis = var_9512, interleave = doubled_213_interleave_0, values = (hidden_states_265_cast_fp16, var_9514_cast_fp16))[name = string("doubled_213_cast_fp16")];
            tensor<int32, [1]> out_107_axes_0 = const()[name = string("out_107_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_107_gamma_0_to_fp16 = const()[name = string("out_107_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1559278208)))];
            fp16 var_9524_to_fp16 = const()[name = string("op_9524_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_107_cast_fp16 = layer_norm(axes = out_107_axes_0, epsilon = var_9524_to_fp16, gamma = out_107_gamma_0_to_fp16, x = doubled_213_cast_fp16)[name = string("out_107_cast_fp16")];
            tensor<int32, [2]> var_9535_split_sizes_0 = const()[name = string("op_9535_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9535_axis_0 = const()[name = string("op_9535_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_9535_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_9535_cast_fp16_1 = split(axis = var_9535_axis_0, split_sizes = var_9535_split_sizes_0, x = out_107_cast_fp16)[name = string("op_9535_cast_fp16")];
            tensor<int32, [2]> input_53_strides_0 = const()[name = string("input_53_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_53_pad_type_0 = const()[name = string("input_53_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_53_dilations_0 = const()[name = string("input_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_53_groups_0 = const()[name = string("input_53_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_53_cast_fp16 = conv(dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = layers_26_mlp_gate_proj_weight_cast_fp16, x = var_9535_cast_fp16_0)[name = string("input_53_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_9552_cast_fp16 = silu(x = input_53_cast_fp16)[name = string("op_9552_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_26_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_26_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1559286464)))];
            tensor<int32, [2]> var_9558_strides_0 = const()[name = string("op_9558_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_9558_pad_type_0 = const()[name = string("op_9558_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_9558_pad_0 = const()[name = string("op_9558_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_9558_dilations_0 = const()[name = string("op_9558_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9558_groups_0 = const()[name = string("op_9558_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_9558_cast_fp16 = conv(dilations = var_9558_dilations_0, groups = var_9558_groups_0, pad = var_9558_pad_0, pad_type = var_9558_pad_type_0, strides = var_9558_strides_0, weight = layers_26_mlp_up_proj_weight_to_fp16, x = var_9535_cast_fp16_0)[name = string("op_9558_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_269_cast_fp16 = mul(x = var_9552_cast_fp16, y = var_9558_cast_fp16)[name = string("x_269_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_26_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_26_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1584452352)))];
            tensor<int32, [2]> hidden_states_267_strides_0 = const()[name = string("hidden_states_267_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_267_pad_type_0 = const()[name = string("hidden_states_267_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_267_pad_0 = const()[name = string("hidden_states_267_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_267_dilations_0 = const()[name = string("hidden_states_267_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_267_groups_0 = const()[name = string("hidden_states_267_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_267_cast_fp16 = conv(dilations = hidden_states_267_dilations_0, groups = hidden_states_267_groups_0, pad = hidden_states_267_pad_0, pad_type = hidden_states_267_pad_type_0, strides = hidden_states_267_strides_0, weight = layers_26_mlp_down_proj_weight_to_fp16, x = x_269_cast_fp16)[name = string("hidden_states_267_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_269_cast_fp16 = add(x = hidden_states_265_cast_fp16, y = hidden_states_267_cast_fp16)[name = string("hidden_states_269_cast_fp16")];
            fp16 const_272_promoted_to_fp16 = const()[name = string("const_272_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_9576_cast_fp16 = mul(x = hidden_states_269_cast_fp16, y = const_272_promoted_to_fp16)[name = string("op_9576_cast_fp16")];
            int32 var_9574 = const()[name = string("op_9574"), val = int32(1)];
            bool doubled_217_interleave_0 = const()[name = string("doubled_217_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_217_cast_fp16 = concat(axis = var_9574, interleave = doubled_217_interleave_0, values = (hidden_states_269_cast_fp16, var_9576_cast_fp16))[name = string("doubled_217_cast_fp16")];
            tensor<int32, [1]> out_109_axes_0 = const()[name = string("out_109_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_109_gamma_0_to_fp16 = const()[name = string("out_109_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1609618240)))];
            fp16 var_9586_to_fp16 = const()[name = string("op_9586_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_109_cast_fp16 = layer_norm(axes = out_109_axes_0, epsilon = var_9586_to_fp16, gamma = out_109_gamma_0_to_fp16, x = doubled_217_cast_fp16)[name = string("out_109_cast_fp16")];
            tensor<int32, [2]> var_9597_split_sizes_0 = const()[name = string("op_9597_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9597_axis_0 = const()[name = string("op_9597_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_9597_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_9597_cast_fp16_1 = split(axis = var_9597_axis_0, split_sizes = var_9597_split_sizes_0, x = out_109_cast_fp16)[name = string("op_9597_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_27_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1609626496)))];
            tensor<int32, [2]> query_states_163_strides_0 = const()[name = string("query_states_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_163_pad_type_0 = const()[name = string("query_states_163_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_163_pad_0 = const()[name = string("query_states_163_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_163_dilations_0 = const()[name = string("query_states_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_163_groups_0 = const()[name = string("query_states_163_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> query_states_163_cast_fp16 = conv(dilations = query_states_163_dilations_0, groups = query_states_163_groups_0, pad = query_states_163_pad_0, pad_type = query_states_163_pad_type_0, strides = query_states_163_strides_0, weight = layers_27_self_attn_q_proj_weight_to_fp16, x = var_9597_cast_fp16_0)[name = string("query_states_163_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_27_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1618015168)))];
            tensor<int32, [2]> key_states_271_strides_0 = const()[name = string("key_states_271_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_271_pad_type_0 = const()[name = string("key_states_271_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_271_pad_0 = const()[name = string("key_states_271_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_271_dilations_0 = const()[name = string("key_states_271_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_271_groups_0 = const()[name = string("key_states_271_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> key_states_271_cast_fp16 = conv(dilations = key_states_271_dilations_0, groups = key_states_271_groups_0, pad = key_states_271_pad_0, pad_type = key_states_271_pad_type_0, strides = key_states_271_strides_0, weight = layers_27_self_attn_k_proj_weight_to_fp16, x = var_9597_cast_fp16_0)[name = string("key_states_271_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_27_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1619063808)))];
            tensor<int32, [2]> value_states_163_strides_0 = const()[name = string("value_states_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_163_pad_type_0 = const()[name = string("value_states_163_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_163_pad_0 = const()[name = string("value_states_163_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_163_dilations_0 = const()[name = string("value_states_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_163_groups_0 = const()[name = string("value_states_163_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 8]> value_states_163_cast_fp16 = conv(dilations = value_states_163_dilations_0, groups = value_states_163_groups_0, pad = value_states_163_pad_0, pad_type = value_states_163_pad_type_0, strides = value_states_163_strides_0, weight = layers_27_self_attn_v_proj_weight_to_fp16, x = var_9597_cast_fp16_0)[name = string("value_states_163_cast_fp16")];
            tensor<int32, [4]> concat_324x = const()[name = string("concat_324x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 8]> x_271_cast_fp16 = reshape(shape = concat_324x, x = query_states_163_cast_fp16)[name = string("x_271_cast_fp16")];
            tensor<int32, [4]> concat_325x = const()[name = string("concat_325x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_9654_cast_fp16 = reshape(shape = concat_325x, x = key_states_271_cast_fp16)[name = string("op_9654_cast_fp16")];
            tensor<int32, [4]> concat_326x = const()[name = string("concat_326x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 8]> var_9661_cast_fp16 = reshape(shape = concat_326x, x = value_states_163_cast_fp16)[name = string("op_9661_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_9665_cast_fp16 = mul(x = x_271_cast_fp16, y = var_859_cast_fp16)[name = string("op_9665_cast_fp16")];
            tensor<int32, [2]> var_9666_split_sizes_0 = const()[name = string("op_9666_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9666_axis_0 = const()[name = string("op_9666_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 8]> var_9666_cast_fp16_0, tensor<fp16, [1, 16, 64, 8]> var_9666_cast_fp16_1 = split(axis = var_9666_axis_0, split_sizes = var_9666_split_sizes_0, x = x_271_cast_fp16)[name = string("op_9666_cast_fp16")];
            fp16 const_274_promoted_to_fp16 = const()[name = string("const_274_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 8]> var_9668_cast_fp16 = mul(x = var_9666_cast_fp16_1, y = const_274_promoted_to_fp16)[name = string("op_9668_cast_fp16")];
            int32 var_9670 = const()[name = string("op_9670"), val = int32(-2)];
            bool var_9671_interleave_0 = const()[name = string("op_9671_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 8]> var_9671_cast_fp16 = concat(axis = var_9670, interleave = var_9671_interleave_0, values = (var_9668_cast_fp16, var_9666_cast_fp16_0))[name = string("op_9671_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> var_9672_cast_fp16 = mul(x = var_9671_cast_fp16, y = var_866_cast_fp16)[name = string("op_9672_cast_fp16")];
            tensor<fp16, [1, 16, 128, 8]> query_states_165_cast_fp16 = add(x = var_9665_cast_fp16, y = var_9672_cast_fp16)[name = string("query_states_165_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_9678_cast_fp16 = mul(x = var_9654_cast_fp16, y = var_859_cast_fp16)[name = string("op_9678_cast_fp16")];
            tensor<int32, [2]> var_9679_split_sizes_0 = const()[name = string("op_9679_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9679_axis_0 = const()[name = string("op_9679_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 8]> var_9679_cast_fp16_0, tensor<fp16, [1, 2, 64, 8]> var_9679_cast_fp16_1 = split(axis = var_9679_axis_0, split_sizes = var_9679_split_sizes_0, x = var_9654_cast_fp16)[name = string("op_9679_cast_fp16")];
            fp16 const_275_promoted_to_fp16 = const()[name = string("const_275_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 8]> var_9681_cast_fp16 = mul(x = var_9679_cast_fp16_1, y = const_275_promoted_to_fp16)[name = string("op_9681_cast_fp16")];
            int32 var_9683 = const()[name = string("op_9683"), val = int32(-2)];
            bool var_9684_interleave_0 = const()[name = string("op_9684_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 8]> var_9684_cast_fp16 = concat(axis = var_9683, interleave = var_9684_interleave_0, values = (var_9681_cast_fp16, var_9679_cast_fp16_0))[name = string("op_9684_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> var_9685_cast_fp16 = mul(x = var_9684_cast_fp16, y = var_866_cast_fp16)[name = string("op_9685_cast_fp16")];
            tensor<fp16, [1, 2, 128, 8]> key_states_275_cast_fp16 = add(x = var_9678_cast_fp16, y = var_9685_cast_fp16)[name = string("key_states_275_cast_fp16")];
            tensor<int32, [1]> expand_dims_324 = const()[name = string("expand_dims_324"), val = tensor<int32, [1]>([27])];
            tensor<int32, [1]> expand_dims_325 = const()[name = string("expand_dims_325"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_327 = const()[name = string("expand_dims_327"), val = tensor<int32, [1]>([0])];
            int32 concat_329_axis_0 = const()[name = string("concat_329_axis_0"), val = int32(0)];
            bool concat_329_interleave_0 = const()[name = string("concat_329_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_329 = concat(axis = concat_329_axis_0, interleave = concat_329_interleave_0, values = (expand_dims_324, expand_dims_325, position_id, expand_dims_327))[name = string("concat_329")];
            tensor<int32, [1]> expand_dims_328 = const()[name = string("expand_dims_328"), val = tensor<int32, [1]>([28])];
            tensor<int32, [1]> concat_330_values1_0 = const()[name = string("concat_330_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_330_values3_0 = const()[name = string("concat_330_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_330_axis_0 = const()[name = string("concat_330_axis_0"), val = int32(0)];
            bool concat_330_interleave_0 = const()[name = string("concat_330_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_330 = concat(axis = concat_330_axis_0, interleave = concat_330_interleave_0, values = (expand_dims_328, concat_330_values1_0, cache_position_end, concat_330_values3_0))[name = string("concat_330")];
            tensor<int32, [4]> key_states_277_perm_0 = const()[name = string("key_states_277_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_28_stride_0 = const()[name = string("key_cache_internal_tensor_assign_28_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_28_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_28_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_28_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_28_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_28_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_28_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> key_states_277_cast_fp16 = transpose(perm = key_states_277_perm_0, x = key_states_275_cast_fp16)[name = string("transpose_176")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_28_cast_fp16 = slice_update(begin = concat_329, begin_mask = key_cache_internal_tensor_assign_28_begin_mask_0, end = concat_330, end_mask = key_cache_internal_tensor_assign_28_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_28_squeeze_mask_0, stride = key_cache_internal_tensor_assign_28_stride_0, update = key_states_277_cast_fp16, x = coreml_update_state_164)[name = string("key_cache_internal_tensor_assign_28_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_28_cast_fp16, input = key_cache)[name = string("coreml_update_state_166_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_166 = read_state(input = key_cache)[name = string("coreml_update_state_166")];
            tensor<int32, [4]> value_states_165_perm_0 = const()[name = string("value_states_165_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_28_stride_0 = const()[name = string("value_cache_internal_tensor_assign_28_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_28_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_28_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_28_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_28_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_28_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_28_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 8, 128]> value_states_165_cast_fp16 = transpose(perm = value_states_165_perm_0, x = var_9661_cast_fp16)[name = string("transpose_175")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_28_cast_fp16 = slice_update(begin = concat_329, begin_mask = value_cache_internal_tensor_assign_28_begin_mask_0, end = concat_330, end_mask = value_cache_internal_tensor_assign_28_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_28_squeeze_mask_0, stride = value_cache_internal_tensor_assign_28_stride_0, update = value_states_165_cast_fp16, x = coreml_update_state_165)[name = string("value_cache_internal_tensor_assign_28_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_28_cast_fp16, input = value_cache)[name = string("coreml_update_state_167_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_167 = read_state(input = value_cache)[name = string("coreml_update_state_167")];
            tensor<int32, [4]> var_9755_begin_0 = const()[name = string("op_9755_begin_0"), val = tensor<int32, [4]>([27, 0, 0, 0])];
            tensor<int32, [4]> var_9755_end_0 = const()[name = string("op_9755_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_9755_end_mask_0 = const()[name = string("op_9755_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9755_cast_fp16 = slice_by_index(begin = var_9755_begin_0, end = var_9755_end_0, end_mask = var_9755_end_mask_0, x = coreml_update_state_166)[name = string("op_9755_cast_fp16")];
            tensor<int32, [2]> tile_54 = const()[name = string("tile_54"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9758_axis_0 = const()[name = string("op_9758_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9758_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9758_cast_fp16_1 = split(axis = var_9758_axis_0, split_sizes = tile_54, x = var_9755_cast_fp16)[name = string("op_9758_cast_fp16")];
            tensor<int32, [4]> var_9765_begin_0 = const()[name = string("op_9765_begin_0"), val = tensor<int32, [4]>([27, 0, 0, 0])];
            tensor<int32, [4]> var_9765_end_0 = const()[name = string("op_9765_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_9765_end_mask_0 = const()[name = string("op_9765_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9765_cast_fp16 = slice_by_index(begin = var_9765_begin_0, end = var_9765_end_0, end_mask = var_9765_end_mask_0, x = coreml_update_state_167)[name = string("op_9765_cast_fp16")];
            tensor<int32, [2]> tile_55 = const()[name = string("tile_55"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9768_axis_0 = const()[name = string("op_9768_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9768_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9768_cast_fp16_1 = split(axis = var_9768_axis_0, split_sizes = tile_55, x = var_9765_cast_fp16)[name = string("op_9768_cast_fp16")];
            tensor<int32, [2]> var_9771_split_sizes_0 = const()[name = string("op_9771_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_9771_axis_0 = const()[name = string("op_9771_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 8]> var_9771_0, tensor<fp16, [1, 8, 128, 8]> var_9771_1 = split(axis = var_9771_axis_0, split_sizes = var_9771_split_sizes_0, x = query_states_165_cast_fp16)[name = string("op_9771")];
            bool attn_weights_433_transpose_x_0 = const()[name = string("attn_weights_433_transpose_x_0"), val = bool(false)];
            bool attn_weights_433_transpose_y_0 = const()[name = string("attn_weights_433_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_433_cast_fp16 = matmul(transpose_x = attn_weights_433_transpose_x_0, transpose_y = attn_weights_433_transpose_y_0, x = var_9758_cast_fp16_0, y = var_9771_0)[name = string("attn_weights_433_cast_fp16")];
            fp16 var_9774_to_fp16 = const()[name = string("op_9774_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_435_cast_fp16 = mul(x = attn_weights_433_cast_fp16, y = var_9774_to_fp16)[name = string("attn_weights_435_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_437_cast_fp16 = add(x = attn_weights_435_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_437_cast_fp16")];
            int32 var_9778 = const()[name = string("op_9778"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_439_cast_fp16 = softmax(axis = var_9778, x = attn_weights_437_cast_fp16)[name = string("attn_weights_439_cast_fp16")];
            bool var_9784_transpose_x_1 = const()[name = string("op_9784_transpose_x_1"), val = bool(true)];
            bool var_9784_transpose_y_1 = const()[name = string("op_9784_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> var_9784_cast_fp16 = matmul(transpose_x = var_9784_transpose_x_1, transpose_y = var_9784_transpose_y_1, x = attn_weights_439_cast_fp16, y = var_9768_cast_fp16_0)[name = string("op_9784_cast_fp16")];
            bool attn_weights_441_transpose_x_0 = const()[name = string("attn_weights_441_transpose_x_0"), val = bool(false)];
            bool attn_weights_441_transpose_y_0 = const()[name = string("attn_weights_441_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_441_cast_fp16 = matmul(transpose_x = attn_weights_441_transpose_x_0, transpose_y = attn_weights_441_transpose_y_0, x = var_9758_cast_fp16_1, y = var_9771_1)[name = string("attn_weights_441_cast_fp16")];
            fp16 var_9786_to_fp16 = const()[name = string("op_9786_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_443_cast_fp16 = mul(x = attn_weights_441_cast_fp16, y = var_9786_to_fp16)[name = string("attn_weights_443_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_445_cast_fp16 = add(x = attn_weights_443_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_445_cast_fp16")];
            int32 var_9790 = const()[name = string("op_9790"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 8]> attn_weights_cast_fp16 = softmax(axis = var_9790, x = attn_weights_445_cast_fp16)[name = string("attn_weights_cast_fp16")];
            bool attn_output_217_transpose_x_1 = const()[name = string("attn_output_217_transpose_x_1"), val = bool(true)];
            bool attn_output_217_transpose_y_1 = const()[name = string("attn_output_217_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 8, 128]> attn_output_217_cast_fp16 = matmul(transpose_x = attn_output_217_transpose_x_1, transpose_y = attn_output_217_transpose_y_1, x = attn_weights_cast_fp16, y = var_9768_cast_fp16_1)[name = string("attn_output_217_cast_fp16")];
            int32 var_9798 = const()[name = string("op_9798"), val = int32(1)];
            bool attn_output_219_interleave_0 = const()[name = string("attn_output_219_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 8, 128]> attn_output_219_cast_fp16 = concat(axis = var_9798, interleave = attn_output_219_interleave_0, values = (var_9784_cast_fp16, attn_output_217_cast_fp16))[name = string("attn_output_219_cast_fp16")];
            tensor<int32, [4]> var_9802_perm_0 = const()[name = string("op_9802_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_335x = const()[name = string("concat_335x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 8]> var_9802_cast_fp16 = transpose(perm = var_9802_perm_0, x = attn_output_219_cast_fp16)[name = string("transpose_174")];
            tensor<fp16, [1, 2048, 1, 8]> attn_output_cast_fp16 = reshape(shape = concat_335x, x = var_9802_cast_fp16)[name = string("attn_output_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_27_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1620112448)))];
            tensor<int32, [2]> hidden_states_273_strides_0 = const()[name = string("hidden_states_273_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_273_pad_type_0 = const()[name = string("hidden_states_273_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_273_pad_0 = const()[name = string("hidden_states_273_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_273_dilations_0 = const()[name = string("hidden_states_273_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_273_groups_0 = const()[name = string("hidden_states_273_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_273_cast_fp16 = conv(dilations = hidden_states_273_dilations_0, groups = hidden_states_273_groups_0, pad = hidden_states_273_pad_0, pad_type = hidden_states_273_pad_type_0, strides = hidden_states_273_strides_0, weight = layers_27_self_attn_o_proj_weight_to_fp16, x = attn_output_cast_fp16)[name = string("hidden_states_273_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_275_cast_fp16 = add(x = hidden_states_269_cast_fp16, y = hidden_states_273_cast_fp16)[name = string("hidden_states_275_cast_fp16")];
            fp16 const_280_promoted_to_fp16 = const()[name = string("const_280_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_9835_cast_fp16 = mul(x = hidden_states_275_cast_fp16, y = const_280_promoted_to_fp16)[name = string("op_9835_cast_fp16")];
            int32 var_9833 = const()[name = string("op_9833"), val = int32(1)];
            bool doubled_221_interleave_0 = const()[name = string("doubled_221_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_221_cast_fp16 = concat(axis = var_9833, interleave = doubled_221_interleave_0, values = (hidden_states_275_cast_fp16, var_9835_cast_fp16))[name = string("doubled_221_cast_fp16")];
            tensor<int32, [1]> out_111_axes_0 = const()[name = string("out_111_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_111_gamma_0_to_fp16 = const()[name = string("out_111_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1628501120)))];
            fp16 var_9845_to_fp16 = const()[name = string("op_9845_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_111_cast_fp16 = layer_norm(axes = out_111_axes_0, epsilon = var_9845_to_fp16, gamma = out_111_gamma_0_to_fp16, x = doubled_221_cast_fp16)[name = string("out_111_cast_fp16")];
            tensor<int32, [2]> var_9856_split_sizes_0 = const()[name = string("op_9856_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9856_axis_0 = const()[name = string("op_9856_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> var_9856_cast_fp16_0, tensor<fp16, [1, 2048, 1, 8]> var_9856_cast_fp16_1 = split(axis = var_9856_axis_0, split_sizes = var_9856_split_sizes_0, x = out_111_cast_fp16)[name = string("op_9856_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_27_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_27_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1628509376)))];
            tensor<int32, [2]> input_strides_0 = const()[name = string("input_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_pad_type_0 = const()[name = string("input_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_pad_0 = const()[name = string("input_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_dilations_0 = const()[name = string("input_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_groups_0 = const()[name = string("input_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> input_cast_fp16 = conv(dilations = input_dilations_0, groups = input_groups_0, pad = input_pad_0, pad_type = input_pad_type_0, strides = input_strides_0, weight = layers_27_mlp_gate_proj_weight_to_fp16, x = var_9856_cast_fp16_0)[name = string("input_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> var_9873_cast_fp16 = silu(x = input_cast_fp16)[name = string("op_9873_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_27_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_27_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1653675264)))];
            tensor<int32, [2]> var_9879_strides_0 = const()[name = string("op_9879_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_9879_pad_type_0 = const()[name = string("op_9879_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_9879_pad_0 = const()[name = string("op_9879_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_9879_dilations_0 = const()[name = string("op_9879_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9879_groups_0 = const()[name = string("op_9879_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 8]> var_9879_cast_fp16 = conv(dilations = var_9879_dilations_0, groups = var_9879_groups_0, pad = var_9879_pad_0, pad_type = var_9879_pad_type_0, strides = var_9879_strides_0, weight = layers_27_mlp_up_proj_weight_to_fp16, x = var_9856_cast_fp16_0)[name = string("op_9879_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 8]> x_cast_fp16 = mul(x = var_9873_cast_fp16, y = var_9879_cast_fp16)[name = string("x_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_27_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_27_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1678841152)))];
            tensor<int32, [2]> hidden_states_277_strides_0 = const()[name = string("hidden_states_277_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_277_pad_type_0 = const()[name = string("hidden_states_277_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_277_pad_0 = const()[name = string("hidden_states_277_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_277_dilations_0 = const()[name = string("hidden_states_277_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_277_groups_0 = const()[name = string("hidden_states_277_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_277_cast_fp16 = conv(dilations = hidden_states_277_dilations_0, groups = hidden_states_277_groups_0, pad = hidden_states_277_pad_0, pad_type = hidden_states_277_pad_type_0, strides = hidden_states_277_strides_0, weight = layers_27_mlp_down_proj_weight_to_fp16, x = x_cast_fp16)[name = string("hidden_states_277_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states_cast_fp16 = add(x = hidden_states_275_cast_fp16, y = hidden_states_277_cast_fp16)[name = string("hidden_states_cast_fp16")];
            fp16 const_282_promoted_to_fp16 = const()[name = string("const_282_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 8]> var_9897_cast_fp16 = mul(x = hidden_states_cast_fp16, y = const_282_promoted_to_fp16)[name = string("op_9897_cast_fp16")];
            int32 var_9895 = const()[name = string("op_9895"), val = int32(1)];
            bool doubled_225_interleave_0 = const()[name = string("doubled_225_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 8]> doubled_225_cast_fp16 = concat(axis = var_9895, interleave = doubled_225_interleave_0, values = (hidden_states_cast_fp16, var_9897_cast_fp16))[name = string("doubled_225_cast_fp16")];
            tensor<int32, [1]> out_axes_0 = const()[name = string("out_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_gamma_0_to_fp16 = const()[name = string("out_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1704007040)))];
            fp16 var_9907_to_fp16 = const()[name = string("op_9907_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 8]> out_cast_fp16 = layer_norm(axes = out_axes_0, epsilon = var_9907_to_fp16, gamma = out_gamma_0_to_fp16, x = doubled_225_cast_fp16)[name = string("out_cast_fp16")];
            tensor<int32, [2]> var_9918_split_sizes_0 = const()[name = string("op_9918_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9918_axis_0 = const()[name = string("op_9918_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 8]> hidden_states, tensor<fp16, [1, 2048, 1, 8]> var_9918_cast_fp16_1 = split(axis = var_9918_axis_0, split_sizes = var_9918_split_sizes_0, x = out_cast_fp16)[name = string("op_9918_cast_fp16")];
        } -> (hidden_states);
    func main<ios18>(tensor<fp32, [1, 2048, 1, 1]> inputs_embeds, state<tensor<fp16, [28, 2, 2048, 128]>> key_cache, tensor<int32, [1]> position_id, tensor<int32, [1]> position_index_seed, state<tensor<fp16, [28, 2, 2048, 128]>> value_cache) {
            tensor<fp16, [256, 2048, 1, 1]> layers_1_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524992))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(524416))))[name = string("layers_1_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_1_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(525312))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13120640))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13108288))))[name = string("layers_1_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_2_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13126848))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13651776))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13651200))))[name = string("layers_2_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_2_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13652096))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26247424))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26235072))))[name = string("layers_2_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_3_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26253632))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26778560))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26777984))))[name = string("layers_3_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_3_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(26778880))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30977408))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30973248))))[name = string("layers_3_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_3_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(30979520))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43566656))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43562496))))[name = string("layers_3_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_4_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(43568768))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44093696))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44093120))))[name = string("layers_4_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_4_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(44094016))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48292544))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48288384))))[name = string("layers_4_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_4_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(48294656))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60889984))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60877632))))[name = string("layers_4_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_4_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(60896192))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73491520))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73479168))))[name = string("layers_4_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_4_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(73497728))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86084864))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86080704))))[name = string("layers_4_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_5_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86086976))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86611904))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86611328))))[name = string("layers_5_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_5_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(86612224))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90810752))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90806592))))[name = string("layers_5_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_5_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(90812864))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103408192))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103395840))))[name = string("layers_5_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_5_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(103414400))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116001536))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(115997376))))[name = string("layers_5_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_6_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116003648))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116528576))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116528000))))[name = string("layers_6_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_6_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116528896))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120727424))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120723264))))[name = string("layers_6_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_6_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120729536))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133324864))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133312512))))[name = string("layers_6_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_6_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(133331072))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145926400))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145914048))))[name = string("layers_6_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_6_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(145932608))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158519744))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158515584))))[name = string("layers_6_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_7_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(158521856))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159046784))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159046208))))[name = string("layers_7_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_7_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(159047104))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163245632))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163241472))))[name = string("layers_7_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_7_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(163247744))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175843072))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175830720))))[name = string("layers_7_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_8_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(175849280))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176374208))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176373632))))[name = string("layers_8_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_8_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(176374528))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180573056))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180568896))))[name = string("layers_8_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_8_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(180575168))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193170496))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193158144))))[name = string("layers_8_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_8_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(193176704))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205772032))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205759680))))[name = string("layers_8_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_8_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(205778240))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218365376))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218361216))))[name = string("layers_8_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_9_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218367488))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218892416))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218891840))))[name = string("layers_9_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_9_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(218892736))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223091264))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223087104))))[name = string("layers_9_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_9_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(223093376))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235688704))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235676352))))[name = string("layers_9_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_9_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(235694912))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248290240))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248277888))))[name = string("layers_9_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_9_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(248296448))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260883584))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260879424))))[name = string("layers_9_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_10_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(260885696))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261410624))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261410048))))[name = string("layers_10_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_10_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(261410944))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265609472))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265605312))))[name = string("layers_10_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_10_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(265611584))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278206912))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278194560))))[name = string("layers_10_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_10_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(278213120))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290808448))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290796096))))[name = string("layers_10_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_10_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(290814656))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303401792))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303397632))))[name = string("layers_10_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_11_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(303403904))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307602432))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307598272))))[name = string("layers_11_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_11_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(307604544))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308129472))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308128896))))[name = string("layers_11_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_11_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308129792))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308654720))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308654144))))[name = string("layers_11_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_11_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(308655040))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312853568))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312849408))))[name = string("layers_11_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_11_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(312855680))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325451008))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325438656))))[name = string("layers_11_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_11_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(325457216))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338052544))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338040192))))[name = string("layers_11_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_11_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(338058752))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350645888))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350641728))))[name = string("layers_11_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_12_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(350648000))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354846528))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354842368))))[name = string("layers_12_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_12_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(354848640))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355373568))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355372992))))[name = string("layers_12_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_12_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355373888))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355898816))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355898240))))[name = string("layers_12_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_12_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(355899136))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360097664))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360093504))))[name = string("layers_12_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_12_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(360099776))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372695104))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372682752))))[name = string("layers_12_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_12_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(372701312))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385296640))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385284288))))[name = string("layers_12_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_12_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(385302848))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397889984))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397885824))))[name = string("layers_12_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_13_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(397892096))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402090624))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402086464))))[name = string("layers_13_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_13_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402092736))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402617664))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402617088))))[name = string("layers_13_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_13_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(402617984))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403142912))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403142336))))[name = string("layers_13_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_13_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(403143232))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407341760))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407337600))))[name = string("layers_13_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_13_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(407343872))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419939200))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419926848))))[name = string("layers_13_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_13_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(419945408))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432532544))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432528384))))[name = string("layers_13_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_14_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(432534656))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436733184))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436729024))))[name = string("layers_14_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_14_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(436735296))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437260224))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437259648))))[name = string("layers_14_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_14_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(437260544))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441459072))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441454912))))[name = string("layers_14_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_14_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(441461184))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454056512))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454044160))))[name = string("layers_14_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_14_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(454062720))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(466658048))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(466645696))))[name = string("layers_14_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_14_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(466664256))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(479251392))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(479247232))))[name = string("layers_14_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_15_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(479253504))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483452032))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483447872))))[name = string("layers_15_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_15_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483454144))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483979072))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483978496))))[name = string("layers_15_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_15_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(483979392))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484504320))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484503744))))[name = string("layers_15_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_15_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(484504640))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488703168))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488699008))))[name = string("layers_15_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_15_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(488705280))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(501300608))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(501288256))))[name = string("layers_15_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_15_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(501306816))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513902144))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513889792))))[name = string("layers_15_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_15_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(513908352))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526495488))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526491328))))[name = string("layers_15_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_16_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(526497600))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530696128))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530691968))))[name = string("layers_16_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_16_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(530698240))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531223168))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531222592))))[name = string("layers_16_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_16_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531223488))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531748416))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531747840))))[name = string("layers_16_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_16_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(531748736))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535947264))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535943104))))[name = string("layers_16_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_16_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(535949376))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(548536512))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(548532352))))[name = string("layers_16_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_17_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(548538624))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552737152))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552732992))))[name = string("layers_17_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_17_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(552739264))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553264192))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553263616))))[name = string("layers_17_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_17_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553264512))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553789440))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553788864))))[name = string("layers_17_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_17_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(553789760))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557988288))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557984128))))[name = string("layers_17_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_17_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(557990400))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570585728))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570573376))))[name = string("layers_17_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_17_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(570591936))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583187264))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583174912))))[name = string("layers_17_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_17_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(583193472))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(595780608))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(595776448))))[name = string("layers_17_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_18_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(595782720))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(599981248))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(599977088))))[name = string("layers_18_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_18_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(599983360))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(600508288))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(600507712))))[name = string("layers_18_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_18_self_attn_v_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(600508608))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(601033536))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(601032960))))[name = string("layers_18_self_attn_v_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_18_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(601033856))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(605232384))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(605228224))))[name = string("layers_18_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_18_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(605234496))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(617829824))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(617817472))))[name = string("layers_18_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_18_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(617836032))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(630431360))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(630419008))))[name = string("layers_18_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_18_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(630437568))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(643024704))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(643020544))))[name = string("layers_18_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_19_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(643026816))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647225344))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647221184))))[name = string("layers_19_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_19_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647227456))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647752384))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647751808))))[name = string("layers_19_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_19_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(647752704))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(660348032))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(660335680))))[name = string("layers_19_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_19_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(660354240))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(672949568))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(672937216))))[name = string("layers_19_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_19_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(672955776))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(685542912))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(685538752))))[name = string("layers_19_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_20_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(685545024))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(689743552))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(689739392))))[name = string("layers_20_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_20_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(689745664))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(690270592))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(690270016))))[name = string("layers_20_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_20_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(690270912))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(694469440))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(694465280))))[name = string("layers_20_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_20_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(694471552))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(707066880))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(707054528))))[name = string("layers_20_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_20_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(707073088))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(719660224))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(719656064))))[name = string("layers_20_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_21_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(719662336))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(723860864))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(723856704))))[name = string("layers_21_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_21_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(723862976))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724387904))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724387328))))[name = string("layers_21_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_21_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(724388224))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(728586752))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(728582592))))[name = string("layers_21_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_21_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(728588864))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(741184192))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(741171840))))[name = string("layers_21_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_21_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(741190400))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(753785728))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(753773376))))[name = string("layers_21_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_21_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(753791936))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(766379072))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(766374912))))[name = string("layers_21_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_22_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(766381184))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(770579712))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(770575552))))[name = string("layers_22_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_22_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(770581824))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(771106752))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(771106176))))[name = string("layers_22_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_22_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(771107072))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783702400))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783690048))))[name = string("layers_22_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_22_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(783708608))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(796303936))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(796291584))))[name = string("layers_22_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_22_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(796310144))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808897280))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808893120))))[name = string("layers_22_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_23_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(808899392))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813097920))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813093760))))[name = string("layers_23_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_23_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813100032))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813624960))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813624384))))[name = string("layers_23_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_23_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(813625280))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(817823808))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(817819648))))[name = string("layers_23_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_23_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(817825920))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(830421248))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(830408896))))[name = string("layers_23_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_23_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(830427456))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(843022784))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(843010432))))[name = string("layers_23_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_23_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(843028992))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855616128))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855611968))))[name = string("layers_23_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_24_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(855618240))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(859816768))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(859812608))))[name = string("layers_24_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_24_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(859818880))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(860343808))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(860343232))))[name = string("layers_24_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_24_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(860344128))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(864542656))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(864538496))))[name = string("layers_24_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_24_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(864544768))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(877140096))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(877127744))))[name = string("layers_24_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_24_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(877146304))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889741632))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889729280))))[name = string("layers_24_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_24_mlp_down_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(889747840))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(902334976))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(902330816))))[name = string("layers_24_mlp_down_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_25_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(902337088))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(906535616))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(906531456))))[name = string("layers_25_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_25_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(906537728))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(907062656))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(907062080))))[name = string("layers_25_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_25_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(907062976))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(911261504))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(911257344))))[name = string("layers_25_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_25_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(911263616))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(923858944))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(923846592))))[name = string("layers_25_mlp_gate_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_25_mlp_up_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(923865152))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936460480))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936448128))))[name = string("layers_25_mlp_up_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_26_self_attn_q_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(936466688))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(940665216))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(940661056))))[name = string("layers_26_self_attn_q_proj_weight_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_26_self_attn_k_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(940667328))), offset = tensor<int8, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(941192256))), scale = tensor<fp16, [256, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(941191680))))[name = string("layers_26_self_attn_k_proj_weight_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_26_self_attn_o_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(941192576))), offset = tensor<int8, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(945391104))), scale = tensor<fp16, [2048, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(945386944))))[name = string("layers_26_self_attn_o_proj_weight_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_26_mlp_gate_proj_weight_cast_fp16 = constexpr_blockwise_shift_scale(data = tensor<int8, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(945393216))), offset = tensor<int8, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(957988544))), scale = tensor<fp16, [6144, 1, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(957976192))))[name = string("layers_26_mlp_gate_proj_weight_cast_fp16")];
            int32 gather_0_cast_uint16_to_int32 = const()[name = string("gather_0_cast_uint16_to_int32"), val = int32(1)];
            tensor<int32, [1]> cache_position_end = add(x = position_id, y = gather_0_cast_uint16_to_int32)[name = string("cache_position_end")];
            fp16 fill_like_0_value_0_to_fp16 = const()[name = string("fill_like_0_value_0_to_fp16"), val = fp16(0x1p+0)];
            tensor<fp16, [1]> fill_like_0_cast_fp16 = fill_like(ref_tensor = position_index_seed, value = fill_like_0_value_0_to_fp16)[name = string("fill_like_0_cast_fp16")];
            int32 var_831 = const()[name = string("op_831"), val = int32(0)];
            bool var_833_exclusive_0 = const()[name = string("op_833_exclusive_0"), val = bool(false)];
            bool var_833_reverse_0 = const()[name = string("op_833_reverse_0"), val = bool(false)];
            tensor<fp16, [1]> var_833_cast_fp16 = cumsum(axis = var_831, exclusive = var_833_exclusive_0, reverse = var_833_reverse_0, x = fill_like_0_cast_fp16)[name = string("op_833_cast_fp16")];
            fp16 var_835_promoted_to_fp16 = const()[name = string("op_835_promoted_to_fp16"), val = fp16(0x1p+0)];
            tensor<fp16, [1]> position_offsets_cast_fp16 = sub(x = var_833_cast_fp16, y = var_835_promoted_to_fp16)[name = string("position_offsets_cast_fp16")];
            tensor<int32, [1]> var_838_axes_0 = const()[name = string("op_838_axes_0"), val = tensor<int32, [1]>([0])];
            tensor<fp16, [1, 1]> var_838_cast_fp16 = expand_dims(axes = var_838_axes_0, x = position_offsets_cast_fp16)[name = string("op_838_cast_fp16")];
            string position_id_promoted_to_fp16_dtype_0 = const()[name = string("position_id_promoted_to_fp16_dtype_0"), val = string("fp16")];
            tensor<fp16, [1]> position_id_to_fp16 = cast(dtype = position_id_promoted_to_fp16_dtype_0, x = position_id)[name = string("cast_3")];
            tensor<fp16, [1, 1]> position_ids_1_cast_fp16 = add(x = var_838_cast_fp16, y = position_id_to_fp16)[name = string("position_ids_1_cast_fp16")];
            string position_ids_dtype_0 = const()[name = string("position_ids_dtype_0"), val = string("int32")];
            int32 greater_equal_0_y_0 = const()[name = string("greater_equal_0_y_0"), val = int32(0)];
            tensor<int32, [1, 1]> position_ids_1_cast_fp16_to_int32 = cast(dtype = position_ids_dtype_0, x = position_ids_1_cast_fp16)[name = string("cast_2")];
            tensor<bool, [1, 1]> greater_equal_0 = greater_equal(x = position_ids_1_cast_fp16_to_int32, y = greater_equal_0_y_0)[name = string("greater_equal_0")];
            int32 slice_by_index_0 = const()[name = string("slice_by_index_0"), val = int32(32768)];
            tensor<int32, [1, 1]> add_0 = add(x = position_ids_1_cast_fp16_to_int32, y = slice_by_index_0)[name = string("add_0")];
            tensor<int32, [1, 1]> select_0 = select(a = position_ids_1_cast_fp16_to_int32, b = add_0, cond = greater_equal_0)[name = string("select_0")];
            tensor<fp16, [32768, 128]> rope_emb_cos_cached_to_fp16 = const()[name = string("rope_emb_cos_cached_to_fp16"), val = tensor<fp16, [32768, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(957994752)))];
            int32 cos_1_batch_dims_0 = const()[name = string("cos_1_batch_dims_0"), val = int32(0)];
            bool cos_1_validate_indices_0 = const()[name = string("cos_1_validate_indices_0"), val = bool(false)];
            int32 greater_equal_0_y_0_1 = const()[name = string("greater_equal_0_y_0_1"), val = int32(0)];
            tensor<bool, [1, 1]> greater_equal_0_1 = greater_equal(x = select_0, y = greater_equal_0_y_0_1)[name = string("greater_equal_0_1")];
            int32 slice_by_index_0_1 = const()[name = string("slice_by_index_0_1"), val = int32(32768)];
            tensor<int32, [1, 1]> add_0_1 = add(x = select_0, y = slice_by_index_0_1)[name = string("add_0_1")];
            tensor<int32, [1, 1]> select_0_1 = select(a = select_0, b = add_0_1, cond = greater_equal_0_1)[name = string("select_0_1")];
            int32 cos_1_cast_fp16_axis_0 = const()[name = string("cos_1_cast_fp16_axis_0"), val = int32(0)];
            tensor<fp16, [1, 1, 128]> cos_1_cast_fp16 = gather(axis = cos_1_cast_fp16_axis_0, batch_dims = cos_1_batch_dims_0, indices = select_0_1, validate_indices = cos_1_validate_indices_0, x = rope_emb_cos_cached_to_fp16)[name = string("cos_1_cast_fp16")];
            tensor<fp16, [32768, 128]> rope_emb_sin_cached_to_fp16 = const()[name = string("rope_emb_sin_cached_to_fp16"), val = tensor<fp16, [32768, 128]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(966383424)))];
            int32 sin_1_batch_dims_0 = const()[name = string("sin_1_batch_dims_0"), val = int32(0)];
            bool sin_1_validate_indices_0 = const()[name = string("sin_1_validate_indices_0"), val = bool(false)];
            int32 sin_1_cast_fp16_axis_0 = const()[name = string("sin_1_cast_fp16_axis_0"), val = int32(0)];
            tensor<fp16, [1, 1, 128]> sin_1_cast_fp16 = gather(axis = sin_1_cast_fp16_axis_0, batch_dims = sin_1_batch_dims_0, indices = select_0_1, validate_indices = sin_1_validate_indices_0, x = rope_emb_sin_cached_to_fp16)[name = string("sin_1_cast_fp16")];
            tensor<int32, [3]> var_857_perm_0 = const()[name = string("op_857_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<int32, [1]> var_859_axes_0 = const()[name = string("op_859_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 128, 1]> var_857_cast_fp16 = transpose(perm = var_857_perm_0, x = cos_1_cast_fp16)[name = string("transpose_86")];
            tensor<fp16, [1, 1, 128, 1]> var_859_cast_fp16 = expand_dims(axes = var_859_axes_0, x = var_857_cast_fp16)[name = string("op_859_cast_fp16")];
            tensor<int32, [3]> var_864_perm_0 = const()[name = string("op_864_perm_0"), val = tensor<int32, [3]>([0, -1, -2])];
            tensor<int32, [1]> var_866_axes_0 = const()[name = string("op_866_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 128, 1]> var_864_cast_fp16 = transpose(perm = var_864_perm_0, x = sin_1_cast_fp16)[name = string("transpose_85")];
            tensor<fp16, [1, 1, 128, 1]> var_866_cast_fp16 = expand_dims(axes = var_866_axes_0, x = var_864_cast_fp16)[name = string("op_866_cast_fp16")];
            tensor<int32, [1]> var_885_axes_0 = const()[name = string("op_885_axes_0"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1, 1, 1]> var_885 = expand_dims(axes = var_885_axes_0, x = position_ids_1_cast_fp16_to_int32)[name = string("op_885")];
            tensor<int32, [1, 1, 2048]> var_878 = const()[name = string("op_878"), val = tensor<int32, [1, 1, 2048]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(974772096)))];
            tensor<bool, [1, 1, 2048]> var_886 = greater(x = var_878, y = var_885)[name = string("op_886")];
            string mask_1_to_fp16_dtype_0 = const()[name = string("mask_1_to_fp16_dtype_0"), val = string("fp16")];
            tensor<int32, [1]> var_893_axes_0 = const()[name = string("op_893_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [1, 1, 2048]> var_886_to_fp16 = cast(dtype = mask_1_to_fp16_dtype_0, x = var_886)[name = string("cast_1")];
            tensor<fp16, [1, 1, 1, 2048]> var_893_cast_fp16 = expand_dims(axes = var_893_axes_0, x = var_886_to_fp16)[name = string("op_893_cast_fp16")];
            tensor<int32, [4]> mask_perm_0 = const()[name = string("mask_perm_0"), val = tensor<int32, [4]>([0, 1, -1, -2])];
            fp16 var_897_promoted_to_fp16 = const()[name = string("op_897_promoted_to_fp16"), val = fp16(0x0p+0)];
            tensor<fp16, [1, 1, 2048, 1]> mask_cast_fp16 = transpose(perm = mask_perm_0, x = var_893_cast_fp16)[name = string("transpose_84")];
            tensor<bool, [1, 1, 2048, 1]> var_898_cast_fp16 = equal(x = mask_cast_fp16, y = var_897_promoted_to_fp16)[name = string("op_898_cast_fp16")];
            fp16 var_899_to_fp16 = const()[name = string("op_899_to_fp16"), val = fp16(-inf)];
            tensor<fp16, [1, 1, 2048, 1]> attn_mask_1_cast_fp16 = select(a = mask_cast_fp16, b = var_899_to_fp16, cond = var_898_cast_fp16)[name = string("attn_mask_1_cast_fp16")];
            string inputs_embeds_to_fp16_dtype_0 = const()[name = string("inputs_embeds_to_fp16_dtype_0"), val = string("fp16")];
            fp16 const_2_promoted_to_fp16 = const()[name = string("const_2_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> inputs_embeds_to_fp16 = cast(dtype = inputs_embeds_to_fp16_dtype_0, x = inputs_embeds)[name = string("cast_0")];
            tensor<fp16, [1, 2048, 1, 1]> var_909_cast_fp16 = mul(x = inputs_embeds_to_fp16, y = const_2_promoted_to_fp16)[name = string("op_909_cast_fp16")];
            int32 var_907 = const()[name = string("op_907"), val = int32(1)];
            bool doubled_1_interleave_0 = const()[name = string("doubled_1_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_1_cast_fp16 = concat(axis = var_907, interleave = doubled_1_interleave_0, values = (inputs_embeds_to_fp16, var_909_cast_fp16))[name = string("doubled_1_cast_fp16")];
            tensor<int32, [1]> out_1_axes_0 = const()[name = string("out_1_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_1_gamma_0_to_fp16 = const()[name = string("out_1_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(974780352)))];
            fp16 var_919_to_fp16 = const()[name = string("op_919_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_1_cast_fp16 = layer_norm(axes = out_1_axes_0, epsilon = var_919_to_fp16, gamma = out_1_gamma_0_to_fp16, x = doubled_1_cast_fp16)[name = string("out_1_cast_fp16")];
            tensor<int32, [2]> var_930_split_sizes_0 = const()[name = string("op_930_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_930_axis_0 = const()[name = string("op_930_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_930_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_930_cast_fp16_1 = split(axis = var_930_axis_0, split_sizes = var_930_split_sizes_0, x = out_1_cast_fp16)[name = string("op_930_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_0_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(974788608)))];
            tensor<int32, [2]> query_states_1_strides_0 = const()[name = string("query_states_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_1_pad_type_0 = const()[name = string("query_states_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_1_pad_0 = const()[name = string("query_states_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_1_dilations_0 = const()[name = string("query_states_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_1_groups_0 = const()[name = string("query_states_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_1_cast_fp16 = conv(dilations = query_states_1_dilations_0, groups = query_states_1_groups_0, pad = query_states_1_pad_0, pad_type = query_states_1_pad_type_0, strides = query_states_1_strides_0, weight = layers_0_self_attn_q_proj_weight_to_fp16, x = var_930_cast_fp16_0)[name = string("query_states_1_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_0_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(983177280)))];
            tensor<int32, [2]> key_states_1_strides_0 = const()[name = string("key_states_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_1_pad_type_0 = const()[name = string("key_states_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_1_pad_0 = const()[name = string("key_states_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_1_dilations_0 = const()[name = string("key_states_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_1_groups_0 = const()[name = string("key_states_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_1_cast_fp16 = conv(dilations = key_states_1_dilations_0, groups = key_states_1_groups_0, pad = key_states_1_pad_0, pad_type = key_states_1_pad_type_0, strides = key_states_1_strides_0, weight = layers_0_self_attn_k_proj_weight_to_fp16, x = var_930_cast_fp16_0)[name = string("key_states_1_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_0_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(984225920)))];
            tensor<int32, [2]> value_states_1_strides_0 = const()[name = string("value_states_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_1_pad_type_0 = const()[name = string("value_states_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_1_pad_0 = const()[name = string("value_states_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_1_dilations_0 = const()[name = string("value_states_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_1_groups_0 = const()[name = string("value_states_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_1_cast_fp16 = conv(dilations = value_states_1_dilations_0, groups = value_states_1_groups_0, pad = value_states_1_pad_0, pad_type = value_states_1_pad_type_0, strides = value_states_1_strides_0, weight = layers_0_self_attn_v_proj_weight_to_fp16, x = var_930_cast_fp16_0)[name = string("value_states_1_cast_fp16")];
            tensor<int32, [4]> concat_0x = const()[name = string("concat_0x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_1_cast_fp16 = reshape(shape = concat_0x, x = query_states_1_cast_fp16)[name = string("x_1_cast_fp16")];
            tensor<int32, [4]> concat_1x = const()[name = string("concat_1x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_987_cast_fp16 = reshape(shape = concat_1x, x = key_states_1_cast_fp16)[name = string("op_987_cast_fp16")];
            tensor<int32, [4]> concat_2x = const()[name = string("concat_2x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_994_cast_fp16 = reshape(shape = concat_2x, x = value_states_1_cast_fp16)[name = string("op_994_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_998_cast_fp16 = mul(x = x_1_cast_fp16, y = var_859_cast_fp16)[name = string("op_998_cast_fp16")];
            tensor<int32, [2]> var_999_split_sizes_0 = const()[name = string("op_999_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_999_axis_0 = const()[name = string("op_999_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_999_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_999_cast_fp16_1 = split(axis = var_999_axis_0, split_sizes = var_999_split_sizes_0, x = x_1_cast_fp16)[name = string("op_999_cast_fp16")];
            fp16 const_4_promoted_to_fp16 = const()[name = string("const_4_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_1001_cast_fp16 = mul(x = var_999_cast_fp16_1, y = const_4_promoted_to_fp16)[name = string("op_1001_cast_fp16")];
            int32 var_1003 = const()[name = string("op_1003"), val = int32(-2)];
            bool var_1004_interleave_0 = const()[name = string("op_1004_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_1004_cast_fp16 = concat(axis = var_1003, interleave = var_1004_interleave_0, values = (var_1001_cast_fp16, var_999_cast_fp16_0))[name = string("op_1004_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_1005_cast_fp16 = mul(x = var_1004_cast_fp16, y = var_866_cast_fp16)[name = string("op_1005_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_3_cast_fp16 = add(x = var_998_cast_fp16, y = var_1005_cast_fp16)[name = string("query_states_3_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_1011_cast_fp16 = mul(x = var_987_cast_fp16, y = var_859_cast_fp16)[name = string("op_1011_cast_fp16")];
            tensor<int32, [2]> var_1012_split_sizes_0 = const()[name = string("op_1012_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1012_axis_0 = const()[name = string("op_1012_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_1012_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_1012_cast_fp16_1 = split(axis = var_1012_axis_0, split_sizes = var_1012_split_sizes_0, x = var_987_cast_fp16)[name = string("op_1012_cast_fp16")];
            fp16 const_5_promoted_to_fp16 = const()[name = string("const_5_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_1014_cast_fp16 = mul(x = var_1012_cast_fp16_1, y = const_5_promoted_to_fp16)[name = string("op_1014_cast_fp16")];
            int32 var_1016 = const()[name = string("op_1016"), val = int32(-2)];
            bool var_1017_interleave_0 = const()[name = string("op_1017_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_1017_cast_fp16 = concat(axis = var_1016, interleave = var_1017_interleave_0, values = (var_1014_cast_fp16, var_1012_cast_fp16_0))[name = string("op_1017_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_1018_cast_fp16 = mul(x = var_1017_cast_fp16, y = var_866_cast_fp16)[name = string("op_1018_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_5_cast_fp16 = add(x = var_1011_cast_fp16, y = var_1018_cast_fp16)[name = string("key_states_5_cast_fp16")];
            tensor<fp16, [28, 2, 2048, 128]> read_state_0 = read_state(input = key_cache)[name = string("read_state_0")];
            tensor<int32, [1]> expand_dims_0 = const()[name = string("expand_dims_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_1 = const()[name = string("expand_dims_1"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_3 = const()[name = string("expand_dims_3"), val = tensor<int32, [1]>([0])];
            int32 concat_5_axis_0 = const()[name = string("concat_5_axis_0"), val = int32(0)];
            bool concat_5_interleave_0 = const()[name = string("concat_5_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_5 = concat(axis = concat_5_axis_0, interleave = concat_5_interleave_0, values = (expand_dims_0, expand_dims_1, position_id, expand_dims_3))[name = string("concat_5")];
            tensor<int32, [1]> expand_dims_4 = const()[name = string("expand_dims_4"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> concat_6_values1_0 = const()[name = string("concat_6_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_6_values3_0 = const()[name = string("concat_6_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_6_axis_0 = const()[name = string("concat_6_axis_0"), val = int32(0)];
            bool concat_6_interleave_0 = const()[name = string("concat_6_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_6 = concat(axis = concat_6_axis_0, interleave = concat_6_interleave_0, values = (expand_dims_4, concat_6_values1_0, cache_position_end, concat_6_values3_0))[name = string("concat_6")];
            tensor<int32, [4]> key_states_7_perm_0 = const()[name = string("key_states_7_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_1_stride_0 = const()[name = string("key_cache_internal_tensor_assign_1_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_1_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_1_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_1_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_1_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_1_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_1_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_7_cast_fp16 = transpose(perm = key_states_7_perm_0, x = key_states_5_cast_fp16)[name = string("transpose_83")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_1_cast_fp16 = slice_update(begin = concat_5, begin_mask = key_cache_internal_tensor_assign_1_begin_mask_0, end = concat_6, end_mask = key_cache_internal_tensor_assign_1_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_1_squeeze_mask_0, stride = key_cache_internal_tensor_assign_1_stride_0, update = key_states_7_cast_fp16, x = read_state_0)[name = string("key_cache_internal_tensor_assign_1_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_1_cast_fp16, input = key_cache)[name = string("coreml_update_state_0_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_0 = read_state(input = key_cache)[name = string("coreml_update_state_0")];
            tensor<fp16, [28, 2, 2048, 128]> read_state_1 = read_state(input = value_cache)[name = string("read_state_1")];
            tensor<int32, [4]> value_states_3_perm_0 = const()[name = string("value_states_3_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_1_stride_0 = const()[name = string("value_cache_internal_tensor_assign_1_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_1_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_1_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_1_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_1_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_1_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_1_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_3_cast_fp16 = transpose(perm = value_states_3_perm_0, x = var_994_cast_fp16)[name = string("transpose_82")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_1_cast_fp16 = slice_update(begin = concat_5, begin_mask = value_cache_internal_tensor_assign_1_begin_mask_0, end = concat_6, end_mask = value_cache_internal_tensor_assign_1_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_1_squeeze_mask_0, stride = value_cache_internal_tensor_assign_1_stride_0, update = value_states_3_cast_fp16, x = read_state_1)[name = string("value_cache_internal_tensor_assign_1_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_1_cast_fp16, input = value_cache)[name = string("coreml_update_state_1_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_1 = read_state(input = value_cache)[name = string("coreml_update_state_1")];
            tensor<int32, [4]> var_1088_begin_0 = const()[name = string("op_1088_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> var_1088_end_0 = const()[name = string("op_1088_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_1088_end_mask_0 = const()[name = string("op_1088_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1088_cast_fp16 = slice_by_index(begin = var_1088_begin_0, end = var_1088_end_0, end_mask = var_1088_end_mask_0, x = coreml_update_state_0)[name = string("op_1088_cast_fp16")];
            tensor<int32, [2]> tile_0 = const()[name = string("tile_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1091_axis_0 = const()[name = string("op_1091_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1091_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1091_cast_fp16_1 = split(axis = var_1091_axis_0, split_sizes = tile_0, x = var_1088_cast_fp16)[name = string("op_1091_cast_fp16")];
            tensor<int32, [4]> var_1098_begin_0 = const()[name = string("op_1098_begin_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [4]> var_1098_end_0 = const()[name = string("op_1098_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_1098_end_mask_0 = const()[name = string("op_1098_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1098_cast_fp16 = slice_by_index(begin = var_1098_begin_0, end = var_1098_end_0, end_mask = var_1098_end_mask_0, x = coreml_update_state_1)[name = string("op_1098_cast_fp16")];
            tensor<int32, [2]> tile_1 = const()[name = string("tile_1"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1101_axis_0 = const()[name = string("op_1101_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1101_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1101_cast_fp16_1 = split(axis = var_1101_axis_0, split_sizes = tile_1, x = var_1098_cast_fp16)[name = string("op_1101_cast_fp16")];
            tensor<int32, [2]> var_1104_split_sizes_0 = const()[name = string("op_1104_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_1104_axis_0 = const()[name = string("op_1104_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_1104_0, tensor<fp16, [1, 8, 128, 1]> var_1104_1 = split(axis = var_1104_axis_0, split_sizes = var_1104_split_sizes_0, x = query_states_3_cast_fp16)[name = string("op_1104")];
            bool attn_weights_1_transpose_x_0 = const()[name = string("attn_weights_1_transpose_x_0"), val = bool(false)];
            bool attn_weights_1_transpose_y_0 = const()[name = string("attn_weights_1_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_1_cast_fp16 = matmul(transpose_x = attn_weights_1_transpose_x_0, transpose_y = attn_weights_1_transpose_y_0, x = var_1091_cast_fp16_0, y = var_1104_0)[name = string("attn_weights_1_cast_fp16")];
            fp16 var_1107_to_fp16 = const()[name = string("op_1107_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_3_cast_fp16 = mul(x = attn_weights_1_cast_fp16, y = var_1107_to_fp16)[name = string("attn_weights_3_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_5_cast_fp16 = add(x = attn_weights_3_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_5_cast_fp16")];
            int32 var_1111 = const()[name = string("op_1111"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_7_cast_fp16 = softmax(axis = var_1111, x = attn_weights_5_cast_fp16)[name = string("attn_weights_7_cast_fp16")];
            bool var_1117_transpose_x_1 = const()[name = string("op_1117_transpose_x_1"), val = bool(true)];
            bool var_1117_transpose_y_1 = const()[name = string("op_1117_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_1117_cast_fp16 = matmul(transpose_x = var_1117_transpose_x_1, transpose_y = var_1117_transpose_y_1, x = attn_weights_7_cast_fp16, y = var_1101_cast_fp16_0)[name = string("op_1117_cast_fp16")];
            bool attn_weights_9_transpose_x_0 = const()[name = string("attn_weights_9_transpose_x_0"), val = bool(false)];
            bool attn_weights_9_transpose_y_0 = const()[name = string("attn_weights_9_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_9_cast_fp16 = matmul(transpose_x = attn_weights_9_transpose_x_0, transpose_y = attn_weights_9_transpose_y_0, x = var_1091_cast_fp16_1, y = var_1104_1)[name = string("attn_weights_9_cast_fp16")];
            fp16 var_1119_to_fp16 = const()[name = string("op_1119_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_11_cast_fp16 = mul(x = attn_weights_9_cast_fp16, y = var_1119_to_fp16)[name = string("attn_weights_11_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_13_cast_fp16 = add(x = attn_weights_11_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_13_cast_fp16")];
            int32 var_1123 = const()[name = string("op_1123"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_15_cast_fp16 = softmax(axis = var_1123, x = attn_weights_13_cast_fp16)[name = string("attn_weights_15_cast_fp16")];
            bool attn_output_1_transpose_x_1 = const()[name = string("attn_output_1_transpose_x_1"), val = bool(true)];
            bool attn_output_1_transpose_y_1 = const()[name = string("attn_output_1_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_1_cast_fp16 = matmul(transpose_x = attn_output_1_transpose_x_1, transpose_y = attn_output_1_transpose_y_1, x = attn_weights_15_cast_fp16, y = var_1101_cast_fp16_1)[name = string("attn_output_1_cast_fp16")];
            int32 var_1131 = const()[name = string("op_1131"), val = int32(1)];
            bool attn_output_3_interleave_0 = const()[name = string("attn_output_3_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_3_cast_fp16 = concat(axis = var_1131, interleave = attn_output_3_interleave_0, values = (var_1117_cast_fp16, attn_output_1_cast_fp16))[name = string("attn_output_3_cast_fp16")];
            tensor<int32, [4]> var_1135_perm_0 = const()[name = string("op_1135_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_11x = const()[name = string("concat_11x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_1135_cast_fp16 = transpose(perm = var_1135_perm_0, x = attn_output_3_cast_fp16)[name = string("transpose_81")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_7_cast_fp16 = reshape(shape = concat_11x, x = var_1135_cast_fp16)[name = string("attn_output_7_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_0_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_0_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(985274560)))];
            tensor<int32, [2]> hidden_states_3_strides_0 = const()[name = string("hidden_states_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_3_pad_type_0 = const()[name = string("hidden_states_3_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_3_pad_0 = const()[name = string("hidden_states_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_3_dilations_0 = const()[name = string("hidden_states_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_3_groups_0 = const()[name = string("hidden_states_3_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_3_cast_fp16 = conv(dilations = hidden_states_3_dilations_0, groups = hidden_states_3_groups_0, pad = hidden_states_3_pad_0, pad_type = hidden_states_3_pad_type_0, strides = hidden_states_3_strides_0, weight = layers_0_self_attn_o_proj_weight_to_fp16, x = attn_output_7_cast_fp16)[name = string("hidden_states_3_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_5_cast_fp16 = add(x = inputs_embeds_to_fp16, y = hidden_states_3_cast_fp16)[name = string("hidden_states_5_cast_fp16")];
            fp16 const_10_promoted_to_fp16 = const()[name = string("const_10_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_1168_cast_fp16 = mul(x = hidden_states_5_cast_fp16, y = const_10_promoted_to_fp16)[name = string("op_1168_cast_fp16")];
            int32 var_1166 = const()[name = string("op_1166"), val = int32(1)];
            bool doubled_5_interleave_0 = const()[name = string("doubled_5_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_5_cast_fp16 = concat(axis = var_1166, interleave = doubled_5_interleave_0, values = (hidden_states_5_cast_fp16, var_1168_cast_fp16))[name = string("doubled_5_cast_fp16")];
            tensor<int32, [1]> out_3_axes_0 = const()[name = string("out_3_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_3_gamma_0_to_fp16 = const()[name = string("out_3_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(993663232)))];
            fp16 var_1178_to_fp16 = const()[name = string("op_1178_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_3_cast_fp16 = layer_norm(axes = out_3_axes_0, epsilon = var_1178_to_fp16, gamma = out_3_gamma_0_to_fp16, x = doubled_5_cast_fp16)[name = string("out_3_cast_fp16")];
            tensor<int32, [2]> var_1189_split_sizes_0 = const()[name = string("op_1189_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1189_axis_0 = const()[name = string("op_1189_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_1189_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_1189_cast_fp16_1 = split(axis = var_1189_axis_0, split_sizes = var_1189_split_sizes_0, x = out_3_cast_fp16)[name = string("op_1189_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_0_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_0_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(993671488)))];
            tensor<int32, [2]> input_1_strides_0 = const()[name = string("input_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_1_pad_type_0 = const()[name = string("input_1_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_1_pad_0 = const()[name = string("input_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_1_dilations_0 = const()[name = string("input_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_1_groups_0 = const()[name = string("input_1_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_1_cast_fp16 = conv(dilations = input_1_dilations_0, groups = input_1_groups_0, pad = input_1_pad_0, pad_type = input_1_pad_type_0, strides = input_1_strides_0, weight = layers_0_mlp_gate_proj_weight_to_fp16, x = var_1189_cast_fp16_0)[name = string("input_1_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_1206_cast_fp16 = silu(x = input_1_cast_fp16)[name = string("op_1206_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_0_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_0_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1018837376)))];
            tensor<int32, [2]> var_1212_strides_0 = const()[name = string("op_1212_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_1212_pad_type_0 = const()[name = string("op_1212_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_1212_pad_0 = const()[name = string("op_1212_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_1212_dilations_0 = const()[name = string("op_1212_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1212_groups_0 = const()[name = string("op_1212_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_1212_cast_fp16 = conv(dilations = var_1212_dilations_0, groups = var_1212_groups_0, pad = var_1212_pad_0, pad_type = var_1212_pad_type_0, strides = var_1212_strides_0, weight = layers_0_mlp_up_proj_weight_to_fp16, x = var_1189_cast_fp16_0)[name = string("op_1212_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_9_cast_fp16 = mul(x = var_1206_cast_fp16, y = var_1212_cast_fp16)[name = string("x_9_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_0_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_0_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1044003264)))];
            tensor<int32, [2]> hidden_states_7_strides_0 = const()[name = string("hidden_states_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_7_pad_type_0 = const()[name = string("hidden_states_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_7_pad_0 = const()[name = string("hidden_states_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_7_dilations_0 = const()[name = string("hidden_states_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_7_groups_0 = const()[name = string("hidden_states_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_7_cast_fp16 = conv(dilations = hidden_states_7_dilations_0, groups = hidden_states_7_groups_0, pad = hidden_states_7_pad_0, pad_type = hidden_states_7_pad_type_0, strides = hidden_states_7_strides_0, weight = layers_0_mlp_down_proj_weight_to_fp16, x = x_9_cast_fp16)[name = string("hidden_states_7_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_9_cast_fp16 = add(x = hidden_states_5_cast_fp16, y = hidden_states_7_cast_fp16)[name = string("hidden_states_9_cast_fp16")];
            fp16 const_12_promoted_to_fp16 = const()[name = string("const_12_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_1230_cast_fp16 = mul(x = hidden_states_9_cast_fp16, y = const_12_promoted_to_fp16)[name = string("op_1230_cast_fp16")];
            int32 var_1228 = const()[name = string("op_1228"), val = int32(1)];
            bool doubled_9_interleave_0 = const()[name = string("doubled_9_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_9_cast_fp16 = concat(axis = var_1228, interleave = doubled_9_interleave_0, values = (hidden_states_9_cast_fp16, var_1230_cast_fp16))[name = string("doubled_9_cast_fp16")];
            tensor<int32, [1]> out_5_axes_0 = const()[name = string("out_5_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_5_gamma_0_to_fp16 = const()[name = string("out_5_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1069169152)))];
            fp16 var_1240_to_fp16 = const()[name = string("op_1240_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_5_cast_fp16 = layer_norm(axes = out_5_axes_0, epsilon = var_1240_to_fp16, gamma = out_5_gamma_0_to_fp16, x = doubled_9_cast_fp16)[name = string("out_5_cast_fp16")];
            tensor<int32, [2]> var_1251_split_sizes_0 = const()[name = string("op_1251_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1251_axis_0 = const()[name = string("op_1251_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_1251_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_1251_cast_fp16_1 = split(axis = var_1251_axis_0, split_sizes = var_1251_split_sizes_0, x = out_5_cast_fp16)[name = string("op_1251_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_1_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_1_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1069177408)))];
            tensor<int32, [2]> query_states_7_strides_0 = const()[name = string("query_states_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_7_pad_type_0 = const()[name = string("query_states_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_7_pad_0 = const()[name = string("query_states_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_7_dilations_0 = const()[name = string("query_states_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_7_groups_0 = const()[name = string("query_states_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_7_cast_fp16 = conv(dilations = query_states_7_dilations_0, groups = query_states_7_groups_0, pad = query_states_7_pad_0, pad_type = query_states_7_pad_type_0, strides = query_states_7_strides_0, weight = layers_1_self_attn_q_proj_weight_to_fp16, x = var_1251_cast_fp16_0)[name = string("query_states_7_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_1_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_1_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1077566080)))];
            tensor<int32, [2]> key_states_11_strides_0 = const()[name = string("key_states_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_11_pad_type_0 = const()[name = string("key_states_11_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_11_pad_0 = const()[name = string("key_states_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_11_dilations_0 = const()[name = string("key_states_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_11_groups_0 = const()[name = string("key_states_11_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_11_cast_fp16 = conv(dilations = key_states_11_dilations_0, groups = key_states_11_groups_0, pad = key_states_11_pad_0, pad_type = key_states_11_pad_type_0, strides = key_states_11_strides_0, weight = layers_1_self_attn_k_proj_weight_to_fp16, x = var_1251_cast_fp16_0)[name = string("key_states_11_cast_fp16")];
            tensor<int32, [2]> value_states_7_strides_0 = const()[name = string("value_states_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_7_pad_type_0 = const()[name = string("value_states_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_7_pad_0 = const()[name = string("value_states_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_7_dilations_0 = const()[name = string("value_states_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_7_groups_0 = const()[name = string("value_states_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_7_cast_fp16 = conv(dilations = value_states_7_dilations_0, groups = value_states_7_groups_0, pad = value_states_7_pad_0, pad_type = value_states_7_pad_type_0, strides = value_states_7_strides_0, weight = layers_1_self_attn_v_proj_weight_cast_fp16, x = var_1251_cast_fp16_0)[name = string("value_states_7_cast_fp16")];
            tensor<int32, [4]> concat_12x = const()[name = string("concat_12x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_11_cast_fp16 = reshape(shape = concat_12x, x = query_states_7_cast_fp16)[name = string("x_11_cast_fp16")];
            tensor<int32, [4]> concat_13x = const()[name = string("concat_13x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_1308_cast_fp16 = reshape(shape = concat_13x, x = key_states_11_cast_fp16)[name = string("op_1308_cast_fp16")];
            tensor<int32, [4]> concat_14x = const()[name = string("concat_14x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_1315_cast_fp16 = reshape(shape = concat_14x, x = value_states_7_cast_fp16)[name = string("op_1315_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_1319_cast_fp16 = mul(x = x_11_cast_fp16, y = var_859_cast_fp16)[name = string("op_1319_cast_fp16")];
            tensor<int32, [2]> var_1320_split_sizes_0 = const()[name = string("op_1320_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1320_axis_0 = const()[name = string("op_1320_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_1320_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_1320_cast_fp16_1 = split(axis = var_1320_axis_0, split_sizes = var_1320_split_sizes_0, x = x_11_cast_fp16)[name = string("op_1320_cast_fp16")];
            fp16 const_14_promoted_to_fp16 = const()[name = string("const_14_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_1322_cast_fp16 = mul(x = var_1320_cast_fp16_1, y = const_14_promoted_to_fp16)[name = string("op_1322_cast_fp16")];
            int32 var_1324 = const()[name = string("op_1324"), val = int32(-2)];
            bool var_1325_interleave_0 = const()[name = string("op_1325_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_1325_cast_fp16 = concat(axis = var_1324, interleave = var_1325_interleave_0, values = (var_1322_cast_fp16, var_1320_cast_fp16_0))[name = string("op_1325_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_1326_cast_fp16 = mul(x = var_1325_cast_fp16, y = var_866_cast_fp16)[name = string("op_1326_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_9_cast_fp16 = add(x = var_1319_cast_fp16, y = var_1326_cast_fp16)[name = string("query_states_9_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_1332_cast_fp16 = mul(x = var_1308_cast_fp16, y = var_859_cast_fp16)[name = string("op_1332_cast_fp16")];
            tensor<int32, [2]> var_1333_split_sizes_0 = const()[name = string("op_1333_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1333_axis_0 = const()[name = string("op_1333_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_1333_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_1333_cast_fp16_1 = split(axis = var_1333_axis_0, split_sizes = var_1333_split_sizes_0, x = var_1308_cast_fp16)[name = string("op_1333_cast_fp16")];
            fp16 const_15_promoted_to_fp16 = const()[name = string("const_15_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_1335_cast_fp16 = mul(x = var_1333_cast_fp16_1, y = const_15_promoted_to_fp16)[name = string("op_1335_cast_fp16")];
            int32 var_1337 = const()[name = string("op_1337"), val = int32(-2)];
            bool var_1338_interleave_0 = const()[name = string("op_1338_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_1338_cast_fp16 = concat(axis = var_1337, interleave = var_1338_interleave_0, values = (var_1335_cast_fp16, var_1333_cast_fp16_0))[name = string("op_1338_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_1339_cast_fp16 = mul(x = var_1338_cast_fp16, y = var_866_cast_fp16)[name = string("op_1339_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_15_cast_fp16 = add(x = var_1332_cast_fp16, y = var_1339_cast_fp16)[name = string("key_states_15_cast_fp16")];
            tensor<int32, [1]> expand_dims_12 = const()[name = string("expand_dims_12"), val = tensor<int32, [1]>([1])];
            tensor<int32, [1]> expand_dims_13 = const()[name = string("expand_dims_13"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_15 = const()[name = string("expand_dims_15"), val = tensor<int32, [1]>([0])];
            int32 concat_17_axis_0 = const()[name = string("concat_17_axis_0"), val = int32(0)];
            bool concat_17_interleave_0 = const()[name = string("concat_17_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_17 = concat(axis = concat_17_axis_0, interleave = concat_17_interleave_0, values = (expand_dims_12, expand_dims_13, position_id, expand_dims_15))[name = string("concat_17")];
            tensor<int32, [1]> expand_dims_16 = const()[name = string("expand_dims_16"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> concat_18_values1_0 = const()[name = string("concat_18_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_18_values3_0 = const()[name = string("concat_18_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_18_axis_0 = const()[name = string("concat_18_axis_0"), val = int32(0)];
            bool concat_18_interleave_0 = const()[name = string("concat_18_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_18 = concat(axis = concat_18_axis_0, interleave = concat_18_interleave_0, values = (expand_dims_16, concat_18_values1_0, cache_position_end, concat_18_values3_0))[name = string("concat_18")];
            tensor<int32, [4]> key_states_17_perm_0 = const()[name = string("key_states_17_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_2_stride_0 = const()[name = string("key_cache_internal_tensor_assign_2_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_2_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_2_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_2_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_2_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_2_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_2_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_17_cast_fp16 = transpose(perm = key_states_17_perm_0, x = key_states_15_cast_fp16)[name = string("transpose_80")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_2_cast_fp16 = slice_update(begin = concat_17, begin_mask = key_cache_internal_tensor_assign_2_begin_mask_0, end = concat_18, end_mask = key_cache_internal_tensor_assign_2_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_2_squeeze_mask_0, stride = key_cache_internal_tensor_assign_2_stride_0, update = key_states_17_cast_fp16, x = coreml_update_state_0)[name = string("key_cache_internal_tensor_assign_2_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_2_cast_fp16, input = key_cache)[name = string("coreml_update_state_2_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_2 = read_state(input = key_cache)[name = string("coreml_update_state_2")];
            tensor<int32, [4]> value_states_9_perm_0 = const()[name = string("value_states_9_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_2_stride_0 = const()[name = string("value_cache_internal_tensor_assign_2_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_2_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_2_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_2_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_2_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_2_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_2_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_9_cast_fp16 = transpose(perm = value_states_9_perm_0, x = var_1315_cast_fp16)[name = string("transpose_79")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_2_cast_fp16 = slice_update(begin = concat_17, begin_mask = value_cache_internal_tensor_assign_2_begin_mask_0, end = concat_18, end_mask = value_cache_internal_tensor_assign_2_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_2_squeeze_mask_0, stride = value_cache_internal_tensor_assign_2_stride_0, update = value_states_9_cast_fp16, x = coreml_update_state_1)[name = string("value_cache_internal_tensor_assign_2_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_2_cast_fp16, input = value_cache)[name = string("coreml_update_state_3_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_3 = read_state(input = value_cache)[name = string("coreml_update_state_3")];
            tensor<int32, [4]> var_1409_begin_0 = const()[name = string("op_1409_begin_0"), val = tensor<int32, [4]>([1, 0, 0, 0])];
            tensor<int32, [4]> var_1409_end_0 = const()[name = string("op_1409_end_0"), val = tensor<int32, [4]>([2, 2, 2048, 128])];
            tensor<bool, [4]> var_1409_end_mask_0 = const()[name = string("op_1409_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1409_cast_fp16 = slice_by_index(begin = var_1409_begin_0, end = var_1409_end_0, end_mask = var_1409_end_mask_0, x = coreml_update_state_2)[name = string("op_1409_cast_fp16")];
            tensor<int32, [2]> tile_2 = const()[name = string("tile_2"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1412_axis_0 = const()[name = string("op_1412_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1412_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1412_cast_fp16_1 = split(axis = var_1412_axis_0, split_sizes = tile_2, x = var_1409_cast_fp16)[name = string("op_1412_cast_fp16")];
            tensor<int32, [4]> var_1419_begin_0 = const()[name = string("op_1419_begin_0"), val = tensor<int32, [4]>([1, 0, 0, 0])];
            tensor<int32, [4]> var_1419_end_0 = const()[name = string("op_1419_end_0"), val = tensor<int32, [4]>([2, 2, 2048, 128])];
            tensor<bool, [4]> var_1419_end_mask_0 = const()[name = string("op_1419_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1419_cast_fp16 = slice_by_index(begin = var_1419_begin_0, end = var_1419_end_0, end_mask = var_1419_end_mask_0, x = coreml_update_state_3)[name = string("op_1419_cast_fp16")];
            tensor<int32, [2]> tile_3 = const()[name = string("tile_3"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1422_axis_0 = const()[name = string("op_1422_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1422_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1422_cast_fp16_1 = split(axis = var_1422_axis_0, split_sizes = tile_3, x = var_1419_cast_fp16)[name = string("op_1422_cast_fp16")];
            tensor<int32, [2]> var_1425_split_sizes_0 = const()[name = string("op_1425_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_1425_axis_0 = const()[name = string("op_1425_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_1425_0, tensor<fp16, [1, 8, 128, 1]> var_1425_1 = split(axis = var_1425_axis_0, split_sizes = var_1425_split_sizes_0, x = query_states_9_cast_fp16)[name = string("op_1425")];
            bool attn_weights_17_transpose_x_0 = const()[name = string("attn_weights_17_transpose_x_0"), val = bool(false)];
            bool attn_weights_17_transpose_y_0 = const()[name = string("attn_weights_17_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_17_cast_fp16 = matmul(transpose_x = attn_weights_17_transpose_x_0, transpose_y = attn_weights_17_transpose_y_0, x = var_1412_cast_fp16_0, y = var_1425_0)[name = string("attn_weights_17_cast_fp16")];
            fp16 var_1428_to_fp16 = const()[name = string("op_1428_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_19_cast_fp16 = mul(x = attn_weights_17_cast_fp16, y = var_1428_to_fp16)[name = string("attn_weights_19_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_21_cast_fp16 = add(x = attn_weights_19_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_21_cast_fp16")];
            int32 var_1432 = const()[name = string("op_1432"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_23_cast_fp16 = softmax(axis = var_1432, x = attn_weights_21_cast_fp16)[name = string("attn_weights_23_cast_fp16")];
            bool var_1438_transpose_x_1 = const()[name = string("op_1438_transpose_x_1"), val = bool(true)];
            bool var_1438_transpose_y_1 = const()[name = string("op_1438_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_1438_cast_fp16 = matmul(transpose_x = var_1438_transpose_x_1, transpose_y = var_1438_transpose_y_1, x = attn_weights_23_cast_fp16, y = var_1422_cast_fp16_0)[name = string("op_1438_cast_fp16")];
            bool attn_weights_25_transpose_x_0 = const()[name = string("attn_weights_25_transpose_x_0"), val = bool(false)];
            bool attn_weights_25_transpose_y_0 = const()[name = string("attn_weights_25_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_25_cast_fp16 = matmul(transpose_x = attn_weights_25_transpose_x_0, transpose_y = attn_weights_25_transpose_y_0, x = var_1412_cast_fp16_1, y = var_1425_1)[name = string("attn_weights_25_cast_fp16")];
            fp16 var_1440_to_fp16 = const()[name = string("op_1440_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_27_cast_fp16 = mul(x = attn_weights_25_cast_fp16, y = var_1440_to_fp16)[name = string("attn_weights_27_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_29_cast_fp16 = add(x = attn_weights_27_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_29_cast_fp16")];
            int32 var_1444 = const()[name = string("op_1444"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_31_cast_fp16 = softmax(axis = var_1444, x = attn_weights_29_cast_fp16)[name = string("attn_weights_31_cast_fp16")];
            bool attn_output_9_transpose_x_1 = const()[name = string("attn_output_9_transpose_x_1"), val = bool(true)];
            bool attn_output_9_transpose_y_1 = const()[name = string("attn_output_9_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_9_cast_fp16 = matmul(transpose_x = attn_output_9_transpose_x_1, transpose_y = attn_output_9_transpose_y_1, x = attn_weights_31_cast_fp16, y = var_1422_cast_fp16_1)[name = string("attn_output_9_cast_fp16")];
            int32 var_1452 = const()[name = string("op_1452"), val = int32(1)];
            bool attn_output_11_interleave_0 = const()[name = string("attn_output_11_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_11_cast_fp16 = concat(axis = var_1452, interleave = attn_output_11_interleave_0, values = (var_1438_cast_fp16, attn_output_9_cast_fp16))[name = string("attn_output_11_cast_fp16")];
            tensor<int32, [4]> var_1456_perm_0 = const()[name = string("op_1456_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_23x = const()[name = string("concat_23x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_1456_cast_fp16 = transpose(perm = var_1456_perm_0, x = attn_output_11_cast_fp16)[name = string("transpose_78")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_15_cast_fp16 = reshape(shape = concat_23x, x = var_1456_cast_fp16)[name = string("attn_output_15_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_1_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_1_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1078614720)))];
            tensor<int32, [2]> hidden_states_13_strides_0 = const()[name = string("hidden_states_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_13_pad_type_0 = const()[name = string("hidden_states_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_13_pad_0 = const()[name = string("hidden_states_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_13_dilations_0 = const()[name = string("hidden_states_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_13_groups_0 = const()[name = string("hidden_states_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_13_cast_fp16 = conv(dilations = hidden_states_13_dilations_0, groups = hidden_states_13_groups_0, pad = hidden_states_13_pad_0, pad_type = hidden_states_13_pad_type_0, strides = hidden_states_13_strides_0, weight = layers_1_self_attn_o_proj_weight_to_fp16, x = attn_output_15_cast_fp16)[name = string("hidden_states_13_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_15_cast_fp16 = add(x = hidden_states_9_cast_fp16, y = hidden_states_13_cast_fp16)[name = string("hidden_states_15_cast_fp16")];
            fp16 const_20_promoted_to_fp16 = const()[name = string("const_20_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_1489_cast_fp16 = mul(x = hidden_states_15_cast_fp16, y = const_20_promoted_to_fp16)[name = string("op_1489_cast_fp16")];
            int32 var_1487 = const()[name = string("op_1487"), val = int32(1)];
            bool doubled_13_interleave_0 = const()[name = string("doubled_13_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_13_cast_fp16 = concat(axis = var_1487, interleave = doubled_13_interleave_0, values = (hidden_states_15_cast_fp16, var_1489_cast_fp16))[name = string("doubled_13_cast_fp16")];
            tensor<int32, [1]> out_7_axes_0 = const()[name = string("out_7_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_7_gamma_0_to_fp16 = const()[name = string("out_7_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1087003392)))];
            fp16 var_1499_to_fp16 = const()[name = string("op_1499_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_7_cast_fp16 = layer_norm(axes = out_7_axes_0, epsilon = var_1499_to_fp16, gamma = out_7_gamma_0_to_fp16, x = doubled_13_cast_fp16)[name = string("out_7_cast_fp16")];
            tensor<int32, [2]> var_1510_split_sizes_0 = const()[name = string("op_1510_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1510_axis_0 = const()[name = string("op_1510_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_1510_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_1510_cast_fp16_1 = split(axis = var_1510_axis_0, split_sizes = var_1510_split_sizes_0, x = out_7_cast_fp16)[name = string("op_1510_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_1_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_1_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1087011648)))];
            tensor<int32, [2]> input_3_strides_0 = const()[name = string("input_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_3_pad_type_0 = const()[name = string("input_3_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_3_pad_0 = const()[name = string("input_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_3_dilations_0 = const()[name = string("input_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_3_groups_0 = const()[name = string("input_3_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_3_cast_fp16 = conv(dilations = input_3_dilations_0, groups = input_3_groups_0, pad = input_3_pad_0, pad_type = input_3_pad_type_0, strides = input_3_strides_0, weight = layers_1_mlp_gate_proj_weight_to_fp16, x = var_1510_cast_fp16_0)[name = string("input_3_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_1527_cast_fp16 = silu(x = input_3_cast_fp16)[name = string("op_1527_cast_fp16")];
            tensor<int32, [2]> var_1533_strides_0 = const()[name = string("op_1533_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_1533_pad_type_0 = const()[name = string("op_1533_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_1533_pad_0 = const()[name = string("op_1533_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_1533_dilations_0 = const()[name = string("op_1533_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1533_groups_0 = const()[name = string("op_1533_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_1533_cast_fp16 = conv(dilations = var_1533_dilations_0, groups = var_1533_groups_0, pad = var_1533_pad_0, pad_type = var_1533_pad_type_0, strides = var_1533_strides_0, weight = layers_1_mlp_up_proj_weight_cast_fp16, x = var_1510_cast_fp16_0)[name = string("op_1533_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_19_cast_fp16 = mul(x = var_1527_cast_fp16, y = var_1533_cast_fp16)[name = string("x_19_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_1_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_1_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1112177536)))];
            tensor<int32, [2]> hidden_states_17_strides_0 = const()[name = string("hidden_states_17_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_17_pad_type_0 = const()[name = string("hidden_states_17_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_17_pad_0 = const()[name = string("hidden_states_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_17_dilations_0 = const()[name = string("hidden_states_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_17_groups_0 = const()[name = string("hidden_states_17_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_17_cast_fp16 = conv(dilations = hidden_states_17_dilations_0, groups = hidden_states_17_groups_0, pad = hidden_states_17_pad_0, pad_type = hidden_states_17_pad_type_0, strides = hidden_states_17_strides_0, weight = layers_1_mlp_down_proj_weight_to_fp16, x = x_19_cast_fp16)[name = string("hidden_states_17_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_19_cast_fp16 = add(x = hidden_states_15_cast_fp16, y = hidden_states_17_cast_fp16)[name = string("hidden_states_19_cast_fp16")];
            fp16 const_22_promoted_to_fp16 = const()[name = string("const_22_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_1551_cast_fp16 = mul(x = hidden_states_19_cast_fp16, y = const_22_promoted_to_fp16)[name = string("op_1551_cast_fp16")];
            int32 var_1549 = const()[name = string("op_1549"), val = int32(1)];
            bool doubled_17_interleave_0 = const()[name = string("doubled_17_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_17_cast_fp16 = concat(axis = var_1549, interleave = doubled_17_interleave_0, values = (hidden_states_19_cast_fp16, var_1551_cast_fp16))[name = string("doubled_17_cast_fp16")];
            tensor<int32, [1]> out_9_axes_0 = const()[name = string("out_9_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_9_gamma_0_to_fp16 = const()[name = string("out_9_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1137343424)))];
            fp16 var_1561_to_fp16 = const()[name = string("op_1561_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_9_cast_fp16 = layer_norm(axes = out_9_axes_0, epsilon = var_1561_to_fp16, gamma = out_9_gamma_0_to_fp16, x = doubled_17_cast_fp16)[name = string("out_9_cast_fp16")];
            tensor<int32, [2]> var_1572_split_sizes_0 = const()[name = string("op_1572_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1572_axis_0 = const()[name = string("op_1572_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_1572_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_1572_cast_fp16_1 = split(axis = var_1572_axis_0, split_sizes = var_1572_split_sizes_0, x = out_9_cast_fp16)[name = string("op_1572_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_2_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_2_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1137351680)))];
            tensor<int32, [2]> query_states_13_strides_0 = const()[name = string("query_states_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_13_pad_type_0 = const()[name = string("query_states_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_13_pad_0 = const()[name = string("query_states_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_13_dilations_0 = const()[name = string("query_states_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_13_groups_0 = const()[name = string("query_states_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_13_cast_fp16 = conv(dilations = query_states_13_dilations_0, groups = query_states_13_groups_0, pad = query_states_13_pad_0, pad_type = query_states_13_pad_type_0, strides = query_states_13_strides_0, weight = layers_2_self_attn_q_proj_weight_to_fp16, x = var_1572_cast_fp16_0)[name = string("query_states_13_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_2_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_2_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1145740352)))];
            tensor<int32, [2]> key_states_21_strides_0 = const()[name = string("key_states_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_21_pad_type_0 = const()[name = string("key_states_21_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_21_pad_0 = const()[name = string("key_states_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_21_dilations_0 = const()[name = string("key_states_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_21_groups_0 = const()[name = string("key_states_21_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_21_cast_fp16 = conv(dilations = key_states_21_dilations_0, groups = key_states_21_groups_0, pad = key_states_21_pad_0, pad_type = key_states_21_pad_type_0, strides = key_states_21_strides_0, weight = layers_2_self_attn_k_proj_weight_to_fp16, x = var_1572_cast_fp16_0)[name = string("key_states_21_cast_fp16")];
            tensor<int32, [2]> value_states_13_strides_0 = const()[name = string("value_states_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_13_pad_type_0 = const()[name = string("value_states_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_13_pad_0 = const()[name = string("value_states_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_13_dilations_0 = const()[name = string("value_states_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_13_groups_0 = const()[name = string("value_states_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_13_cast_fp16 = conv(dilations = value_states_13_dilations_0, groups = value_states_13_groups_0, pad = value_states_13_pad_0, pad_type = value_states_13_pad_type_0, strides = value_states_13_strides_0, weight = layers_2_self_attn_v_proj_weight_cast_fp16, x = var_1572_cast_fp16_0)[name = string("value_states_13_cast_fp16")];
            tensor<int32, [4]> concat_24x = const()[name = string("concat_24x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_21_cast_fp16 = reshape(shape = concat_24x, x = query_states_13_cast_fp16)[name = string("x_21_cast_fp16")];
            tensor<int32, [4]> concat_25x = const()[name = string("concat_25x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_1629_cast_fp16 = reshape(shape = concat_25x, x = key_states_21_cast_fp16)[name = string("op_1629_cast_fp16")];
            tensor<int32, [4]> concat_26x = const()[name = string("concat_26x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_1636_cast_fp16 = reshape(shape = concat_26x, x = value_states_13_cast_fp16)[name = string("op_1636_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_1640_cast_fp16 = mul(x = x_21_cast_fp16, y = var_859_cast_fp16)[name = string("op_1640_cast_fp16")];
            tensor<int32, [2]> var_1641_split_sizes_0 = const()[name = string("op_1641_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1641_axis_0 = const()[name = string("op_1641_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_1641_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_1641_cast_fp16_1 = split(axis = var_1641_axis_0, split_sizes = var_1641_split_sizes_0, x = x_21_cast_fp16)[name = string("op_1641_cast_fp16")];
            fp16 const_24_promoted_to_fp16 = const()[name = string("const_24_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_1643_cast_fp16 = mul(x = var_1641_cast_fp16_1, y = const_24_promoted_to_fp16)[name = string("op_1643_cast_fp16")];
            int32 var_1645 = const()[name = string("op_1645"), val = int32(-2)];
            bool var_1646_interleave_0 = const()[name = string("op_1646_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_1646_cast_fp16 = concat(axis = var_1645, interleave = var_1646_interleave_0, values = (var_1643_cast_fp16, var_1641_cast_fp16_0))[name = string("op_1646_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_1647_cast_fp16 = mul(x = var_1646_cast_fp16, y = var_866_cast_fp16)[name = string("op_1647_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_15_cast_fp16 = add(x = var_1640_cast_fp16, y = var_1647_cast_fp16)[name = string("query_states_15_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_1653_cast_fp16 = mul(x = var_1629_cast_fp16, y = var_859_cast_fp16)[name = string("op_1653_cast_fp16")];
            tensor<int32, [2]> var_1654_split_sizes_0 = const()[name = string("op_1654_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1654_axis_0 = const()[name = string("op_1654_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_1654_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_1654_cast_fp16_1 = split(axis = var_1654_axis_0, split_sizes = var_1654_split_sizes_0, x = var_1629_cast_fp16)[name = string("op_1654_cast_fp16")];
            fp16 const_25_promoted_to_fp16 = const()[name = string("const_25_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_1656_cast_fp16 = mul(x = var_1654_cast_fp16_1, y = const_25_promoted_to_fp16)[name = string("op_1656_cast_fp16")];
            int32 var_1658 = const()[name = string("op_1658"), val = int32(-2)];
            bool var_1659_interleave_0 = const()[name = string("op_1659_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_1659_cast_fp16 = concat(axis = var_1658, interleave = var_1659_interleave_0, values = (var_1656_cast_fp16, var_1654_cast_fp16_0))[name = string("op_1659_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_1660_cast_fp16 = mul(x = var_1659_cast_fp16, y = var_866_cast_fp16)[name = string("op_1660_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_25_cast_fp16 = add(x = var_1653_cast_fp16, y = var_1660_cast_fp16)[name = string("key_states_25_cast_fp16")];
            tensor<int32, [1]> expand_dims_24 = const()[name = string("expand_dims_24"), val = tensor<int32, [1]>([2])];
            tensor<int32, [1]> expand_dims_25 = const()[name = string("expand_dims_25"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_27 = const()[name = string("expand_dims_27"), val = tensor<int32, [1]>([0])];
            int32 concat_29_axis_0 = const()[name = string("concat_29_axis_0"), val = int32(0)];
            bool concat_29_interleave_0 = const()[name = string("concat_29_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_29 = concat(axis = concat_29_axis_0, interleave = concat_29_interleave_0, values = (expand_dims_24, expand_dims_25, position_id, expand_dims_27))[name = string("concat_29")];
            tensor<int32, [1]> expand_dims_28 = const()[name = string("expand_dims_28"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> concat_30_values1_0 = const()[name = string("concat_30_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_30_values3_0 = const()[name = string("concat_30_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_30_axis_0 = const()[name = string("concat_30_axis_0"), val = int32(0)];
            bool concat_30_interleave_0 = const()[name = string("concat_30_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_30 = concat(axis = concat_30_axis_0, interleave = concat_30_interleave_0, values = (expand_dims_28, concat_30_values1_0, cache_position_end, concat_30_values3_0))[name = string("concat_30")];
            tensor<int32, [4]> key_states_27_perm_0 = const()[name = string("key_states_27_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_3_stride_0 = const()[name = string("key_cache_internal_tensor_assign_3_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_3_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_3_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_3_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_3_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_3_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_3_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_27_cast_fp16 = transpose(perm = key_states_27_perm_0, x = key_states_25_cast_fp16)[name = string("transpose_77")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_3_cast_fp16 = slice_update(begin = concat_29, begin_mask = key_cache_internal_tensor_assign_3_begin_mask_0, end = concat_30, end_mask = key_cache_internal_tensor_assign_3_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_3_squeeze_mask_0, stride = key_cache_internal_tensor_assign_3_stride_0, update = key_states_27_cast_fp16, x = coreml_update_state_2)[name = string("key_cache_internal_tensor_assign_3_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_3_cast_fp16, input = key_cache)[name = string("coreml_update_state_4_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_4 = read_state(input = key_cache)[name = string("coreml_update_state_4")];
            tensor<int32, [4]> value_states_15_perm_0 = const()[name = string("value_states_15_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_3_stride_0 = const()[name = string("value_cache_internal_tensor_assign_3_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_3_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_3_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_3_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_3_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_3_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_3_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_15_cast_fp16 = transpose(perm = value_states_15_perm_0, x = var_1636_cast_fp16)[name = string("transpose_76")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_3_cast_fp16 = slice_update(begin = concat_29, begin_mask = value_cache_internal_tensor_assign_3_begin_mask_0, end = concat_30, end_mask = value_cache_internal_tensor_assign_3_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_3_squeeze_mask_0, stride = value_cache_internal_tensor_assign_3_stride_0, update = value_states_15_cast_fp16, x = coreml_update_state_3)[name = string("value_cache_internal_tensor_assign_3_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_3_cast_fp16, input = value_cache)[name = string("coreml_update_state_5_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_5 = read_state(input = value_cache)[name = string("coreml_update_state_5")];
            tensor<int32, [4]> var_1730_begin_0 = const()[name = string("op_1730_begin_0"), val = tensor<int32, [4]>([2, 0, 0, 0])];
            tensor<int32, [4]> var_1730_end_0 = const()[name = string("op_1730_end_0"), val = tensor<int32, [4]>([3, 2, 2048, 128])];
            tensor<bool, [4]> var_1730_end_mask_0 = const()[name = string("op_1730_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1730_cast_fp16 = slice_by_index(begin = var_1730_begin_0, end = var_1730_end_0, end_mask = var_1730_end_mask_0, x = coreml_update_state_4)[name = string("op_1730_cast_fp16")];
            tensor<int32, [2]> tile_4 = const()[name = string("tile_4"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1733_axis_0 = const()[name = string("op_1733_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1733_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1733_cast_fp16_1 = split(axis = var_1733_axis_0, split_sizes = tile_4, x = var_1730_cast_fp16)[name = string("op_1733_cast_fp16")];
            tensor<int32, [4]> var_1740_begin_0 = const()[name = string("op_1740_begin_0"), val = tensor<int32, [4]>([2, 0, 0, 0])];
            tensor<int32, [4]> var_1740_end_0 = const()[name = string("op_1740_end_0"), val = tensor<int32, [4]>([3, 2, 2048, 128])];
            tensor<bool, [4]> var_1740_end_mask_0 = const()[name = string("op_1740_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_1740_cast_fp16 = slice_by_index(begin = var_1740_begin_0, end = var_1740_end_0, end_mask = var_1740_end_mask_0, x = coreml_update_state_5)[name = string("op_1740_cast_fp16")];
            tensor<int32, [2]> tile_5 = const()[name = string("tile_5"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1743_axis_0 = const()[name = string("op_1743_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_1743_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_1743_cast_fp16_1 = split(axis = var_1743_axis_0, split_sizes = tile_5, x = var_1740_cast_fp16)[name = string("op_1743_cast_fp16")];
            tensor<int32, [2]> var_1746_split_sizes_0 = const()[name = string("op_1746_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_1746_axis_0 = const()[name = string("op_1746_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_1746_0, tensor<fp16, [1, 8, 128, 1]> var_1746_1 = split(axis = var_1746_axis_0, split_sizes = var_1746_split_sizes_0, x = query_states_15_cast_fp16)[name = string("op_1746")];
            bool attn_weights_33_transpose_x_0 = const()[name = string("attn_weights_33_transpose_x_0"), val = bool(false)];
            bool attn_weights_33_transpose_y_0 = const()[name = string("attn_weights_33_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_33_cast_fp16 = matmul(transpose_x = attn_weights_33_transpose_x_0, transpose_y = attn_weights_33_transpose_y_0, x = var_1733_cast_fp16_0, y = var_1746_0)[name = string("attn_weights_33_cast_fp16")];
            fp16 var_1749_to_fp16 = const()[name = string("op_1749_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_35_cast_fp16 = mul(x = attn_weights_33_cast_fp16, y = var_1749_to_fp16)[name = string("attn_weights_35_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_37_cast_fp16 = add(x = attn_weights_35_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_37_cast_fp16")];
            int32 var_1753 = const()[name = string("op_1753"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_39_cast_fp16 = softmax(axis = var_1753, x = attn_weights_37_cast_fp16)[name = string("attn_weights_39_cast_fp16")];
            bool var_1759_transpose_x_1 = const()[name = string("op_1759_transpose_x_1"), val = bool(true)];
            bool var_1759_transpose_y_1 = const()[name = string("op_1759_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_1759_cast_fp16 = matmul(transpose_x = var_1759_transpose_x_1, transpose_y = var_1759_transpose_y_1, x = attn_weights_39_cast_fp16, y = var_1743_cast_fp16_0)[name = string("op_1759_cast_fp16")];
            bool attn_weights_41_transpose_x_0 = const()[name = string("attn_weights_41_transpose_x_0"), val = bool(false)];
            bool attn_weights_41_transpose_y_0 = const()[name = string("attn_weights_41_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_41_cast_fp16 = matmul(transpose_x = attn_weights_41_transpose_x_0, transpose_y = attn_weights_41_transpose_y_0, x = var_1733_cast_fp16_1, y = var_1746_1)[name = string("attn_weights_41_cast_fp16")];
            fp16 var_1761_to_fp16 = const()[name = string("op_1761_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_43_cast_fp16 = mul(x = attn_weights_41_cast_fp16, y = var_1761_to_fp16)[name = string("attn_weights_43_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_45_cast_fp16 = add(x = attn_weights_43_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_45_cast_fp16")];
            int32 var_1765 = const()[name = string("op_1765"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_47_cast_fp16 = softmax(axis = var_1765, x = attn_weights_45_cast_fp16)[name = string("attn_weights_47_cast_fp16")];
            bool attn_output_17_transpose_x_1 = const()[name = string("attn_output_17_transpose_x_1"), val = bool(true)];
            bool attn_output_17_transpose_y_1 = const()[name = string("attn_output_17_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_17_cast_fp16 = matmul(transpose_x = attn_output_17_transpose_x_1, transpose_y = attn_output_17_transpose_y_1, x = attn_weights_47_cast_fp16, y = var_1743_cast_fp16_1)[name = string("attn_output_17_cast_fp16")];
            int32 var_1773 = const()[name = string("op_1773"), val = int32(1)];
            bool attn_output_19_interleave_0 = const()[name = string("attn_output_19_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_19_cast_fp16 = concat(axis = var_1773, interleave = attn_output_19_interleave_0, values = (var_1759_cast_fp16, attn_output_17_cast_fp16))[name = string("attn_output_19_cast_fp16")];
            tensor<int32, [4]> var_1777_perm_0 = const()[name = string("op_1777_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_35x = const()[name = string("concat_35x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_1777_cast_fp16 = transpose(perm = var_1777_perm_0, x = attn_output_19_cast_fp16)[name = string("transpose_75")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_23_cast_fp16 = reshape(shape = concat_35x, x = var_1777_cast_fp16)[name = string("attn_output_23_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_2_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_2_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1146788992)))];
            tensor<int32, [2]> hidden_states_23_strides_0 = const()[name = string("hidden_states_23_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_23_pad_type_0 = const()[name = string("hidden_states_23_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_23_pad_0 = const()[name = string("hidden_states_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_23_dilations_0 = const()[name = string("hidden_states_23_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_23_groups_0 = const()[name = string("hidden_states_23_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_23_cast_fp16 = conv(dilations = hidden_states_23_dilations_0, groups = hidden_states_23_groups_0, pad = hidden_states_23_pad_0, pad_type = hidden_states_23_pad_type_0, strides = hidden_states_23_strides_0, weight = layers_2_self_attn_o_proj_weight_to_fp16, x = attn_output_23_cast_fp16)[name = string("hidden_states_23_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_25_cast_fp16 = add(x = hidden_states_19_cast_fp16, y = hidden_states_23_cast_fp16)[name = string("hidden_states_25_cast_fp16")];
            fp16 const_30_promoted_to_fp16 = const()[name = string("const_30_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_1810_cast_fp16 = mul(x = hidden_states_25_cast_fp16, y = const_30_promoted_to_fp16)[name = string("op_1810_cast_fp16")];
            int32 var_1808 = const()[name = string("op_1808"), val = int32(1)];
            bool doubled_21_interleave_0 = const()[name = string("doubled_21_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_21_cast_fp16 = concat(axis = var_1808, interleave = doubled_21_interleave_0, values = (hidden_states_25_cast_fp16, var_1810_cast_fp16))[name = string("doubled_21_cast_fp16")];
            tensor<int32, [1]> out_11_axes_0 = const()[name = string("out_11_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_11_gamma_0_to_fp16 = const()[name = string("out_11_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1155177664)))];
            fp16 var_1820_to_fp16 = const()[name = string("op_1820_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_11_cast_fp16 = layer_norm(axes = out_11_axes_0, epsilon = var_1820_to_fp16, gamma = out_11_gamma_0_to_fp16, x = doubled_21_cast_fp16)[name = string("out_11_cast_fp16")];
            tensor<int32, [2]> var_1831_split_sizes_0 = const()[name = string("op_1831_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1831_axis_0 = const()[name = string("op_1831_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_1831_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_1831_cast_fp16_1 = split(axis = var_1831_axis_0, split_sizes = var_1831_split_sizes_0, x = out_11_cast_fp16)[name = string("op_1831_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_2_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_2_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1155185920)))];
            tensor<int32, [2]> input_5_strides_0 = const()[name = string("input_5_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_5_pad_type_0 = const()[name = string("input_5_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_5_pad_0 = const()[name = string("input_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_5_dilations_0 = const()[name = string("input_5_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_5_groups_0 = const()[name = string("input_5_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_5_cast_fp16 = conv(dilations = input_5_dilations_0, groups = input_5_groups_0, pad = input_5_pad_0, pad_type = input_5_pad_type_0, strides = input_5_strides_0, weight = layers_2_mlp_gate_proj_weight_to_fp16, x = var_1831_cast_fp16_0)[name = string("input_5_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_1848_cast_fp16 = silu(x = input_5_cast_fp16)[name = string("op_1848_cast_fp16")];
            tensor<int32, [2]> var_1854_strides_0 = const()[name = string("op_1854_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_1854_pad_type_0 = const()[name = string("op_1854_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_1854_pad_0 = const()[name = string("op_1854_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_1854_dilations_0 = const()[name = string("op_1854_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_1854_groups_0 = const()[name = string("op_1854_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_1854_cast_fp16 = conv(dilations = var_1854_dilations_0, groups = var_1854_groups_0, pad = var_1854_pad_0, pad_type = var_1854_pad_type_0, strides = var_1854_strides_0, weight = layers_2_mlp_up_proj_weight_cast_fp16, x = var_1831_cast_fp16_0)[name = string("op_1854_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_29_cast_fp16 = mul(x = var_1848_cast_fp16, y = var_1854_cast_fp16)[name = string("x_29_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_2_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_2_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1180351808)))];
            tensor<int32, [2]> hidden_states_27_strides_0 = const()[name = string("hidden_states_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_27_pad_type_0 = const()[name = string("hidden_states_27_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_27_pad_0 = const()[name = string("hidden_states_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_27_dilations_0 = const()[name = string("hidden_states_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_27_groups_0 = const()[name = string("hidden_states_27_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_27_cast_fp16 = conv(dilations = hidden_states_27_dilations_0, groups = hidden_states_27_groups_0, pad = hidden_states_27_pad_0, pad_type = hidden_states_27_pad_type_0, strides = hidden_states_27_strides_0, weight = layers_2_mlp_down_proj_weight_to_fp16, x = x_29_cast_fp16)[name = string("hidden_states_27_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_29_cast_fp16 = add(x = hidden_states_25_cast_fp16, y = hidden_states_27_cast_fp16)[name = string("hidden_states_29_cast_fp16")];
            fp16 const_32_promoted_to_fp16 = const()[name = string("const_32_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_1872_cast_fp16 = mul(x = hidden_states_29_cast_fp16, y = const_32_promoted_to_fp16)[name = string("op_1872_cast_fp16")];
            int32 var_1870 = const()[name = string("op_1870"), val = int32(1)];
            bool doubled_25_interleave_0 = const()[name = string("doubled_25_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_25_cast_fp16 = concat(axis = var_1870, interleave = doubled_25_interleave_0, values = (hidden_states_29_cast_fp16, var_1872_cast_fp16))[name = string("doubled_25_cast_fp16")];
            tensor<int32, [1]> out_13_axes_0 = const()[name = string("out_13_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_13_gamma_0_to_fp16 = const()[name = string("out_13_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1205517696)))];
            fp16 var_1882_to_fp16 = const()[name = string("op_1882_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_13_cast_fp16 = layer_norm(axes = out_13_axes_0, epsilon = var_1882_to_fp16, gamma = out_13_gamma_0_to_fp16, x = doubled_25_cast_fp16)[name = string("out_13_cast_fp16")];
            tensor<int32, [2]> var_1893_split_sizes_0 = const()[name = string("op_1893_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_1893_axis_0 = const()[name = string("op_1893_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_1893_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_1893_cast_fp16_1 = split(axis = var_1893_axis_0, split_sizes = var_1893_split_sizes_0, x = out_13_cast_fp16)[name = string("op_1893_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_3_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_3_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1205525952)))];
            tensor<int32, [2]> query_states_19_strides_0 = const()[name = string("query_states_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_19_pad_type_0 = const()[name = string("query_states_19_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_19_pad_0 = const()[name = string("query_states_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_19_dilations_0 = const()[name = string("query_states_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_19_groups_0 = const()[name = string("query_states_19_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_19_cast_fp16 = conv(dilations = query_states_19_dilations_0, groups = query_states_19_groups_0, pad = query_states_19_pad_0, pad_type = query_states_19_pad_type_0, strides = query_states_19_strides_0, weight = layers_3_self_attn_q_proj_weight_to_fp16, x = var_1893_cast_fp16_0)[name = string("query_states_19_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_3_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_3_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1213914624)))];
            tensor<int32, [2]> key_states_31_strides_0 = const()[name = string("key_states_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_31_pad_type_0 = const()[name = string("key_states_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_31_pad_0 = const()[name = string("key_states_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_31_dilations_0 = const()[name = string("key_states_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_31_groups_0 = const()[name = string("key_states_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_31_cast_fp16 = conv(dilations = key_states_31_dilations_0, groups = key_states_31_groups_0, pad = key_states_31_pad_0, pad_type = key_states_31_pad_type_0, strides = key_states_31_strides_0, weight = layers_3_self_attn_k_proj_weight_to_fp16, x = var_1893_cast_fp16_0)[name = string("key_states_31_cast_fp16")];
            tensor<int32, [2]> value_states_19_strides_0 = const()[name = string("value_states_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_19_pad_type_0 = const()[name = string("value_states_19_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_19_pad_0 = const()[name = string("value_states_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_19_dilations_0 = const()[name = string("value_states_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_19_groups_0 = const()[name = string("value_states_19_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_19_cast_fp16 = conv(dilations = value_states_19_dilations_0, groups = value_states_19_groups_0, pad = value_states_19_pad_0, pad_type = value_states_19_pad_type_0, strides = value_states_19_strides_0, weight = layers_3_self_attn_v_proj_weight_cast_fp16, x = var_1893_cast_fp16_0)[name = string("value_states_19_cast_fp16")];
            tensor<int32, [4]> concat_36x = const()[name = string("concat_36x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_31_cast_fp16 = reshape(shape = concat_36x, x = query_states_19_cast_fp16)[name = string("x_31_cast_fp16")];
            tensor<int32, [4]> concat_37x = const()[name = string("concat_37x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_1950_cast_fp16 = reshape(shape = concat_37x, x = key_states_31_cast_fp16)[name = string("op_1950_cast_fp16")];
            tensor<int32, [4]> concat_38x = const()[name = string("concat_38x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_1957_cast_fp16 = reshape(shape = concat_38x, x = value_states_19_cast_fp16)[name = string("op_1957_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_1961_cast_fp16 = mul(x = x_31_cast_fp16, y = var_859_cast_fp16)[name = string("op_1961_cast_fp16")];
            tensor<int32, [2]> var_1962_split_sizes_0 = const()[name = string("op_1962_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1962_axis_0 = const()[name = string("op_1962_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_1962_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_1962_cast_fp16_1 = split(axis = var_1962_axis_0, split_sizes = var_1962_split_sizes_0, x = x_31_cast_fp16)[name = string("op_1962_cast_fp16")];
            fp16 const_34_promoted_to_fp16 = const()[name = string("const_34_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_1964_cast_fp16 = mul(x = var_1962_cast_fp16_1, y = const_34_promoted_to_fp16)[name = string("op_1964_cast_fp16")];
            int32 var_1966 = const()[name = string("op_1966"), val = int32(-2)];
            bool var_1967_interleave_0 = const()[name = string("op_1967_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_1967_cast_fp16 = concat(axis = var_1966, interleave = var_1967_interleave_0, values = (var_1964_cast_fp16, var_1962_cast_fp16_0))[name = string("op_1967_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_1968_cast_fp16 = mul(x = var_1967_cast_fp16, y = var_866_cast_fp16)[name = string("op_1968_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_21_cast_fp16 = add(x = var_1961_cast_fp16, y = var_1968_cast_fp16)[name = string("query_states_21_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_1974_cast_fp16 = mul(x = var_1950_cast_fp16, y = var_859_cast_fp16)[name = string("op_1974_cast_fp16")];
            tensor<int32, [2]> var_1975_split_sizes_0 = const()[name = string("op_1975_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_1975_axis_0 = const()[name = string("op_1975_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_1975_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_1975_cast_fp16_1 = split(axis = var_1975_axis_0, split_sizes = var_1975_split_sizes_0, x = var_1950_cast_fp16)[name = string("op_1975_cast_fp16")];
            fp16 const_35_promoted_to_fp16 = const()[name = string("const_35_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_1977_cast_fp16 = mul(x = var_1975_cast_fp16_1, y = const_35_promoted_to_fp16)[name = string("op_1977_cast_fp16")];
            int32 var_1979 = const()[name = string("op_1979"), val = int32(-2)];
            bool var_1980_interleave_0 = const()[name = string("op_1980_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_1980_cast_fp16 = concat(axis = var_1979, interleave = var_1980_interleave_0, values = (var_1977_cast_fp16, var_1975_cast_fp16_0))[name = string("op_1980_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_1981_cast_fp16 = mul(x = var_1980_cast_fp16, y = var_866_cast_fp16)[name = string("op_1981_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_35_cast_fp16 = add(x = var_1974_cast_fp16, y = var_1981_cast_fp16)[name = string("key_states_35_cast_fp16")];
            tensor<int32, [1]> expand_dims_36 = const()[name = string("expand_dims_36"), val = tensor<int32, [1]>([3])];
            tensor<int32, [1]> expand_dims_37 = const()[name = string("expand_dims_37"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_39 = const()[name = string("expand_dims_39"), val = tensor<int32, [1]>([0])];
            int32 concat_41_axis_0 = const()[name = string("concat_41_axis_0"), val = int32(0)];
            bool concat_41_interleave_0 = const()[name = string("concat_41_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_41 = concat(axis = concat_41_axis_0, interleave = concat_41_interleave_0, values = (expand_dims_36, expand_dims_37, position_id, expand_dims_39))[name = string("concat_41")];
            tensor<int32, [1]> expand_dims_40 = const()[name = string("expand_dims_40"), val = tensor<int32, [1]>([4])];
            tensor<int32, [1]> concat_42_values1_0 = const()[name = string("concat_42_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_42_values3_0 = const()[name = string("concat_42_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_42_axis_0 = const()[name = string("concat_42_axis_0"), val = int32(0)];
            bool concat_42_interleave_0 = const()[name = string("concat_42_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_42 = concat(axis = concat_42_axis_0, interleave = concat_42_interleave_0, values = (expand_dims_40, concat_42_values1_0, cache_position_end, concat_42_values3_0))[name = string("concat_42")];
            tensor<int32, [4]> key_states_37_perm_0 = const()[name = string("key_states_37_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_4_stride_0 = const()[name = string("key_cache_internal_tensor_assign_4_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_4_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_4_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_4_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_4_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_4_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_4_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_37_cast_fp16 = transpose(perm = key_states_37_perm_0, x = key_states_35_cast_fp16)[name = string("transpose_74")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_4_cast_fp16 = slice_update(begin = concat_41, begin_mask = key_cache_internal_tensor_assign_4_begin_mask_0, end = concat_42, end_mask = key_cache_internal_tensor_assign_4_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_4_squeeze_mask_0, stride = key_cache_internal_tensor_assign_4_stride_0, update = key_states_37_cast_fp16, x = coreml_update_state_4)[name = string("key_cache_internal_tensor_assign_4_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_4_cast_fp16, input = key_cache)[name = string("coreml_update_state_6_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_6 = read_state(input = key_cache)[name = string("coreml_update_state_6")];
            tensor<int32, [4]> value_states_21_perm_0 = const()[name = string("value_states_21_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_4_stride_0 = const()[name = string("value_cache_internal_tensor_assign_4_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_4_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_4_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_4_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_4_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_4_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_4_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_21_cast_fp16 = transpose(perm = value_states_21_perm_0, x = var_1957_cast_fp16)[name = string("transpose_73")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_4_cast_fp16 = slice_update(begin = concat_41, begin_mask = value_cache_internal_tensor_assign_4_begin_mask_0, end = concat_42, end_mask = value_cache_internal_tensor_assign_4_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_4_squeeze_mask_0, stride = value_cache_internal_tensor_assign_4_stride_0, update = value_states_21_cast_fp16, x = coreml_update_state_5)[name = string("value_cache_internal_tensor_assign_4_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_4_cast_fp16, input = value_cache)[name = string("coreml_update_state_7_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_7 = read_state(input = value_cache)[name = string("coreml_update_state_7")];
            tensor<int32, [4]> var_2051_begin_0 = const()[name = string("op_2051_begin_0"), val = tensor<int32, [4]>([3, 0, 0, 0])];
            tensor<int32, [4]> var_2051_end_0 = const()[name = string("op_2051_end_0"), val = tensor<int32, [4]>([4, 2, 2048, 128])];
            tensor<bool, [4]> var_2051_end_mask_0 = const()[name = string("op_2051_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2051_cast_fp16 = slice_by_index(begin = var_2051_begin_0, end = var_2051_end_0, end_mask = var_2051_end_mask_0, x = coreml_update_state_6)[name = string("op_2051_cast_fp16")];
            tensor<int32, [2]> tile_6 = const()[name = string("tile_6"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2054_axis_0 = const()[name = string("op_2054_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2054_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2054_cast_fp16_1 = split(axis = var_2054_axis_0, split_sizes = tile_6, x = var_2051_cast_fp16)[name = string("op_2054_cast_fp16")];
            tensor<int32, [4]> var_2061_begin_0 = const()[name = string("op_2061_begin_0"), val = tensor<int32, [4]>([3, 0, 0, 0])];
            tensor<int32, [4]> var_2061_end_0 = const()[name = string("op_2061_end_0"), val = tensor<int32, [4]>([4, 2, 2048, 128])];
            tensor<bool, [4]> var_2061_end_mask_0 = const()[name = string("op_2061_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2061_cast_fp16 = slice_by_index(begin = var_2061_begin_0, end = var_2061_end_0, end_mask = var_2061_end_mask_0, x = coreml_update_state_7)[name = string("op_2061_cast_fp16")];
            tensor<int32, [2]> tile_7 = const()[name = string("tile_7"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2064_axis_0 = const()[name = string("op_2064_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2064_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2064_cast_fp16_1 = split(axis = var_2064_axis_0, split_sizes = tile_7, x = var_2061_cast_fp16)[name = string("op_2064_cast_fp16")];
            tensor<int32, [2]> var_2067_split_sizes_0 = const()[name = string("op_2067_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_2067_axis_0 = const()[name = string("op_2067_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_2067_0, tensor<fp16, [1, 8, 128, 1]> var_2067_1 = split(axis = var_2067_axis_0, split_sizes = var_2067_split_sizes_0, x = query_states_21_cast_fp16)[name = string("op_2067")];
            bool attn_weights_49_transpose_x_0 = const()[name = string("attn_weights_49_transpose_x_0"), val = bool(false)];
            bool attn_weights_49_transpose_y_0 = const()[name = string("attn_weights_49_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_49_cast_fp16 = matmul(transpose_x = attn_weights_49_transpose_x_0, transpose_y = attn_weights_49_transpose_y_0, x = var_2054_cast_fp16_0, y = var_2067_0)[name = string("attn_weights_49_cast_fp16")];
            fp16 var_2070_to_fp16 = const()[name = string("op_2070_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_51_cast_fp16 = mul(x = attn_weights_49_cast_fp16, y = var_2070_to_fp16)[name = string("attn_weights_51_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_53_cast_fp16 = add(x = attn_weights_51_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_53_cast_fp16")];
            int32 var_2074 = const()[name = string("op_2074"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_55_cast_fp16 = softmax(axis = var_2074, x = attn_weights_53_cast_fp16)[name = string("attn_weights_55_cast_fp16")];
            bool var_2080_transpose_x_1 = const()[name = string("op_2080_transpose_x_1"), val = bool(true)];
            bool var_2080_transpose_y_1 = const()[name = string("op_2080_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_2080_cast_fp16 = matmul(transpose_x = var_2080_transpose_x_1, transpose_y = var_2080_transpose_y_1, x = attn_weights_55_cast_fp16, y = var_2064_cast_fp16_0)[name = string("op_2080_cast_fp16")];
            bool attn_weights_57_transpose_x_0 = const()[name = string("attn_weights_57_transpose_x_0"), val = bool(false)];
            bool attn_weights_57_transpose_y_0 = const()[name = string("attn_weights_57_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_57_cast_fp16 = matmul(transpose_x = attn_weights_57_transpose_x_0, transpose_y = attn_weights_57_transpose_y_0, x = var_2054_cast_fp16_1, y = var_2067_1)[name = string("attn_weights_57_cast_fp16")];
            fp16 var_2082_to_fp16 = const()[name = string("op_2082_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_59_cast_fp16 = mul(x = attn_weights_57_cast_fp16, y = var_2082_to_fp16)[name = string("attn_weights_59_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_61_cast_fp16 = add(x = attn_weights_59_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_61_cast_fp16")];
            int32 var_2086 = const()[name = string("op_2086"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_63_cast_fp16 = softmax(axis = var_2086, x = attn_weights_61_cast_fp16)[name = string("attn_weights_63_cast_fp16")];
            bool attn_output_25_transpose_x_1 = const()[name = string("attn_output_25_transpose_x_1"), val = bool(true)];
            bool attn_output_25_transpose_y_1 = const()[name = string("attn_output_25_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_25_cast_fp16 = matmul(transpose_x = attn_output_25_transpose_x_1, transpose_y = attn_output_25_transpose_y_1, x = attn_weights_63_cast_fp16, y = var_2064_cast_fp16_1)[name = string("attn_output_25_cast_fp16")];
            int32 var_2094 = const()[name = string("op_2094"), val = int32(1)];
            bool attn_output_27_interleave_0 = const()[name = string("attn_output_27_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_27_cast_fp16 = concat(axis = var_2094, interleave = attn_output_27_interleave_0, values = (var_2080_cast_fp16, attn_output_25_cast_fp16))[name = string("attn_output_27_cast_fp16")];
            tensor<int32, [4]> var_2098_perm_0 = const()[name = string("op_2098_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_47x = const()[name = string("concat_47x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_2098_cast_fp16 = transpose(perm = var_2098_perm_0, x = attn_output_27_cast_fp16)[name = string("transpose_72")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_31_cast_fp16 = reshape(shape = concat_47x, x = var_2098_cast_fp16)[name = string("attn_output_31_cast_fp16")];
            tensor<int32, [2]> hidden_states_33_strides_0 = const()[name = string("hidden_states_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_33_pad_type_0 = const()[name = string("hidden_states_33_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_33_pad_0 = const()[name = string("hidden_states_33_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_33_dilations_0 = const()[name = string("hidden_states_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_33_groups_0 = const()[name = string("hidden_states_33_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_33_cast_fp16 = conv(dilations = hidden_states_33_dilations_0, groups = hidden_states_33_groups_0, pad = hidden_states_33_pad_0, pad_type = hidden_states_33_pad_type_0, strides = hidden_states_33_strides_0, weight = layers_3_self_attn_o_proj_weight_cast_fp16, x = attn_output_31_cast_fp16)[name = string("hidden_states_33_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_35_cast_fp16 = add(x = hidden_states_29_cast_fp16, y = hidden_states_33_cast_fp16)[name = string("hidden_states_35_cast_fp16")];
            fp16 const_40_promoted_to_fp16 = const()[name = string("const_40_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_2131_cast_fp16 = mul(x = hidden_states_35_cast_fp16, y = const_40_promoted_to_fp16)[name = string("op_2131_cast_fp16")];
            int32 var_2129 = const()[name = string("op_2129"), val = int32(1)];
            bool doubled_29_interleave_0 = const()[name = string("doubled_29_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_29_cast_fp16 = concat(axis = var_2129, interleave = doubled_29_interleave_0, values = (hidden_states_35_cast_fp16, var_2131_cast_fp16))[name = string("doubled_29_cast_fp16")];
            tensor<int32, [1]> out_15_axes_0 = const()[name = string("out_15_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_15_gamma_0_to_fp16 = const()[name = string("out_15_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1214963264)))];
            fp16 var_2141_to_fp16 = const()[name = string("op_2141_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_15_cast_fp16 = layer_norm(axes = out_15_axes_0, epsilon = var_2141_to_fp16, gamma = out_15_gamma_0_to_fp16, x = doubled_29_cast_fp16)[name = string("out_15_cast_fp16")];
            tensor<int32, [2]> var_2152_split_sizes_0 = const()[name = string("op_2152_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2152_axis_0 = const()[name = string("op_2152_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_2152_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_2152_cast_fp16_1 = split(axis = var_2152_axis_0, split_sizes = var_2152_split_sizes_0, x = out_15_cast_fp16)[name = string("op_2152_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_3_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_3_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1214971520)))];
            tensor<int32, [2]> input_7_strides_0 = const()[name = string("input_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_7_pad_type_0 = const()[name = string("input_7_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_7_pad_0 = const()[name = string("input_7_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_7_dilations_0 = const()[name = string("input_7_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_7_groups_0 = const()[name = string("input_7_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_7_cast_fp16 = conv(dilations = input_7_dilations_0, groups = input_7_groups_0, pad = input_7_pad_0, pad_type = input_7_pad_type_0, strides = input_7_strides_0, weight = layers_3_mlp_gate_proj_weight_to_fp16, x = var_2152_cast_fp16_0)[name = string("input_7_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_2169_cast_fp16 = silu(x = input_7_cast_fp16)[name = string("op_2169_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_3_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_3_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1240137408)))];
            tensor<int32, [2]> var_2175_strides_0 = const()[name = string("op_2175_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_2175_pad_type_0 = const()[name = string("op_2175_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_2175_pad_0 = const()[name = string("op_2175_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_2175_dilations_0 = const()[name = string("op_2175_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2175_groups_0 = const()[name = string("op_2175_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_2175_cast_fp16 = conv(dilations = var_2175_dilations_0, groups = var_2175_groups_0, pad = var_2175_pad_0, pad_type = var_2175_pad_type_0, strides = var_2175_strides_0, weight = layers_3_mlp_up_proj_weight_to_fp16, x = var_2152_cast_fp16_0)[name = string("op_2175_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_39_cast_fp16 = mul(x = var_2169_cast_fp16, y = var_2175_cast_fp16)[name = string("x_39_cast_fp16")];
            tensor<int32, [2]> hidden_states_37_strides_0 = const()[name = string("hidden_states_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_37_pad_type_0 = const()[name = string("hidden_states_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_37_pad_0 = const()[name = string("hidden_states_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_37_dilations_0 = const()[name = string("hidden_states_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_37_groups_0 = const()[name = string("hidden_states_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_37_cast_fp16 = conv(dilations = hidden_states_37_dilations_0, groups = hidden_states_37_groups_0, pad = hidden_states_37_pad_0, pad_type = hidden_states_37_pad_type_0, strides = hidden_states_37_strides_0, weight = layers_3_mlp_down_proj_weight_cast_fp16, x = x_39_cast_fp16)[name = string("hidden_states_37_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_39_cast_fp16 = add(x = hidden_states_35_cast_fp16, y = hidden_states_37_cast_fp16)[name = string("hidden_states_39_cast_fp16")];
            fp16 const_42_promoted_to_fp16 = const()[name = string("const_42_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_2193_cast_fp16 = mul(x = hidden_states_39_cast_fp16, y = const_42_promoted_to_fp16)[name = string("op_2193_cast_fp16")];
            int32 var_2191 = const()[name = string("op_2191"), val = int32(1)];
            bool doubled_33_interleave_0 = const()[name = string("doubled_33_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_33_cast_fp16 = concat(axis = var_2191, interleave = doubled_33_interleave_0, values = (hidden_states_39_cast_fp16, var_2193_cast_fp16))[name = string("doubled_33_cast_fp16")];
            tensor<int32, [1]> out_17_axes_0 = const()[name = string("out_17_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_17_gamma_0_to_fp16 = const()[name = string("out_17_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1265303296)))];
            fp16 var_2203_to_fp16 = const()[name = string("op_2203_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_17_cast_fp16 = layer_norm(axes = out_17_axes_0, epsilon = var_2203_to_fp16, gamma = out_17_gamma_0_to_fp16, x = doubled_33_cast_fp16)[name = string("out_17_cast_fp16")];
            tensor<int32, [2]> var_2214_split_sizes_0 = const()[name = string("op_2214_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2214_axis_0 = const()[name = string("op_2214_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_2214_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_2214_cast_fp16_1 = split(axis = var_2214_axis_0, split_sizes = var_2214_split_sizes_0, x = out_17_cast_fp16)[name = string("op_2214_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_4_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_4_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1265311552)))];
            tensor<int32, [2]> query_states_25_strides_0 = const()[name = string("query_states_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_25_pad_type_0 = const()[name = string("query_states_25_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_25_pad_0 = const()[name = string("query_states_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_25_dilations_0 = const()[name = string("query_states_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_25_groups_0 = const()[name = string("query_states_25_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_25_cast_fp16 = conv(dilations = query_states_25_dilations_0, groups = query_states_25_groups_0, pad = query_states_25_pad_0, pad_type = query_states_25_pad_type_0, strides = query_states_25_strides_0, weight = layers_4_self_attn_q_proj_weight_to_fp16, x = var_2214_cast_fp16_0)[name = string("query_states_25_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_4_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_4_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1273700224)))];
            tensor<int32, [2]> key_states_41_strides_0 = const()[name = string("key_states_41_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_41_pad_type_0 = const()[name = string("key_states_41_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_41_pad_0 = const()[name = string("key_states_41_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_41_dilations_0 = const()[name = string("key_states_41_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_41_groups_0 = const()[name = string("key_states_41_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_41_cast_fp16 = conv(dilations = key_states_41_dilations_0, groups = key_states_41_groups_0, pad = key_states_41_pad_0, pad_type = key_states_41_pad_type_0, strides = key_states_41_strides_0, weight = layers_4_self_attn_k_proj_weight_to_fp16, x = var_2214_cast_fp16_0)[name = string("key_states_41_cast_fp16")];
            tensor<int32, [2]> value_states_25_strides_0 = const()[name = string("value_states_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_25_pad_type_0 = const()[name = string("value_states_25_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_25_pad_0 = const()[name = string("value_states_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_25_dilations_0 = const()[name = string("value_states_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_25_groups_0 = const()[name = string("value_states_25_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_25_cast_fp16 = conv(dilations = value_states_25_dilations_0, groups = value_states_25_groups_0, pad = value_states_25_pad_0, pad_type = value_states_25_pad_type_0, strides = value_states_25_strides_0, weight = layers_4_self_attn_v_proj_weight_cast_fp16, x = var_2214_cast_fp16_0)[name = string("value_states_25_cast_fp16")];
            tensor<int32, [4]> concat_48x = const()[name = string("concat_48x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_41_cast_fp16 = reshape(shape = concat_48x, x = query_states_25_cast_fp16)[name = string("x_41_cast_fp16")];
            tensor<int32, [4]> concat_49x = const()[name = string("concat_49x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_2271_cast_fp16 = reshape(shape = concat_49x, x = key_states_41_cast_fp16)[name = string("op_2271_cast_fp16")];
            tensor<int32, [4]> concat_50x = const()[name = string("concat_50x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_2278_cast_fp16 = reshape(shape = concat_50x, x = value_states_25_cast_fp16)[name = string("op_2278_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_2282_cast_fp16 = mul(x = x_41_cast_fp16, y = var_859_cast_fp16)[name = string("op_2282_cast_fp16")];
            tensor<int32, [2]> var_2283_split_sizes_0 = const()[name = string("op_2283_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2283_axis_0 = const()[name = string("op_2283_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_2283_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_2283_cast_fp16_1 = split(axis = var_2283_axis_0, split_sizes = var_2283_split_sizes_0, x = x_41_cast_fp16)[name = string("op_2283_cast_fp16")];
            fp16 const_44_promoted_to_fp16 = const()[name = string("const_44_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_2285_cast_fp16 = mul(x = var_2283_cast_fp16_1, y = const_44_promoted_to_fp16)[name = string("op_2285_cast_fp16")];
            int32 var_2287 = const()[name = string("op_2287"), val = int32(-2)];
            bool var_2288_interleave_0 = const()[name = string("op_2288_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_2288_cast_fp16 = concat(axis = var_2287, interleave = var_2288_interleave_0, values = (var_2285_cast_fp16, var_2283_cast_fp16_0))[name = string("op_2288_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_2289_cast_fp16 = mul(x = var_2288_cast_fp16, y = var_866_cast_fp16)[name = string("op_2289_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_27_cast_fp16 = add(x = var_2282_cast_fp16, y = var_2289_cast_fp16)[name = string("query_states_27_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_2295_cast_fp16 = mul(x = var_2271_cast_fp16, y = var_859_cast_fp16)[name = string("op_2295_cast_fp16")];
            tensor<int32, [2]> var_2296_split_sizes_0 = const()[name = string("op_2296_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2296_axis_0 = const()[name = string("op_2296_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_2296_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_2296_cast_fp16_1 = split(axis = var_2296_axis_0, split_sizes = var_2296_split_sizes_0, x = var_2271_cast_fp16)[name = string("op_2296_cast_fp16")];
            fp16 const_45_promoted_to_fp16 = const()[name = string("const_45_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_2298_cast_fp16 = mul(x = var_2296_cast_fp16_1, y = const_45_promoted_to_fp16)[name = string("op_2298_cast_fp16")];
            int32 var_2300 = const()[name = string("op_2300"), val = int32(-2)];
            bool var_2301_interleave_0 = const()[name = string("op_2301_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_2301_cast_fp16 = concat(axis = var_2300, interleave = var_2301_interleave_0, values = (var_2298_cast_fp16, var_2296_cast_fp16_0))[name = string("op_2301_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_2302_cast_fp16 = mul(x = var_2301_cast_fp16, y = var_866_cast_fp16)[name = string("op_2302_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_45_cast_fp16 = add(x = var_2295_cast_fp16, y = var_2302_cast_fp16)[name = string("key_states_45_cast_fp16")];
            tensor<int32, [1]> expand_dims_48 = const()[name = string("expand_dims_48"), val = tensor<int32, [1]>([4])];
            tensor<int32, [1]> expand_dims_49 = const()[name = string("expand_dims_49"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_51 = const()[name = string("expand_dims_51"), val = tensor<int32, [1]>([0])];
            int32 concat_53_axis_0 = const()[name = string("concat_53_axis_0"), val = int32(0)];
            bool concat_53_interleave_0 = const()[name = string("concat_53_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_53 = concat(axis = concat_53_axis_0, interleave = concat_53_interleave_0, values = (expand_dims_48, expand_dims_49, position_id, expand_dims_51))[name = string("concat_53")];
            tensor<int32, [1]> expand_dims_52 = const()[name = string("expand_dims_52"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> concat_54_values1_0 = const()[name = string("concat_54_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_54_values3_0 = const()[name = string("concat_54_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_54_axis_0 = const()[name = string("concat_54_axis_0"), val = int32(0)];
            bool concat_54_interleave_0 = const()[name = string("concat_54_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_54 = concat(axis = concat_54_axis_0, interleave = concat_54_interleave_0, values = (expand_dims_52, concat_54_values1_0, cache_position_end, concat_54_values3_0))[name = string("concat_54")];
            tensor<int32, [4]> key_states_47_perm_0 = const()[name = string("key_states_47_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_5_stride_0 = const()[name = string("key_cache_internal_tensor_assign_5_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_5_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_5_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_5_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_5_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_5_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_5_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_47_cast_fp16 = transpose(perm = key_states_47_perm_0, x = key_states_45_cast_fp16)[name = string("transpose_71")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_5_cast_fp16 = slice_update(begin = concat_53, begin_mask = key_cache_internal_tensor_assign_5_begin_mask_0, end = concat_54, end_mask = key_cache_internal_tensor_assign_5_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_5_squeeze_mask_0, stride = key_cache_internal_tensor_assign_5_stride_0, update = key_states_47_cast_fp16, x = coreml_update_state_6)[name = string("key_cache_internal_tensor_assign_5_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_5_cast_fp16, input = key_cache)[name = string("coreml_update_state_8_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_8 = read_state(input = key_cache)[name = string("coreml_update_state_8")];
            tensor<int32, [4]> value_states_27_perm_0 = const()[name = string("value_states_27_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_5_stride_0 = const()[name = string("value_cache_internal_tensor_assign_5_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_5_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_5_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_5_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_5_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_5_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_5_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_27_cast_fp16 = transpose(perm = value_states_27_perm_0, x = var_2278_cast_fp16)[name = string("transpose_70")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_5_cast_fp16 = slice_update(begin = concat_53, begin_mask = value_cache_internal_tensor_assign_5_begin_mask_0, end = concat_54, end_mask = value_cache_internal_tensor_assign_5_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_5_squeeze_mask_0, stride = value_cache_internal_tensor_assign_5_stride_0, update = value_states_27_cast_fp16, x = coreml_update_state_7)[name = string("value_cache_internal_tensor_assign_5_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_5_cast_fp16, input = value_cache)[name = string("coreml_update_state_9_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_9 = read_state(input = value_cache)[name = string("coreml_update_state_9")];
            tensor<int32, [4]> var_2372_begin_0 = const()[name = string("op_2372_begin_0"), val = tensor<int32, [4]>([4, 0, 0, 0])];
            tensor<int32, [4]> var_2372_end_0 = const()[name = string("op_2372_end_0"), val = tensor<int32, [4]>([5, 2, 2048, 128])];
            tensor<bool, [4]> var_2372_end_mask_0 = const()[name = string("op_2372_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2372_cast_fp16 = slice_by_index(begin = var_2372_begin_0, end = var_2372_end_0, end_mask = var_2372_end_mask_0, x = coreml_update_state_8)[name = string("op_2372_cast_fp16")];
            tensor<int32, [2]> tile_8 = const()[name = string("tile_8"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2375_axis_0 = const()[name = string("op_2375_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2375_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2375_cast_fp16_1 = split(axis = var_2375_axis_0, split_sizes = tile_8, x = var_2372_cast_fp16)[name = string("op_2375_cast_fp16")];
            tensor<int32, [4]> var_2382_begin_0 = const()[name = string("op_2382_begin_0"), val = tensor<int32, [4]>([4, 0, 0, 0])];
            tensor<int32, [4]> var_2382_end_0 = const()[name = string("op_2382_end_0"), val = tensor<int32, [4]>([5, 2, 2048, 128])];
            tensor<bool, [4]> var_2382_end_mask_0 = const()[name = string("op_2382_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2382_cast_fp16 = slice_by_index(begin = var_2382_begin_0, end = var_2382_end_0, end_mask = var_2382_end_mask_0, x = coreml_update_state_9)[name = string("op_2382_cast_fp16")];
            tensor<int32, [2]> tile_9 = const()[name = string("tile_9"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2385_axis_0 = const()[name = string("op_2385_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2385_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2385_cast_fp16_1 = split(axis = var_2385_axis_0, split_sizes = tile_9, x = var_2382_cast_fp16)[name = string("op_2385_cast_fp16")];
            tensor<int32, [2]> var_2388_split_sizes_0 = const()[name = string("op_2388_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_2388_axis_0 = const()[name = string("op_2388_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_2388_0, tensor<fp16, [1, 8, 128, 1]> var_2388_1 = split(axis = var_2388_axis_0, split_sizes = var_2388_split_sizes_0, x = query_states_27_cast_fp16)[name = string("op_2388")];
            bool attn_weights_65_transpose_x_0 = const()[name = string("attn_weights_65_transpose_x_0"), val = bool(false)];
            bool attn_weights_65_transpose_y_0 = const()[name = string("attn_weights_65_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_65_cast_fp16 = matmul(transpose_x = attn_weights_65_transpose_x_0, transpose_y = attn_weights_65_transpose_y_0, x = var_2375_cast_fp16_0, y = var_2388_0)[name = string("attn_weights_65_cast_fp16")];
            fp16 var_2391_to_fp16 = const()[name = string("op_2391_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_67_cast_fp16 = mul(x = attn_weights_65_cast_fp16, y = var_2391_to_fp16)[name = string("attn_weights_67_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_69_cast_fp16 = add(x = attn_weights_67_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_69_cast_fp16")];
            int32 var_2395 = const()[name = string("op_2395"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_71_cast_fp16 = softmax(axis = var_2395, x = attn_weights_69_cast_fp16)[name = string("attn_weights_71_cast_fp16")];
            bool var_2401_transpose_x_1 = const()[name = string("op_2401_transpose_x_1"), val = bool(true)];
            bool var_2401_transpose_y_1 = const()[name = string("op_2401_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_2401_cast_fp16 = matmul(transpose_x = var_2401_transpose_x_1, transpose_y = var_2401_transpose_y_1, x = attn_weights_71_cast_fp16, y = var_2385_cast_fp16_0)[name = string("op_2401_cast_fp16")];
            bool attn_weights_73_transpose_x_0 = const()[name = string("attn_weights_73_transpose_x_0"), val = bool(false)];
            bool attn_weights_73_transpose_y_0 = const()[name = string("attn_weights_73_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_73_cast_fp16 = matmul(transpose_x = attn_weights_73_transpose_x_0, transpose_y = attn_weights_73_transpose_y_0, x = var_2375_cast_fp16_1, y = var_2388_1)[name = string("attn_weights_73_cast_fp16")];
            fp16 var_2403_to_fp16 = const()[name = string("op_2403_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_75_cast_fp16 = mul(x = attn_weights_73_cast_fp16, y = var_2403_to_fp16)[name = string("attn_weights_75_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_77_cast_fp16 = add(x = attn_weights_75_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_77_cast_fp16")];
            int32 var_2407 = const()[name = string("op_2407"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_79_cast_fp16 = softmax(axis = var_2407, x = attn_weights_77_cast_fp16)[name = string("attn_weights_79_cast_fp16")];
            bool attn_output_33_transpose_x_1 = const()[name = string("attn_output_33_transpose_x_1"), val = bool(true)];
            bool attn_output_33_transpose_y_1 = const()[name = string("attn_output_33_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_33_cast_fp16 = matmul(transpose_x = attn_output_33_transpose_x_1, transpose_y = attn_output_33_transpose_y_1, x = attn_weights_79_cast_fp16, y = var_2385_cast_fp16_1)[name = string("attn_output_33_cast_fp16")];
            int32 var_2415 = const()[name = string("op_2415"), val = int32(1)];
            bool attn_output_35_interleave_0 = const()[name = string("attn_output_35_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_35_cast_fp16 = concat(axis = var_2415, interleave = attn_output_35_interleave_0, values = (var_2401_cast_fp16, attn_output_33_cast_fp16))[name = string("attn_output_35_cast_fp16")];
            tensor<int32, [4]> var_2419_perm_0 = const()[name = string("op_2419_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_59x = const()[name = string("concat_59x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_2419_cast_fp16 = transpose(perm = var_2419_perm_0, x = attn_output_35_cast_fp16)[name = string("transpose_69")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_39_cast_fp16 = reshape(shape = concat_59x, x = var_2419_cast_fp16)[name = string("attn_output_39_cast_fp16")];
            tensor<int32, [2]> hidden_states_43_strides_0 = const()[name = string("hidden_states_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_43_pad_type_0 = const()[name = string("hidden_states_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_43_pad_0 = const()[name = string("hidden_states_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_43_dilations_0 = const()[name = string("hidden_states_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_43_groups_0 = const()[name = string("hidden_states_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_43_cast_fp16 = conv(dilations = hidden_states_43_dilations_0, groups = hidden_states_43_groups_0, pad = hidden_states_43_pad_0, pad_type = hidden_states_43_pad_type_0, strides = hidden_states_43_strides_0, weight = layers_4_self_attn_o_proj_weight_cast_fp16, x = attn_output_39_cast_fp16)[name = string("hidden_states_43_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_45_cast_fp16 = add(x = hidden_states_39_cast_fp16, y = hidden_states_43_cast_fp16)[name = string("hidden_states_45_cast_fp16")];
            fp16 const_50_promoted_to_fp16 = const()[name = string("const_50_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_2452_cast_fp16 = mul(x = hidden_states_45_cast_fp16, y = const_50_promoted_to_fp16)[name = string("op_2452_cast_fp16")];
            int32 var_2450 = const()[name = string("op_2450"), val = int32(1)];
            bool doubled_37_interleave_0 = const()[name = string("doubled_37_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_37_cast_fp16 = concat(axis = var_2450, interleave = doubled_37_interleave_0, values = (hidden_states_45_cast_fp16, var_2452_cast_fp16))[name = string("doubled_37_cast_fp16")];
            tensor<int32, [1]> out_19_axes_0 = const()[name = string("out_19_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_19_gamma_0_to_fp16 = const()[name = string("out_19_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1274748864)))];
            fp16 var_2462_to_fp16 = const()[name = string("op_2462_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_19_cast_fp16 = layer_norm(axes = out_19_axes_0, epsilon = var_2462_to_fp16, gamma = out_19_gamma_0_to_fp16, x = doubled_37_cast_fp16)[name = string("out_19_cast_fp16")];
            tensor<int32, [2]> var_2473_split_sizes_0 = const()[name = string("op_2473_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2473_axis_0 = const()[name = string("op_2473_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_2473_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_2473_cast_fp16_1 = split(axis = var_2473_axis_0, split_sizes = var_2473_split_sizes_0, x = out_19_cast_fp16)[name = string("op_2473_cast_fp16")];
            tensor<int32, [2]> input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_9_pad_0 = const()[name = string("input_9_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_9_dilations_0 = const()[name = string("input_9_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_9_groups_0 = const()[name = string("input_9_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_9_cast_fp16 = conv(dilations = input_9_dilations_0, groups = input_9_groups_0, pad = input_9_pad_0, pad_type = input_9_pad_type_0, strides = input_9_strides_0, weight = layers_4_mlp_gate_proj_weight_cast_fp16, x = var_2473_cast_fp16_0)[name = string("input_9_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_2490_cast_fp16 = silu(x = input_9_cast_fp16)[name = string("op_2490_cast_fp16")];
            tensor<int32, [2]> var_2496_strides_0 = const()[name = string("op_2496_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_2496_pad_type_0 = const()[name = string("op_2496_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_2496_pad_0 = const()[name = string("op_2496_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_2496_dilations_0 = const()[name = string("op_2496_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2496_groups_0 = const()[name = string("op_2496_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_2496_cast_fp16 = conv(dilations = var_2496_dilations_0, groups = var_2496_groups_0, pad = var_2496_pad_0, pad_type = var_2496_pad_type_0, strides = var_2496_strides_0, weight = layers_4_mlp_up_proj_weight_cast_fp16, x = var_2473_cast_fp16_0)[name = string("op_2496_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_49_cast_fp16 = mul(x = var_2490_cast_fp16, y = var_2496_cast_fp16)[name = string("x_49_cast_fp16")];
            tensor<int32, [2]> hidden_states_47_strides_0 = const()[name = string("hidden_states_47_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_47_pad_type_0 = const()[name = string("hidden_states_47_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_47_pad_0 = const()[name = string("hidden_states_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_47_dilations_0 = const()[name = string("hidden_states_47_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_47_groups_0 = const()[name = string("hidden_states_47_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_47_cast_fp16 = conv(dilations = hidden_states_47_dilations_0, groups = hidden_states_47_groups_0, pad = hidden_states_47_pad_0, pad_type = hidden_states_47_pad_type_0, strides = hidden_states_47_strides_0, weight = layers_4_mlp_down_proj_weight_cast_fp16, x = x_49_cast_fp16)[name = string("hidden_states_47_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_49_cast_fp16 = add(x = hidden_states_45_cast_fp16, y = hidden_states_47_cast_fp16)[name = string("hidden_states_49_cast_fp16")];
            fp16 const_52_promoted_to_fp16 = const()[name = string("const_52_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_2514_cast_fp16 = mul(x = hidden_states_49_cast_fp16, y = const_52_promoted_to_fp16)[name = string("op_2514_cast_fp16")];
            int32 var_2512 = const()[name = string("op_2512"), val = int32(1)];
            bool doubled_41_interleave_0 = const()[name = string("doubled_41_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_41_cast_fp16 = concat(axis = var_2512, interleave = doubled_41_interleave_0, values = (hidden_states_49_cast_fp16, var_2514_cast_fp16))[name = string("doubled_41_cast_fp16")];
            tensor<int32, [1]> out_21_axes_0 = const()[name = string("out_21_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_21_gamma_0_to_fp16 = const()[name = string("out_21_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1274757120)))];
            fp16 var_2524_to_fp16 = const()[name = string("op_2524_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_21_cast_fp16 = layer_norm(axes = out_21_axes_0, epsilon = var_2524_to_fp16, gamma = out_21_gamma_0_to_fp16, x = doubled_41_cast_fp16)[name = string("out_21_cast_fp16")];
            tensor<int32, [2]> var_2535_split_sizes_0 = const()[name = string("op_2535_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2535_axis_0 = const()[name = string("op_2535_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_2535_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_2535_cast_fp16_1 = split(axis = var_2535_axis_0, split_sizes = var_2535_split_sizes_0, x = out_21_cast_fp16)[name = string("op_2535_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_5_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_5_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1274765376)))];
            tensor<int32, [2]> query_states_31_strides_0 = const()[name = string("query_states_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_31_pad_type_0 = const()[name = string("query_states_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_31_pad_0 = const()[name = string("query_states_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_31_dilations_0 = const()[name = string("query_states_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_31_groups_0 = const()[name = string("query_states_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_31_cast_fp16 = conv(dilations = query_states_31_dilations_0, groups = query_states_31_groups_0, pad = query_states_31_pad_0, pad_type = query_states_31_pad_type_0, strides = query_states_31_strides_0, weight = layers_5_self_attn_q_proj_weight_to_fp16, x = var_2535_cast_fp16_0)[name = string("query_states_31_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_5_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_5_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1283154048)))];
            tensor<int32, [2]> key_states_51_strides_0 = const()[name = string("key_states_51_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_51_pad_type_0 = const()[name = string("key_states_51_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_51_pad_0 = const()[name = string("key_states_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_51_dilations_0 = const()[name = string("key_states_51_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_51_groups_0 = const()[name = string("key_states_51_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_51_cast_fp16 = conv(dilations = key_states_51_dilations_0, groups = key_states_51_groups_0, pad = key_states_51_pad_0, pad_type = key_states_51_pad_type_0, strides = key_states_51_strides_0, weight = layers_5_self_attn_k_proj_weight_to_fp16, x = var_2535_cast_fp16_0)[name = string("key_states_51_cast_fp16")];
            tensor<int32, [2]> value_states_31_strides_0 = const()[name = string("value_states_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_31_pad_type_0 = const()[name = string("value_states_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_31_pad_0 = const()[name = string("value_states_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_31_dilations_0 = const()[name = string("value_states_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_31_groups_0 = const()[name = string("value_states_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_31_cast_fp16 = conv(dilations = value_states_31_dilations_0, groups = value_states_31_groups_0, pad = value_states_31_pad_0, pad_type = value_states_31_pad_type_0, strides = value_states_31_strides_0, weight = layers_5_self_attn_v_proj_weight_cast_fp16, x = var_2535_cast_fp16_0)[name = string("value_states_31_cast_fp16")];
            tensor<int32, [4]> concat_60x = const()[name = string("concat_60x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_51_cast_fp16 = reshape(shape = concat_60x, x = query_states_31_cast_fp16)[name = string("x_51_cast_fp16")];
            tensor<int32, [4]> concat_61x = const()[name = string("concat_61x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_2592_cast_fp16 = reshape(shape = concat_61x, x = key_states_51_cast_fp16)[name = string("op_2592_cast_fp16")];
            tensor<int32, [4]> concat_62x = const()[name = string("concat_62x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_2599_cast_fp16 = reshape(shape = concat_62x, x = value_states_31_cast_fp16)[name = string("op_2599_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_2603_cast_fp16 = mul(x = x_51_cast_fp16, y = var_859_cast_fp16)[name = string("op_2603_cast_fp16")];
            tensor<int32, [2]> var_2604_split_sizes_0 = const()[name = string("op_2604_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2604_axis_0 = const()[name = string("op_2604_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_2604_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_2604_cast_fp16_1 = split(axis = var_2604_axis_0, split_sizes = var_2604_split_sizes_0, x = x_51_cast_fp16)[name = string("op_2604_cast_fp16")];
            fp16 const_54_promoted_to_fp16 = const()[name = string("const_54_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_2606_cast_fp16 = mul(x = var_2604_cast_fp16_1, y = const_54_promoted_to_fp16)[name = string("op_2606_cast_fp16")];
            int32 var_2608 = const()[name = string("op_2608"), val = int32(-2)];
            bool var_2609_interleave_0 = const()[name = string("op_2609_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_2609_cast_fp16 = concat(axis = var_2608, interleave = var_2609_interleave_0, values = (var_2606_cast_fp16, var_2604_cast_fp16_0))[name = string("op_2609_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_2610_cast_fp16 = mul(x = var_2609_cast_fp16, y = var_866_cast_fp16)[name = string("op_2610_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_33_cast_fp16 = add(x = var_2603_cast_fp16, y = var_2610_cast_fp16)[name = string("query_states_33_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_2616_cast_fp16 = mul(x = var_2592_cast_fp16, y = var_859_cast_fp16)[name = string("op_2616_cast_fp16")];
            tensor<int32, [2]> var_2617_split_sizes_0 = const()[name = string("op_2617_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2617_axis_0 = const()[name = string("op_2617_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_2617_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_2617_cast_fp16_1 = split(axis = var_2617_axis_0, split_sizes = var_2617_split_sizes_0, x = var_2592_cast_fp16)[name = string("op_2617_cast_fp16")];
            fp16 const_55_promoted_to_fp16 = const()[name = string("const_55_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_2619_cast_fp16 = mul(x = var_2617_cast_fp16_1, y = const_55_promoted_to_fp16)[name = string("op_2619_cast_fp16")];
            int32 var_2621 = const()[name = string("op_2621"), val = int32(-2)];
            bool var_2622_interleave_0 = const()[name = string("op_2622_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_2622_cast_fp16 = concat(axis = var_2621, interleave = var_2622_interleave_0, values = (var_2619_cast_fp16, var_2617_cast_fp16_0))[name = string("op_2622_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_2623_cast_fp16 = mul(x = var_2622_cast_fp16, y = var_866_cast_fp16)[name = string("op_2623_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_55_cast_fp16 = add(x = var_2616_cast_fp16, y = var_2623_cast_fp16)[name = string("key_states_55_cast_fp16")];
            tensor<int32, [1]> expand_dims_60 = const()[name = string("expand_dims_60"), val = tensor<int32, [1]>([5])];
            tensor<int32, [1]> expand_dims_61 = const()[name = string("expand_dims_61"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_63 = const()[name = string("expand_dims_63"), val = tensor<int32, [1]>([0])];
            int32 concat_65_axis_0 = const()[name = string("concat_65_axis_0"), val = int32(0)];
            bool concat_65_interleave_0 = const()[name = string("concat_65_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_65 = concat(axis = concat_65_axis_0, interleave = concat_65_interleave_0, values = (expand_dims_60, expand_dims_61, position_id, expand_dims_63))[name = string("concat_65")];
            tensor<int32, [1]> expand_dims_64 = const()[name = string("expand_dims_64"), val = tensor<int32, [1]>([6])];
            tensor<int32, [1]> concat_66_values1_0 = const()[name = string("concat_66_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_66_values3_0 = const()[name = string("concat_66_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_66_axis_0 = const()[name = string("concat_66_axis_0"), val = int32(0)];
            bool concat_66_interleave_0 = const()[name = string("concat_66_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_66 = concat(axis = concat_66_axis_0, interleave = concat_66_interleave_0, values = (expand_dims_64, concat_66_values1_0, cache_position_end, concat_66_values3_0))[name = string("concat_66")];
            tensor<int32, [4]> key_states_57_perm_0 = const()[name = string("key_states_57_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_6_stride_0 = const()[name = string("key_cache_internal_tensor_assign_6_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_6_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_6_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_6_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_6_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_6_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_6_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_57_cast_fp16 = transpose(perm = key_states_57_perm_0, x = key_states_55_cast_fp16)[name = string("transpose_68")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_6_cast_fp16 = slice_update(begin = concat_65, begin_mask = key_cache_internal_tensor_assign_6_begin_mask_0, end = concat_66, end_mask = key_cache_internal_tensor_assign_6_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_6_squeeze_mask_0, stride = key_cache_internal_tensor_assign_6_stride_0, update = key_states_57_cast_fp16, x = coreml_update_state_8)[name = string("key_cache_internal_tensor_assign_6_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_6_cast_fp16, input = key_cache)[name = string("coreml_update_state_10_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_10 = read_state(input = key_cache)[name = string("coreml_update_state_10")];
            tensor<int32, [4]> value_states_33_perm_0 = const()[name = string("value_states_33_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_6_stride_0 = const()[name = string("value_cache_internal_tensor_assign_6_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_6_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_6_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_6_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_6_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_6_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_6_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_33_cast_fp16 = transpose(perm = value_states_33_perm_0, x = var_2599_cast_fp16)[name = string("transpose_67")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_6_cast_fp16 = slice_update(begin = concat_65, begin_mask = value_cache_internal_tensor_assign_6_begin_mask_0, end = concat_66, end_mask = value_cache_internal_tensor_assign_6_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_6_squeeze_mask_0, stride = value_cache_internal_tensor_assign_6_stride_0, update = value_states_33_cast_fp16, x = coreml_update_state_9)[name = string("value_cache_internal_tensor_assign_6_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_6_cast_fp16, input = value_cache)[name = string("coreml_update_state_11_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_11 = read_state(input = value_cache)[name = string("coreml_update_state_11")];
            tensor<int32, [4]> var_2693_begin_0 = const()[name = string("op_2693_begin_0"), val = tensor<int32, [4]>([5, 0, 0, 0])];
            tensor<int32, [4]> var_2693_end_0 = const()[name = string("op_2693_end_0"), val = tensor<int32, [4]>([6, 2, 2048, 128])];
            tensor<bool, [4]> var_2693_end_mask_0 = const()[name = string("op_2693_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2693_cast_fp16 = slice_by_index(begin = var_2693_begin_0, end = var_2693_end_0, end_mask = var_2693_end_mask_0, x = coreml_update_state_10)[name = string("op_2693_cast_fp16")];
            tensor<int32, [2]> tile_10 = const()[name = string("tile_10"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2696_axis_0 = const()[name = string("op_2696_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2696_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2696_cast_fp16_1 = split(axis = var_2696_axis_0, split_sizes = tile_10, x = var_2693_cast_fp16)[name = string("op_2696_cast_fp16")];
            tensor<int32, [4]> var_2703_begin_0 = const()[name = string("op_2703_begin_0"), val = tensor<int32, [4]>([5, 0, 0, 0])];
            tensor<int32, [4]> var_2703_end_0 = const()[name = string("op_2703_end_0"), val = tensor<int32, [4]>([6, 2, 2048, 128])];
            tensor<bool, [4]> var_2703_end_mask_0 = const()[name = string("op_2703_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_2703_cast_fp16 = slice_by_index(begin = var_2703_begin_0, end = var_2703_end_0, end_mask = var_2703_end_mask_0, x = coreml_update_state_11)[name = string("op_2703_cast_fp16")];
            tensor<int32, [2]> tile_11 = const()[name = string("tile_11"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2706_axis_0 = const()[name = string("op_2706_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_2706_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_2706_cast_fp16_1 = split(axis = var_2706_axis_0, split_sizes = tile_11, x = var_2703_cast_fp16)[name = string("op_2706_cast_fp16")];
            tensor<int32, [2]> var_2709_split_sizes_0 = const()[name = string("op_2709_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_2709_axis_0 = const()[name = string("op_2709_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_2709_0, tensor<fp16, [1, 8, 128, 1]> var_2709_1 = split(axis = var_2709_axis_0, split_sizes = var_2709_split_sizes_0, x = query_states_33_cast_fp16)[name = string("op_2709")];
            bool attn_weights_81_transpose_x_0 = const()[name = string("attn_weights_81_transpose_x_0"), val = bool(false)];
            bool attn_weights_81_transpose_y_0 = const()[name = string("attn_weights_81_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_81_cast_fp16 = matmul(transpose_x = attn_weights_81_transpose_x_0, transpose_y = attn_weights_81_transpose_y_0, x = var_2696_cast_fp16_0, y = var_2709_0)[name = string("attn_weights_81_cast_fp16")];
            fp16 var_2712_to_fp16 = const()[name = string("op_2712_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_83_cast_fp16 = mul(x = attn_weights_81_cast_fp16, y = var_2712_to_fp16)[name = string("attn_weights_83_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_85_cast_fp16 = add(x = attn_weights_83_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_85_cast_fp16")];
            int32 var_2716 = const()[name = string("op_2716"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_87_cast_fp16 = softmax(axis = var_2716, x = attn_weights_85_cast_fp16)[name = string("attn_weights_87_cast_fp16")];
            bool var_2722_transpose_x_1 = const()[name = string("op_2722_transpose_x_1"), val = bool(true)];
            bool var_2722_transpose_y_1 = const()[name = string("op_2722_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_2722_cast_fp16 = matmul(transpose_x = var_2722_transpose_x_1, transpose_y = var_2722_transpose_y_1, x = attn_weights_87_cast_fp16, y = var_2706_cast_fp16_0)[name = string("op_2722_cast_fp16")];
            bool attn_weights_89_transpose_x_0 = const()[name = string("attn_weights_89_transpose_x_0"), val = bool(false)];
            bool attn_weights_89_transpose_y_0 = const()[name = string("attn_weights_89_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_89_cast_fp16 = matmul(transpose_x = attn_weights_89_transpose_x_0, transpose_y = attn_weights_89_transpose_y_0, x = var_2696_cast_fp16_1, y = var_2709_1)[name = string("attn_weights_89_cast_fp16")];
            fp16 var_2724_to_fp16 = const()[name = string("op_2724_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_91_cast_fp16 = mul(x = attn_weights_89_cast_fp16, y = var_2724_to_fp16)[name = string("attn_weights_91_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_93_cast_fp16 = add(x = attn_weights_91_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_93_cast_fp16")];
            int32 var_2728 = const()[name = string("op_2728"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_95_cast_fp16 = softmax(axis = var_2728, x = attn_weights_93_cast_fp16)[name = string("attn_weights_95_cast_fp16")];
            bool attn_output_41_transpose_x_1 = const()[name = string("attn_output_41_transpose_x_1"), val = bool(true)];
            bool attn_output_41_transpose_y_1 = const()[name = string("attn_output_41_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_41_cast_fp16 = matmul(transpose_x = attn_output_41_transpose_x_1, transpose_y = attn_output_41_transpose_y_1, x = attn_weights_95_cast_fp16, y = var_2706_cast_fp16_1)[name = string("attn_output_41_cast_fp16")];
            int32 var_2736 = const()[name = string("op_2736"), val = int32(1)];
            bool attn_output_43_interleave_0 = const()[name = string("attn_output_43_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_43_cast_fp16 = concat(axis = var_2736, interleave = attn_output_43_interleave_0, values = (var_2722_cast_fp16, attn_output_41_cast_fp16))[name = string("attn_output_43_cast_fp16")];
            tensor<int32, [4]> var_2740_perm_0 = const()[name = string("op_2740_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_71x = const()[name = string("concat_71x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_2740_cast_fp16 = transpose(perm = var_2740_perm_0, x = attn_output_43_cast_fp16)[name = string("transpose_66")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_47_cast_fp16 = reshape(shape = concat_71x, x = var_2740_cast_fp16)[name = string("attn_output_47_cast_fp16")];
            tensor<int32, [2]> hidden_states_53_strides_0 = const()[name = string("hidden_states_53_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_53_pad_type_0 = const()[name = string("hidden_states_53_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_53_pad_0 = const()[name = string("hidden_states_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_53_dilations_0 = const()[name = string("hidden_states_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_53_groups_0 = const()[name = string("hidden_states_53_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_53_cast_fp16 = conv(dilations = hidden_states_53_dilations_0, groups = hidden_states_53_groups_0, pad = hidden_states_53_pad_0, pad_type = hidden_states_53_pad_type_0, strides = hidden_states_53_strides_0, weight = layers_5_self_attn_o_proj_weight_cast_fp16, x = attn_output_47_cast_fp16)[name = string("hidden_states_53_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_55_cast_fp16 = add(x = hidden_states_49_cast_fp16, y = hidden_states_53_cast_fp16)[name = string("hidden_states_55_cast_fp16")];
            fp16 const_60_promoted_to_fp16 = const()[name = string("const_60_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_2773_cast_fp16 = mul(x = hidden_states_55_cast_fp16, y = const_60_promoted_to_fp16)[name = string("op_2773_cast_fp16")];
            int32 var_2771 = const()[name = string("op_2771"), val = int32(1)];
            bool doubled_45_interleave_0 = const()[name = string("doubled_45_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_45_cast_fp16 = concat(axis = var_2771, interleave = doubled_45_interleave_0, values = (hidden_states_55_cast_fp16, var_2773_cast_fp16))[name = string("doubled_45_cast_fp16")];
            tensor<int32, [1]> out_23_axes_0 = const()[name = string("out_23_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_23_gamma_0_to_fp16 = const()[name = string("out_23_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1284202688)))];
            fp16 var_2783_to_fp16 = const()[name = string("op_2783_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_23_cast_fp16 = layer_norm(axes = out_23_axes_0, epsilon = var_2783_to_fp16, gamma = out_23_gamma_0_to_fp16, x = doubled_45_cast_fp16)[name = string("out_23_cast_fp16")];
            tensor<int32, [2]> var_2794_split_sizes_0 = const()[name = string("op_2794_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2794_axis_0 = const()[name = string("op_2794_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_2794_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_2794_cast_fp16_1 = split(axis = var_2794_axis_0, split_sizes = var_2794_split_sizes_0, x = out_23_cast_fp16)[name = string("op_2794_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_5_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_5_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1284210944)))];
            tensor<int32, [2]> input_11_strides_0 = const()[name = string("input_11_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_11_pad_type_0 = const()[name = string("input_11_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_11_pad_0 = const()[name = string("input_11_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_11_dilations_0 = const()[name = string("input_11_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_11_groups_0 = const()[name = string("input_11_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_11_cast_fp16 = conv(dilations = input_11_dilations_0, groups = input_11_groups_0, pad = input_11_pad_0, pad_type = input_11_pad_type_0, strides = input_11_strides_0, weight = layers_5_mlp_gate_proj_weight_to_fp16, x = var_2794_cast_fp16_0)[name = string("input_11_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_2811_cast_fp16 = silu(x = input_11_cast_fp16)[name = string("op_2811_cast_fp16")];
            tensor<int32, [2]> var_2817_strides_0 = const()[name = string("op_2817_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_2817_pad_type_0 = const()[name = string("op_2817_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_2817_pad_0 = const()[name = string("op_2817_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_2817_dilations_0 = const()[name = string("op_2817_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_2817_groups_0 = const()[name = string("op_2817_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_2817_cast_fp16 = conv(dilations = var_2817_dilations_0, groups = var_2817_groups_0, pad = var_2817_pad_0, pad_type = var_2817_pad_type_0, strides = var_2817_strides_0, weight = layers_5_mlp_up_proj_weight_cast_fp16, x = var_2794_cast_fp16_0)[name = string("op_2817_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_59_cast_fp16 = mul(x = var_2811_cast_fp16, y = var_2817_cast_fp16)[name = string("x_59_cast_fp16")];
            tensor<int32, [2]> hidden_states_57_strides_0 = const()[name = string("hidden_states_57_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_57_pad_type_0 = const()[name = string("hidden_states_57_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_57_pad_0 = const()[name = string("hidden_states_57_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_57_dilations_0 = const()[name = string("hidden_states_57_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_57_groups_0 = const()[name = string("hidden_states_57_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_57_cast_fp16 = conv(dilations = hidden_states_57_dilations_0, groups = hidden_states_57_groups_0, pad = hidden_states_57_pad_0, pad_type = hidden_states_57_pad_type_0, strides = hidden_states_57_strides_0, weight = layers_5_mlp_down_proj_weight_cast_fp16, x = x_59_cast_fp16)[name = string("hidden_states_57_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_59_cast_fp16 = add(x = hidden_states_55_cast_fp16, y = hidden_states_57_cast_fp16)[name = string("hidden_states_59_cast_fp16")];
            fp16 const_62_promoted_to_fp16 = const()[name = string("const_62_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_2835_cast_fp16 = mul(x = hidden_states_59_cast_fp16, y = const_62_promoted_to_fp16)[name = string("op_2835_cast_fp16")];
            int32 var_2833 = const()[name = string("op_2833"), val = int32(1)];
            bool doubled_49_interleave_0 = const()[name = string("doubled_49_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_49_cast_fp16 = concat(axis = var_2833, interleave = doubled_49_interleave_0, values = (hidden_states_59_cast_fp16, var_2835_cast_fp16))[name = string("doubled_49_cast_fp16")];
            tensor<int32, [1]> out_25_axes_0 = const()[name = string("out_25_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_25_gamma_0_to_fp16 = const()[name = string("out_25_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1309376832)))];
            fp16 var_2845_to_fp16 = const()[name = string("op_2845_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_25_cast_fp16 = layer_norm(axes = out_25_axes_0, epsilon = var_2845_to_fp16, gamma = out_25_gamma_0_to_fp16, x = doubled_49_cast_fp16)[name = string("out_25_cast_fp16")];
            tensor<int32, [2]> var_2856_split_sizes_0 = const()[name = string("op_2856_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_2856_axis_0 = const()[name = string("op_2856_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_2856_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_2856_cast_fp16_1 = split(axis = var_2856_axis_0, split_sizes = var_2856_split_sizes_0, x = out_25_cast_fp16)[name = string("op_2856_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_6_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_6_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1309385088)))];
            tensor<int32, [2]> query_states_37_strides_0 = const()[name = string("query_states_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_37_pad_type_0 = const()[name = string("query_states_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_37_pad_0 = const()[name = string("query_states_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_37_dilations_0 = const()[name = string("query_states_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_37_groups_0 = const()[name = string("query_states_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_37_cast_fp16 = conv(dilations = query_states_37_dilations_0, groups = query_states_37_groups_0, pad = query_states_37_pad_0, pad_type = query_states_37_pad_type_0, strides = query_states_37_strides_0, weight = layers_6_self_attn_q_proj_weight_to_fp16, x = var_2856_cast_fp16_0)[name = string("query_states_37_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_6_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_6_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1317773760)))];
            tensor<int32, [2]> key_states_61_strides_0 = const()[name = string("key_states_61_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_61_pad_type_0 = const()[name = string("key_states_61_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_61_pad_0 = const()[name = string("key_states_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_61_dilations_0 = const()[name = string("key_states_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_61_groups_0 = const()[name = string("key_states_61_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_61_cast_fp16 = conv(dilations = key_states_61_dilations_0, groups = key_states_61_groups_0, pad = key_states_61_pad_0, pad_type = key_states_61_pad_type_0, strides = key_states_61_strides_0, weight = layers_6_self_attn_k_proj_weight_to_fp16, x = var_2856_cast_fp16_0)[name = string("key_states_61_cast_fp16")];
            tensor<int32, [2]> value_states_37_strides_0 = const()[name = string("value_states_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_37_pad_type_0 = const()[name = string("value_states_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_37_pad_0 = const()[name = string("value_states_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_37_dilations_0 = const()[name = string("value_states_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_37_groups_0 = const()[name = string("value_states_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_37_cast_fp16 = conv(dilations = value_states_37_dilations_0, groups = value_states_37_groups_0, pad = value_states_37_pad_0, pad_type = value_states_37_pad_type_0, strides = value_states_37_strides_0, weight = layers_6_self_attn_v_proj_weight_cast_fp16, x = var_2856_cast_fp16_0)[name = string("value_states_37_cast_fp16")];
            tensor<int32, [4]> concat_72x = const()[name = string("concat_72x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_61_cast_fp16 = reshape(shape = concat_72x, x = query_states_37_cast_fp16)[name = string("x_61_cast_fp16")];
            tensor<int32, [4]> concat_73x = const()[name = string("concat_73x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_2913_cast_fp16 = reshape(shape = concat_73x, x = key_states_61_cast_fp16)[name = string("op_2913_cast_fp16")];
            tensor<int32, [4]> concat_74x = const()[name = string("concat_74x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_2920_cast_fp16 = reshape(shape = concat_74x, x = value_states_37_cast_fp16)[name = string("op_2920_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_2924_cast_fp16 = mul(x = x_61_cast_fp16, y = var_859_cast_fp16)[name = string("op_2924_cast_fp16")];
            tensor<int32, [2]> var_2925_split_sizes_0 = const()[name = string("op_2925_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2925_axis_0 = const()[name = string("op_2925_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_2925_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_2925_cast_fp16_1 = split(axis = var_2925_axis_0, split_sizes = var_2925_split_sizes_0, x = x_61_cast_fp16)[name = string("op_2925_cast_fp16")];
            fp16 const_64_promoted_to_fp16 = const()[name = string("const_64_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_2927_cast_fp16 = mul(x = var_2925_cast_fp16_1, y = const_64_promoted_to_fp16)[name = string("op_2927_cast_fp16")];
            int32 var_2929 = const()[name = string("op_2929"), val = int32(-2)];
            bool var_2930_interleave_0 = const()[name = string("op_2930_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_2930_cast_fp16 = concat(axis = var_2929, interleave = var_2930_interleave_0, values = (var_2927_cast_fp16, var_2925_cast_fp16_0))[name = string("op_2930_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_2931_cast_fp16 = mul(x = var_2930_cast_fp16, y = var_866_cast_fp16)[name = string("op_2931_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_39_cast_fp16 = add(x = var_2924_cast_fp16, y = var_2931_cast_fp16)[name = string("query_states_39_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_2937_cast_fp16 = mul(x = var_2913_cast_fp16, y = var_859_cast_fp16)[name = string("op_2937_cast_fp16")];
            tensor<int32, [2]> var_2938_split_sizes_0 = const()[name = string("op_2938_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_2938_axis_0 = const()[name = string("op_2938_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_2938_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_2938_cast_fp16_1 = split(axis = var_2938_axis_0, split_sizes = var_2938_split_sizes_0, x = var_2913_cast_fp16)[name = string("op_2938_cast_fp16")];
            fp16 const_65_promoted_to_fp16 = const()[name = string("const_65_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_2940_cast_fp16 = mul(x = var_2938_cast_fp16_1, y = const_65_promoted_to_fp16)[name = string("op_2940_cast_fp16")];
            int32 var_2942 = const()[name = string("op_2942"), val = int32(-2)];
            bool var_2943_interleave_0 = const()[name = string("op_2943_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_2943_cast_fp16 = concat(axis = var_2942, interleave = var_2943_interleave_0, values = (var_2940_cast_fp16, var_2938_cast_fp16_0))[name = string("op_2943_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_2944_cast_fp16 = mul(x = var_2943_cast_fp16, y = var_866_cast_fp16)[name = string("op_2944_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_65_cast_fp16 = add(x = var_2937_cast_fp16, y = var_2944_cast_fp16)[name = string("key_states_65_cast_fp16")];
            tensor<int32, [1]> expand_dims_72 = const()[name = string("expand_dims_72"), val = tensor<int32, [1]>([6])];
            tensor<int32, [1]> expand_dims_73 = const()[name = string("expand_dims_73"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_75 = const()[name = string("expand_dims_75"), val = tensor<int32, [1]>([0])];
            int32 concat_77_axis_0 = const()[name = string("concat_77_axis_0"), val = int32(0)];
            bool concat_77_interleave_0 = const()[name = string("concat_77_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_77 = concat(axis = concat_77_axis_0, interleave = concat_77_interleave_0, values = (expand_dims_72, expand_dims_73, position_id, expand_dims_75))[name = string("concat_77")];
            tensor<int32, [1]> expand_dims_76 = const()[name = string("expand_dims_76"), val = tensor<int32, [1]>([7])];
            tensor<int32, [1]> concat_78_values1_0 = const()[name = string("concat_78_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_78_values3_0 = const()[name = string("concat_78_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_78_axis_0 = const()[name = string("concat_78_axis_0"), val = int32(0)];
            bool concat_78_interleave_0 = const()[name = string("concat_78_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_78 = concat(axis = concat_78_axis_0, interleave = concat_78_interleave_0, values = (expand_dims_76, concat_78_values1_0, cache_position_end, concat_78_values3_0))[name = string("concat_78")];
            tensor<int32, [4]> key_states_67_perm_0 = const()[name = string("key_states_67_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_7_stride_0 = const()[name = string("key_cache_internal_tensor_assign_7_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_7_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_7_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_7_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_7_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_7_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_7_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_67_cast_fp16 = transpose(perm = key_states_67_perm_0, x = key_states_65_cast_fp16)[name = string("transpose_65")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_7_cast_fp16 = slice_update(begin = concat_77, begin_mask = key_cache_internal_tensor_assign_7_begin_mask_0, end = concat_78, end_mask = key_cache_internal_tensor_assign_7_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_7_squeeze_mask_0, stride = key_cache_internal_tensor_assign_7_stride_0, update = key_states_67_cast_fp16, x = coreml_update_state_10)[name = string("key_cache_internal_tensor_assign_7_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_7_cast_fp16, input = key_cache)[name = string("coreml_update_state_12_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_12 = read_state(input = key_cache)[name = string("coreml_update_state_12")];
            tensor<int32, [4]> value_states_39_perm_0 = const()[name = string("value_states_39_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_7_stride_0 = const()[name = string("value_cache_internal_tensor_assign_7_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_7_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_7_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_7_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_7_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_7_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_7_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_39_cast_fp16 = transpose(perm = value_states_39_perm_0, x = var_2920_cast_fp16)[name = string("transpose_64")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_7_cast_fp16 = slice_update(begin = concat_77, begin_mask = value_cache_internal_tensor_assign_7_begin_mask_0, end = concat_78, end_mask = value_cache_internal_tensor_assign_7_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_7_squeeze_mask_0, stride = value_cache_internal_tensor_assign_7_stride_0, update = value_states_39_cast_fp16, x = coreml_update_state_11)[name = string("value_cache_internal_tensor_assign_7_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_7_cast_fp16, input = value_cache)[name = string("coreml_update_state_13_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_13 = read_state(input = value_cache)[name = string("coreml_update_state_13")];
            tensor<int32, [4]> var_3014_begin_0 = const()[name = string("op_3014_begin_0"), val = tensor<int32, [4]>([6, 0, 0, 0])];
            tensor<int32, [4]> var_3014_end_0 = const()[name = string("op_3014_end_0"), val = tensor<int32, [4]>([7, 2, 2048, 128])];
            tensor<bool, [4]> var_3014_end_mask_0 = const()[name = string("op_3014_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3014_cast_fp16 = slice_by_index(begin = var_3014_begin_0, end = var_3014_end_0, end_mask = var_3014_end_mask_0, x = coreml_update_state_12)[name = string("op_3014_cast_fp16")];
            tensor<int32, [2]> tile_12 = const()[name = string("tile_12"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3017_axis_0 = const()[name = string("op_3017_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3017_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3017_cast_fp16_1 = split(axis = var_3017_axis_0, split_sizes = tile_12, x = var_3014_cast_fp16)[name = string("op_3017_cast_fp16")];
            tensor<int32, [4]> var_3024_begin_0 = const()[name = string("op_3024_begin_0"), val = tensor<int32, [4]>([6, 0, 0, 0])];
            tensor<int32, [4]> var_3024_end_0 = const()[name = string("op_3024_end_0"), val = tensor<int32, [4]>([7, 2, 2048, 128])];
            tensor<bool, [4]> var_3024_end_mask_0 = const()[name = string("op_3024_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3024_cast_fp16 = slice_by_index(begin = var_3024_begin_0, end = var_3024_end_0, end_mask = var_3024_end_mask_0, x = coreml_update_state_13)[name = string("op_3024_cast_fp16")];
            tensor<int32, [2]> tile_13 = const()[name = string("tile_13"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3027_axis_0 = const()[name = string("op_3027_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3027_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3027_cast_fp16_1 = split(axis = var_3027_axis_0, split_sizes = tile_13, x = var_3024_cast_fp16)[name = string("op_3027_cast_fp16")];
            tensor<int32, [2]> var_3030_split_sizes_0 = const()[name = string("op_3030_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3030_axis_0 = const()[name = string("op_3030_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_3030_0, tensor<fp16, [1, 8, 128, 1]> var_3030_1 = split(axis = var_3030_axis_0, split_sizes = var_3030_split_sizes_0, x = query_states_39_cast_fp16)[name = string("op_3030")];
            bool attn_weights_97_transpose_x_0 = const()[name = string("attn_weights_97_transpose_x_0"), val = bool(false)];
            bool attn_weights_97_transpose_y_0 = const()[name = string("attn_weights_97_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_97_cast_fp16 = matmul(transpose_x = attn_weights_97_transpose_x_0, transpose_y = attn_weights_97_transpose_y_0, x = var_3017_cast_fp16_0, y = var_3030_0)[name = string("attn_weights_97_cast_fp16")];
            fp16 var_3033_to_fp16 = const()[name = string("op_3033_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_99_cast_fp16 = mul(x = attn_weights_97_cast_fp16, y = var_3033_to_fp16)[name = string("attn_weights_99_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_101_cast_fp16 = add(x = attn_weights_99_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_101_cast_fp16")];
            int32 var_3037 = const()[name = string("op_3037"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_103_cast_fp16 = softmax(axis = var_3037, x = attn_weights_101_cast_fp16)[name = string("attn_weights_103_cast_fp16")];
            bool var_3043_transpose_x_1 = const()[name = string("op_3043_transpose_x_1"), val = bool(true)];
            bool var_3043_transpose_y_1 = const()[name = string("op_3043_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_3043_cast_fp16 = matmul(transpose_x = var_3043_transpose_x_1, transpose_y = var_3043_transpose_y_1, x = attn_weights_103_cast_fp16, y = var_3027_cast_fp16_0)[name = string("op_3043_cast_fp16")];
            bool attn_weights_105_transpose_x_0 = const()[name = string("attn_weights_105_transpose_x_0"), val = bool(false)];
            bool attn_weights_105_transpose_y_0 = const()[name = string("attn_weights_105_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_105_cast_fp16 = matmul(transpose_x = attn_weights_105_transpose_x_0, transpose_y = attn_weights_105_transpose_y_0, x = var_3017_cast_fp16_1, y = var_3030_1)[name = string("attn_weights_105_cast_fp16")];
            fp16 var_3045_to_fp16 = const()[name = string("op_3045_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_107_cast_fp16 = mul(x = attn_weights_105_cast_fp16, y = var_3045_to_fp16)[name = string("attn_weights_107_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_109_cast_fp16 = add(x = attn_weights_107_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_109_cast_fp16")];
            int32 var_3049 = const()[name = string("op_3049"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_111_cast_fp16 = softmax(axis = var_3049, x = attn_weights_109_cast_fp16)[name = string("attn_weights_111_cast_fp16")];
            bool attn_output_49_transpose_x_1 = const()[name = string("attn_output_49_transpose_x_1"), val = bool(true)];
            bool attn_output_49_transpose_y_1 = const()[name = string("attn_output_49_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_49_cast_fp16 = matmul(transpose_x = attn_output_49_transpose_x_1, transpose_y = attn_output_49_transpose_y_1, x = attn_weights_111_cast_fp16, y = var_3027_cast_fp16_1)[name = string("attn_output_49_cast_fp16")];
            int32 var_3057 = const()[name = string("op_3057"), val = int32(1)];
            bool attn_output_51_interleave_0 = const()[name = string("attn_output_51_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_51_cast_fp16 = concat(axis = var_3057, interleave = attn_output_51_interleave_0, values = (var_3043_cast_fp16, attn_output_49_cast_fp16))[name = string("attn_output_51_cast_fp16")];
            tensor<int32, [4]> var_3061_perm_0 = const()[name = string("op_3061_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_83x = const()[name = string("concat_83x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_3061_cast_fp16 = transpose(perm = var_3061_perm_0, x = attn_output_51_cast_fp16)[name = string("transpose_63")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_55_cast_fp16 = reshape(shape = concat_83x, x = var_3061_cast_fp16)[name = string("attn_output_55_cast_fp16")];
            tensor<int32, [2]> hidden_states_63_strides_0 = const()[name = string("hidden_states_63_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_63_pad_type_0 = const()[name = string("hidden_states_63_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_63_pad_0 = const()[name = string("hidden_states_63_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_63_dilations_0 = const()[name = string("hidden_states_63_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_63_groups_0 = const()[name = string("hidden_states_63_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_63_cast_fp16 = conv(dilations = hidden_states_63_dilations_0, groups = hidden_states_63_groups_0, pad = hidden_states_63_pad_0, pad_type = hidden_states_63_pad_type_0, strides = hidden_states_63_strides_0, weight = layers_6_self_attn_o_proj_weight_cast_fp16, x = attn_output_55_cast_fp16)[name = string("hidden_states_63_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_65_cast_fp16 = add(x = hidden_states_59_cast_fp16, y = hidden_states_63_cast_fp16)[name = string("hidden_states_65_cast_fp16")];
            fp16 const_70_promoted_to_fp16 = const()[name = string("const_70_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_3094_cast_fp16 = mul(x = hidden_states_65_cast_fp16, y = const_70_promoted_to_fp16)[name = string("op_3094_cast_fp16")];
            int32 var_3092 = const()[name = string("op_3092"), val = int32(1)];
            bool doubled_53_interleave_0 = const()[name = string("doubled_53_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_53_cast_fp16 = concat(axis = var_3092, interleave = doubled_53_interleave_0, values = (hidden_states_65_cast_fp16, var_3094_cast_fp16))[name = string("doubled_53_cast_fp16")];
            tensor<int32, [1]> out_27_axes_0 = const()[name = string("out_27_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_27_gamma_0_to_fp16 = const()[name = string("out_27_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1318822400)))];
            fp16 var_3104_to_fp16 = const()[name = string("op_3104_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_27_cast_fp16 = layer_norm(axes = out_27_axes_0, epsilon = var_3104_to_fp16, gamma = out_27_gamma_0_to_fp16, x = doubled_53_cast_fp16)[name = string("out_27_cast_fp16")];
            tensor<int32, [2]> var_3115_split_sizes_0 = const()[name = string("op_3115_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3115_axis_0 = const()[name = string("op_3115_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_3115_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_3115_cast_fp16_1 = split(axis = var_3115_axis_0, split_sizes = var_3115_split_sizes_0, x = out_27_cast_fp16)[name = string("op_3115_cast_fp16")];
            tensor<int32, [2]> input_13_strides_0 = const()[name = string("input_13_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_13_pad_type_0 = const()[name = string("input_13_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_13_pad_0 = const()[name = string("input_13_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_13_dilations_0 = const()[name = string("input_13_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_13_groups_0 = const()[name = string("input_13_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_13_cast_fp16 = conv(dilations = input_13_dilations_0, groups = input_13_groups_0, pad = input_13_pad_0, pad_type = input_13_pad_type_0, strides = input_13_strides_0, weight = layers_6_mlp_gate_proj_weight_cast_fp16, x = var_3115_cast_fp16_0)[name = string("input_13_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_3132_cast_fp16 = silu(x = input_13_cast_fp16)[name = string("op_3132_cast_fp16")];
            tensor<int32, [2]> var_3138_strides_0 = const()[name = string("op_3138_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_3138_pad_type_0 = const()[name = string("op_3138_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_3138_pad_0 = const()[name = string("op_3138_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_3138_dilations_0 = const()[name = string("op_3138_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3138_groups_0 = const()[name = string("op_3138_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_3138_cast_fp16 = conv(dilations = var_3138_dilations_0, groups = var_3138_groups_0, pad = var_3138_pad_0, pad_type = var_3138_pad_type_0, strides = var_3138_strides_0, weight = layers_6_mlp_up_proj_weight_cast_fp16, x = var_3115_cast_fp16_0)[name = string("op_3138_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_69_cast_fp16 = mul(x = var_3132_cast_fp16, y = var_3138_cast_fp16)[name = string("x_69_cast_fp16")];
            tensor<int32, [2]> hidden_states_67_strides_0 = const()[name = string("hidden_states_67_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_67_pad_type_0 = const()[name = string("hidden_states_67_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_67_pad_0 = const()[name = string("hidden_states_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_67_dilations_0 = const()[name = string("hidden_states_67_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_67_groups_0 = const()[name = string("hidden_states_67_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_67_cast_fp16 = conv(dilations = hidden_states_67_dilations_0, groups = hidden_states_67_groups_0, pad = hidden_states_67_pad_0, pad_type = hidden_states_67_pad_type_0, strides = hidden_states_67_strides_0, weight = layers_6_mlp_down_proj_weight_cast_fp16, x = x_69_cast_fp16)[name = string("hidden_states_67_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_69_cast_fp16 = add(x = hidden_states_65_cast_fp16, y = hidden_states_67_cast_fp16)[name = string("hidden_states_69_cast_fp16")];
            fp16 const_72_promoted_to_fp16 = const()[name = string("const_72_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_3156_cast_fp16 = mul(x = hidden_states_69_cast_fp16, y = const_72_promoted_to_fp16)[name = string("op_3156_cast_fp16")];
            int32 var_3154 = const()[name = string("op_3154"), val = int32(1)];
            bool doubled_57_interleave_0 = const()[name = string("doubled_57_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_57_cast_fp16 = concat(axis = var_3154, interleave = doubled_57_interleave_0, values = (hidden_states_69_cast_fp16, var_3156_cast_fp16))[name = string("doubled_57_cast_fp16")];
            tensor<int32, [1]> out_29_axes_0 = const()[name = string("out_29_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_29_gamma_0_to_fp16 = const()[name = string("out_29_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1318830656)))];
            fp16 var_3166_to_fp16 = const()[name = string("op_3166_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_29_cast_fp16 = layer_norm(axes = out_29_axes_0, epsilon = var_3166_to_fp16, gamma = out_29_gamma_0_to_fp16, x = doubled_57_cast_fp16)[name = string("out_29_cast_fp16")];
            tensor<int32, [2]> var_3177_split_sizes_0 = const()[name = string("op_3177_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3177_axis_0 = const()[name = string("op_3177_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_3177_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_3177_cast_fp16_1 = split(axis = var_3177_axis_0, split_sizes = var_3177_split_sizes_0, x = out_29_cast_fp16)[name = string("op_3177_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_7_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_7_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1318838912)))];
            tensor<int32, [2]> query_states_43_strides_0 = const()[name = string("query_states_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_43_pad_type_0 = const()[name = string("query_states_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_43_pad_0 = const()[name = string("query_states_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_43_dilations_0 = const()[name = string("query_states_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_43_groups_0 = const()[name = string("query_states_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_43_cast_fp16 = conv(dilations = query_states_43_dilations_0, groups = query_states_43_groups_0, pad = query_states_43_pad_0, pad_type = query_states_43_pad_type_0, strides = query_states_43_strides_0, weight = layers_7_self_attn_q_proj_weight_to_fp16, x = var_3177_cast_fp16_0)[name = string("query_states_43_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_7_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_7_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1327227584)))];
            tensor<int32, [2]> key_states_71_strides_0 = const()[name = string("key_states_71_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_71_pad_type_0 = const()[name = string("key_states_71_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_71_pad_0 = const()[name = string("key_states_71_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_71_dilations_0 = const()[name = string("key_states_71_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_71_groups_0 = const()[name = string("key_states_71_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_71_cast_fp16 = conv(dilations = key_states_71_dilations_0, groups = key_states_71_groups_0, pad = key_states_71_pad_0, pad_type = key_states_71_pad_type_0, strides = key_states_71_strides_0, weight = layers_7_self_attn_k_proj_weight_to_fp16, x = var_3177_cast_fp16_0)[name = string("key_states_71_cast_fp16")];
            tensor<int32, [2]> value_states_43_strides_0 = const()[name = string("value_states_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_43_pad_type_0 = const()[name = string("value_states_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_43_pad_0 = const()[name = string("value_states_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_43_dilations_0 = const()[name = string("value_states_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_43_groups_0 = const()[name = string("value_states_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_43_cast_fp16 = conv(dilations = value_states_43_dilations_0, groups = value_states_43_groups_0, pad = value_states_43_pad_0, pad_type = value_states_43_pad_type_0, strides = value_states_43_strides_0, weight = layers_7_self_attn_v_proj_weight_cast_fp16, x = var_3177_cast_fp16_0)[name = string("value_states_43_cast_fp16")];
            tensor<int32, [4]> concat_84x = const()[name = string("concat_84x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_71_cast_fp16 = reshape(shape = concat_84x, x = query_states_43_cast_fp16)[name = string("x_71_cast_fp16")];
            tensor<int32, [4]> concat_85x = const()[name = string("concat_85x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_3234_cast_fp16 = reshape(shape = concat_85x, x = key_states_71_cast_fp16)[name = string("op_3234_cast_fp16")];
            tensor<int32, [4]> concat_86x = const()[name = string("concat_86x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_3241_cast_fp16 = reshape(shape = concat_86x, x = value_states_43_cast_fp16)[name = string("op_3241_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_3245_cast_fp16 = mul(x = x_71_cast_fp16, y = var_859_cast_fp16)[name = string("op_3245_cast_fp16")];
            tensor<int32, [2]> var_3246_split_sizes_0 = const()[name = string("op_3246_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3246_axis_0 = const()[name = string("op_3246_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_3246_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_3246_cast_fp16_1 = split(axis = var_3246_axis_0, split_sizes = var_3246_split_sizes_0, x = x_71_cast_fp16)[name = string("op_3246_cast_fp16")];
            fp16 const_74_promoted_to_fp16 = const()[name = string("const_74_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_3248_cast_fp16 = mul(x = var_3246_cast_fp16_1, y = const_74_promoted_to_fp16)[name = string("op_3248_cast_fp16")];
            int32 var_3250 = const()[name = string("op_3250"), val = int32(-2)];
            bool var_3251_interleave_0 = const()[name = string("op_3251_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_3251_cast_fp16 = concat(axis = var_3250, interleave = var_3251_interleave_0, values = (var_3248_cast_fp16, var_3246_cast_fp16_0))[name = string("op_3251_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_3252_cast_fp16 = mul(x = var_3251_cast_fp16, y = var_866_cast_fp16)[name = string("op_3252_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_45_cast_fp16 = add(x = var_3245_cast_fp16, y = var_3252_cast_fp16)[name = string("query_states_45_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_3258_cast_fp16 = mul(x = var_3234_cast_fp16, y = var_859_cast_fp16)[name = string("op_3258_cast_fp16")];
            tensor<int32, [2]> var_3259_split_sizes_0 = const()[name = string("op_3259_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3259_axis_0 = const()[name = string("op_3259_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_3259_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_3259_cast_fp16_1 = split(axis = var_3259_axis_0, split_sizes = var_3259_split_sizes_0, x = var_3234_cast_fp16)[name = string("op_3259_cast_fp16")];
            fp16 const_75_promoted_to_fp16 = const()[name = string("const_75_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_3261_cast_fp16 = mul(x = var_3259_cast_fp16_1, y = const_75_promoted_to_fp16)[name = string("op_3261_cast_fp16")];
            int32 var_3263 = const()[name = string("op_3263"), val = int32(-2)];
            bool var_3264_interleave_0 = const()[name = string("op_3264_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_3264_cast_fp16 = concat(axis = var_3263, interleave = var_3264_interleave_0, values = (var_3261_cast_fp16, var_3259_cast_fp16_0))[name = string("op_3264_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_3265_cast_fp16 = mul(x = var_3264_cast_fp16, y = var_866_cast_fp16)[name = string("op_3265_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_75_cast_fp16 = add(x = var_3258_cast_fp16, y = var_3265_cast_fp16)[name = string("key_states_75_cast_fp16")];
            tensor<int32, [1]> expand_dims_84 = const()[name = string("expand_dims_84"), val = tensor<int32, [1]>([7])];
            tensor<int32, [1]> expand_dims_85 = const()[name = string("expand_dims_85"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_87 = const()[name = string("expand_dims_87"), val = tensor<int32, [1]>([0])];
            int32 concat_89_axis_0 = const()[name = string("concat_89_axis_0"), val = int32(0)];
            bool concat_89_interleave_0 = const()[name = string("concat_89_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_89 = concat(axis = concat_89_axis_0, interleave = concat_89_interleave_0, values = (expand_dims_84, expand_dims_85, position_id, expand_dims_87))[name = string("concat_89")];
            tensor<int32, [1]> expand_dims_88 = const()[name = string("expand_dims_88"), val = tensor<int32, [1]>([8])];
            tensor<int32, [1]> concat_90_values1_0 = const()[name = string("concat_90_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_90_values3_0 = const()[name = string("concat_90_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_90_axis_0 = const()[name = string("concat_90_axis_0"), val = int32(0)];
            bool concat_90_interleave_0 = const()[name = string("concat_90_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_90 = concat(axis = concat_90_axis_0, interleave = concat_90_interleave_0, values = (expand_dims_88, concat_90_values1_0, cache_position_end, concat_90_values3_0))[name = string("concat_90")];
            tensor<int32, [4]> key_states_77_perm_0 = const()[name = string("key_states_77_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_8_stride_0 = const()[name = string("key_cache_internal_tensor_assign_8_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_8_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_8_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_8_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_8_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_8_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_8_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_77_cast_fp16 = transpose(perm = key_states_77_perm_0, x = key_states_75_cast_fp16)[name = string("transpose_62")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_8_cast_fp16 = slice_update(begin = concat_89, begin_mask = key_cache_internal_tensor_assign_8_begin_mask_0, end = concat_90, end_mask = key_cache_internal_tensor_assign_8_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_8_squeeze_mask_0, stride = key_cache_internal_tensor_assign_8_stride_0, update = key_states_77_cast_fp16, x = coreml_update_state_12)[name = string("key_cache_internal_tensor_assign_8_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_8_cast_fp16, input = key_cache)[name = string("coreml_update_state_14_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_14 = read_state(input = key_cache)[name = string("coreml_update_state_14")];
            tensor<int32, [4]> value_states_45_perm_0 = const()[name = string("value_states_45_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_8_stride_0 = const()[name = string("value_cache_internal_tensor_assign_8_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_8_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_8_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_8_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_8_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_8_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_8_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_45_cast_fp16 = transpose(perm = value_states_45_perm_0, x = var_3241_cast_fp16)[name = string("transpose_61")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_8_cast_fp16 = slice_update(begin = concat_89, begin_mask = value_cache_internal_tensor_assign_8_begin_mask_0, end = concat_90, end_mask = value_cache_internal_tensor_assign_8_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_8_squeeze_mask_0, stride = value_cache_internal_tensor_assign_8_stride_0, update = value_states_45_cast_fp16, x = coreml_update_state_13)[name = string("value_cache_internal_tensor_assign_8_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_8_cast_fp16, input = value_cache)[name = string("coreml_update_state_15_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_15 = read_state(input = value_cache)[name = string("coreml_update_state_15")];
            tensor<int32, [4]> var_3335_begin_0 = const()[name = string("op_3335_begin_0"), val = tensor<int32, [4]>([7, 0, 0, 0])];
            tensor<int32, [4]> var_3335_end_0 = const()[name = string("op_3335_end_0"), val = tensor<int32, [4]>([8, 2, 2048, 128])];
            tensor<bool, [4]> var_3335_end_mask_0 = const()[name = string("op_3335_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3335_cast_fp16 = slice_by_index(begin = var_3335_begin_0, end = var_3335_end_0, end_mask = var_3335_end_mask_0, x = coreml_update_state_14)[name = string("op_3335_cast_fp16")];
            tensor<int32, [2]> tile_14 = const()[name = string("tile_14"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3338_axis_0 = const()[name = string("op_3338_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3338_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3338_cast_fp16_1 = split(axis = var_3338_axis_0, split_sizes = tile_14, x = var_3335_cast_fp16)[name = string("op_3338_cast_fp16")];
            tensor<int32, [4]> var_3345_begin_0 = const()[name = string("op_3345_begin_0"), val = tensor<int32, [4]>([7, 0, 0, 0])];
            tensor<int32, [4]> var_3345_end_0 = const()[name = string("op_3345_end_0"), val = tensor<int32, [4]>([8, 2, 2048, 128])];
            tensor<bool, [4]> var_3345_end_mask_0 = const()[name = string("op_3345_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3345_cast_fp16 = slice_by_index(begin = var_3345_begin_0, end = var_3345_end_0, end_mask = var_3345_end_mask_0, x = coreml_update_state_15)[name = string("op_3345_cast_fp16")];
            tensor<int32, [2]> tile_15 = const()[name = string("tile_15"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3348_axis_0 = const()[name = string("op_3348_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3348_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3348_cast_fp16_1 = split(axis = var_3348_axis_0, split_sizes = tile_15, x = var_3345_cast_fp16)[name = string("op_3348_cast_fp16")];
            tensor<int32, [2]> var_3351_split_sizes_0 = const()[name = string("op_3351_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3351_axis_0 = const()[name = string("op_3351_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_3351_0, tensor<fp16, [1, 8, 128, 1]> var_3351_1 = split(axis = var_3351_axis_0, split_sizes = var_3351_split_sizes_0, x = query_states_45_cast_fp16)[name = string("op_3351")];
            bool attn_weights_113_transpose_x_0 = const()[name = string("attn_weights_113_transpose_x_0"), val = bool(false)];
            bool attn_weights_113_transpose_y_0 = const()[name = string("attn_weights_113_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_113_cast_fp16 = matmul(transpose_x = attn_weights_113_transpose_x_0, transpose_y = attn_weights_113_transpose_y_0, x = var_3338_cast_fp16_0, y = var_3351_0)[name = string("attn_weights_113_cast_fp16")];
            fp16 var_3354_to_fp16 = const()[name = string("op_3354_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_115_cast_fp16 = mul(x = attn_weights_113_cast_fp16, y = var_3354_to_fp16)[name = string("attn_weights_115_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_117_cast_fp16 = add(x = attn_weights_115_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_117_cast_fp16")];
            int32 var_3358 = const()[name = string("op_3358"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_119_cast_fp16 = softmax(axis = var_3358, x = attn_weights_117_cast_fp16)[name = string("attn_weights_119_cast_fp16")];
            bool var_3364_transpose_x_1 = const()[name = string("op_3364_transpose_x_1"), val = bool(true)];
            bool var_3364_transpose_y_1 = const()[name = string("op_3364_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_3364_cast_fp16 = matmul(transpose_x = var_3364_transpose_x_1, transpose_y = var_3364_transpose_y_1, x = attn_weights_119_cast_fp16, y = var_3348_cast_fp16_0)[name = string("op_3364_cast_fp16")];
            bool attn_weights_121_transpose_x_0 = const()[name = string("attn_weights_121_transpose_x_0"), val = bool(false)];
            bool attn_weights_121_transpose_y_0 = const()[name = string("attn_weights_121_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_121_cast_fp16 = matmul(transpose_x = attn_weights_121_transpose_x_0, transpose_y = attn_weights_121_transpose_y_0, x = var_3338_cast_fp16_1, y = var_3351_1)[name = string("attn_weights_121_cast_fp16")];
            fp16 var_3366_to_fp16 = const()[name = string("op_3366_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_123_cast_fp16 = mul(x = attn_weights_121_cast_fp16, y = var_3366_to_fp16)[name = string("attn_weights_123_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_125_cast_fp16 = add(x = attn_weights_123_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_125_cast_fp16")];
            int32 var_3370 = const()[name = string("op_3370"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_127_cast_fp16 = softmax(axis = var_3370, x = attn_weights_125_cast_fp16)[name = string("attn_weights_127_cast_fp16")];
            bool attn_output_57_transpose_x_1 = const()[name = string("attn_output_57_transpose_x_1"), val = bool(true)];
            bool attn_output_57_transpose_y_1 = const()[name = string("attn_output_57_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_57_cast_fp16 = matmul(transpose_x = attn_output_57_transpose_x_1, transpose_y = attn_output_57_transpose_y_1, x = attn_weights_127_cast_fp16, y = var_3348_cast_fp16_1)[name = string("attn_output_57_cast_fp16")];
            int32 var_3378 = const()[name = string("op_3378"), val = int32(1)];
            bool attn_output_59_interleave_0 = const()[name = string("attn_output_59_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_59_cast_fp16 = concat(axis = var_3378, interleave = attn_output_59_interleave_0, values = (var_3364_cast_fp16, attn_output_57_cast_fp16))[name = string("attn_output_59_cast_fp16")];
            tensor<int32, [4]> var_3382_perm_0 = const()[name = string("op_3382_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_95x = const()[name = string("concat_95x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_3382_cast_fp16 = transpose(perm = var_3382_perm_0, x = attn_output_59_cast_fp16)[name = string("transpose_60")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_63_cast_fp16 = reshape(shape = concat_95x, x = var_3382_cast_fp16)[name = string("attn_output_63_cast_fp16")];
            tensor<int32, [2]> hidden_states_73_strides_0 = const()[name = string("hidden_states_73_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_73_pad_type_0 = const()[name = string("hidden_states_73_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_73_pad_0 = const()[name = string("hidden_states_73_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_73_dilations_0 = const()[name = string("hidden_states_73_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_73_groups_0 = const()[name = string("hidden_states_73_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_73_cast_fp16 = conv(dilations = hidden_states_73_dilations_0, groups = hidden_states_73_groups_0, pad = hidden_states_73_pad_0, pad_type = hidden_states_73_pad_type_0, strides = hidden_states_73_strides_0, weight = layers_7_self_attn_o_proj_weight_cast_fp16, x = attn_output_63_cast_fp16)[name = string("hidden_states_73_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_75_cast_fp16 = add(x = hidden_states_69_cast_fp16, y = hidden_states_73_cast_fp16)[name = string("hidden_states_75_cast_fp16")];
            fp16 const_80_promoted_to_fp16 = const()[name = string("const_80_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_3415_cast_fp16 = mul(x = hidden_states_75_cast_fp16, y = const_80_promoted_to_fp16)[name = string("op_3415_cast_fp16")];
            int32 var_3413 = const()[name = string("op_3413"), val = int32(1)];
            bool doubled_61_interleave_0 = const()[name = string("doubled_61_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_61_cast_fp16 = concat(axis = var_3413, interleave = doubled_61_interleave_0, values = (hidden_states_75_cast_fp16, var_3415_cast_fp16))[name = string("doubled_61_cast_fp16")];
            tensor<int32, [1]> out_31_axes_0 = const()[name = string("out_31_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_31_gamma_0_to_fp16 = const()[name = string("out_31_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1328276224)))];
            fp16 var_3425_to_fp16 = const()[name = string("op_3425_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_31_cast_fp16 = layer_norm(axes = out_31_axes_0, epsilon = var_3425_to_fp16, gamma = out_31_gamma_0_to_fp16, x = doubled_61_cast_fp16)[name = string("out_31_cast_fp16")];
            tensor<int32, [2]> var_3436_split_sizes_0 = const()[name = string("op_3436_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3436_axis_0 = const()[name = string("op_3436_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_3436_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_3436_cast_fp16_1 = split(axis = var_3436_axis_0, split_sizes = var_3436_split_sizes_0, x = out_31_cast_fp16)[name = string("op_3436_cast_fp16")];
            tensor<int32, [2]> input_15_strides_0 = const()[name = string("input_15_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_15_pad_type_0 = const()[name = string("input_15_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_15_pad_0 = const()[name = string("input_15_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_15_dilations_0 = const()[name = string("input_15_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_15_groups_0 = const()[name = string("input_15_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_15_cast_fp16 = conv(dilations = input_15_dilations_0, groups = input_15_groups_0, pad = input_15_pad_0, pad_type = input_15_pad_type_0, strides = input_15_strides_0, weight = layers_7_mlp_gate_proj_weight_cast_fp16, x = var_3436_cast_fp16_0)[name = string("input_15_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_3453_cast_fp16 = silu(x = input_15_cast_fp16)[name = string("op_3453_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_7_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_7_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1328284480)))];
            tensor<int32, [2]> var_3459_strides_0 = const()[name = string("op_3459_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_3459_pad_type_0 = const()[name = string("op_3459_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_3459_pad_0 = const()[name = string("op_3459_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_3459_dilations_0 = const()[name = string("op_3459_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3459_groups_0 = const()[name = string("op_3459_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_3459_cast_fp16 = conv(dilations = var_3459_dilations_0, groups = var_3459_groups_0, pad = var_3459_pad_0, pad_type = var_3459_pad_type_0, strides = var_3459_strides_0, weight = layers_7_mlp_up_proj_weight_to_fp16, x = var_3436_cast_fp16_0)[name = string("op_3459_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_79_cast_fp16 = mul(x = var_3453_cast_fp16, y = var_3459_cast_fp16)[name = string("x_79_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_7_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_7_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1353450368)))];
            tensor<int32, [2]> hidden_states_77_strides_0 = const()[name = string("hidden_states_77_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_77_pad_type_0 = const()[name = string("hidden_states_77_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_77_pad_0 = const()[name = string("hidden_states_77_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_77_dilations_0 = const()[name = string("hidden_states_77_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_77_groups_0 = const()[name = string("hidden_states_77_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_77_cast_fp16 = conv(dilations = hidden_states_77_dilations_0, groups = hidden_states_77_groups_0, pad = hidden_states_77_pad_0, pad_type = hidden_states_77_pad_type_0, strides = hidden_states_77_strides_0, weight = layers_7_mlp_down_proj_weight_to_fp16, x = x_79_cast_fp16)[name = string("hidden_states_77_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_79_cast_fp16 = add(x = hidden_states_75_cast_fp16, y = hidden_states_77_cast_fp16)[name = string("hidden_states_79_cast_fp16")];
            fp16 const_82_promoted_to_fp16 = const()[name = string("const_82_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_3477_cast_fp16 = mul(x = hidden_states_79_cast_fp16, y = const_82_promoted_to_fp16)[name = string("op_3477_cast_fp16")];
            int32 var_3475 = const()[name = string("op_3475"), val = int32(1)];
            bool doubled_65_interleave_0 = const()[name = string("doubled_65_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_65_cast_fp16 = concat(axis = var_3475, interleave = doubled_65_interleave_0, values = (hidden_states_79_cast_fp16, var_3477_cast_fp16))[name = string("doubled_65_cast_fp16")];
            tensor<int32, [1]> out_33_axes_0 = const()[name = string("out_33_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_33_gamma_0_to_fp16 = const()[name = string("out_33_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1378616256)))];
            fp16 var_3487_to_fp16 = const()[name = string("op_3487_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_33_cast_fp16 = layer_norm(axes = out_33_axes_0, epsilon = var_3487_to_fp16, gamma = out_33_gamma_0_to_fp16, x = doubled_65_cast_fp16)[name = string("out_33_cast_fp16")];
            tensor<int32, [2]> var_3498_split_sizes_0 = const()[name = string("op_3498_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3498_axis_0 = const()[name = string("op_3498_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_3498_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_3498_cast_fp16_1 = split(axis = var_3498_axis_0, split_sizes = var_3498_split_sizes_0, x = out_33_cast_fp16)[name = string("op_3498_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_8_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_8_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1378624512)))];
            tensor<int32, [2]> query_states_49_strides_0 = const()[name = string("query_states_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_49_pad_type_0 = const()[name = string("query_states_49_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_49_pad_0 = const()[name = string("query_states_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_49_dilations_0 = const()[name = string("query_states_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_49_groups_0 = const()[name = string("query_states_49_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_49_cast_fp16 = conv(dilations = query_states_49_dilations_0, groups = query_states_49_groups_0, pad = query_states_49_pad_0, pad_type = query_states_49_pad_type_0, strides = query_states_49_strides_0, weight = layers_8_self_attn_q_proj_weight_to_fp16, x = var_3498_cast_fp16_0)[name = string("query_states_49_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_8_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_8_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1387013184)))];
            tensor<int32, [2]> key_states_81_strides_0 = const()[name = string("key_states_81_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_81_pad_type_0 = const()[name = string("key_states_81_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_81_pad_0 = const()[name = string("key_states_81_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_81_dilations_0 = const()[name = string("key_states_81_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_81_groups_0 = const()[name = string("key_states_81_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_81_cast_fp16 = conv(dilations = key_states_81_dilations_0, groups = key_states_81_groups_0, pad = key_states_81_pad_0, pad_type = key_states_81_pad_type_0, strides = key_states_81_strides_0, weight = layers_8_self_attn_k_proj_weight_to_fp16, x = var_3498_cast_fp16_0)[name = string("key_states_81_cast_fp16")];
            tensor<int32, [2]> value_states_49_strides_0 = const()[name = string("value_states_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_49_pad_type_0 = const()[name = string("value_states_49_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_49_pad_0 = const()[name = string("value_states_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_49_dilations_0 = const()[name = string("value_states_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_49_groups_0 = const()[name = string("value_states_49_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_49_cast_fp16 = conv(dilations = value_states_49_dilations_0, groups = value_states_49_groups_0, pad = value_states_49_pad_0, pad_type = value_states_49_pad_type_0, strides = value_states_49_strides_0, weight = layers_8_self_attn_v_proj_weight_cast_fp16, x = var_3498_cast_fp16_0)[name = string("value_states_49_cast_fp16")];
            tensor<int32, [4]> concat_96x = const()[name = string("concat_96x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_81_cast_fp16 = reshape(shape = concat_96x, x = query_states_49_cast_fp16)[name = string("x_81_cast_fp16")];
            tensor<int32, [4]> concat_97x = const()[name = string("concat_97x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_3555_cast_fp16 = reshape(shape = concat_97x, x = key_states_81_cast_fp16)[name = string("op_3555_cast_fp16")];
            tensor<int32, [4]> concat_98x = const()[name = string("concat_98x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_3562_cast_fp16 = reshape(shape = concat_98x, x = value_states_49_cast_fp16)[name = string("op_3562_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_3566_cast_fp16 = mul(x = x_81_cast_fp16, y = var_859_cast_fp16)[name = string("op_3566_cast_fp16")];
            tensor<int32, [2]> var_3567_split_sizes_0 = const()[name = string("op_3567_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3567_axis_0 = const()[name = string("op_3567_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_3567_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_3567_cast_fp16_1 = split(axis = var_3567_axis_0, split_sizes = var_3567_split_sizes_0, x = x_81_cast_fp16)[name = string("op_3567_cast_fp16")];
            fp16 const_84_promoted_to_fp16 = const()[name = string("const_84_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_3569_cast_fp16 = mul(x = var_3567_cast_fp16_1, y = const_84_promoted_to_fp16)[name = string("op_3569_cast_fp16")];
            int32 var_3571 = const()[name = string("op_3571"), val = int32(-2)];
            bool var_3572_interleave_0 = const()[name = string("op_3572_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_3572_cast_fp16 = concat(axis = var_3571, interleave = var_3572_interleave_0, values = (var_3569_cast_fp16, var_3567_cast_fp16_0))[name = string("op_3572_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_3573_cast_fp16 = mul(x = var_3572_cast_fp16, y = var_866_cast_fp16)[name = string("op_3573_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_51_cast_fp16 = add(x = var_3566_cast_fp16, y = var_3573_cast_fp16)[name = string("query_states_51_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_3579_cast_fp16 = mul(x = var_3555_cast_fp16, y = var_859_cast_fp16)[name = string("op_3579_cast_fp16")];
            tensor<int32, [2]> var_3580_split_sizes_0 = const()[name = string("op_3580_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3580_axis_0 = const()[name = string("op_3580_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_3580_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_3580_cast_fp16_1 = split(axis = var_3580_axis_0, split_sizes = var_3580_split_sizes_0, x = var_3555_cast_fp16)[name = string("op_3580_cast_fp16")];
            fp16 const_85_promoted_to_fp16 = const()[name = string("const_85_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_3582_cast_fp16 = mul(x = var_3580_cast_fp16_1, y = const_85_promoted_to_fp16)[name = string("op_3582_cast_fp16")];
            int32 var_3584 = const()[name = string("op_3584"), val = int32(-2)];
            bool var_3585_interleave_0 = const()[name = string("op_3585_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_3585_cast_fp16 = concat(axis = var_3584, interleave = var_3585_interleave_0, values = (var_3582_cast_fp16, var_3580_cast_fp16_0))[name = string("op_3585_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_3586_cast_fp16 = mul(x = var_3585_cast_fp16, y = var_866_cast_fp16)[name = string("op_3586_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_85_cast_fp16 = add(x = var_3579_cast_fp16, y = var_3586_cast_fp16)[name = string("key_states_85_cast_fp16")];
            tensor<int32, [1]> expand_dims_96 = const()[name = string("expand_dims_96"), val = tensor<int32, [1]>([8])];
            tensor<int32, [1]> expand_dims_97 = const()[name = string("expand_dims_97"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_99 = const()[name = string("expand_dims_99"), val = tensor<int32, [1]>([0])];
            int32 concat_101_axis_0 = const()[name = string("concat_101_axis_0"), val = int32(0)];
            bool concat_101_interleave_0 = const()[name = string("concat_101_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_101 = concat(axis = concat_101_axis_0, interleave = concat_101_interleave_0, values = (expand_dims_96, expand_dims_97, position_id, expand_dims_99))[name = string("concat_101")];
            tensor<int32, [1]> expand_dims_100 = const()[name = string("expand_dims_100"), val = tensor<int32, [1]>([9])];
            tensor<int32, [1]> concat_102_values1_0 = const()[name = string("concat_102_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_102_values3_0 = const()[name = string("concat_102_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_102_axis_0 = const()[name = string("concat_102_axis_0"), val = int32(0)];
            bool concat_102_interleave_0 = const()[name = string("concat_102_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_102 = concat(axis = concat_102_axis_0, interleave = concat_102_interleave_0, values = (expand_dims_100, concat_102_values1_0, cache_position_end, concat_102_values3_0))[name = string("concat_102")];
            tensor<int32, [4]> key_states_87_perm_0 = const()[name = string("key_states_87_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_9_stride_0 = const()[name = string("key_cache_internal_tensor_assign_9_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_9_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_9_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_9_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_9_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_9_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_9_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_87_cast_fp16 = transpose(perm = key_states_87_perm_0, x = key_states_85_cast_fp16)[name = string("transpose_59")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_9_cast_fp16 = slice_update(begin = concat_101, begin_mask = key_cache_internal_tensor_assign_9_begin_mask_0, end = concat_102, end_mask = key_cache_internal_tensor_assign_9_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_9_squeeze_mask_0, stride = key_cache_internal_tensor_assign_9_stride_0, update = key_states_87_cast_fp16, x = coreml_update_state_14)[name = string("key_cache_internal_tensor_assign_9_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_9_cast_fp16, input = key_cache)[name = string("coreml_update_state_16_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_16 = read_state(input = key_cache)[name = string("coreml_update_state_16")];
            tensor<int32, [4]> value_states_51_perm_0 = const()[name = string("value_states_51_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_9_stride_0 = const()[name = string("value_cache_internal_tensor_assign_9_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_9_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_9_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_9_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_9_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_9_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_9_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_51_cast_fp16 = transpose(perm = value_states_51_perm_0, x = var_3562_cast_fp16)[name = string("transpose_58")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_9_cast_fp16 = slice_update(begin = concat_101, begin_mask = value_cache_internal_tensor_assign_9_begin_mask_0, end = concat_102, end_mask = value_cache_internal_tensor_assign_9_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_9_squeeze_mask_0, stride = value_cache_internal_tensor_assign_9_stride_0, update = value_states_51_cast_fp16, x = coreml_update_state_15)[name = string("value_cache_internal_tensor_assign_9_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_9_cast_fp16, input = value_cache)[name = string("coreml_update_state_17_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_17 = read_state(input = value_cache)[name = string("coreml_update_state_17")];
            tensor<int32, [4]> var_3656_begin_0 = const()[name = string("op_3656_begin_0"), val = tensor<int32, [4]>([8, 0, 0, 0])];
            tensor<int32, [4]> var_3656_end_0 = const()[name = string("op_3656_end_0"), val = tensor<int32, [4]>([9, 2, 2048, 128])];
            tensor<bool, [4]> var_3656_end_mask_0 = const()[name = string("op_3656_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3656_cast_fp16 = slice_by_index(begin = var_3656_begin_0, end = var_3656_end_0, end_mask = var_3656_end_mask_0, x = coreml_update_state_16)[name = string("op_3656_cast_fp16")];
            tensor<int32, [2]> tile_16 = const()[name = string("tile_16"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3659_axis_0 = const()[name = string("op_3659_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3659_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3659_cast_fp16_1 = split(axis = var_3659_axis_0, split_sizes = tile_16, x = var_3656_cast_fp16)[name = string("op_3659_cast_fp16")];
            tensor<int32, [4]> var_3666_begin_0 = const()[name = string("op_3666_begin_0"), val = tensor<int32, [4]>([8, 0, 0, 0])];
            tensor<int32, [4]> var_3666_end_0 = const()[name = string("op_3666_end_0"), val = tensor<int32, [4]>([9, 2, 2048, 128])];
            tensor<bool, [4]> var_3666_end_mask_0 = const()[name = string("op_3666_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3666_cast_fp16 = slice_by_index(begin = var_3666_begin_0, end = var_3666_end_0, end_mask = var_3666_end_mask_0, x = coreml_update_state_17)[name = string("op_3666_cast_fp16")];
            tensor<int32, [2]> tile_17 = const()[name = string("tile_17"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3669_axis_0 = const()[name = string("op_3669_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3669_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3669_cast_fp16_1 = split(axis = var_3669_axis_0, split_sizes = tile_17, x = var_3666_cast_fp16)[name = string("op_3669_cast_fp16")];
            tensor<int32, [2]> var_3672_split_sizes_0 = const()[name = string("op_3672_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3672_axis_0 = const()[name = string("op_3672_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_3672_0, tensor<fp16, [1, 8, 128, 1]> var_3672_1 = split(axis = var_3672_axis_0, split_sizes = var_3672_split_sizes_0, x = query_states_51_cast_fp16)[name = string("op_3672")];
            bool attn_weights_129_transpose_x_0 = const()[name = string("attn_weights_129_transpose_x_0"), val = bool(false)];
            bool attn_weights_129_transpose_y_0 = const()[name = string("attn_weights_129_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_129_cast_fp16 = matmul(transpose_x = attn_weights_129_transpose_x_0, transpose_y = attn_weights_129_transpose_y_0, x = var_3659_cast_fp16_0, y = var_3672_0)[name = string("attn_weights_129_cast_fp16")];
            fp16 var_3675_to_fp16 = const()[name = string("op_3675_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_131_cast_fp16 = mul(x = attn_weights_129_cast_fp16, y = var_3675_to_fp16)[name = string("attn_weights_131_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_133_cast_fp16 = add(x = attn_weights_131_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_133_cast_fp16")];
            int32 var_3679 = const()[name = string("op_3679"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_135_cast_fp16 = softmax(axis = var_3679, x = attn_weights_133_cast_fp16)[name = string("attn_weights_135_cast_fp16")];
            bool var_3685_transpose_x_1 = const()[name = string("op_3685_transpose_x_1"), val = bool(true)];
            bool var_3685_transpose_y_1 = const()[name = string("op_3685_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_3685_cast_fp16 = matmul(transpose_x = var_3685_transpose_x_1, transpose_y = var_3685_transpose_y_1, x = attn_weights_135_cast_fp16, y = var_3669_cast_fp16_0)[name = string("op_3685_cast_fp16")];
            bool attn_weights_137_transpose_x_0 = const()[name = string("attn_weights_137_transpose_x_0"), val = bool(false)];
            bool attn_weights_137_transpose_y_0 = const()[name = string("attn_weights_137_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_137_cast_fp16 = matmul(transpose_x = attn_weights_137_transpose_x_0, transpose_y = attn_weights_137_transpose_y_0, x = var_3659_cast_fp16_1, y = var_3672_1)[name = string("attn_weights_137_cast_fp16")];
            fp16 var_3687_to_fp16 = const()[name = string("op_3687_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_139_cast_fp16 = mul(x = attn_weights_137_cast_fp16, y = var_3687_to_fp16)[name = string("attn_weights_139_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_141_cast_fp16 = add(x = attn_weights_139_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_141_cast_fp16")];
            int32 var_3691 = const()[name = string("op_3691"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_143_cast_fp16 = softmax(axis = var_3691, x = attn_weights_141_cast_fp16)[name = string("attn_weights_143_cast_fp16")];
            bool attn_output_65_transpose_x_1 = const()[name = string("attn_output_65_transpose_x_1"), val = bool(true)];
            bool attn_output_65_transpose_y_1 = const()[name = string("attn_output_65_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_65_cast_fp16 = matmul(transpose_x = attn_output_65_transpose_x_1, transpose_y = attn_output_65_transpose_y_1, x = attn_weights_143_cast_fp16, y = var_3669_cast_fp16_1)[name = string("attn_output_65_cast_fp16")];
            int32 var_3699 = const()[name = string("op_3699"), val = int32(1)];
            bool attn_output_67_interleave_0 = const()[name = string("attn_output_67_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_67_cast_fp16 = concat(axis = var_3699, interleave = attn_output_67_interleave_0, values = (var_3685_cast_fp16, attn_output_65_cast_fp16))[name = string("attn_output_67_cast_fp16")];
            tensor<int32, [4]> var_3703_perm_0 = const()[name = string("op_3703_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_107x = const()[name = string("concat_107x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_3703_cast_fp16 = transpose(perm = var_3703_perm_0, x = attn_output_67_cast_fp16)[name = string("transpose_57")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_71_cast_fp16 = reshape(shape = concat_107x, x = var_3703_cast_fp16)[name = string("attn_output_71_cast_fp16")];
            tensor<int32, [2]> hidden_states_83_strides_0 = const()[name = string("hidden_states_83_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_83_pad_type_0 = const()[name = string("hidden_states_83_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_83_pad_0 = const()[name = string("hidden_states_83_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_83_dilations_0 = const()[name = string("hidden_states_83_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_83_groups_0 = const()[name = string("hidden_states_83_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_83_cast_fp16 = conv(dilations = hidden_states_83_dilations_0, groups = hidden_states_83_groups_0, pad = hidden_states_83_pad_0, pad_type = hidden_states_83_pad_type_0, strides = hidden_states_83_strides_0, weight = layers_8_self_attn_o_proj_weight_cast_fp16, x = attn_output_71_cast_fp16)[name = string("hidden_states_83_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_85_cast_fp16 = add(x = hidden_states_79_cast_fp16, y = hidden_states_83_cast_fp16)[name = string("hidden_states_85_cast_fp16")];
            fp16 const_90_promoted_to_fp16 = const()[name = string("const_90_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_3736_cast_fp16 = mul(x = hidden_states_85_cast_fp16, y = const_90_promoted_to_fp16)[name = string("op_3736_cast_fp16")];
            int32 var_3734 = const()[name = string("op_3734"), val = int32(1)];
            bool doubled_69_interleave_0 = const()[name = string("doubled_69_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_69_cast_fp16 = concat(axis = var_3734, interleave = doubled_69_interleave_0, values = (hidden_states_85_cast_fp16, var_3736_cast_fp16))[name = string("doubled_69_cast_fp16")];
            tensor<int32, [1]> out_35_axes_0 = const()[name = string("out_35_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_35_gamma_0_to_fp16 = const()[name = string("out_35_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1388061824)))];
            fp16 var_3746_to_fp16 = const()[name = string("op_3746_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_35_cast_fp16 = layer_norm(axes = out_35_axes_0, epsilon = var_3746_to_fp16, gamma = out_35_gamma_0_to_fp16, x = doubled_69_cast_fp16)[name = string("out_35_cast_fp16")];
            tensor<int32, [2]> var_3757_split_sizes_0 = const()[name = string("op_3757_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3757_axis_0 = const()[name = string("op_3757_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_3757_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_3757_cast_fp16_1 = split(axis = var_3757_axis_0, split_sizes = var_3757_split_sizes_0, x = out_35_cast_fp16)[name = string("op_3757_cast_fp16")];
            tensor<int32, [2]> input_17_strides_0 = const()[name = string("input_17_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_17_pad_type_0 = const()[name = string("input_17_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_17_pad_0 = const()[name = string("input_17_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_17_dilations_0 = const()[name = string("input_17_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_17_groups_0 = const()[name = string("input_17_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_17_cast_fp16 = conv(dilations = input_17_dilations_0, groups = input_17_groups_0, pad = input_17_pad_0, pad_type = input_17_pad_type_0, strides = input_17_strides_0, weight = layers_8_mlp_gate_proj_weight_cast_fp16, x = var_3757_cast_fp16_0)[name = string("input_17_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_3774_cast_fp16 = silu(x = input_17_cast_fp16)[name = string("op_3774_cast_fp16")];
            tensor<int32, [2]> var_3780_strides_0 = const()[name = string("op_3780_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_3780_pad_type_0 = const()[name = string("op_3780_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_3780_pad_0 = const()[name = string("op_3780_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_3780_dilations_0 = const()[name = string("op_3780_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3780_groups_0 = const()[name = string("op_3780_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_3780_cast_fp16 = conv(dilations = var_3780_dilations_0, groups = var_3780_groups_0, pad = var_3780_pad_0, pad_type = var_3780_pad_type_0, strides = var_3780_strides_0, weight = layers_8_mlp_up_proj_weight_cast_fp16, x = var_3757_cast_fp16_0)[name = string("op_3780_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_89_cast_fp16 = mul(x = var_3774_cast_fp16, y = var_3780_cast_fp16)[name = string("x_89_cast_fp16")];
            tensor<int32, [2]> hidden_states_87_strides_0 = const()[name = string("hidden_states_87_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_87_pad_type_0 = const()[name = string("hidden_states_87_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_87_pad_0 = const()[name = string("hidden_states_87_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_87_dilations_0 = const()[name = string("hidden_states_87_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_87_groups_0 = const()[name = string("hidden_states_87_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_87_cast_fp16 = conv(dilations = hidden_states_87_dilations_0, groups = hidden_states_87_groups_0, pad = hidden_states_87_pad_0, pad_type = hidden_states_87_pad_type_0, strides = hidden_states_87_strides_0, weight = layers_8_mlp_down_proj_weight_cast_fp16, x = x_89_cast_fp16)[name = string("hidden_states_87_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_89_cast_fp16 = add(x = hidden_states_85_cast_fp16, y = hidden_states_87_cast_fp16)[name = string("hidden_states_89_cast_fp16")];
            fp16 const_92_promoted_to_fp16 = const()[name = string("const_92_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_3798_cast_fp16 = mul(x = hidden_states_89_cast_fp16, y = const_92_promoted_to_fp16)[name = string("op_3798_cast_fp16")];
            int32 var_3796 = const()[name = string("op_3796"), val = int32(1)];
            bool doubled_73_interleave_0 = const()[name = string("doubled_73_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_73_cast_fp16 = concat(axis = var_3796, interleave = doubled_73_interleave_0, values = (hidden_states_89_cast_fp16, var_3798_cast_fp16))[name = string("doubled_73_cast_fp16")];
            tensor<int32, [1]> out_37_axes_0 = const()[name = string("out_37_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_37_gamma_0_to_fp16 = const()[name = string("out_37_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1388070080)))];
            fp16 var_3808_to_fp16 = const()[name = string("op_3808_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_37_cast_fp16 = layer_norm(axes = out_37_axes_0, epsilon = var_3808_to_fp16, gamma = out_37_gamma_0_to_fp16, x = doubled_73_cast_fp16)[name = string("out_37_cast_fp16")];
            tensor<int32, [2]> var_3819_split_sizes_0 = const()[name = string("op_3819_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_3819_axis_0 = const()[name = string("op_3819_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_3819_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_3819_cast_fp16_1 = split(axis = var_3819_axis_0, split_sizes = var_3819_split_sizes_0, x = out_37_cast_fp16)[name = string("op_3819_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_9_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_9_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1388078336)))];
            tensor<int32, [2]> query_states_55_strides_0 = const()[name = string("query_states_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_55_pad_type_0 = const()[name = string("query_states_55_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_55_pad_0 = const()[name = string("query_states_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_55_dilations_0 = const()[name = string("query_states_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_55_groups_0 = const()[name = string("query_states_55_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_55_cast_fp16 = conv(dilations = query_states_55_dilations_0, groups = query_states_55_groups_0, pad = query_states_55_pad_0, pad_type = query_states_55_pad_type_0, strides = query_states_55_strides_0, weight = layers_9_self_attn_q_proj_weight_to_fp16, x = var_3819_cast_fp16_0)[name = string("query_states_55_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_9_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_9_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1396467008)))];
            tensor<int32, [2]> key_states_91_strides_0 = const()[name = string("key_states_91_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_91_pad_type_0 = const()[name = string("key_states_91_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_91_pad_0 = const()[name = string("key_states_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_91_dilations_0 = const()[name = string("key_states_91_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_91_groups_0 = const()[name = string("key_states_91_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_91_cast_fp16 = conv(dilations = key_states_91_dilations_0, groups = key_states_91_groups_0, pad = key_states_91_pad_0, pad_type = key_states_91_pad_type_0, strides = key_states_91_strides_0, weight = layers_9_self_attn_k_proj_weight_to_fp16, x = var_3819_cast_fp16_0)[name = string("key_states_91_cast_fp16")];
            tensor<int32, [2]> value_states_55_strides_0 = const()[name = string("value_states_55_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_55_pad_type_0 = const()[name = string("value_states_55_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_55_pad_0 = const()[name = string("value_states_55_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_55_dilations_0 = const()[name = string("value_states_55_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_55_groups_0 = const()[name = string("value_states_55_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_55_cast_fp16 = conv(dilations = value_states_55_dilations_0, groups = value_states_55_groups_0, pad = value_states_55_pad_0, pad_type = value_states_55_pad_type_0, strides = value_states_55_strides_0, weight = layers_9_self_attn_v_proj_weight_cast_fp16, x = var_3819_cast_fp16_0)[name = string("value_states_55_cast_fp16")];
            tensor<int32, [4]> concat_108x = const()[name = string("concat_108x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_91_cast_fp16 = reshape(shape = concat_108x, x = query_states_55_cast_fp16)[name = string("x_91_cast_fp16")];
            tensor<int32, [4]> concat_109x = const()[name = string("concat_109x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_3876_cast_fp16 = reshape(shape = concat_109x, x = key_states_91_cast_fp16)[name = string("op_3876_cast_fp16")];
            tensor<int32, [4]> concat_110x = const()[name = string("concat_110x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_3883_cast_fp16 = reshape(shape = concat_110x, x = value_states_55_cast_fp16)[name = string("op_3883_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_3887_cast_fp16 = mul(x = x_91_cast_fp16, y = var_859_cast_fp16)[name = string("op_3887_cast_fp16")];
            tensor<int32, [2]> var_3888_split_sizes_0 = const()[name = string("op_3888_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3888_axis_0 = const()[name = string("op_3888_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_3888_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_3888_cast_fp16_1 = split(axis = var_3888_axis_0, split_sizes = var_3888_split_sizes_0, x = x_91_cast_fp16)[name = string("op_3888_cast_fp16")];
            fp16 const_94_promoted_to_fp16 = const()[name = string("const_94_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_3890_cast_fp16 = mul(x = var_3888_cast_fp16_1, y = const_94_promoted_to_fp16)[name = string("op_3890_cast_fp16")];
            int32 var_3892 = const()[name = string("op_3892"), val = int32(-2)];
            bool var_3893_interleave_0 = const()[name = string("op_3893_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_3893_cast_fp16 = concat(axis = var_3892, interleave = var_3893_interleave_0, values = (var_3890_cast_fp16, var_3888_cast_fp16_0))[name = string("op_3893_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_3894_cast_fp16 = mul(x = var_3893_cast_fp16, y = var_866_cast_fp16)[name = string("op_3894_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_57_cast_fp16 = add(x = var_3887_cast_fp16, y = var_3894_cast_fp16)[name = string("query_states_57_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_3900_cast_fp16 = mul(x = var_3876_cast_fp16, y = var_859_cast_fp16)[name = string("op_3900_cast_fp16")];
            tensor<int32, [2]> var_3901_split_sizes_0 = const()[name = string("op_3901_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_3901_axis_0 = const()[name = string("op_3901_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_3901_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_3901_cast_fp16_1 = split(axis = var_3901_axis_0, split_sizes = var_3901_split_sizes_0, x = var_3876_cast_fp16)[name = string("op_3901_cast_fp16")];
            fp16 const_95_promoted_to_fp16 = const()[name = string("const_95_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_3903_cast_fp16 = mul(x = var_3901_cast_fp16_1, y = const_95_promoted_to_fp16)[name = string("op_3903_cast_fp16")];
            int32 var_3905 = const()[name = string("op_3905"), val = int32(-2)];
            bool var_3906_interleave_0 = const()[name = string("op_3906_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_3906_cast_fp16 = concat(axis = var_3905, interleave = var_3906_interleave_0, values = (var_3903_cast_fp16, var_3901_cast_fp16_0))[name = string("op_3906_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_3907_cast_fp16 = mul(x = var_3906_cast_fp16, y = var_866_cast_fp16)[name = string("op_3907_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_95_cast_fp16 = add(x = var_3900_cast_fp16, y = var_3907_cast_fp16)[name = string("key_states_95_cast_fp16")];
            tensor<int32, [1]> expand_dims_108 = const()[name = string("expand_dims_108"), val = tensor<int32, [1]>([9])];
            tensor<int32, [1]> expand_dims_109 = const()[name = string("expand_dims_109"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_111 = const()[name = string("expand_dims_111"), val = tensor<int32, [1]>([0])];
            int32 concat_113_axis_0 = const()[name = string("concat_113_axis_0"), val = int32(0)];
            bool concat_113_interleave_0 = const()[name = string("concat_113_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_113 = concat(axis = concat_113_axis_0, interleave = concat_113_interleave_0, values = (expand_dims_108, expand_dims_109, position_id, expand_dims_111))[name = string("concat_113")];
            tensor<int32, [1]> expand_dims_112 = const()[name = string("expand_dims_112"), val = tensor<int32, [1]>([10])];
            tensor<int32, [1]> concat_114_values1_0 = const()[name = string("concat_114_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_114_values3_0 = const()[name = string("concat_114_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_114_axis_0 = const()[name = string("concat_114_axis_0"), val = int32(0)];
            bool concat_114_interleave_0 = const()[name = string("concat_114_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_114 = concat(axis = concat_114_axis_0, interleave = concat_114_interleave_0, values = (expand_dims_112, concat_114_values1_0, cache_position_end, concat_114_values3_0))[name = string("concat_114")];
            tensor<int32, [4]> key_states_97_perm_0 = const()[name = string("key_states_97_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_10_stride_0 = const()[name = string("key_cache_internal_tensor_assign_10_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_10_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_10_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_10_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_10_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_10_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_10_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_97_cast_fp16 = transpose(perm = key_states_97_perm_0, x = key_states_95_cast_fp16)[name = string("transpose_56")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_10_cast_fp16 = slice_update(begin = concat_113, begin_mask = key_cache_internal_tensor_assign_10_begin_mask_0, end = concat_114, end_mask = key_cache_internal_tensor_assign_10_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_10_squeeze_mask_0, stride = key_cache_internal_tensor_assign_10_stride_0, update = key_states_97_cast_fp16, x = coreml_update_state_16)[name = string("key_cache_internal_tensor_assign_10_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_10_cast_fp16, input = key_cache)[name = string("coreml_update_state_18_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_18 = read_state(input = key_cache)[name = string("coreml_update_state_18")];
            tensor<int32, [4]> value_states_57_perm_0 = const()[name = string("value_states_57_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_10_stride_0 = const()[name = string("value_cache_internal_tensor_assign_10_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_10_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_10_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_10_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_10_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_10_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_10_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_57_cast_fp16 = transpose(perm = value_states_57_perm_0, x = var_3883_cast_fp16)[name = string("transpose_55")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_10_cast_fp16 = slice_update(begin = concat_113, begin_mask = value_cache_internal_tensor_assign_10_begin_mask_0, end = concat_114, end_mask = value_cache_internal_tensor_assign_10_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_10_squeeze_mask_0, stride = value_cache_internal_tensor_assign_10_stride_0, update = value_states_57_cast_fp16, x = coreml_update_state_17)[name = string("value_cache_internal_tensor_assign_10_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_10_cast_fp16, input = value_cache)[name = string("coreml_update_state_19_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_19 = read_state(input = value_cache)[name = string("coreml_update_state_19")];
            tensor<int32, [4]> var_3977_begin_0 = const()[name = string("op_3977_begin_0"), val = tensor<int32, [4]>([9, 0, 0, 0])];
            tensor<int32, [4]> var_3977_end_0 = const()[name = string("op_3977_end_0"), val = tensor<int32, [4]>([10, 2, 2048, 128])];
            tensor<bool, [4]> var_3977_end_mask_0 = const()[name = string("op_3977_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3977_cast_fp16 = slice_by_index(begin = var_3977_begin_0, end = var_3977_end_0, end_mask = var_3977_end_mask_0, x = coreml_update_state_18)[name = string("op_3977_cast_fp16")];
            tensor<int32, [2]> tile_18 = const()[name = string("tile_18"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3980_axis_0 = const()[name = string("op_3980_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3980_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3980_cast_fp16_1 = split(axis = var_3980_axis_0, split_sizes = tile_18, x = var_3977_cast_fp16)[name = string("op_3980_cast_fp16")];
            tensor<int32, [4]> var_3987_begin_0 = const()[name = string("op_3987_begin_0"), val = tensor<int32, [4]>([9, 0, 0, 0])];
            tensor<int32, [4]> var_3987_end_0 = const()[name = string("op_3987_end_0"), val = tensor<int32, [4]>([10, 2, 2048, 128])];
            tensor<bool, [4]> var_3987_end_mask_0 = const()[name = string("op_3987_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_3987_cast_fp16 = slice_by_index(begin = var_3987_begin_0, end = var_3987_end_0, end_mask = var_3987_end_mask_0, x = coreml_update_state_19)[name = string("op_3987_cast_fp16")];
            tensor<int32, [2]> tile_19 = const()[name = string("tile_19"), val = tensor<int32, [2]>([1, 1])];
            int32 var_3990_axis_0 = const()[name = string("op_3990_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_3990_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_3990_cast_fp16_1 = split(axis = var_3990_axis_0, split_sizes = tile_19, x = var_3987_cast_fp16)[name = string("op_3990_cast_fp16")];
            tensor<int32, [2]> var_3993_split_sizes_0 = const()[name = string("op_3993_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_3993_axis_0 = const()[name = string("op_3993_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_3993_0, tensor<fp16, [1, 8, 128, 1]> var_3993_1 = split(axis = var_3993_axis_0, split_sizes = var_3993_split_sizes_0, x = query_states_57_cast_fp16)[name = string("op_3993")];
            bool attn_weights_145_transpose_x_0 = const()[name = string("attn_weights_145_transpose_x_0"), val = bool(false)];
            bool attn_weights_145_transpose_y_0 = const()[name = string("attn_weights_145_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_145_cast_fp16 = matmul(transpose_x = attn_weights_145_transpose_x_0, transpose_y = attn_weights_145_transpose_y_0, x = var_3980_cast_fp16_0, y = var_3993_0)[name = string("attn_weights_145_cast_fp16")];
            fp16 var_3996_to_fp16 = const()[name = string("op_3996_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_147_cast_fp16 = mul(x = attn_weights_145_cast_fp16, y = var_3996_to_fp16)[name = string("attn_weights_147_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_149_cast_fp16 = add(x = attn_weights_147_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_149_cast_fp16")];
            int32 var_4000 = const()[name = string("op_4000"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_151_cast_fp16 = softmax(axis = var_4000, x = attn_weights_149_cast_fp16)[name = string("attn_weights_151_cast_fp16")];
            bool var_4006_transpose_x_1 = const()[name = string("op_4006_transpose_x_1"), val = bool(true)];
            bool var_4006_transpose_y_1 = const()[name = string("op_4006_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_4006_cast_fp16 = matmul(transpose_x = var_4006_transpose_x_1, transpose_y = var_4006_transpose_y_1, x = attn_weights_151_cast_fp16, y = var_3990_cast_fp16_0)[name = string("op_4006_cast_fp16")];
            bool attn_weights_153_transpose_x_0 = const()[name = string("attn_weights_153_transpose_x_0"), val = bool(false)];
            bool attn_weights_153_transpose_y_0 = const()[name = string("attn_weights_153_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_153_cast_fp16 = matmul(transpose_x = attn_weights_153_transpose_x_0, transpose_y = attn_weights_153_transpose_y_0, x = var_3980_cast_fp16_1, y = var_3993_1)[name = string("attn_weights_153_cast_fp16")];
            fp16 var_4008_to_fp16 = const()[name = string("op_4008_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_155_cast_fp16 = mul(x = attn_weights_153_cast_fp16, y = var_4008_to_fp16)[name = string("attn_weights_155_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_157_cast_fp16 = add(x = attn_weights_155_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_157_cast_fp16")];
            int32 var_4012 = const()[name = string("op_4012"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_159_cast_fp16 = softmax(axis = var_4012, x = attn_weights_157_cast_fp16)[name = string("attn_weights_159_cast_fp16")];
            bool attn_output_73_transpose_x_1 = const()[name = string("attn_output_73_transpose_x_1"), val = bool(true)];
            bool attn_output_73_transpose_y_1 = const()[name = string("attn_output_73_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_73_cast_fp16 = matmul(transpose_x = attn_output_73_transpose_x_1, transpose_y = attn_output_73_transpose_y_1, x = attn_weights_159_cast_fp16, y = var_3990_cast_fp16_1)[name = string("attn_output_73_cast_fp16")];
            int32 var_4020 = const()[name = string("op_4020"), val = int32(1)];
            bool attn_output_75_interleave_0 = const()[name = string("attn_output_75_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_75_cast_fp16 = concat(axis = var_4020, interleave = attn_output_75_interleave_0, values = (var_4006_cast_fp16, attn_output_73_cast_fp16))[name = string("attn_output_75_cast_fp16")];
            tensor<int32, [4]> var_4024_perm_0 = const()[name = string("op_4024_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_119x = const()[name = string("concat_119x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_4024_cast_fp16 = transpose(perm = var_4024_perm_0, x = attn_output_75_cast_fp16)[name = string("transpose_54")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_79_cast_fp16 = reshape(shape = concat_119x, x = var_4024_cast_fp16)[name = string("attn_output_79_cast_fp16")];
            tensor<int32, [2]> hidden_states_93_strides_0 = const()[name = string("hidden_states_93_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_93_pad_type_0 = const()[name = string("hidden_states_93_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_93_pad_0 = const()[name = string("hidden_states_93_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_93_dilations_0 = const()[name = string("hidden_states_93_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_93_groups_0 = const()[name = string("hidden_states_93_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_93_cast_fp16 = conv(dilations = hidden_states_93_dilations_0, groups = hidden_states_93_groups_0, pad = hidden_states_93_pad_0, pad_type = hidden_states_93_pad_type_0, strides = hidden_states_93_strides_0, weight = layers_9_self_attn_o_proj_weight_cast_fp16, x = attn_output_79_cast_fp16)[name = string("hidden_states_93_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_95_cast_fp16 = add(x = hidden_states_89_cast_fp16, y = hidden_states_93_cast_fp16)[name = string("hidden_states_95_cast_fp16")];
            fp16 const_100_promoted_to_fp16 = const()[name = string("const_100_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_4057_cast_fp16 = mul(x = hidden_states_95_cast_fp16, y = const_100_promoted_to_fp16)[name = string("op_4057_cast_fp16")];
            int32 var_4055 = const()[name = string("op_4055"), val = int32(1)];
            bool doubled_77_interleave_0 = const()[name = string("doubled_77_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_77_cast_fp16 = concat(axis = var_4055, interleave = doubled_77_interleave_0, values = (hidden_states_95_cast_fp16, var_4057_cast_fp16))[name = string("doubled_77_cast_fp16")];
            tensor<int32, [1]> out_39_axes_0 = const()[name = string("out_39_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_39_gamma_0_to_fp16 = const()[name = string("out_39_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1397515648)))];
            fp16 var_4067_to_fp16 = const()[name = string("op_4067_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_39_cast_fp16 = layer_norm(axes = out_39_axes_0, epsilon = var_4067_to_fp16, gamma = out_39_gamma_0_to_fp16, x = doubled_77_cast_fp16)[name = string("out_39_cast_fp16")];
            tensor<int32, [2]> var_4078_split_sizes_0 = const()[name = string("op_4078_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4078_axis_0 = const()[name = string("op_4078_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_4078_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_4078_cast_fp16_1 = split(axis = var_4078_axis_0, split_sizes = var_4078_split_sizes_0, x = out_39_cast_fp16)[name = string("op_4078_cast_fp16")];
            tensor<int32, [2]> input_19_strides_0 = const()[name = string("input_19_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_19_pad_type_0 = const()[name = string("input_19_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_19_pad_0 = const()[name = string("input_19_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_19_dilations_0 = const()[name = string("input_19_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_19_groups_0 = const()[name = string("input_19_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_19_cast_fp16 = conv(dilations = input_19_dilations_0, groups = input_19_groups_0, pad = input_19_pad_0, pad_type = input_19_pad_type_0, strides = input_19_strides_0, weight = layers_9_mlp_gate_proj_weight_cast_fp16, x = var_4078_cast_fp16_0)[name = string("input_19_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_4095_cast_fp16 = silu(x = input_19_cast_fp16)[name = string("op_4095_cast_fp16")];
            tensor<int32, [2]> var_4101_strides_0 = const()[name = string("op_4101_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_4101_pad_type_0 = const()[name = string("op_4101_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_4101_pad_0 = const()[name = string("op_4101_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_4101_dilations_0 = const()[name = string("op_4101_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4101_groups_0 = const()[name = string("op_4101_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_4101_cast_fp16 = conv(dilations = var_4101_dilations_0, groups = var_4101_groups_0, pad = var_4101_pad_0, pad_type = var_4101_pad_type_0, strides = var_4101_strides_0, weight = layers_9_mlp_up_proj_weight_cast_fp16, x = var_4078_cast_fp16_0)[name = string("op_4101_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_99_cast_fp16 = mul(x = var_4095_cast_fp16, y = var_4101_cast_fp16)[name = string("x_99_cast_fp16")];
            tensor<int32, [2]> hidden_states_97_strides_0 = const()[name = string("hidden_states_97_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_97_pad_type_0 = const()[name = string("hidden_states_97_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_97_pad_0 = const()[name = string("hidden_states_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_97_dilations_0 = const()[name = string("hidden_states_97_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_97_groups_0 = const()[name = string("hidden_states_97_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_97_cast_fp16 = conv(dilations = hidden_states_97_dilations_0, groups = hidden_states_97_groups_0, pad = hidden_states_97_pad_0, pad_type = hidden_states_97_pad_type_0, strides = hidden_states_97_strides_0, weight = layers_9_mlp_down_proj_weight_cast_fp16, x = x_99_cast_fp16)[name = string("hidden_states_97_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_99_cast_fp16 = add(x = hidden_states_95_cast_fp16, y = hidden_states_97_cast_fp16)[name = string("hidden_states_99_cast_fp16")];
            fp16 const_102_promoted_to_fp16 = const()[name = string("const_102_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_4119_cast_fp16 = mul(x = hidden_states_99_cast_fp16, y = const_102_promoted_to_fp16)[name = string("op_4119_cast_fp16")];
            int32 var_4117 = const()[name = string("op_4117"), val = int32(1)];
            bool doubled_81_interleave_0 = const()[name = string("doubled_81_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_81_cast_fp16 = concat(axis = var_4117, interleave = doubled_81_interleave_0, values = (hidden_states_99_cast_fp16, var_4119_cast_fp16))[name = string("doubled_81_cast_fp16")];
            tensor<int32, [1]> out_41_axes_0 = const()[name = string("out_41_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_41_gamma_0_to_fp16 = const()[name = string("out_41_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1397523904)))];
            fp16 var_4129_to_fp16 = const()[name = string("op_4129_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_41_cast_fp16 = layer_norm(axes = out_41_axes_0, epsilon = var_4129_to_fp16, gamma = out_41_gamma_0_to_fp16, x = doubled_81_cast_fp16)[name = string("out_41_cast_fp16")];
            tensor<int32, [2]> var_4140_split_sizes_0 = const()[name = string("op_4140_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4140_axis_0 = const()[name = string("op_4140_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_4140_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_4140_cast_fp16_1 = split(axis = var_4140_axis_0, split_sizes = var_4140_split_sizes_0, x = out_41_cast_fp16)[name = string("op_4140_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_10_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_10_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1397532160)))];
            tensor<int32, [2]> query_states_61_strides_0 = const()[name = string("query_states_61_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_61_pad_type_0 = const()[name = string("query_states_61_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_61_pad_0 = const()[name = string("query_states_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_61_dilations_0 = const()[name = string("query_states_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_61_groups_0 = const()[name = string("query_states_61_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_61_cast_fp16 = conv(dilations = query_states_61_dilations_0, groups = query_states_61_groups_0, pad = query_states_61_pad_0, pad_type = query_states_61_pad_type_0, strides = query_states_61_strides_0, weight = layers_10_self_attn_q_proj_weight_to_fp16, x = var_4140_cast_fp16_0)[name = string("query_states_61_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_10_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_10_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1405920832)))];
            tensor<int32, [2]> key_states_101_strides_0 = const()[name = string("key_states_101_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_101_pad_type_0 = const()[name = string("key_states_101_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_101_pad_0 = const()[name = string("key_states_101_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_101_dilations_0 = const()[name = string("key_states_101_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_101_groups_0 = const()[name = string("key_states_101_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_101_cast_fp16 = conv(dilations = key_states_101_dilations_0, groups = key_states_101_groups_0, pad = key_states_101_pad_0, pad_type = key_states_101_pad_type_0, strides = key_states_101_strides_0, weight = layers_10_self_attn_k_proj_weight_to_fp16, x = var_4140_cast_fp16_0)[name = string("key_states_101_cast_fp16")];
            tensor<int32, [2]> value_states_61_strides_0 = const()[name = string("value_states_61_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_61_pad_type_0 = const()[name = string("value_states_61_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_61_pad_0 = const()[name = string("value_states_61_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_61_dilations_0 = const()[name = string("value_states_61_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_61_groups_0 = const()[name = string("value_states_61_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_61_cast_fp16 = conv(dilations = value_states_61_dilations_0, groups = value_states_61_groups_0, pad = value_states_61_pad_0, pad_type = value_states_61_pad_type_0, strides = value_states_61_strides_0, weight = layers_10_self_attn_v_proj_weight_cast_fp16, x = var_4140_cast_fp16_0)[name = string("value_states_61_cast_fp16")];
            tensor<int32, [4]> concat_120x = const()[name = string("concat_120x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_101_cast_fp16 = reshape(shape = concat_120x, x = query_states_61_cast_fp16)[name = string("x_101_cast_fp16")];
            tensor<int32, [4]> concat_121x = const()[name = string("concat_121x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_4197_cast_fp16 = reshape(shape = concat_121x, x = key_states_101_cast_fp16)[name = string("op_4197_cast_fp16")];
            tensor<int32, [4]> concat_122x = const()[name = string("concat_122x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_4204_cast_fp16 = reshape(shape = concat_122x, x = value_states_61_cast_fp16)[name = string("op_4204_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_4208_cast_fp16 = mul(x = x_101_cast_fp16, y = var_859_cast_fp16)[name = string("op_4208_cast_fp16")];
            tensor<int32, [2]> var_4209_split_sizes_0 = const()[name = string("op_4209_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4209_axis_0 = const()[name = string("op_4209_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_4209_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_4209_cast_fp16_1 = split(axis = var_4209_axis_0, split_sizes = var_4209_split_sizes_0, x = x_101_cast_fp16)[name = string("op_4209_cast_fp16")];
            fp16 const_104_promoted_to_fp16 = const()[name = string("const_104_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_4211_cast_fp16 = mul(x = var_4209_cast_fp16_1, y = const_104_promoted_to_fp16)[name = string("op_4211_cast_fp16")];
            int32 var_4213 = const()[name = string("op_4213"), val = int32(-2)];
            bool var_4214_interleave_0 = const()[name = string("op_4214_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_4214_cast_fp16 = concat(axis = var_4213, interleave = var_4214_interleave_0, values = (var_4211_cast_fp16, var_4209_cast_fp16_0))[name = string("op_4214_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_4215_cast_fp16 = mul(x = var_4214_cast_fp16, y = var_866_cast_fp16)[name = string("op_4215_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_63_cast_fp16 = add(x = var_4208_cast_fp16, y = var_4215_cast_fp16)[name = string("query_states_63_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_4221_cast_fp16 = mul(x = var_4197_cast_fp16, y = var_859_cast_fp16)[name = string("op_4221_cast_fp16")];
            tensor<int32, [2]> var_4222_split_sizes_0 = const()[name = string("op_4222_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4222_axis_0 = const()[name = string("op_4222_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_4222_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_4222_cast_fp16_1 = split(axis = var_4222_axis_0, split_sizes = var_4222_split_sizes_0, x = var_4197_cast_fp16)[name = string("op_4222_cast_fp16")];
            fp16 const_105_promoted_to_fp16 = const()[name = string("const_105_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_4224_cast_fp16 = mul(x = var_4222_cast_fp16_1, y = const_105_promoted_to_fp16)[name = string("op_4224_cast_fp16")];
            int32 var_4226 = const()[name = string("op_4226"), val = int32(-2)];
            bool var_4227_interleave_0 = const()[name = string("op_4227_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_4227_cast_fp16 = concat(axis = var_4226, interleave = var_4227_interleave_0, values = (var_4224_cast_fp16, var_4222_cast_fp16_0))[name = string("op_4227_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_4228_cast_fp16 = mul(x = var_4227_cast_fp16, y = var_866_cast_fp16)[name = string("op_4228_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_105_cast_fp16 = add(x = var_4221_cast_fp16, y = var_4228_cast_fp16)[name = string("key_states_105_cast_fp16")];
            tensor<int32, [1]> expand_dims_120 = const()[name = string("expand_dims_120"), val = tensor<int32, [1]>([10])];
            tensor<int32, [1]> expand_dims_121 = const()[name = string("expand_dims_121"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_123 = const()[name = string("expand_dims_123"), val = tensor<int32, [1]>([0])];
            int32 concat_125_axis_0 = const()[name = string("concat_125_axis_0"), val = int32(0)];
            bool concat_125_interleave_0 = const()[name = string("concat_125_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_125 = concat(axis = concat_125_axis_0, interleave = concat_125_interleave_0, values = (expand_dims_120, expand_dims_121, position_id, expand_dims_123))[name = string("concat_125")];
            tensor<int32, [1]> expand_dims_124 = const()[name = string("expand_dims_124"), val = tensor<int32, [1]>([11])];
            tensor<int32, [1]> concat_126_values1_0 = const()[name = string("concat_126_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_126_values3_0 = const()[name = string("concat_126_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_126_axis_0 = const()[name = string("concat_126_axis_0"), val = int32(0)];
            bool concat_126_interleave_0 = const()[name = string("concat_126_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_126 = concat(axis = concat_126_axis_0, interleave = concat_126_interleave_0, values = (expand_dims_124, concat_126_values1_0, cache_position_end, concat_126_values3_0))[name = string("concat_126")];
            tensor<int32, [4]> key_states_107_perm_0 = const()[name = string("key_states_107_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_11_stride_0 = const()[name = string("key_cache_internal_tensor_assign_11_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_11_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_11_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_11_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_11_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_11_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_11_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_107_cast_fp16 = transpose(perm = key_states_107_perm_0, x = key_states_105_cast_fp16)[name = string("transpose_53")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_11_cast_fp16 = slice_update(begin = concat_125, begin_mask = key_cache_internal_tensor_assign_11_begin_mask_0, end = concat_126, end_mask = key_cache_internal_tensor_assign_11_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_11_squeeze_mask_0, stride = key_cache_internal_tensor_assign_11_stride_0, update = key_states_107_cast_fp16, x = coreml_update_state_18)[name = string("key_cache_internal_tensor_assign_11_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_11_cast_fp16, input = key_cache)[name = string("coreml_update_state_20_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_20 = read_state(input = key_cache)[name = string("coreml_update_state_20")];
            tensor<int32, [4]> value_states_63_perm_0 = const()[name = string("value_states_63_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_11_stride_0 = const()[name = string("value_cache_internal_tensor_assign_11_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_11_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_11_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_11_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_11_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_11_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_11_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_63_cast_fp16 = transpose(perm = value_states_63_perm_0, x = var_4204_cast_fp16)[name = string("transpose_52")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_11_cast_fp16 = slice_update(begin = concat_125, begin_mask = value_cache_internal_tensor_assign_11_begin_mask_0, end = concat_126, end_mask = value_cache_internal_tensor_assign_11_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_11_squeeze_mask_0, stride = value_cache_internal_tensor_assign_11_stride_0, update = value_states_63_cast_fp16, x = coreml_update_state_19)[name = string("value_cache_internal_tensor_assign_11_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_11_cast_fp16, input = value_cache)[name = string("coreml_update_state_21_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_21 = read_state(input = value_cache)[name = string("coreml_update_state_21")];
            tensor<int32, [4]> var_4298_begin_0 = const()[name = string("op_4298_begin_0"), val = tensor<int32, [4]>([10, 0, 0, 0])];
            tensor<int32, [4]> var_4298_end_0 = const()[name = string("op_4298_end_0"), val = tensor<int32, [4]>([11, 2, 2048, 128])];
            tensor<bool, [4]> var_4298_end_mask_0 = const()[name = string("op_4298_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4298_cast_fp16 = slice_by_index(begin = var_4298_begin_0, end = var_4298_end_0, end_mask = var_4298_end_mask_0, x = coreml_update_state_20)[name = string("op_4298_cast_fp16")];
            tensor<int32, [2]> tile_20 = const()[name = string("tile_20"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4301_axis_0 = const()[name = string("op_4301_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4301_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4301_cast_fp16_1 = split(axis = var_4301_axis_0, split_sizes = tile_20, x = var_4298_cast_fp16)[name = string("op_4301_cast_fp16")];
            tensor<int32, [4]> var_4308_begin_0 = const()[name = string("op_4308_begin_0"), val = tensor<int32, [4]>([10, 0, 0, 0])];
            tensor<int32, [4]> var_4308_end_0 = const()[name = string("op_4308_end_0"), val = tensor<int32, [4]>([11, 2, 2048, 128])];
            tensor<bool, [4]> var_4308_end_mask_0 = const()[name = string("op_4308_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4308_cast_fp16 = slice_by_index(begin = var_4308_begin_0, end = var_4308_end_0, end_mask = var_4308_end_mask_0, x = coreml_update_state_21)[name = string("op_4308_cast_fp16")];
            tensor<int32, [2]> tile_21 = const()[name = string("tile_21"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4311_axis_0 = const()[name = string("op_4311_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4311_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4311_cast_fp16_1 = split(axis = var_4311_axis_0, split_sizes = tile_21, x = var_4308_cast_fp16)[name = string("op_4311_cast_fp16")];
            tensor<int32, [2]> var_4314_split_sizes_0 = const()[name = string("op_4314_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_4314_axis_0 = const()[name = string("op_4314_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_4314_0, tensor<fp16, [1, 8, 128, 1]> var_4314_1 = split(axis = var_4314_axis_0, split_sizes = var_4314_split_sizes_0, x = query_states_63_cast_fp16)[name = string("op_4314")];
            bool attn_weights_161_transpose_x_0 = const()[name = string("attn_weights_161_transpose_x_0"), val = bool(false)];
            bool attn_weights_161_transpose_y_0 = const()[name = string("attn_weights_161_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_161_cast_fp16 = matmul(transpose_x = attn_weights_161_transpose_x_0, transpose_y = attn_weights_161_transpose_y_0, x = var_4301_cast_fp16_0, y = var_4314_0)[name = string("attn_weights_161_cast_fp16")];
            fp16 var_4317_to_fp16 = const()[name = string("op_4317_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_163_cast_fp16 = mul(x = attn_weights_161_cast_fp16, y = var_4317_to_fp16)[name = string("attn_weights_163_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_165_cast_fp16 = add(x = attn_weights_163_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_165_cast_fp16")];
            int32 var_4321 = const()[name = string("op_4321"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_167_cast_fp16 = softmax(axis = var_4321, x = attn_weights_165_cast_fp16)[name = string("attn_weights_167_cast_fp16")];
            bool var_4327_transpose_x_1 = const()[name = string("op_4327_transpose_x_1"), val = bool(true)];
            bool var_4327_transpose_y_1 = const()[name = string("op_4327_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_4327_cast_fp16 = matmul(transpose_x = var_4327_transpose_x_1, transpose_y = var_4327_transpose_y_1, x = attn_weights_167_cast_fp16, y = var_4311_cast_fp16_0)[name = string("op_4327_cast_fp16")];
            bool attn_weights_169_transpose_x_0 = const()[name = string("attn_weights_169_transpose_x_0"), val = bool(false)];
            bool attn_weights_169_transpose_y_0 = const()[name = string("attn_weights_169_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_169_cast_fp16 = matmul(transpose_x = attn_weights_169_transpose_x_0, transpose_y = attn_weights_169_transpose_y_0, x = var_4301_cast_fp16_1, y = var_4314_1)[name = string("attn_weights_169_cast_fp16")];
            fp16 var_4329_to_fp16 = const()[name = string("op_4329_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_171_cast_fp16 = mul(x = attn_weights_169_cast_fp16, y = var_4329_to_fp16)[name = string("attn_weights_171_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_173_cast_fp16 = add(x = attn_weights_171_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_173_cast_fp16")];
            int32 var_4333 = const()[name = string("op_4333"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_175_cast_fp16 = softmax(axis = var_4333, x = attn_weights_173_cast_fp16)[name = string("attn_weights_175_cast_fp16")];
            bool attn_output_81_transpose_x_1 = const()[name = string("attn_output_81_transpose_x_1"), val = bool(true)];
            bool attn_output_81_transpose_y_1 = const()[name = string("attn_output_81_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_81_cast_fp16 = matmul(transpose_x = attn_output_81_transpose_x_1, transpose_y = attn_output_81_transpose_y_1, x = attn_weights_175_cast_fp16, y = var_4311_cast_fp16_1)[name = string("attn_output_81_cast_fp16")];
            int32 var_4341 = const()[name = string("op_4341"), val = int32(1)];
            bool attn_output_83_interleave_0 = const()[name = string("attn_output_83_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_83_cast_fp16 = concat(axis = var_4341, interleave = attn_output_83_interleave_0, values = (var_4327_cast_fp16, attn_output_81_cast_fp16))[name = string("attn_output_83_cast_fp16")];
            tensor<int32, [4]> var_4345_perm_0 = const()[name = string("op_4345_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_131x = const()[name = string("concat_131x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_4345_cast_fp16 = transpose(perm = var_4345_perm_0, x = attn_output_83_cast_fp16)[name = string("transpose_51")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_87_cast_fp16 = reshape(shape = concat_131x, x = var_4345_cast_fp16)[name = string("attn_output_87_cast_fp16")];
            tensor<int32, [2]> hidden_states_103_strides_0 = const()[name = string("hidden_states_103_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_103_pad_type_0 = const()[name = string("hidden_states_103_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_103_pad_0 = const()[name = string("hidden_states_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_103_dilations_0 = const()[name = string("hidden_states_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_103_groups_0 = const()[name = string("hidden_states_103_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_103_cast_fp16 = conv(dilations = hidden_states_103_dilations_0, groups = hidden_states_103_groups_0, pad = hidden_states_103_pad_0, pad_type = hidden_states_103_pad_type_0, strides = hidden_states_103_strides_0, weight = layers_10_self_attn_o_proj_weight_cast_fp16, x = attn_output_87_cast_fp16)[name = string("hidden_states_103_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_105_cast_fp16 = add(x = hidden_states_99_cast_fp16, y = hidden_states_103_cast_fp16)[name = string("hidden_states_105_cast_fp16")];
            fp16 const_110_promoted_to_fp16 = const()[name = string("const_110_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_4378_cast_fp16 = mul(x = hidden_states_105_cast_fp16, y = const_110_promoted_to_fp16)[name = string("op_4378_cast_fp16")];
            int32 var_4376 = const()[name = string("op_4376"), val = int32(1)];
            bool doubled_85_interleave_0 = const()[name = string("doubled_85_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_85_cast_fp16 = concat(axis = var_4376, interleave = doubled_85_interleave_0, values = (hidden_states_105_cast_fp16, var_4378_cast_fp16))[name = string("doubled_85_cast_fp16")];
            tensor<int32, [1]> out_43_axes_0 = const()[name = string("out_43_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_43_gamma_0_to_fp16 = const()[name = string("out_43_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406969472)))];
            fp16 var_4388_to_fp16 = const()[name = string("op_4388_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_43_cast_fp16 = layer_norm(axes = out_43_axes_0, epsilon = var_4388_to_fp16, gamma = out_43_gamma_0_to_fp16, x = doubled_85_cast_fp16)[name = string("out_43_cast_fp16")];
            tensor<int32, [2]> var_4399_split_sizes_0 = const()[name = string("op_4399_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4399_axis_0 = const()[name = string("op_4399_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_4399_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_4399_cast_fp16_1 = split(axis = var_4399_axis_0, split_sizes = var_4399_split_sizes_0, x = out_43_cast_fp16)[name = string("op_4399_cast_fp16")];
            tensor<int32, [2]> input_21_strides_0 = const()[name = string("input_21_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_21_pad_type_0 = const()[name = string("input_21_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_21_pad_0 = const()[name = string("input_21_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_21_dilations_0 = const()[name = string("input_21_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_21_groups_0 = const()[name = string("input_21_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_21_cast_fp16 = conv(dilations = input_21_dilations_0, groups = input_21_groups_0, pad = input_21_pad_0, pad_type = input_21_pad_type_0, strides = input_21_strides_0, weight = layers_10_mlp_gate_proj_weight_cast_fp16, x = var_4399_cast_fp16_0)[name = string("input_21_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_4416_cast_fp16 = silu(x = input_21_cast_fp16)[name = string("op_4416_cast_fp16")];
            tensor<int32, [2]> var_4422_strides_0 = const()[name = string("op_4422_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_4422_pad_type_0 = const()[name = string("op_4422_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_4422_pad_0 = const()[name = string("op_4422_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_4422_dilations_0 = const()[name = string("op_4422_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4422_groups_0 = const()[name = string("op_4422_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_4422_cast_fp16 = conv(dilations = var_4422_dilations_0, groups = var_4422_groups_0, pad = var_4422_pad_0, pad_type = var_4422_pad_type_0, strides = var_4422_strides_0, weight = layers_10_mlp_up_proj_weight_cast_fp16, x = var_4399_cast_fp16_0)[name = string("op_4422_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_109_cast_fp16 = mul(x = var_4416_cast_fp16, y = var_4422_cast_fp16)[name = string("x_109_cast_fp16")];
            tensor<int32, [2]> hidden_states_107_strides_0 = const()[name = string("hidden_states_107_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_107_pad_type_0 = const()[name = string("hidden_states_107_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_107_pad_0 = const()[name = string("hidden_states_107_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_107_dilations_0 = const()[name = string("hidden_states_107_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_107_groups_0 = const()[name = string("hidden_states_107_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_107_cast_fp16 = conv(dilations = hidden_states_107_dilations_0, groups = hidden_states_107_groups_0, pad = hidden_states_107_pad_0, pad_type = hidden_states_107_pad_type_0, strides = hidden_states_107_strides_0, weight = layers_10_mlp_down_proj_weight_cast_fp16, x = x_109_cast_fp16)[name = string("hidden_states_107_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_109_cast_fp16 = add(x = hidden_states_105_cast_fp16, y = hidden_states_107_cast_fp16)[name = string("hidden_states_109_cast_fp16")];
            fp16 const_112_promoted_to_fp16 = const()[name = string("const_112_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_4440_cast_fp16 = mul(x = hidden_states_109_cast_fp16, y = const_112_promoted_to_fp16)[name = string("op_4440_cast_fp16")];
            int32 var_4438 = const()[name = string("op_4438"), val = int32(1)];
            bool doubled_89_interleave_0 = const()[name = string("doubled_89_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_89_cast_fp16 = concat(axis = var_4438, interleave = doubled_89_interleave_0, values = (hidden_states_109_cast_fp16, var_4440_cast_fp16))[name = string("doubled_89_cast_fp16")];
            tensor<int32, [1]> out_45_axes_0 = const()[name = string("out_45_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_45_gamma_0_to_fp16 = const()[name = string("out_45_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406977728)))];
            fp16 var_4450_to_fp16 = const()[name = string("op_4450_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_45_cast_fp16 = layer_norm(axes = out_45_axes_0, epsilon = var_4450_to_fp16, gamma = out_45_gamma_0_to_fp16, x = doubled_89_cast_fp16)[name = string("out_45_cast_fp16")];
            tensor<int32, [2]> var_4461_split_sizes_0 = const()[name = string("op_4461_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4461_axis_0 = const()[name = string("op_4461_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_4461_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_4461_cast_fp16_1 = split(axis = var_4461_axis_0, split_sizes = var_4461_split_sizes_0, x = out_45_cast_fp16)[name = string("op_4461_cast_fp16")];
            tensor<int32, [2]> query_states_67_strides_0 = const()[name = string("query_states_67_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_67_pad_type_0 = const()[name = string("query_states_67_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_67_pad_0 = const()[name = string("query_states_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_67_dilations_0 = const()[name = string("query_states_67_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_67_groups_0 = const()[name = string("query_states_67_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_67_cast_fp16 = conv(dilations = query_states_67_dilations_0, groups = query_states_67_groups_0, pad = query_states_67_pad_0, pad_type = query_states_67_pad_type_0, strides = query_states_67_strides_0, weight = layers_11_self_attn_q_proj_weight_cast_fp16, x = var_4461_cast_fp16_0)[name = string("query_states_67_cast_fp16")];
            tensor<int32, [2]> key_states_111_strides_0 = const()[name = string("key_states_111_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_111_pad_type_0 = const()[name = string("key_states_111_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_111_pad_0 = const()[name = string("key_states_111_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_111_dilations_0 = const()[name = string("key_states_111_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_111_groups_0 = const()[name = string("key_states_111_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_111_cast_fp16 = conv(dilations = key_states_111_dilations_0, groups = key_states_111_groups_0, pad = key_states_111_pad_0, pad_type = key_states_111_pad_type_0, strides = key_states_111_strides_0, weight = layers_11_self_attn_k_proj_weight_cast_fp16, x = var_4461_cast_fp16_0)[name = string("key_states_111_cast_fp16")];
            tensor<int32, [2]> value_states_67_strides_0 = const()[name = string("value_states_67_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_67_pad_type_0 = const()[name = string("value_states_67_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_67_pad_0 = const()[name = string("value_states_67_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_67_dilations_0 = const()[name = string("value_states_67_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_67_groups_0 = const()[name = string("value_states_67_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_67_cast_fp16 = conv(dilations = value_states_67_dilations_0, groups = value_states_67_groups_0, pad = value_states_67_pad_0, pad_type = value_states_67_pad_type_0, strides = value_states_67_strides_0, weight = layers_11_self_attn_v_proj_weight_cast_fp16, x = var_4461_cast_fp16_0)[name = string("value_states_67_cast_fp16")];
            tensor<int32, [4]> concat_132x = const()[name = string("concat_132x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_111_cast_fp16 = reshape(shape = concat_132x, x = query_states_67_cast_fp16)[name = string("x_111_cast_fp16")];
            tensor<int32, [4]> concat_133x = const()[name = string("concat_133x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_4518_cast_fp16 = reshape(shape = concat_133x, x = key_states_111_cast_fp16)[name = string("op_4518_cast_fp16")];
            tensor<int32, [4]> concat_134x = const()[name = string("concat_134x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_4525_cast_fp16 = reshape(shape = concat_134x, x = value_states_67_cast_fp16)[name = string("op_4525_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_4529_cast_fp16 = mul(x = x_111_cast_fp16, y = var_859_cast_fp16)[name = string("op_4529_cast_fp16")];
            tensor<int32, [2]> var_4530_split_sizes_0 = const()[name = string("op_4530_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4530_axis_0 = const()[name = string("op_4530_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_4530_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_4530_cast_fp16_1 = split(axis = var_4530_axis_0, split_sizes = var_4530_split_sizes_0, x = x_111_cast_fp16)[name = string("op_4530_cast_fp16")];
            fp16 const_114_promoted_to_fp16 = const()[name = string("const_114_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_4532_cast_fp16 = mul(x = var_4530_cast_fp16_1, y = const_114_promoted_to_fp16)[name = string("op_4532_cast_fp16")];
            int32 var_4534 = const()[name = string("op_4534"), val = int32(-2)];
            bool var_4535_interleave_0 = const()[name = string("op_4535_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_4535_cast_fp16 = concat(axis = var_4534, interleave = var_4535_interleave_0, values = (var_4532_cast_fp16, var_4530_cast_fp16_0))[name = string("op_4535_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_4536_cast_fp16 = mul(x = var_4535_cast_fp16, y = var_866_cast_fp16)[name = string("op_4536_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_69_cast_fp16 = add(x = var_4529_cast_fp16, y = var_4536_cast_fp16)[name = string("query_states_69_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_4542_cast_fp16 = mul(x = var_4518_cast_fp16, y = var_859_cast_fp16)[name = string("op_4542_cast_fp16")];
            tensor<int32, [2]> var_4543_split_sizes_0 = const()[name = string("op_4543_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4543_axis_0 = const()[name = string("op_4543_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_4543_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_4543_cast_fp16_1 = split(axis = var_4543_axis_0, split_sizes = var_4543_split_sizes_0, x = var_4518_cast_fp16)[name = string("op_4543_cast_fp16")];
            fp16 const_115_promoted_to_fp16 = const()[name = string("const_115_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_4545_cast_fp16 = mul(x = var_4543_cast_fp16_1, y = const_115_promoted_to_fp16)[name = string("op_4545_cast_fp16")];
            int32 var_4547 = const()[name = string("op_4547"), val = int32(-2)];
            bool var_4548_interleave_0 = const()[name = string("op_4548_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_4548_cast_fp16 = concat(axis = var_4547, interleave = var_4548_interleave_0, values = (var_4545_cast_fp16, var_4543_cast_fp16_0))[name = string("op_4548_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_4549_cast_fp16 = mul(x = var_4548_cast_fp16, y = var_866_cast_fp16)[name = string("op_4549_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_115_cast_fp16 = add(x = var_4542_cast_fp16, y = var_4549_cast_fp16)[name = string("key_states_115_cast_fp16")];
            tensor<int32, [1]> expand_dims_132 = const()[name = string("expand_dims_132"), val = tensor<int32, [1]>([11])];
            tensor<int32, [1]> expand_dims_133 = const()[name = string("expand_dims_133"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_135 = const()[name = string("expand_dims_135"), val = tensor<int32, [1]>([0])];
            int32 concat_137_axis_0 = const()[name = string("concat_137_axis_0"), val = int32(0)];
            bool concat_137_interleave_0 = const()[name = string("concat_137_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_137 = concat(axis = concat_137_axis_0, interleave = concat_137_interleave_0, values = (expand_dims_132, expand_dims_133, position_id, expand_dims_135))[name = string("concat_137")];
            tensor<int32, [1]> expand_dims_136 = const()[name = string("expand_dims_136"), val = tensor<int32, [1]>([12])];
            tensor<int32, [1]> concat_138_values1_0 = const()[name = string("concat_138_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_138_values3_0 = const()[name = string("concat_138_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_138_axis_0 = const()[name = string("concat_138_axis_0"), val = int32(0)];
            bool concat_138_interleave_0 = const()[name = string("concat_138_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_138 = concat(axis = concat_138_axis_0, interleave = concat_138_interleave_0, values = (expand_dims_136, concat_138_values1_0, cache_position_end, concat_138_values3_0))[name = string("concat_138")];
            tensor<int32, [4]> key_states_117_perm_0 = const()[name = string("key_states_117_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_12_stride_0 = const()[name = string("key_cache_internal_tensor_assign_12_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_12_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_12_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_12_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_12_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_12_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_12_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_117_cast_fp16 = transpose(perm = key_states_117_perm_0, x = key_states_115_cast_fp16)[name = string("transpose_50")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_12_cast_fp16 = slice_update(begin = concat_137, begin_mask = key_cache_internal_tensor_assign_12_begin_mask_0, end = concat_138, end_mask = key_cache_internal_tensor_assign_12_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_12_squeeze_mask_0, stride = key_cache_internal_tensor_assign_12_stride_0, update = key_states_117_cast_fp16, x = coreml_update_state_20)[name = string("key_cache_internal_tensor_assign_12_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_12_cast_fp16, input = key_cache)[name = string("coreml_update_state_22_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_22 = read_state(input = key_cache)[name = string("coreml_update_state_22")];
            tensor<int32, [4]> value_states_69_perm_0 = const()[name = string("value_states_69_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_12_stride_0 = const()[name = string("value_cache_internal_tensor_assign_12_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_12_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_12_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_12_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_12_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_12_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_12_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_69_cast_fp16 = transpose(perm = value_states_69_perm_0, x = var_4525_cast_fp16)[name = string("transpose_49")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_12_cast_fp16 = slice_update(begin = concat_137, begin_mask = value_cache_internal_tensor_assign_12_begin_mask_0, end = concat_138, end_mask = value_cache_internal_tensor_assign_12_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_12_squeeze_mask_0, stride = value_cache_internal_tensor_assign_12_stride_0, update = value_states_69_cast_fp16, x = coreml_update_state_21)[name = string("value_cache_internal_tensor_assign_12_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_12_cast_fp16, input = value_cache)[name = string("coreml_update_state_23_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_23 = read_state(input = value_cache)[name = string("coreml_update_state_23")];
            tensor<int32, [4]> var_4619_begin_0 = const()[name = string("op_4619_begin_0"), val = tensor<int32, [4]>([11, 0, 0, 0])];
            tensor<int32, [4]> var_4619_end_0 = const()[name = string("op_4619_end_0"), val = tensor<int32, [4]>([12, 2, 2048, 128])];
            tensor<bool, [4]> var_4619_end_mask_0 = const()[name = string("op_4619_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4619_cast_fp16 = slice_by_index(begin = var_4619_begin_0, end = var_4619_end_0, end_mask = var_4619_end_mask_0, x = coreml_update_state_22)[name = string("op_4619_cast_fp16")];
            tensor<int32, [2]> tile_22 = const()[name = string("tile_22"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4622_axis_0 = const()[name = string("op_4622_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4622_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4622_cast_fp16_1 = split(axis = var_4622_axis_0, split_sizes = tile_22, x = var_4619_cast_fp16)[name = string("op_4622_cast_fp16")];
            tensor<int32, [4]> var_4629_begin_0 = const()[name = string("op_4629_begin_0"), val = tensor<int32, [4]>([11, 0, 0, 0])];
            tensor<int32, [4]> var_4629_end_0 = const()[name = string("op_4629_end_0"), val = tensor<int32, [4]>([12, 2, 2048, 128])];
            tensor<bool, [4]> var_4629_end_mask_0 = const()[name = string("op_4629_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4629_cast_fp16 = slice_by_index(begin = var_4629_begin_0, end = var_4629_end_0, end_mask = var_4629_end_mask_0, x = coreml_update_state_23)[name = string("op_4629_cast_fp16")];
            tensor<int32, [2]> tile_23 = const()[name = string("tile_23"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4632_axis_0 = const()[name = string("op_4632_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4632_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4632_cast_fp16_1 = split(axis = var_4632_axis_0, split_sizes = tile_23, x = var_4629_cast_fp16)[name = string("op_4632_cast_fp16")];
            tensor<int32, [2]> var_4635_split_sizes_0 = const()[name = string("op_4635_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_4635_axis_0 = const()[name = string("op_4635_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_4635_0, tensor<fp16, [1, 8, 128, 1]> var_4635_1 = split(axis = var_4635_axis_0, split_sizes = var_4635_split_sizes_0, x = query_states_69_cast_fp16)[name = string("op_4635")];
            bool attn_weights_177_transpose_x_0 = const()[name = string("attn_weights_177_transpose_x_0"), val = bool(false)];
            bool attn_weights_177_transpose_y_0 = const()[name = string("attn_weights_177_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_177_cast_fp16 = matmul(transpose_x = attn_weights_177_transpose_x_0, transpose_y = attn_weights_177_transpose_y_0, x = var_4622_cast_fp16_0, y = var_4635_0)[name = string("attn_weights_177_cast_fp16")];
            fp16 var_4638_to_fp16 = const()[name = string("op_4638_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_179_cast_fp16 = mul(x = attn_weights_177_cast_fp16, y = var_4638_to_fp16)[name = string("attn_weights_179_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_181_cast_fp16 = add(x = attn_weights_179_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_181_cast_fp16")];
            int32 var_4642 = const()[name = string("op_4642"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_183_cast_fp16 = softmax(axis = var_4642, x = attn_weights_181_cast_fp16)[name = string("attn_weights_183_cast_fp16")];
            bool var_4648_transpose_x_1 = const()[name = string("op_4648_transpose_x_1"), val = bool(true)];
            bool var_4648_transpose_y_1 = const()[name = string("op_4648_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_4648_cast_fp16 = matmul(transpose_x = var_4648_transpose_x_1, transpose_y = var_4648_transpose_y_1, x = attn_weights_183_cast_fp16, y = var_4632_cast_fp16_0)[name = string("op_4648_cast_fp16")];
            bool attn_weights_185_transpose_x_0 = const()[name = string("attn_weights_185_transpose_x_0"), val = bool(false)];
            bool attn_weights_185_transpose_y_0 = const()[name = string("attn_weights_185_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_185_cast_fp16 = matmul(transpose_x = attn_weights_185_transpose_x_0, transpose_y = attn_weights_185_transpose_y_0, x = var_4622_cast_fp16_1, y = var_4635_1)[name = string("attn_weights_185_cast_fp16")];
            fp16 var_4650_to_fp16 = const()[name = string("op_4650_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_187_cast_fp16 = mul(x = attn_weights_185_cast_fp16, y = var_4650_to_fp16)[name = string("attn_weights_187_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_189_cast_fp16 = add(x = attn_weights_187_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_189_cast_fp16")];
            int32 var_4654 = const()[name = string("op_4654"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_191_cast_fp16 = softmax(axis = var_4654, x = attn_weights_189_cast_fp16)[name = string("attn_weights_191_cast_fp16")];
            bool attn_output_89_transpose_x_1 = const()[name = string("attn_output_89_transpose_x_1"), val = bool(true)];
            bool attn_output_89_transpose_y_1 = const()[name = string("attn_output_89_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_89_cast_fp16 = matmul(transpose_x = attn_output_89_transpose_x_1, transpose_y = attn_output_89_transpose_y_1, x = attn_weights_191_cast_fp16, y = var_4632_cast_fp16_1)[name = string("attn_output_89_cast_fp16")];
            int32 var_4662 = const()[name = string("op_4662"), val = int32(1)];
            bool attn_output_91_interleave_0 = const()[name = string("attn_output_91_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_91_cast_fp16 = concat(axis = var_4662, interleave = attn_output_91_interleave_0, values = (var_4648_cast_fp16, attn_output_89_cast_fp16))[name = string("attn_output_91_cast_fp16")];
            tensor<int32, [4]> var_4666_perm_0 = const()[name = string("op_4666_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_143x = const()[name = string("concat_143x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_4666_cast_fp16 = transpose(perm = var_4666_perm_0, x = attn_output_91_cast_fp16)[name = string("transpose_48")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_95_cast_fp16 = reshape(shape = concat_143x, x = var_4666_cast_fp16)[name = string("attn_output_95_cast_fp16")];
            tensor<int32, [2]> hidden_states_113_strides_0 = const()[name = string("hidden_states_113_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_113_pad_type_0 = const()[name = string("hidden_states_113_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_113_pad_0 = const()[name = string("hidden_states_113_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_113_dilations_0 = const()[name = string("hidden_states_113_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_113_groups_0 = const()[name = string("hidden_states_113_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_113_cast_fp16 = conv(dilations = hidden_states_113_dilations_0, groups = hidden_states_113_groups_0, pad = hidden_states_113_pad_0, pad_type = hidden_states_113_pad_type_0, strides = hidden_states_113_strides_0, weight = layers_11_self_attn_o_proj_weight_cast_fp16, x = attn_output_95_cast_fp16)[name = string("hidden_states_113_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_115_cast_fp16 = add(x = hidden_states_109_cast_fp16, y = hidden_states_113_cast_fp16)[name = string("hidden_states_115_cast_fp16")];
            fp16 const_120_promoted_to_fp16 = const()[name = string("const_120_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_4699_cast_fp16 = mul(x = hidden_states_115_cast_fp16, y = const_120_promoted_to_fp16)[name = string("op_4699_cast_fp16")];
            int32 var_4697 = const()[name = string("op_4697"), val = int32(1)];
            bool doubled_93_interleave_0 = const()[name = string("doubled_93_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_93_cast_fp16 = concat(axis = var_4697, interleave = doubled_93_interleave_0, values = (hidden_states_115_cast_fp16, var_4699_cast_fp16))[name = string("doubled_93_cast_fp16")];
            tensor<int32, [1]> out_47_axes_0 = const()[name = string("out_47_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_47_gamma_0_to_fp16 = const()[name = string("out_47_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406985984)))];
            fp16 var_4709_to_fp16 = const()[name = string("op_4709_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_47_cast_fp16 = layer_norm(axes = out_47_axes_0, epsilon = var_4709_to_fp16, gamma = out_47_gamma_0_to_fp16, x = doubled_93_cast_fp16)[name = string("out_47_cast_fp16")];
            tensor<int32, [2]> var_4720_split_sizes_0 = const()[name = string("op_4720_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4720_axis_0 = const()[name = string("op_4720_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_4720_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_4720_cast_fp16_1 = split(axis = var_4720_axis_0, split_sizes = var_4720_split_sizes_0, x = out_47_cast_fp16)[name = string("op_4720_cast_fp16")];
            tensor<int32, [2]> input_23_strides_0 = const()[name = string("input_23_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_23_pad_type_0 = const()[name = string("input_23_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_23_pad_0 = const()[name = string("input_23_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_23_dilations_0 = const()[name = string("input_23_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_23_groups_0 = const()[name = string("input_23_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_23_cast_fp16 = conv(dilations = input_23_dilations_0, groups = input_23_groups_0, pad = input_23_pad_0, pad_type = input_23_pad_type_0, strides = input_23_strides_0, weight = layers_11_mlp_gate_proj_weight_cast_fp16, x = var_4720_cast_fp16_0)[name = string("input_23_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_4737_cast_fp16 = silu(x = input_23_cast_fp16)[name = string("op_4737_cast_fp16")];
            tensor<int32, [2]> var_4743_strides_0 = const()[name = string("op_4743_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_4743_pad_type_0 = const()[name = string("op_4743_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_4743_pad_0 = const()[name = string("op_4743_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_4743_dilations_0 = const()[name = string("op_4743_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4743_groups_0 = const()[name = string("op_4743_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_4743_cast_fp16 = conv(dilations = var_4743_dilations_0, groups = var_4743_groups_0, pad = var_4743_pad_0, pad_type = var_4743_pad_type_0, strides = var_4743_strides_0, weight = layers_11_mlp_up_proj_weight_cast_fp16, x = var_4720_cast_fp16_0)[name = string("op_4743_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_119_cast_fp16 = mul(x = var_4737_cast_fp16, y = var_4743_cast_fp16)[name = string("x_119_cast_fp16")];
            tensor<int32, [2]> hidden_states_117_strides_0 = const()[name = string("hidden_states_117_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_117_pad_type_0 = const()[name = string("hidden_states_117_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_117_pad_0 = const()[name = string("hidden_states_117_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_117_dilations_0 = const()[name = string("hidden_states_117_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_117_groups_0 = const()[name = string("hidden_states_117_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_117_cast_fp16 = conv(dilations = hidden_states_117_dilations_0, groups = hidden_states_117_groups_0, pad = hidden_states_117_pad_0, pad_type = hidden_states_117_pad_type_0, strides = hidden_states_117_strides_0, weight = layers_11_mlp_down_proj_weight_cast_fp16, x = x_119_cast_fp16)[name = string("hidden_states_117_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_119_cast_fp16 = add(x = hidden_states_115_cast_fp16, y = hidden_states_117_cast_fp16)[name = string("hidden_states_119_cast_fp16")];
            fp16 const_122_promoted_to_fp16 = const()[name = string("const_122_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_4761_cast_fp16 = mul(x = hidden_states_119_cast_fp16, y = const_122_promoted_to_fp16)[name = string("op_4761_cast_fp16")];
            int32 var_4759 = const()[name = string("op_4759"), val = int32(1)];
            bool doubled_97_interleave_0 = const()[name = string("doubled_97_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_97_cast_fp16 = concat(axis = var_4759, interleave = doubled_97_interleave_0, values = (hidden_states_119_cast_fp16, var_4761_cast_fp16))[name = string("doubled_97_cast_fp16")];
            tensor<int32, [1]> out_49_axes_0 = const()[name = string("out_49_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_49_gamma_0_to_fp16 = const()[name = string("out_49_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1406994240)))];
            fp16 var_4771_to_fp16 = const()[name = string("op_4771_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_49_cast_fp16 = layer_norm(axes = out_49_axes_0, epsilon = var_4771_to_fp16, gamma = out_49_gamma_0_to_fp16, x = doubled_97_cast_fp16)[name = string("out_49_cast_fp16")];
            tensor<int32, [2]> var_4782_split_sizes_0 = const()[name = string("op_4782_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_4782_axis_0 = const()[name = string("op_4782_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_4782_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_4782_cast_fp16_1 = split(axis = var_4782_axis_0, split_sizes = var_4782_split_sizes_0, x = out_49_cast_fp16)[name = string("op_4782_cast_fp16")];
            tensor<int32, [2]> query_states_73_strides_0 = const()[name = string("query_states_73_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_73_pad_type_0 = const()[name = string("query_states_73_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_73_pad_0 = const()[name = string("query_states_73_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_73_dilations_0 = const()[name = string("query_states_73_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_73_groups_0 = const()[name = string("query_states_73_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_73_cast_fp16 = conv(dilations = query_states_73_dilations_0, groups = query_states_73_groups_0, pad = query_states_73_pad_0, pad_type = query_states_73_pad_type_0, strides = query_states_73_strides_0, weight = layers_12_self_attn_q_proj_weight_cast_fp16, x = var_4782_cast_fp16_0)[name = string("query_states_73_cast_fp16")];
            tensor<int32, [2]> key_states_121_strides_0 = const()[name = string("key_states_121_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_121_pad_type_0 = const()[name = string("key_states_121_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_121_pad_0 = const()[name = string("key_states_121_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_121_dilations_0 = const()[name = string("key_states_121_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_121_groups_0 = const()[name = string("key_states_121_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_121_cast_fp16 = conv(dilations = key_states_121_dilations_0, groups = key_states_121_groups_0, pad = key_states_121_pad_0, pad_type = key_states_121_pad_type_0, strides = key_states_121_strides_0, weight = layers_12_self_attn_k_proj_weight_cast_fp16, x = var_4782_cast_fp16_0)[name = string("key_states_121_cast_fp16")];
            tensor<int32, [2]> value_states_73_strides_0 = const()[name = string("value_states_73_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_73_pad_type_0 = const()[name = string("value_states_73_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_73_pad_0 = const()[name = string("value_states_73_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_73_dilations_0 = const()[name = string("value_states_73_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_73_groups_0 = const()[name = string("value_states_73_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_73_cast_fp16 = conv(dilations = value_states_73_dilations_0, groups = value_states_73_groups_0, pad = value_states_73_pad_0, pad_type = value_states_73_pad_type_0, strides = value_states_73_strides_0, weight = layers_12_self_attn_v_proj_weight_cast_fp16, x = var_4782_cast_fp16_0)[name = string("value_states_73_cast_fp16")];
            tensor<int32, [4]> concat_144x = const()[name = string("concat_144x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_121_cast_fp16 = reshape(shape = concat_144x, x = query_states_73_cast_fp16)[name = string("x_121_cast_fp16")];
            tensor<int32, [4]> concat_145x = const()[name = string("concat_145x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_4839_cast_fp16 = reshape(shape = concat_145x, x = key_states_121_cast_fp16)[name = string("op_4839_cast_fp16")];
            tensor<int32, [4]> concat_146x = const()[name = string("concat_146x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_4846_cast_fp16 = reshape(shape = concat_146x, x = value_states_73_cast_fp16)[name = string("op_4846_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_4850_cast_fp16 = mul(x = x_121_cast_fp16, y = var_859_cast_fp16)[name = string("op_4850_cast_fp16")];
            tensor<int32, [2]> var_4851_split_sizes_0 = const()[name = string("op_4851_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4851_axis_0 = const()[name = string("op_4851_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_4851_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_4851_cast_fp16_1 = split(axis = var_4851_axis_0, split_sizes = var_4851_split_sizes_0, x = x_121_cast_fp16)[name = string("op_4851_cast_fp16")];
            fp16 const_124_promoted_to_fp16 = const()[name = string("const_124_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_4853_cast_fp16 = mul(x = var_4851_cast_fp16_1, y = const_124_promoted_to_fp16)[name = string("op_4853_cast_fp16")];
            int32 var_4855 = const()[name = string("op_4855"), val = int32(-2)];
            bool var_4856_interleave_0 = const()[name = string("op_4856_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_4856_cast_fp16 = concat(axis = var_4855, interleave = var_4856_interleave_0, values = (var_4853_cast_fp16, var_4851_cast_fp16_0))[name = string("op_4856_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_4857_cast_fp16 = mul(x = var_4856_cast_fp16, y = var_866_cast_fp16)[name = string("op_4857_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_75_cast_fp16 = add(x = var_4850_cast_fp16, y = var_4857_cast_fp16)[name = string("query_states_75_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_4863_cast_fp16 = mul(x = var_4839_cast_fp16, y = var_859_cast_fp16)[name = string("op_4863_cast_fp16")];
            tensor<int32, [2]> var_4864_split_sizes_0 = const()[name = string("op_4864_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_4864_axis_0 = const()[name = string("op_4864_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_4864_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_4864_cast_fp16_1 = split(axis = var_4864_axis_0, split_sizes = var_4864_split_sizes_0, x = var_4839_cast_fp16)[name = string("op_4864_cast_fp16")];
            fp16 const_125_promoted_to_fp16 = const()[name = string("const_125_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_4866_cast_fp16 = mul(x = var_4864_cast_fp16_1, y = const_125_promoted_to_fp16)[name = string("op_4866_cast_fp16")];
            int32 var_4868 = const()[name = string("op_4868"), val = int32(-2)];
            bool var_4869_interleave_0 = const()[name = string("op_4869_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_4869_cast_fp16 = concat(axis = var_4868, interleave = var_4869_interleave_0, values = (var_4866_cast_fp16, var_4864_cast_fp16_0))[name = string("op_4869_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_4870_cast_fp16 = mul(x = var_4869_cast_fp16, y = var_866_cast_fp16)[name = string("op_4870_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_125_cast_fp16 = add(x = var_4863_cast_fp16, y = var_4870_cast_fp16)[name = string("key_states_125_cast_fp16")];
            tensor<int32, [1]> expand_dims_144 = const()[name = string("expand_dims_144"), val = tensor<int32, [1]>([12])];
            tensor<int32, [1]> expand_dims_145 = const()[name = string("expand_dims_145"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_147 = const()[name = string("expand_dims_147"), val = tensor<int32, [1]>([0])];
            int32 concat_149_axis_0 = const()[name = string("concat_149_axis_0"), val = int32(0)];
            bool concat_149_interleave_0 = const()[name = string("concat_149_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_149 = concat(axis = concat_149_axis_0, interleave = concat_149_interleave_0, values = (expand_dims_144, expand_dims_145, position_id, expand_dims_147))[name = string("concat_149")];
            tensor<int32, [1]> expand_dims_148 = const()[name = string("expand_dims_148"), val = tensor<int32, [1]>([13])];
            tensor<int32, [1]> concat_150_values1_0 = const()[name = string("concat_150_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_150_values3_0 = const()[name = string("concat_150_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_150_axis_0 = const()[name = string("concat_150_axis_0"), val = int32(0)];
            bool concat_150_interleave_0 = const()[name = string("concat_150_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_150 = concat(axis = concat_150_axis_0, interleave = concat_150_interleave_0, values = (expand_dims_148, concat_150_values1_0, cache_position_end, concat_150_values3_0))[name = string("concat_150")];
            tensor<int32, [4]> key_states_127_perm_0 = const()[name = string("key_states_127_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_13_stride_0 = const()[name = string("key_cache_internal_tensor_assign_13_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_13_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_13_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_13_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_13_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_13_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_13_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_127_cast_fp16 = transpose(perm = key_states_127_perm_0, x = key_states_125_cast_fp16)[name = string("transpose_47")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_13_cast_fp16 = slice_update(begin = concat_149, begin_mask = key_cache_internal_tensor_assign_13_begin_mask_0, end = concat_150, end_mask = key_cache_internal_tensor_assign_13_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_13_squeeze_mask_0, stride = key_cache_internal_tensor_assign_13_stride_0, update = key_states_127_cast_fp16, x = coreml_update_state_22)[name = string("key_cache_internal_tensor_assign_13_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_13_cast_fp16, input = key_cache)[name = string("coreml_update_state_24_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_24 = read_state(input = key_cache)[name = string("coreml_update_state_24")];
            tensor<int32, [4]> value_states_75_perm_0 = const()[name = string("value_states_75_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_13_stride_0 = const()[name = string("value_cache_internal_tensor_assign_13_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_13_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_13_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_13_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_13_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_13_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_13_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_75_cast_fp16 = transpose(perm = value_states_75_perm_0, x = var_4846_cast_fp16)[name = string("transpose_46")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_13_cast_fp16 = slice_update(begin = concat_149, begin_mask = value_cache_internal_tensor_assign_13_begin_mask_0, end = concat_150, end_mask = value_cache_internal_tensor_assign_13_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_13_squeeze_mask_0, stride = value_cache_internal_tensor_assign_13_stride_0, update = value_states_75_cast_fp16, x = coreml_update_state_23)[name = string("value_cache_internal_tensor_assign_13_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_13_cast_fp16, input = value_cache)[name = string("coreml_update_state_25_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_25 = read_state(input = value_cache)[name = string("coreml_update_state_25")];
            tensor<int32, [4]> var_4940_begin_0 = const()[name = string("op_4940_begin_0"), val = tensor<int32, [4]>([12, 0, 0, 0])];
            tensor<int32, [4]> var_4940_end_0 = const()[name = string("op_4940_end_0"), val = tensor<int32, [4]>([13, 2, 2048, 128])];
            tensor<bool, [4]> var_4940_end_mask_0 = const()[name = string("op_4940_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4940_cast_fp16 = slice_by_index(begin = var_4940_begin_0, end = var_4940_end_0, end_mask = var_4940_end_mask_0, x = coreml_update_state_24)[name = string("op_4940_cast_fp16")];
            tensor<int32, [2]> tile_24 = const()[name = string("tile_24"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4943_axis_0 = const()[name = string("op_4943_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4943_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4943_cast_fp16_1 = split(axis = var_4943_axis_0, split_sizes = tile_24, x = var_4940_cast_fp16)[name = string("op_4943_cast_fp16")];
            tensor<int32, [4]> var_4950_begin_0 = const()[name = string("op_4950_begin_0"), val = tensor<int32, [4]>([12, 0, 0, 0])];
            tensor<int32, [4]> var_4950_end_0 = const()[name = string("op_4950_end_0"), val = tensor<int32, [4]>([13, 2, 2048, 128])];
            tensor<bool, [4]> var_4950_end_mask_0 = const()[name = string("op_4950_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_4950_cast_fp16 = slice_by_index(begin = var_4950_begin_0, end = var_4950_end_0, end_mask = var_4950_end_mask_0, x = coreml_update_state_25)[name = string("op_4950_cast_fp16")];
            tensor<int32, [2]> tile_25 = const()[name = string("tile_25"), val = tensor<int32, [2]>([1, 1])];
            int32 var_4953_axis_0 = const()[name = string("op_4953_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_4953_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_4953_cast_fp16_1 = split(axis = var_4953_axis_0, split_sizes = tile_25, x = var_4950_cast_fp16)[name = string("op_4953_cast_fp16")];
            tensor<int32, [2]> var_4956_split_sizes_0 = const()[name = string("op_4956_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_4956_axis_0 = const()[name = string("op_4956_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_4956_0, tensor<fp16, [1, 8, 128, 1]> var_4956_1 = split(axis = var_4956_axis_0, split_sizes = var_4956_split_sizes_0, x = query_states_75_cast_fp16)[name = string("op_4956")];
            bool attn_weights_193_transpose_x_0 = const()[name = string("attn_weights_193_transpose_x_0"), val = bool(false)];
            bool attn_weights_193_transpose_y_0 = const()[name = string("attn_weights_193_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_193_cast_fp16 = matmul(transpose_x = attn_weights_193_transpose_x_0, transpose_y = attn_weights_193_transpose_y_0, x = var_4943_cast_fp16_0, y = var_4956_0)[name = string("attn_weights_193_cast_fp16")];
            fp16 var_4959_to_fp16 = const()[name = string("op_4959_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_195_cast_fp16 = mul(x = attn_weights_193_cast_fp16, y = var_4959_to_fp16)[name = string("attn_weights_195_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_197_cast_fp16 = add(x = attn_weights_195_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_197_cast_fp16")];
            int32 var_4963 = const()[name = string("op_4963"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_199_cast_fp16 = softmax(axis = var_4963, x = attn_weights_197_cast_fp16)[name = string("attn_weights_199_cast_fp16")];
            bool var_4969_transpose_x_1 = const()[name = string("op_4969_transpose_x_1"), val = bool(true)];
            bool var_4969_transpose_y_1 = const()[name = string("op_4969_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_4969_cast_fp16 = matmul(transpose_x = var_4969_transpose_x_1, transpose_y = var_4969_transpose_y_1, x = attn_weights_199_cast_fp16, y = var_4953_cast_fp16_0)[name = string("op_4969_cast_fp16")];
            bool attn_weights_201_transpose_x_0 = const()[name = string("attn_weights_201_transpose_x_0"), val = bool(false)];
            bool attn_weights_201_transpose_y_0 = const()[name = string("attn_weights_201_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_201_cast_fp16 = matmul(transpose_x = attn_weights_201_transpose_x_0, transpose_y = attn_weights_201_transpose_y_0, x = var_4943_cast_fp16_1, y = var_4956_1)[name = string("attn_weights_201_cast_fp16")];
            fp16 var_4971_to_fp16 = const()[name = string("op_4971_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_203_cast_fp16 = mul(x = attn_weights_201_cast_fp16, y = var_4971_to_fp16)[name = string("attn_weights_203_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_205_cast_fp16 = add(x = attn_weights_203_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_205_cast_fp16")];
            int32 var_4975 = const()[name = string("op_4975"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_207_cast_fp16 = softmax(axis = var_4975, x = attn_weights_205_cast_fp16)[name = string("attn_weights_207_cast_fp16")];
            bool attn_output_97_transpose_x_1 = const()[name = string("attn_output_97_transpose_x_1"), val = bool(true)];
            bool attn_output_97_transpose_y_1 = const()[name = string("attn_output_97_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_97_cast_fp16 = matmul(transpose_x = attn_output_97_transpose_x_1, transpose_y = attn_output_97_transpose_y_1, x = attn_weights_207_cast_fp16, y = var_4953_cast_fp16_1)[name = string("attn_output_97_cast_fp16")];
            int32 var_4983 = const()[name = string("op_4983"), val = int32(1)];
            bool attn_output_99_interleave_0 = const()[name = string("attn_output_99_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_99_cast_fp16 = concat(axis = var_4983, interleave = attn_output_99_interleave_0, values = (var_4969_cast_fp16, attn_output_97_cast_fp16))[name = string("attn_output_99_cast_fp16")];
            tensor<int32, [4]> var_4987_perm_0 = const()[name = string("op_4987_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_155x = const()[name = string("concat_155x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_4987_cast_fp16 = transpose(perm = var_4987_perm_0, x = attn_output_99_cast_fp16)[name = string("transpose_45")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_103_cast_fp16 = reshape(shape = concat_155x, x = var_4987_cast_fp16)[name = string("attn_output_103_cast_fp16")];
            tensor<int32, [2]> hidden_states_123_strides_0 = const()[name = string("hidden_states_123_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_123_pad_type_0 = const()[name = string("hidden_states_123_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_123_pad_0 = const()[name = string("hidden_states_123_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_123_dilations_0 = const()[name = string("hidden_states_123_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_123_groups_0 = const()[name = string("hidden_states_123_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_123_cast_fp16 = conv(dilations = hidden_states_123_dilations_0, groups = hidden_states_123_groups_0, pad = hidden_states_123_pad_0, pad_type = hidden_states_123_pad_type_0, strides = hidden_states_123_strides_0, weight = layers_12_self_attn_o_proj_weight_cast_fp16, x = attn_output_103_cast_fp16)[name = string("hidden_states_123_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_125_cast_fp16 = add(x = hidden_states_119_cast_fp16, y = hidden_states_123_cast_fp16)[name = string("hidden_states_125_cast_fp16")];
            fp16 const_130_promoted_to_fp16 = const()[name = string("const_130_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_5020_cast_fp16 = mul(x = hidden_states_125_cast_fp16, y = const_130_promoted_to_fp16)[name = string("op_5020_cast_fp16")];
            int32 var_5018 = const()[name = string("op_5018"), val = int32(1)];
            bool doubled_101_interleave_0 = const()[name = string("doubled_101_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_101_cast_fp16 = concat(axis = var_5018, interleave = doubled_101_interleave_0, values = (hidden_states_125_cast_fp16, var_5020_cast_fp16))[name = string("doubled_101_cast_fp16")];
            tensor<int32, [1]> out_51_axes_0 = const()[name = string("out_51_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_51_gamma_0_to_fp16 = const()[name = string("out_51_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407002496)))];
            fp16 var_5030_to_fp16 = const()[name = string("op_5030_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_51_cast_fp16 = layer_norm(axes = out_51_axes_0, epsilon = var_5030_to_fp16, gamma = out_51_gamma_0_to_fp16, x = doubled_101_cast_fp16)[name = string("out_51_cast_fp16")];
            tensor<int32, [2]> var_5041_split_sizes_0 = const()[name = string("op_5041_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5041_axis_0 = const()[name = string("op_5041_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_5041_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_5041_cast_fp16_1 = split(axis = var_5041_axis_0, split_sizes = var_5041_split_sizes_0, x = out_51_cast_fp16)[name = string("op_5041_cast_fp16")];
            tensor<int32, [2]> input_25_strides_0 = const()[name = string("input_25_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_25_pad_type_0 = const()[name = string("input_25_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_25_pad_0 = const()[name = string("input_25_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_25_dilations_0 = const()[name = string("input_25_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_25_groups_0 = const()[name = string("input_25_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_25_cast_fp16 = conv(dilations = input_25_dilations_0, groups = input_25_groups_0, pad = input_25_pad_0, pad_type = input_25_pad_type_0, strides = input_25_strides_0, weight = layers_12_mlp_gate_proj_weight_cast_fp16, x = var_5041_cast_fp16_0)[name = string("input_25_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_5058_cast_fp16 = silu(x = input_25_cast_fp16)[name = string("op_5058_cast_fp16")];
            tensor<int32, [2]> var_5064_strides_0 = const()[name = string("op_5064_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_5064_pad_type_0 = const()[name = string("op_5064_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_5064_pad_0 = const()[name = string("op_5064_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_5064_dilations_0 = const()[name = string("op_5064_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5064_groups_0 = const()[name = string("op_5064_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_5064_cast_fp16 = conv(dilations = var_5064_dilations_0, groups = var_5064_groups_0, pad = var_5064_pad_0, pad_type = var_5064_pad_type_0, strides = var_5064_strides_0, weight = layers_12_mlp_up_proj_weight_cast_fp16, x = var_5041_cast_fp16_0)[name = string("op_5064_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_129_cast_fp16 = mul(x = var_5058_cast_fp16, y = var_5064_cast_fp16)[name = string("x_129_cast_fp16")];
            tensor<int32, [2]> hidden_states_127_strides_0 = const()[name = string("hidden_states_127_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_127_pad_type_0 = const()[name = string("hidden_states_127_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_127_pad_0 = const()[name = string("hidden_states_127_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_127_dilations_0 = const()[name = string("hidden_states_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_127_groups_0 = const()[name = string("hidden_states_127_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_127_cast_fp16 = conv(dilations = hidden_states_127_dilations_0, groups = hidden_states_127_groups_0, pad = hidden_states_127_pad_0, pad_type = hidden_states_127_pad_type_0, strides = hidden_states_127_strides_0, weight = layers_12_mlp_down_proj_weight_cast_fp16, x = x_129_cast_fp16)[name = string("hidden_states_127_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_129_cast_fp16 = add(x = hidden_states_125_cast_fp16, y = hidden_states_127_cast_fp16)[name = string("hidden_states_129_cast_fp16")];
            fp16 const_132_promoted_to_fp16 = const()[name = string("const_132_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_5082_cast_fp16 = mul(x = hidden_states_129_cast_fp16, y = const_132_promoted_to_fp16)[name = string("op_5082_cast_fp16")];
            int32 var_5080 = const()[name = string("op_5080"), val = int32(1)];
            bool doubled_105_interleave_0 = const()[name = string("doubled_105_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_105_cast_fp16 = concat(axis = var_5080, interleave = doubled_105_interleave_0, values = (hidden_states_129_cast_fp16, var_5082_cast_fp16))[name = string("doubled_105_cast_fp16")];
            tensor<int32, [1]> out_53_axes_0 = const()[name = string("out_53_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_53_gamma_0_to_fp16 = const()[name = string("out_53_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407010752)))];
            fp16 var_5092_to_fp16 = const()[name = string("op_5092_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_53_cast_fp16 = layer_norm(axes = out_53_axes_0, epsilon = var_5092_to_fp16, gamma = out_53_gamma_0_to_fp16, x = doubled_105_cast_fp16)[name = string("out_53_cast_fp16")];
            tensor<int32, [2]> var_5103_split_sizes_0 = const()[name = string("op_5103_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5103_axis_0 = const()[name = string("op_5103_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_5103_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_5103_cast_fp16_1 = split(axis = var_5103_axis_0, split_sizes = var_5103_split_sizes_0, x = out_53_cast_fp16)[name = string("op_5103_cast_fp16")];
            tensor<int32, [2]> query_states_79_strides_0 = const()[name = string("query_states_79_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_79_pad_type_0 = const()[name = string("query_states_79_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_79_pad_0 = const()[name = string("query_states_79_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_79_dilations_0 = const()[name = string("query_states_79_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_79_groups_0 = const()[name = string("query_states_79_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_79_cast_fp16 = conv(dilations = query_states_79_dilations_0, groups = query_states_79_groups_0, pad = query_states_79_pad_0, pad_type = query_states_79_pad_type_0, strides = query_states_79_strides_0, weight = layers_13_self_attn_q_proj_weight_cast_fp16, x = var_5103_cast_fp16_0)[name = string("query_states_79_cast_fp16")];
            tensor<int32, [2]> key_states_131_strides_0 = const()[name = string("key_states_131_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_131_pad_type_0 = const()[name = string("key_states_131_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_131_pad_0 = const()[name = string("key_states_131_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_131_dilations_0 = const()[name = string("key_states_131_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_131_groups_0 = const()[name = string("key_states_131_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_131_cast_fp16 = conv(dilations = key_states_131_dilations_0, groups = key_states_131_groups_0, pad = key_states_131_pad_0, pad_type = key_states_131_pad_type_0, strides = key_states_131_strides_0, weight = layers_13_self_attn_k_proj_weight_cast_fp16, x = var_5103_cast_fp16_0)[name = string("key_states_131_cast_fp16")];
            tensor<int32, [2]> value_states_79_strides_0 = const()[name = string("value_states_79_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_79_pad_type_0 = const()[name = string("value_states_79_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_79_pad_0 = const()[name = string("value_states_79_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_79_dilations_0 = const()[name = string("value_states_79_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_79_groups_0 = const()[name = string("value_states_79_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_79_cast_fp16 = conv(dilations = value_states_79_dilations_0, groups = value_states_79_groups_0, pad = value_states_79_pad_0, pad_type = value_states_79_pad_type_0, strides = value_states_79_strides_0, weight = layers_13_self_attn_v_proj_weight_cast_fp16, x = var_5103_cast_fp16_0)[name = string("value_states_79_cast_fp16")];
            tensor<int32, [4]> concat_156x = const()[name = string("concat_156x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_131_cast_fp16 = reshape(shape = concat_156x, x = query_states_79_cast_fp16)[name = string("x_131_cast_fp16")];
            tensor<int32, [4]> concat_157x = const()[name = string("concat_157x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_5160_cast_fp16 = reshape(shape = concat_157x, x = key_states_131_cast_fp16)[name = string("op_5160_cast_fp16")];
            tensor<int32, [4]> concat_158x = const()[name = string("concat_158x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_5167_cast_fp16 = reshape(shape = concat_158x, x = value_states_79_cast_fp16)[name = string("op_5167_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_5171_cast_fp16 = mul(x = x_131_cast_fp16, y = var_859_cast_fp16)[name = string("op_5171_cast_fp16")];
            tensor<int32, [2]> var_5172_split_sizes_0 = const()[name = string("op_5172_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5172_axis_0 = const()[name = string("op_5172_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_5172_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_5172_cast_fp16_1 = split(axis = var_5172_axis_0, split_sizes = var_5172_split_sizes_0, x = x_131_cast_fp16)[name = string("op_5172_cast_fp16")];
            fp16 const_134_promoted_to_fp16 = const()[name = string("const_134_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_5174_cast_fp16 = mul(x = var_5172_cast_fp16_1, y = const_134_promoted_to_fp16)[name = string("op_5174_cast_fp16")];
            int32 var_5176 = const()[name = string("op_5176"), val = int32(-2)];
            bool var_5177_interleave_0 = const()[name = string("op_5177_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_5177_cast_fp16 = concat(axis = var_5176, interleave = var_5177_interleave_0, values = (var_5174_cast_fp16, var_5172_cast_fp16_0))[name = string("op_5177_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_5178_cast_fp16 = mul(x = var_5177_cast_fp16, y = var_866_cast_fp16)[name = string("op_5178_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_81_cast_fp16 = add(x = var_5171_cast_fp16, y = var_5178_cast_fp16)[name = string("query_states_81_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_5184_cast_fp16 = mul(x = var_5160_cast_fp16, y = var_859_cast_fp16)[name = string("op_5184_cast_fp16")];
            tensor<int32, [2]> var_5185_split_sizes_0 = const()[name = string("op_5185_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5185_axis_0 = const()[name = string("op_5185_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_5185_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_5185_cast_fp16_1 = split(axis = var_5185_axis_0, split_sizes = var_5185_split_sizes_0, x = var_5160_cast_fp16)[name = string("op_5185_cast_fp16")];
            fp16 const_135_promoted_to_fp16 = const()[name = string("const_135_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_5187_cast_fp16 = mul(x = var_5185_cast_fp16_1, y = const_135_promoted_to_fp16)[name = string("op_5187_cast_fp16")];
            int32 var_5189 = const()[name = string("op_5189"), val = int32(-2)];
            bool var_5190_interleave_0 = const()[name = string("op_5190_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_5190_cast_fp16 = concat(axis = var_5189, interleave = var_5190_interleave_0, values = (var_5187_cast_fp16, var_5185_cast_fp16_0))[name = string("op_5190_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_5191_cast_fp16 = mul(x = var_5190_cast_fp16, y = var_866_cast_fp16)[name = string("op_5191_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_135_cast_fp16 = add(x = var_5184_cast_fp16, y = var_5191_cast_fp16)[name = string("key_states_135_cast_fp16")];
            tensor<int32, [1]> expand_dims_156 = const()[name = string("expand_dims_156"), val = tensor<int32, [1]>([13])];
            tensor<int32, [1]> expand_dims_157 = const()[name = string("expand_dims_157"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_159 = const()[name = string("expand_dims_159"), val = tensor<int32, [1]>([0])];
            int32 concat_161_axis_0 = const()[name = string("concat_161_axis_0"), val = int32(0)];
            bool concat_161_interleave_0 = const()[name = string("concat_161_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_161 = concat(axis = concat_161_axis_0, interleave = concat_161_interleave_0, values = (expand_dims_156, expand_dims_157, position_id, expand_dims_159))[name = string("concat_161")];
            tensor<int32, [1]> expand_dims_160 = const()[name = string("expand_dims_160"), val = tensor<int32, [1]>([14])];
            tensor<int32, [1]> concat_162_values1_0 = const()[name = string("concat_162_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_162_values3_0 = const()[name = string("concat_162_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_162_axis_0 = const()[name = string("concat_162_axis_0"), val = int32(0)];
            bool concat_162_interleave_0 = const()[name = string("concat_162_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_162 = concat(axis = concat_162_axis_0, interleave = concat_162_interleave_0, values = (expand_dims_160, concat_162_values1_0, cache_position_end, concat_162_values3_0))[name = string("concat_162")];
            tensor<int32, [4]> key_states_137_perm_0 = const()[name = string("key_states_137_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_14_stride_0 = const()[name = string("key_cache_internal_tensor_assign_14_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_14_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_14_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_14_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_14_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_14_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_14_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_137_cast_fp16 = transpose(perm = key_states_137_perm_0, x = key_states_135_cast_fp16)[name = string("transpose_44")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_14_cast_fp16 = slice_update(begin = concat_161, begin_mask = key_cache_internal_tensor_assign_14_begin_mask_0, end = concat_162, end_mask = key_cache_internal_tensor_assign_14_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_14_squeeze_mask_0, stride = key_cache_internal_tensor_assign_14_stride_0, update = key_states_137_cast_fp16, x = coreml_update_state_24)[name = string("key_cache_internal_tensor_assign_14_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_14_cast_fp16, input = key_cache)[name = string("coreml_update_state_26_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_26 = read_state(input = key_cache)[name = string("coreml_update_state_26")];
            tensor<int32, [4]> value_states_81_perm_0 = const()[name = string("value_states_81_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_14_stride_0 = const()[name = string("value_cache_internal_tensor_assign_14_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_14_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_14_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_14_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_14_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_14_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_14_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_81_cast_fp16 = transpose(perm = value_states_81_perm_0, x = var_5167_cast_fp16)[name = string("transpose_43")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_14_cast_fp16 = slice_update(begin = concat_161, begin_mask = value_cache_internal_tensor_assign_14_begin_mask_0, end = concat_162, end_mask = value_cache_internal_tensor_assign_14_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_14_squeeze_mask_0, stride = value_cache_internal_tensor_assign_14_stride_0, update = value_states_81_cast_fp16, x = coreml_update_state_25)[name = string("value_cache_internal_tensor_assign_14_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_14_cast_fp16, input = value_cache)[name = string("coreml_update_state_27_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_27 = read_state(input = value_cache)[name = string("coreml_update_state_27")];
            tensor<int32, [4]> var_5261_begin_0 = const()[name = string("op_5261_begin_0"), val = tensor<int32, [4]>([13, 0, 0, 0])];
            tensor<int32, [4]> var_5261_end_0 = const()[name = string("op_5261_end_0"), val = tensor<int32, [4]>([14, 2, 2048, 128])];
            tensor<bool, [4]> var_5261_end_mask_0 = const()[name = string("op_5261_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5261_cast_fp16 = slice_by_index(begin = var_5261_begin_0, end = var_5261_end_0, end_mask = var_5261_end_mask_0, x = coreml_update_state_26)[name = string("op_5261_cast_fp16")];
            tensor<int32, [2]> tile_26 = const()[name = string("tile_26"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5264_axis_0 = const()[name = string("op_5264_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5264_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5264_cast_fp16_1 = split(axis = var_5264_axis_0, split_sizes = tile_26, x = var_5261_cast_fp16)[name = string("op_5264_cast_fp16")];
            tensor<int32, [4]> var_5271_begin_0 = const()[name = string("op_5271_begin_0"), val = tensor<int32, [4]>([13, 0, 0, 0])];
            tensor<int32, [4]> var_5271_end_0 = const()[name = string("op_5271_end_0"), val = tensor<int32, [4]>([14, 2, 2048, 128])];
            tensor<bool, [4]> var_5271_end_mask_0 = const()[name = string("op_5271_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5271_cast_fp16 = slice_by_index(begin = var_5271_begin_0, end = var_5271_end_0, end_mask = var_5271_end_mask_0, x = coreml_update_state_27)[name = string("op_5271_cast_fp16")];
            tensor<int32, [2]> tile_27 = const()[name = string("tile_27"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5274_axis_0 = const()[name = string("op_5274_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5274_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5274_cast_fp16_1 = split(axis = var_5274_axis_0, split_sizes = tile_27, x = var_5271_cast_fp16)[name = string("op_5274_cast_fp16")];
            tensor<int32, [2]> var_5277_split_sizes_0 = const()[name = string("op_5277_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_5277_axis_0 = const()[name = string("op_5277_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_5277_0, tensor<fp16, [1, 8, 128, 1]> var_5277_1 = split(axis = var_5277_axis_0, split_sizes = var_5277_split_sizes_0, x = query_states_81_cast_fp16)[name = string("op_5277")];
            bool attn_weights_209_transpose_x_0 = const()[name = string("attn_weights_209_transpose_x_0"), val = bool(false)];
            bool attn_weights_209_transpose_y_0 = const()[name = string("attn_weights_209_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_209_cast_fp16 = matmul(transpose_x = attn_weights_209_transpose_x_0, transpose_y = attn_weights_209_transpose_y_0, x = var_5264_cast_fp16_0, y = var_5277_0)[name = string("attn_weights_209_cast_fp16")];
            fp16 var_5280_to_fp16 = const()[name = string("op_5280_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_211_cast_fp16 = mul(x = attn_weights_209_cast_fp16, y = var_5280_to_fp16)[name = string("attn_weights_211_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_213_cast_fp16 = add(x = attn_weights_211_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_213_cast_fp16")];
            int32 var_5284 = const()[name = string("op_5284"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_215_cast_fp16 = softmax(axis = var_5284, x = attn_weights_213_cast_fp16)[name = string("attn_weights_215_cast_fp16")];
            bool var_5290_transpose_x_1 = const()[name = string("op_5290_transpose_x_1"), val = bool(true)];
            bool var_5290_transpose_y_1 = const()[name = string("op_5290_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_5290_cast_fp16 = matmul(transpose_x = var_5290_transpose_x_1, transpose_y = var_5290_transpose_y_1, x = attn_weights_215_cast_fp16, y = var_5274_cast_fp16_0)[name = string("op_5290_cast_fp16")];
            bool attn_weights_217_transpose_x_0 = const()[name = string("attn_weights_217_transpose_x_0"), val = bool(false)];
            bool attn_weights_217_transpose_y_0 = const()[name = string("attn_weights_217_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_217_cast_fp16 = matmul(transpose_x = attn_weights_217_transpose_x_0, transpose_y = attn_weights_217_transpose_y_0, x = var_5264_cast_fp16_1, y = var_5277_1)[name = string("attn_weights_217_cast_fp16")];
            fp16 var_5292_to_fp16 = const()[name = string("op_5292_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_219_cast_fp16 = mul(x = attn_weights_217_cast_fp16, y = var_5292_to_fp16)[name = string("attn_weights_219_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_221_cast_fp16 = add(x = attn_weights_219_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_221_cast_fp16")];
            int32 var_5296 = const()[name = string("op_5296"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_223_cast_fp16 = softmax(axis = var_5296, x = attn_weights_221_cast_fp16)[name = string("attn_weights_223_cast_fp16")];
            bool attn_output_105_transpose_x_1 = const()[name = string("attn_output_105_transpose_x_1"), val = bool(true)];
            bool attn_output_105_transpose_y_1 = const()[name = string("attn_output_105_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_105_cast_fp16 = matmul(transpose_x = attn_output_105_transpose_x_1, transpose_y = attn_output_105_transpose_y_1, x = attn_weights_223_cast_fp16, y = var_5274_cast_fp16_1)[name = string("attn_output_105_cast_fp16")];
            int32 var_5304 = const()[name = string("op_5304"), val = int32(1)];
            bool attn_output_107_interleave_0 = const()[name = string("attn_output_107_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_107_cast_fp16 = concat(axis = var_5304, interleave = attn_output_107_interleave_0, values = (var_5290_cast_fp16, attn_output_105_cast_fp16))[name = string("attn_output_107_cast_fp16")];
            tensor<int32, [4]> var_5308_perm_0 = const()[name = string("op_5308_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_167x = const()[name = string("concat_167x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_5308_cast_fp16 = transpose(perm = var_5308_perm_0, x = attn_output_107_cast_fp16)[name = string("transpose_42")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_111_cast_fp16 = reshape(shape = concat_167x, x = var_5308_cast_fp16)[name = string("attn_output_111_cast_fp16")];
            tensor<int32, [2]> hidden_states_133_strides_0 = const()[name = string("hidden_states_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_133_pad_type_0 = const()[name = string("hidden_states_133_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_133_pad_0 = const()[name = string("hidden_states_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_133_dilations_0 = const()[name = string("hidden_states_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_133_groups_0 = const()[name = string("hidden_states_133_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_133_cast_fp16 = conv(dilations = hidden_states_133_dilations_0, groups = hidden_states_133_groups_0, pad = hidden_states_133_pad_0, pad_type = hidden_states_133_pad_type_0, strides = hidden_states_133_strides_0, weight = layers_13_self_attn_o_proj_weight_cast_fp16, x = attn_output_111_cast_fp16)[name = string("hidden_states_133_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_135_cast_fp16 = add(x = hidden_states_129_cast_fp16, y = hidden_states_133_cast_fp16)[name = string("hidden_states_135_cast_fp16")];
            fp16 const_140_promoted_to_fp16 = const()[name = string("const_140_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_5341_cast_fp16 = mul(x = hidden_states_135_cast_fp16, y = const_140_promoted_to_fp16)[name = string("op_5341_cast_fp16")];
            int32 var_5339 = const()[name = string("op_5339"), val = int32(1)];
            bool doubled_109_interleave_0 = const()[name = string("doubled_109_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_109_cast_fp16 = concat(axis = var_5339, interleave = doubled_109_interleave_0, values = (hidden_states_135_cast_fp16, var_5341_cast_fp16))[name = string("doubled_109_cast_fp16")];
            tensor<int32, [1]> out_55_axes_0 = const()[name = string("out_55_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_55_gamma_0_to_fp16 = const()[name = string("out_55_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407019008)))];
            fp16 var_5351_to_fp16 = const()[name = string("op_5351_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_55_cast_fp16 = layer_norm(axes = out_55_axes_0, epsilon = var_5351_to_fp16, gamma = out_55_gamma_0_to_fp16, x = doubled_109_cast_fp16)[name = string("out_55_cast_fp16")];
            tensor<int32, [2]> var_5362_split_sizes_0 = const()[name = string("op_5362_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5362_axis_0 = const()[name = string("op_5362_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_5362_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_5362_cast_fp16_1 = split(axis = var_5362_axis_0, split_sizes = var_5362_split_sizes_0, x = out_55_cast_fp16)[name = string("op_5362_cast_fp16")];
            tensor<int32, [2]> input_27_strides_0 = const()[name = string("input_27_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_27_pad_type_0 = const()[name = string("input_27_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_27_pad_0 = const()[name = string("input_27_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_27_dilations_0 = const()[name = string("input_27_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_27_groups_0 = const()[name = string("input_27_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_27_cast_fp16 = conv(dilations = input_27_dilations_0, groups = input_27_groups_0, pad = input_27_pad_0, pad_type = input_27_pad_type_0, strides = input_27_strides_0, weight = layers_13_mlp_gate_proj_weight_cast_fp16, x = var_5362_cast_fp16_0)[name = string("input_27_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_5379_cast_fp16 = silu(x = input_27_cast_fp16)[name = string("op_5379_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_13_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_13_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1407027264)))];
            tensor<int32, [2]> var_5385_strides_0 = const()[name = string("op_5385_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_5385_pad_type_0 = const()[name = string("op_5385_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_5385_pad_0 = const()[name = string("op_5385_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_5385_dilations_0 = const()[name = string("op_5385_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5385_groups_0 = const()[name = string("op_5385_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_5385_cast_fp16 = conv(dilations = var_5385_dilations_0, groups = var_5385_groups_0, pad = var_5385_pad_0, pad_type = var_5385_pad_type_0, strides = var_5385_strides_0, weight = layers_13_mlp_up_proj_weight_to_fp16, x = var_5362_cast_fp16_0)[name = string("op_5385_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_139_cast_fp16 = mul(x = var_5379_cast_fp16, y = var_5385_cast_fp16)[name = string("x_139_cast_fp16")];
            tensor<int32, [2]> hidden_states_137_strides_0 = const()[name = string("hidden_states_137_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_137_pad_type_0 = const()[name = string("hidden_states_137_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_137_pad_0 = const()[name = string("hidden_states_137_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_137_dilations_0 = const()[name = string("hidden_states_137_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_137_groups_0 = const()[name = string("hidden_states_137_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_137_cast_fp16 = conv(dilations = hidden_states_137_dilations_0, groups = hidden_states_137_groups_0, pad = hidden_states_137_pad_0, pad_type = hidden_states_137_pad_type_0, strides = hidden_states_137_strides_0, weight = layers_13_mlp_down_proj_weight_cast_fp16, x = x_139_cast_fp16)[name = string("hidden_states_137_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_139_cast_fp16 = add(x = hidden_states_135_cast_fp16, y = hidden_states_137_cast_fp16)[name = string("hidden_states_139_cast_fp16")];
            fp16 const_142_promoted_to_fp16 = const()[name = string("const_142_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_5403_cast_fp16 = mul(x = hidden_states_139_cast_fp16, y = const_142_promoted_to_fp16)[name = string("op_5403_cast_fp16")];
            int32 var_5401 = const()[name = string("op_5401"), val = int32(1)];
            bool doubled_113_interleave_0 = const()[name = string("doubled_113_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_113_cast_fp16 = concat(axis = var_5401, interleave = doubled_113_interleave_0, values = (hidden_states_139_cast_fp16, var_5403_cast_fp16))[name = string("doubled_113_cast_fp16")];
            tensor<int32, [1]> out_57_axes_0 = const()[name = string("out_57_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_57_gamma_0_to_fp16 = const()[name = string("out_57_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1432193152)))];
            fp16 var_5413_to_fp16 = const()[name = string("op_5413_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_57_cast_fp16 = layer_norm(axes = out_57_axes_0, epsilon = var_5413_to_fp16, gamma = out_57_gamma_0_to_fp16, x = doubled_113_cast_fp16)[name = string("out_57_cast_fp16")];
            tensor<int32, [2]> var_5424_split_sizes_0 = const()[name = string("op_5424_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5424_axis_0 = const()[name = string("op_5424_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_5424_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_5424_cast_fp16_1 = split(axis = var_5424_axis_0, split_sizes = var_5424_split_sizes_0, x = out_57_cast_fp16)[name = string("op_5424_cast_fp16")];
            tensor<int32, [2]> query_states_85_strides_0 = const()[name = string("query_states_85_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_85_pad_type_0 = const()[name = string("query_states_85_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_85_pad_0 = const()[name = string("query_states_85_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_85_dilations_0 = const()[name = string("query_states_85_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_85_groups_0 = const()[name = string("query_states_85_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_85_cast_fp16 = conv(dilations = query_states_85_dilations_0, groups = query_states_85_groups_0, pad = query_states_85_pad_0, pad_type = query_states_85_pad_type_0, strides = query_states_85_strides_0, weight = layers_14_self_attn_q_proj_weight_cast_fp16, x = var_5424_cast_fp16_0)[name = string("query_states_85_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_14_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_14_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1432201408)))];
            tensor<int32, [2]> key_states_141_strides_0 = const()[name = string("key_states_141_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_141_pad_type_0 = const()[name = string("key_states_141_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_141_pad_0 = const()[name = string("key_states_141_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_141_dilations_0 = const()[name = string("key_states_141_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_141_groups_0 = const()[name = string("key_states_141_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_141_cast_fp16 = conv(dilations = key_states_141_dilations_0, groups = key_states_141_groups_0, pad = key_states_141_pad_0, pad_type = key_states_141_pad_type_0, strides = key_states_141_strides_0, weight = layers_14_self_attn_k_proj_weight_to_fp16, x = var_5424_cast_fp16_0)[name = string("key_states_141_cast_fp16")];
            tensor<int32, [2]> value_states_85_strides_0 = const()[name = string("value_states_85_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_85_pad_type_0 = const()[name = string("value_states_85_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_85_pad_0 = const()[name = string("value_states_85_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_85_dilations_0 = const()[name = string("value_states_85_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_85_groups_0 = const()[name = string("value_states_85_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_85_cast_fp16 = conv(dilations = value_states_85_dilations_0, groups = value_states_85_groups_0, pad = value_states_85_pad_0, pad_type = value_states_85_pad_type_0, strides = value_states_85_strides_0, weight = layers_14_self_attn_v_proj_weight_cast_fp16, x = var_5424_cast_fp16_0)[name = string("value_states_85_cast_fp16")];
            tensor<int32, [4]> concat_168x = const()[name = string("concat_168x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_141_cast_fp16 = reshape(shape = concat_168x, x = query_states_85_cast_fp16)[name = string("x_141_cast_fp16")];
            tensor<int32, [4]> concat_169x = const()[name = string("concat_169x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_5481_cast_fp16 = reshape(shape = concat_169x, x = key_states_141_cast_fp16)[name = string("op_5481_cast_fp16")];
            tensor<int32, [4]> concat_170x = const()[name = string("concat_170x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_5488_cast_fp16 = reshape(shape = concat_170x, x = value_states_85_cast_fp16)[name = string("op_5488_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_5492_cast_fp16 = mul(x = x_141_cast_fp16, y = var_859_cast_fp16)[name = string("op_5492_cast_fp16")];
            tensor<int32, [2]> var_5493_split_sizes_0 = const()[name = string("op_5493_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5493_axis_0 = const()[name = string("op_5493_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_5493_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_5493_cast_fp16_1 = split(axis = var_5493_axis_0, split_sizes = var_5493_split_sizes_0, x = x_141_cast_fp16)[name = string("op_5493_cast_fp16")];
            fp16 const_144_promoted_to_fp16 = const()[name = string("const_144_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_5495_cast_fp16 = mul(x = var_5493_cast_fp16_1, y = const_144_promoted_to_fp16)[name = string("op_5495_cast_fp16")];
            int32 var_5497 = const()[name = string("op_5497"), val = int32(-2)];
            bool var_5498_interleave_0 = const()[name = string("op_5498_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_5498_cast_fp16 = concat(axis = var_5497, interleave = var_5498_interleave_0, values = (var_5495_cast_fp16, var_5493_cast_fp16_0))[name = string("op_5498_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_5499_cast_fp16 = mul(x = var_5498_cast_fp16, y = var_866_cast_fp16)[name = string("op_5499_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_87_cast_fp16 = add(x = var_5492_cast_fp16, y = var_5499_cast_fp16)[name = string("query_states_87_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_5505_cast_fp16 = mul(x = var_5481_cast_fp16, y = var_859_cast_fp16)[name = string("op_5505_cast_fp16")];
            tensor<int32, [2]> var_5506_split_sizes_0 = const()[name = string("op_5506_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5506_axis_0 = const()[name = string("op_5506_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_5506_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_5506_cast_fp16_1 = split(axis = var_5506_axis_0, split_sizes = var_5506_split_sizes_0, x = var_5481_cast_fp16)[name = string("op_5506_cast_fp16")];
            fp16 const_145_promoted_to_fp16 = const()[name = string("const_145_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_5508_cast_fp16 = mul(x = var_5506_cast_fp16_1, y = const_145_promoted_to_fp16)[name = string("op_5508_cast_fp16")];
            int32 var_5510 = const()[name = string("op_5510"), val = int32(-2)];
            bool var_5511_interleave_0 = const()[name = string("op_5511_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_5511_cast_fp16 = concat(axis = var_5510, interleave = var_5511_interleave_0, values = (var_5508_cast_fp16, var_5506_cast_fp16_0))[name = string("op_5511_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_5512_cast_fp16 = mul(x = var_5511_cast_fp16, y = var_866_cast_fp16)[name = string("op_5512_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_145_cast_fp16 = add(x = var_5505_cast_fp16, y = var_5512_cast_fp16)[name = string("key_states_145_cast_fp16")];
            tensor<int32, [1]> expand_dims_168 = const()[name = string("expand_dims_168"), val = tensor<int32, [1]>([14])];
            tensor<int32, [1]> expand_dims_169 = const()[name = string("expand_dims_169"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_171 = const()[name = string("expand_dims_171"), val = tensor<int32, [1]>([0])];
            int32 concat_173_axis_0 = const()[name = string("concat_173_axis_0"), val = int32(0)];
            bool concat_173_interleave_0 = const()[name = string("concat_173_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_173 = concat(axis = concat_173_axis_0, interleave = concat_173_interleave_0, values = (expand_dims_168, expand_dims_169, position_id, expand_dims_171))[name = string("concat_173")];
            tensor<int32, [1]> expand_dims_172 = const()[name = string("expand_dims_172"), val = tensor<int32, [1]>([15])];
            tensor<int32, [1]> concat_174_values1_0 = const()[name = string("concat_174_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_174_values3_0 = const()[name = string("concat_174_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_174_axis_0 = const()[name = string("concat_174_axis_0"), val = int32(0)];
            bool concat_174_interleave_0 = const()[name = string("concat_174_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_174 = concat(axis = concat_174_axis_0, interleave = concat_174_interleave_0, values = (expand_dims_172, concat_174_values1_0, cache_position_end, concat_174_values3_0))[name = string("concat_174")];
            tensor<int32, [4]> key_states_147_perm_0 = const()[name = string("key_states_147_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_15_stride_0 = const()[name = string("key_cache_internal_tensor_assign_15_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_15_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_15_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_15_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_15_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_15_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_15_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_147_cast_fp16 = transpose(perm = key_states_147_perm_0, x = key_states_145_cast_fp16)[name = string("transpose_41")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_15_cast_fp16 = slice_update(begin = concat_173, begin_mask = key_cache_internal_tensor_assign_15_begin_mask_0, end = concat_174, end_mask = key_cache_internal_tensor_assign_15_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_15_squeeze_mask_0, stride = key_cache_internal_tensor_assign_15_stride_0, update = key_states_147_cast_fp16, x = coreml_update_state_26)[name = string("key_cache_internal_tensor_assign_15_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_15_cast_fp16, input = key_cache)[name = string("coreml_update_state_28_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_28 = read_state(input = key_cache)[name = string("coreml_update_state_28")];
            tensor<int32, [4]> value_states_87_perm_0 = const()[name = string("value_states_87_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_15_stride_0 = const()[name = string("value_cache_internal_tensor_assign_15_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_15_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_15_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_15_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_15_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_15_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_15_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_87_cast_fp16 = transpose(perm = value_states_87_perm_0, x = var_5488_cast_fp16)[name = string("transpose_40")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_15_cast_fp16 = slice_update(begin = concat_173, begin_mask = value_cache_internal_tensor_assign_15_begin_mask_0, end = concat_174, end_mask = value_cache_internal_tensor_assign_15_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_15_squeeze_mask_0, stride = value_cache_internal_tensor_assign_15_stride_0, update = value_states_87_cast_fp16, x = coreml_update_state_27)[name = string("value_cache_internal_tensor_assign_15_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_15_cast_fp16, input = value_cache)[name = string("coreml_update_state_29_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_29 = read_state(input = value_cache)[name = string("coreml_update_state_29")];
            tensor<int32, [4]> var_5582_begin_0 = const()[name = string("op_5582_begin_0"), val = tensor<int32, [4]>([14, 0, 0, 0])];
            tensor<int32, [4]> var_5582_end_0 = const()[name = string("op_5582_end_0"), val = tensor<int32, [4]>([15, 2, 2048, 128])];
            tensor<bool, [4]> var_5582_end_mask_0 = const()[name = string("op_5582_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5582_cast_fp16 = slice_by_index(begin = var_5582_begin_0, end = var_5582_end_0, end_mask = var_5582_end_mask_0, x = coreml_update_state_28)[name = string("op_5582_cast_fp16")];
            tensor<int32, [2]> tile_28 = const()[name = string("tile_28"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5585_axis_0 = const()[name = string("op_5585_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5585_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5585_cast_fp16_1 = split(axis = var_5585_axis_0, split_sizes = tile_28, x = var_5582_cast_fp16)[name = string("op_5585_cast_fp16")];
            tensor<int32, [4]> var_5592_begin_0 = const()[name = string("op_5592_begin_0"), val = tensor<int32, [4]>([14, 0, 0, 0])];
            tensor<int32, [4]> var_5592_end_0 = const()[name = string("op_5592_end_0"), val = tensor<int32, [4]>([15, 2, 2048, 128])];
            tensor<bool, [4]> var_5592_end_mask_0 = const()[name = string("op_5592_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5592_cast_fp16 = slice_by_index(begin = var_5592_begin_0, end = var_5592_end_0, end_mask = var_5592_end_mask_0, x = coreml_update_state_29)[name = string("op_5592_cast_fp16")];
            tensor<int32, [2]> tile_29 = const()[name = string("tile_29"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5595_axis_0 = const()[name = string("op_5595_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5595_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5595_cast_fp16_1 = split(axis = var_5595_axis_0, split_sizes = tile_29, x = var_5592_cast_fp16)[name = string("op_5595_cast_fp16")];
            tensor<int32, [2]> var_5598_split_sizes_0 = const()[name = string("op_5598_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_5598_axis_0 = const()[name = string("op_5598_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_5598_0, tensor<fp16, [1, 8, 128, 1]> var_5598_1 = split(axis = var_5598_axis_0, split_sizes = var_5598_split_sizes_0, x = query_states_87_cast_fp16)[name = string("op_5598")];
            bool attn_weights_225_transpose_x_0 = const()[name = string("attn_weights_225_transpose_x_0"), val = bool(false)];
            bool attn_weights_225_transpose_y_0 = const()[name = string("attn_weights_225_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_225_cast_fp16 = matmul(transpose_x = attn_weights_225_transpose_x_0, transpose_y = attn_weights_225_transpose_y_0, x = var_5585_cast_fp16_0, y = var_5598_0)[name = string("attn_weights_225_cast_fp16")];
            fp16 var_5601_to_fp16 = const()[name = string("op_5601_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_227_cast_fp16 = mul(x = attn_weights_225_cast_fp16, y = var_5601_to_fp16)[name = string("attn_weights_227_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_229_cast_fp16 = add(x = attn_weights_227_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_229_cast_fp16")];
            int32 var_5605 = const()[name = string("op_5605"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_231_cast_fp16 = softmax(axis = var_5605, x = attn_weights_229_cast_fp16)[name = string("attn_weights_231_cast_fp16")];
            bool var_5611_transpose_x_1 = const()[name = string("op_5611_transpose_x_1"), val = bool(true)];
            bool var_5611_transpose_y_1 = const()[name = string("op_5611_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_5611_cast_fp16 = matmul(transpose_x = var_5611_transpose_x_1, transpose_y = var_5611_transpose_y_1, x = attn_weights_231_cast_fp16, y = var_5595_cast_fp16_0)[name = string("op_5611_cast_fp16")];
            bool attn_weights_233_transpose_x_0 = const()[name = string("attn_weights_233_transpose_x_0"), val = bool(false)];
            bool attn_weights_233_transpose_y_0 = const()[name = string("attn_weights_233_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_233_cast_fp16 = matmul(transpose_x = attn_weights_233_transpose_x_0, transpose_y = attn_weights_233_transpose_y_0, x = var_5585_cast_fp16_1, y = var_5598_1)[name = string("attn_weights_233_cast_fp16")];
            fp16 var_5613_to_fp16 = const()[name = string("op_5613_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_235_cast_fp16 = mul(x = attn_weights_233_cast_fp16, y = var_5613_to_fp16)[name = string("attn_weights_235_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_237_cast_fp16 = add(x = attn_weights_235_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_237_cast_fp16")];
            int32 var_5617 = const()[name = string("op_5617"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_239_cast_fp16 = softmax(axis = var_5617, x = attn_weights_237_cast_fp16)[name = string("attn_weights_239_cast_fp16")];
            bool attn_output_113_transpose_x_1 = const()[name = string("attn_output_113_transpose_x_1"), val = bool(true)];
            bool attn_output_113_transpose_y_1 = const()[name = string("attn_output_113_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_113_cast_fp16 = matmul(transpose_x = attn_output_113_transpose_x_1, transpose_y = attn_output_113_transpose_y_1, x = attn_weights_239_cast_fp16, y = var_5595_cast_fp16_1)[name = string("attn_output_113_cast_fp16")];
            int32 var_5625 = const()[name = string("op_5625"), val = int32(1)];
            bool attn_output_115_interleave_0 = const()[name = string("attn_output_115_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_115_cast_fp16 = concat(axis = var_5625, interleave = attn_output_115_interleave_0, values = (var_5611_cast_fp16, attn_output_113_cast_fp16))[name = string("attn_output_115_cast_fp16")];
            tensor<int32, [4]> var_5629_perm_0 = const()[name = string("op_5629_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_179x = const()[name = string("concat_179x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_5629_cast_fp16 = transpose(perm = var_5629_perm_0, x = attn_output_115_cast_fp16)[name = string("transpose_39")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_119_cast_fp16 = reshape(shape = concat_179x, x = var_5629_cast_fp16)[name = string("attn_output_119_cast_fp16")];
            tensor<int32, [2]> hidden_states_143_strides_0 = const()[name = string("hidden_states_143_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_143_pad_type_0 = const()[name = string("hidden_states_143_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_143_pad_0 = const()[name = string("hidden_states_143_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_143_dilations_0 = const()[name = string("hidden_states_143_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_143_groups_0 = const()[name = string("hidden_states_143_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_143_cast_fp16 = conv(dilations = hidden_states_143_dilations_0, groups = hidden_states_143_groups_0, pad = hidden_states_143_pad_0, pad_type = hidden_states_143_pad_type_0, strides = hidden_states_143_strides_0, weight = layers_14_self_attn_o_proj_weight_cast_fp16, x = attn_output_119_cast_fp16)[name = string("hidden_states_143_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_145_cast_fp16 = add(x = hidden_states_139_cast_fp16, y = hidden_states_143_cast_fp16)[name = string("hidden_states_145_cast_fp16")];
            fp16 const_150_promoted_to_fp16 = const()[name = string("const_150_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_5662_cast_fp16 = mul(x = hidden_states_145_cast_fp16, y = const_150_promoted_to_fp16)[name = string("op_5662_cast_fp16")];
            int32 var_5660 = const()[name = string("op_5660"), val = int32(1)];
            bool doubled_117_interleave_0 = const()[name = string("doubled_117_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_117_cast_fp16 = concat(axis = var_5660, interleave = doubled_117_interleave_0, values = (hidden_states_145_cast_fp16, var_5662_cast_fp16))[name = string("doubled_117_cast_fp16")];
            tensor<int32, [1]> out_59_axes_0 = const()[name = string("out_59_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_59_gamma_0_to_fp16 = const()[name = string("out_59_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433250048)))];
            fp16 var_5672_to_fp16 = const()[name = string("op_5672_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_59_cast_fp16 = layer_norm(axes = out_59_axes_0, epsilon = var_5672_to_fp16, gamma = out_59_gamma_0_to_fp16, x = doubled_117_cast_fp16)[name = string("out_59_cast_fp16")];
            tensor<int32, [2]> var_5683_split_sizes_0 = const()[name = string("op_5683_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5683_axis_0 = const()[name = string("op_5683_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_5683_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_5683_cast_fp16_1 = split(axis = var_5683_axis_0, split_sizes = var_5683_split_sizes_0, x = out_59_cast_fp16)[name = string("op_5683_cast_fp16")];
            tensor<int32, [2]> input_29_strides_0 = const()[name = string("input_29_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_29_pad_type_0 = const()[name = string("input_29_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_29_pad_0 = const()[name = string("input_29_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_29_dilations_0 = const()[name = string("input_29_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_29_groups_0 = const()[name = string("input_29_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_29_cast_fp16 = conv(dilations = input_29_dilations_0, groups = input_29_groups_0, pad = input_29_pad_0, pad_type = input_29_pad_type_0, strides = input_29_strides_0, weight = layers_14_mlp_gate_proj_weight_cast_fp16, x = var_5683_cast_fp16_0)[name = string("input_29_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_5700_cast_fp16 = silu(x = input_29_cast_fp16)[name = string("op_5700_cast_fp16")];
            tensor<int32, [2]> var_5706_strides_0 = const()[name = string("op_5706_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_5706_pad_type_0 = const()[name = string("op_5706_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_5706_pad_0 = const()[name = string("op_5706_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_5706_dilations_0 = const()[name = string("op_5706_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5706_groups_0 = const()[name = string("op_5706_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_5706_cast_fp16 = conv(dilations = var_5706_dilations_0, groups = var_5706_groups_0, pad = var_5706_pad_0, pad_type = var_5706_pad_type_0, strides = var_5706_strides_0, weight = layers_14_mlp_up_proj_weight_cast_fp16, x = var_5683_cast_fp16_0)[name = string("op_5706_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_149_cast_fp16 = mul(x = var_5700_cast_fp16, y = var_5706_cast_fp16)[name = string("x_149_cast_fp16")];
            tensor<int32, [2]> hidden_states_147_strides_0 = const()[name = string("hidden_states_147_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_147_pad_type_0 = const()[name = string("hidden_states_147_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_147_pad_0 = const()[name = string("hidden_states_147_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_147_dilations_0 = const()[name = string("hidden_states_147_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_147_groups_0 = const()[name = string("hidden_states_147_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_147_cast_fp16 = conv(dilations = hidden_states_147_dilations_0, groups = hidden_states_147_groups_0, pad = hidden_states_147_pad_0, pad_type = hidden_states_147_pad_type_0, strides = hidden_states_147_strides_0, weight = layers_14_mlp_down_proj_weight_cast_fp16, x = x_149_cast_fp16)[name = string("hidden_states_147_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_149_cast_fp16 = add(x = hidden_states_145_cast_fp16, y = hidden_states_147_cast_fp16)[name = string("hidden_states_149_cast_fp16")];
            fp16 const_152_promoted_to_fp16 = const()[name = string("const_152_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_5724_cast_fp16 = mul(x = hidden_states_149_cast_fp16, y = const_152_promoted_to_fp16)[name = string("op_5724_cast_fp16")];
            int32 var_5722 = const()[name = string("op_5722"), val = int32(1)];
            bool doubled_121_interleave_0 = const()[name = string("doubled_121_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_121_cast_fp16 = concat(axis = var_5722, interleave = doubled_121_interleave_0, values = (hidden_states_149_cast_fp16, var_5724_cast_fp16))[name = string("doubled_121_cast_fp16")];
            tensor<int32, [1]> out_61_axes_0 = const()[name = string("out_61_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_61_gamma_0_to_fp16 = const()[name = string("out_61_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433258304)))];
            fp16 var_5734_to_fp16 = const()[name = string("op_5734_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_61_cast_fp16 = layer_norm(axes = out_61_axes_0, epsilon = var_5734_to_fp16, gamma = out_61_gamma_0_to_fp16, x = doubled_121_cast_fp16)[name = string("out_61_cast_fp16")];
            tensor<int32, [2]> var_5745_split_sizes_0 = const()[name = string("op_5745_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_5745_axis_0 = const()[name = string("op_5745_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_5745_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_5745_cast_fp16_1 = split(axis = var_5745_axis_0, split_sizes = var_5745_split_sizes_0, x = out_61_cast_fp16)[name = string("op_5745_cast_fp16")];
            tensor<int32, [2]> query_states_91_strides_0 = const()[name = string("query_states_91_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_91_pad_type_0 = const()[name = string("query_states_91_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_91_pad_0 = const()[name = string("query_states_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_91_dilations_0 = const()[name = string("query_states_91_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_91_groups_0 = const()[name = string("query_states_91_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_91_cast_fp16 = conv(dilations = query_states_91_dilations_0, groups = query_states_91_groups_0, pad = query_states_91_pad_0, pad_type = query_states_91_pad_type_0, strides = query_states_91_strides_0, weight = layers_15_self_attn_q_proj_weight_cast_fp16, x = var_5745_cast_fp16_0)[name = string("query_states_91_cast_fp16")];
            tensor<int32, [2]> key_states_151_strides_0 = const()[name = string("key_states_151_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_151_pad_type_0 = const()[name = string("key_states_151_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_151_pad_0 = const()[name = string("key_states_151_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_151_dilations_0 = const()[name = string("key_states_151_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_151_groups_0 = const()[name = string("key_states_151_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_151_cast_fp16 = conv(dilations = key_states_151_dilations_0, groups = key_states_151_groups_0, pad = key_states_151_pad_0, pad_type = key_states_151_pad_type_0, strides = key_states_151_strides_0, weight = layers_15_self_attn_k_proj_weight_cast_fp16, x = var_5745_cast_fp16_0)[name = string("key_states_151_cast_fp16")];
            tensor<int32, [2]> value_states_91_strides_0 = const()[name = string("value_states_91_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_91_pad_type_0 = const()[name = string("value_states_91_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_91_pad_0 = const()[name = string("value_states_91_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_91_dilations_0 = const()[name = string("value_states_91_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_91_groups_0 = const()[name = string("value_states_91_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_91_cast_fp16 = conv(dilations = value_states_91_dilations_0, groups = value_states_91_groups_0, pad = value_states_91_pad_0, pad_type = value_states_91_pad_type_0, strides = value_states_91_strides_0, weight = layers_15_self_attn_v_proj_weight_cast_fp16, x = var_5745_cast_fp16_0)[name = string("value_states_91_cast_fp16")];
            tensor<int32, [4]> concat_180x = const()[name = string("concat_180x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_151_cast_fp16 = reshape(shape = concat_180x, x = query_states_91_cast_fp16)[name = string("x_151_cast_fp16")];
            tensor<int32, [4]> concat_181x = const()[name = string("concat_181x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_5802_cast_fp16 = reshape(shape = concat_181x, x = key_states_151_cast_fp16)[name = string("op_5802_cast_fp16")];
            tensor<int32, [4]> concat_182x = const()[name = string("concat_182x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_5809_cast_fp16 = reshape(shape = concat_182x, x = value_states_91_cast_fp16)[name = string("op_5809_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_5813_cast_fp16 = mul(x = x_151_cast_fp16, y = var_859_cast_fp16)[name = string("op_5813_cast_fp16")];
            tensor<int32, [2]> var_5814_split_sizes_0 = const()[name = string("op_5814_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5814_axis_0 = const()[name = string("op_5814_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_5814_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_5814_cast_fp16_1 = split(axis = var_5814_axis_0, split_sizes = var_5814_split_sizes_0, x = x_151_cast_fp16)[name = string("op_5814_cast_fp16")];
            fp16 const_154_promoted_to_fp16 = const()[name = string("const_154_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_5816_cast_fp16 = mul(x = var_5814_cast_fp16_1, y = const_154_promoted_to_fp16)[name = string("op_5816_cast_fp16")];
            int32 var_5818 = const()[name = string("op_5818"), val = int32(-2)];
            bool var_5819_interleave_0 = const()[name = string("op_5819_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_5819_cast_fp16 = concat(axis = var_5818, interleave = var_5819_interleave_0, values = (var_5816_cast_fp16, var_5814_cast_fp16_0))[name = string("op_5819_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_5820_cast_fp16 = mul(x = var_5819_cast_fp16, y = var_866_cast_fp16)[name = string("op_5820_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_93_cast_fp16 = add(x = var_5813_cast_fp16, y = var_5820_cast_fp16)[name = string("query_states_93_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_5826_cast_fp16 = mul(x = var_5802_cast_fp16, y = var_859_cast_fp16)[name = string("op_5826_cast_fp16")];
            tensor<int32, [2]> var_5827_split_sizes_0 = const()[name = string("op_5827_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_5827_axis_0 = const()[name = string("op_5827_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_5827_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_5827_cast_fp16_1 = split(axis = var_5827_axis_0, split_sizes = var_5827_split_sizes_0, x = var_5802_cast_fp16)[name = string("op_5827_cast_fp16")];
            fp16 const_155_promoted_to_fp16 = const()[name = string("const_155_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_5829_cast_fp16 = mul(x = var_5827_cast_fp16_1, y = const_155_promoted_to_fp16)[name = string("op_5829_cast_fp16")];
            int32 var_5831 = const()[name = string("op_5831"), val = int32(-2)];
            bool var_5832_interleave_0 = const()[name = string("op_5832_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_5832_cast_fp16 = concat(axis = var_5831, interleave = var_5832_interleave_0, values = (var_5829_cast_fp16, var_5827_cast_fp16_0))[name = string("op_5832_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_5833_cast_fp16 = mul(x = var_5832_cast_fp16, y = var_866_cast_fp16)[name = string("op_5833_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_155_cast_fp16 = add(x = var_5826_cast_fp16, y = var_5833_cast_fp16)[name = string("key_states_155_cast_fp16")];
            tensor<int32, [1]> expand_dims_180 = const()[name = string("expand_dims_180"), val = tensor<int32, [1]>([15])];
            tensor<int32, [1]> expand_dims_181 = const()[name = string("expand_dims_181"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_183 = const()[name = string("expand_dims_183"), val = tensor<int32, [1]>([0])];
            int32 concat_185_axis_0 = const()[name = string("concat_185_axis_0"), val = int32(0)];
            bool concat_185_interleave_0 = const()[name = string("concat_185_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_185 = concat(axis = concat_185_axis_0, interleave = concat_185_interleave_0, values = (expand_dims_180, expand_dims_181, position_id, expand_dims_183))[name = string("concat_185")];
            tensor<int32, [1]> expand_dims_184 = const()[name = string("expand_dims_184"), val = tensor<int32, [1]>([16])];
            tensor<int32, [1]> concat_186_values1_0 = const()[name = string("concat_186_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_186_values3_0 = const()[name = string("concat_186_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_186_axis_0 = const()[name = string("concat_186_axis_0"), val = int32(0)];
            bool concat_186_interleave_0 = const()[name = string("concat_186_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_186 = concat(axis = concat_186_axis_0, interleave = concat_186_interleave_0, values = (expand_dims_184, concat_186_values1_0, cache_position_end, concat_186_values3_0))[name = string("concat_186")];
            tensor<int32, [4]> key_states_157_perm_0 = const()[name = string("key_states_157_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_16_stride_0 = const()[name = string("key_cache_internal_tensor_assign_16_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_16_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_16_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_16_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_16_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_16_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_16_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_157_cast_fp16 = transpose(perm = key_states_157_perm_0, x = key_states_155_cast_fp16)[name = string("transpose_38")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_16_cast_fp16 = slice_update(begin = concat_185, begin_mask = key_cache_internal_tensor_assign_16_begin_mask_0, end = concat_186, end_mask = key_cache_internal_tensor_assign_16_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_16_squeeze_mask_0, stride = key_cache_internal_tensor_assign_16_stride_0, update = key_states_157_cast_fp16, x = coreml_update_state_28)[name = string("key_cache_internal_tensor_assign_16_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_16_cast_fp16, input = key_cache)[name = string("coreml_update_state_30_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_30 = read_state(input = key_cache)[name = string("coreml_update_state_30")];
            tensor<int32, [4]> value_states_93_perm_0 = const()[name = string("value_states_93_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_16_stride_0 = const()[name = string("value_cache_internal_tensor_assign_16_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_16_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_16_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_16_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_16_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_16_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_16_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_93_cast_fp16 = transpose(perm = value_states_93_perm_0, x = var_5809_cast_fp16)[name = string("transpose_37")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_16_cast_fp16 = slice_update(begin = concat_185, begin_mask = value_cache_internal_tensor_assign_16_begin_mask_0, end = concat_186, end_mask = value_cache_internal_tensor_assign_16_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_16_squeeze_mask_0, stride = value_cache_internal_tensor_assign_16_stride_0, update = value_states_93_cast_fp16, x = coreml_update_state_29)[name = string("value_cache_internal_tensor_assign_16_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_16_cast_fp16, input = value_cache)[name = string("coreml_update_state_31_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_31 = read_state(input = value_cache)[name = string("coreml_update_state_31")];
            tensor<int32, [4]> var_5903_begin_0 = const()[name = string("op_5903_begin_0"), val = tensor<int32, [4]>([15, 0, 0, 0])];
            tensor<int32, [4]> var_5903_end_0 = const()[name = string("op_5903_end_0"), val = tensor<int32, [4]>([16, 2, 2048, 128])];
            tensor<bool, [4]> var_5903_end_mask_0 = const()[name = string("op_5903_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5903_cast_fp16 = slice_by_index(begin = var_5903_begin_0, end = var_5903_end_0, end_mask = var_5903_end_mask_0, x = coreml_update_state_30)[name = string("op_5903_cast_fp16")];
            tensor<int32, [2]> tile_30 = const()[name = string("tile_30"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5906_axis_0 = const()[name = string("op_5906_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5906_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5906_cast_fp16_1 = split(axis = var_5906_axis_0, split_sizes = tile_30, x = var_5903_cast_fp16)[name = string("op_5906_cast_fp16")];
            tensor<int32, [4]> var_5913_begin_0 = const()[name = string("op_5913_begin_0"), val = tensor<int32, [4]>([15, 0, 0, 0])];
            tensor<int32, [4]> var_5913_end_0 = const()[name = string("op_5913_end_0"), val = tensor<int32, [4]>([16, 2, 2048, 128])];
            tensor<bool, [4]> var_5913_end_mask_0 = const()[name = string("op_5913_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_5913_cast_fp16 = slice_by_index(begin = var_5913_begin_0, end = var_5913_end_0, end_mask = var_5913_end_mask_0, x = coreml_update_state_31)[name = string("op_5913_cast_fp16")];
            tensor<int32, [2]> tile_31 = const()[name = string("tile_31"), val = tensor<int32, [2]>([1, 1])];
            int32 var_5916_axis_0 = const()[name = string("op_5916_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_5916_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_5916_cast_fp16_1 = split(axis = var_5916_axis_0, split_sizes = tile_31, x = var_5913_cast_fp16)[name = string("op_5916_cast_fp16")];
            tensor<int32, [2]> var_5919_split_sizes_0 = const()[name = string("op_5919_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_5919_axis_0 = const()[name = string("op_5919_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_5919_0, tensor<fp16, [1, 8, 128, 1]> var_5919_1 = split(axis = var_5919_axis_0, split_sizes = var_5919_split_sizes_0, x = query_states_93_cast_fp16)[name = string("op_5919")];
            bool attn_weights_241_transpose_x_0 = const()[name = string("attn_weights_241_transpose_x_0"), val = bool(false)];
            bool attn_weights_241_transpose_y_0 = const()[name = string("attn_weights_241_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_241_cast_fp16 = matmul(transpose_x = attn_weights_241_transpose_x_0, transpose_y = attn_weights_241_transpose_y_0, x = var_5906_cast_fp16_0, y = var_5919_0)[name = string("attn_weights_241_cast_fp16")];
            fp16 var_5922_to_fp16 = const()[name = string("op_5922_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_243_cast_fp16 = mul(x = attn_weights_241_cast_fp16, y = var_5922_to_fp16)[name = string("attn_weights_243_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_245_cast_fp16 = add(x = attn_weights_243_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_245_cast_fp16")];
            int32 var_5926 = const()[name = string("op_5926"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_247_cast_fp16 = softmax(axis = var_5926, x = attn_weights_245_cast_fp16)[name = string("attn_weights_247_cast_fp16")];
            bool var_5932_transpose_x_1 = const()[name = string("op_5932_transpose_x_1"), val = bool(true)];
            bool var_5932_transpose_y_1 = const()[name = string("op_5932_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_5932_cast_fp16 = matmul(transpose_x = var_5932_transpose_x_1, transpose_y = var_5932_transpose_y_1, x = attn_weights_247_cast_fp16, y = var_5916_cast_fp16_0)[name = string("op_5932_cast_fp16")];
            bool attn_weights_249_transpose_x_0 = const()[name = string("attn_weights_249_transpose_x_0"), val = bool(false)];
            bool attn_weights_249_transpose_y_0 = const()[name = string("attn_weights_249_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_249_cast_fp16 = matmul(transpose_x = attn_weights_249_transpose_x_0, transpose_y = attn_weights_249_transpose_y_0, x = var_5906_cast_fp16_1, y = var_5919_1)[name = string("attn_weights_249_cast_fp16")];
            fp16 var_5934_to_fp16 = const()[name = string("op_5934_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_251_cast_fp16 = mul(x = attn_weights_249_cast_fp16, y = var_5934_to_fp16)[name = string("attn_weights_251_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_253_cast_fp16 = add(x = attn_weights_251_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_253_cast_fp16")];
            int32 var_5938 = const()[name = string("op_5938"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_255_cast_fp16 = softmax(axis = var_5938, x = attn_weights_253_cast_fp16)[name = string("attn_weights_255_cast_fp16")];
            bool attn_output_121_transpose_x_1 = const()[name = string("attn_output_121_transpose_x_1"), val = bool(true)];
            bool attn_output_121_transpose_y_1 = const()[name = string("attn_output_121_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_121_cast_fp16 = matmul(transpose_x = attn_output_121_transpose_x_1, transpose_y = attn_output_121_transpose_y_1, x = attn_weights_255_cast_fp16, y = var_5916_cast_fp16_1)[name = string("attn_output_121_cast_fp16")];
            int32 var_5946 = const()[name = string("op_5946"), val = int32(1)];
            bool attn_output_123_interleave_0 = const()[name = string("attn_output_123_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_123_cast_fp16 = concat(axis = var_5946, interleave = attn_output_123_interleave_0, values = (var_5932_cast_fp16, attn_output_121_cast_fp16))[name = string("attn_output_123_cast_fp16")];
            tensor<int32, [4]> var_5950_perm_0 = const()[name = string("op_5950_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_191x = const()[name = string("concat_191x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_5950_cast_fp16 = transpose(perm = var_5950_perm_0, x = attn_output_123_cast_fp16)[name = string("transpose_36")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_127_cast_fp16 = reshape(shape = concat_191x, x = var_5950_cast_fp16)[name = string("attn_output_127_cast_fp16")];
            tensor<int32, [2]> hidden_states_153_strides_0 = const()[name = string("hidden_states_153_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_153_pad_type_0 = const()[name = string("hidden_states_153_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_153_pad_0 = const()[name = string("hidden_states_153_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_153_dilations_0 = const()[name = string("hidden_states_153_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_153_groups_0 = const()[name = string("hidden_states_153_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_153_cast_fp16 = conv(dilations = hidden_states_153_dilations_0, groups = hidden_states_153_groups_0, pad = hidden_states_153_pad_0, pad_type = hidden_states_153_pad_type_0, strides = hidden_states_153_strides_0, weight = layers_15_self_attn_o_proj_weight_cast_fp16, x = attn_output_127_cast_fp16)[name = string("hidden_states_153_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_155_cast_fp16 = add(x = hidden_states_149_cast_fp16, y = hidden_states_153_cast_fp16)[name = string("hidden_states_155_cast_fp16")];
            fp16 const_160_promoted_to_fp16 = const()[name = string("const_160_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_5983_cast_fp16 = mul(x = hidden_states_155_cast_fp16, y = const_160_promoted_to_fp16)[name = string("op_5983_cast_fp16")];
            int32 var_5981 = const()[name = string("op_5981"), val = int32(1)];
            bool doubled_125_interleave_0 = const()[name = string("doubled_125_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_125_cast_fp16 = concat(axis = var_5981, interleave = doubled_125_interleave_0, values = (hidden_states_155_cast_fp16, var_5983_cast_fp16))[name = string("doubled_125_cast_fp16")];
            tensor<int32, [1]> out_63_axes_0 = const()[name = string("out_63_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_63_gamma_0_to_fp16 = const()[name = string("out_63_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433266560)))];
            fp16 var_5993_to_fp16 = const()[name = string("op_5993_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_63_cast_fp16 = layer_norm(axes = out_63_axes_0, epsilon = var_5993_to_fp16, gamma = out_63_gamma_0_to_fp16, x = doubled_125_cast_fp16)[name = string("out_63_cast_fp16")];
            tensor<int32, [2]> var_6004_split_sizes_0 = const()[name = string("op_6004_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6004_axis_0 = const()[name = string("op_6004_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_6004_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_6004_cast_fp16_1 = split(axis = var_6004_axis_0, split_sizes = var_6004_split_sizes_0, x = out_63_cast_fp16)[name = string("op_6004_cast_fp16")];
            tensor<int32, [2]> input_31_strides_0 = const()[name = string("input_31_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_31_pad_type_0 = const()[name = string("input_31_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_31_pad_0 = const()[name = string("input_31_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_31_dilations_0 = const()[name = string("input_31_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_31_groups_0 = const()[name = string("input_31_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_31_cast_fp16 = conv(dilations = input_31_dilations_0, groups = input_31_groups_0, pad = input_31_pad_0, pad_type = input_31_pad_type_0, strides = input_31_strides_0, weight = layers_15_mlp_gate_proj_weight_cast_fp16, x = var_6004_cast_fp16_0)[name = string("input_31_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_6021_cast_fp16 = silu(x = input_31_cast_fp16)[name = string("op_6021_cast_fp16")];
            tensor<int32, [2]> var_6027_strides_0 = const()[name = string("op_6027_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6027_pad_type_0 = const()[name = string("op_6027_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6027_pad_0 = const()[name = string("op_6027_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6027_dilations_0 = const()[name = string("op_6027_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6027_groups_0 = const()[name = string("op_6027_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_6027_cast_fp16 = conv(dilations = var_6027_dilations_0, groups = var_6027_groups_0, pad = var_6027_pad_0, pad_type = var_6027_pad_type_0, strides = var_6027_strides_0, weight = layers_15_mlp_up_proj_weight_cast_fp16, x = var_6004_cast_fp16_0)[name = string("op_6027_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_159_cast_fp16 = mul(x = var_6021_cast_fp16, y = var_6027_cast_fp16)[name = string("x_159_cast_fp16")];
            tensor<int32, [2]> hidden_states_157_strides_0 = const()[name = string("hidden_states_157_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_157_pad_type_0 = const()[name = string("hidden_states_157_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_157_pad_0 = const()[name = string("hidden_states_157_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_157_dilations_0 = const()[name = string("hidden_states_157_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_157_groups_0 = const()[name = string("hidden_states_157_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_157_cast_fp16 = conv(dilations = hidden_states_157_dilations_0, groups = hidden_states_157_groups_0, pad = hidden_states_157_pad_0, pad_type = hidden_states_157_pad_type_0, strides = hidden_states_157_strides_0, weight = layers_15_mlp_down_proj_weight_cast_fp16, x = x_159_cast_fp16)[name = string("hidden_states_157_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_159_cast_fp16 = add(x = hidden_states_155_cast_fp16, y = hidden_states_157_cast_fp16)[name = string("hidden_states_159_cast_fp16")];
            fp16 const_162_promoted_to_fp16 = const()[name = string("const_162_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_6045_cast_fp16 = mul(x = hidden_states_159_cast_fp16, y = const_162_promoted_to_fp16)[name = string("op_6045_cast_fp16")];
            int32 var_6043 = const()[name = string("op_6043"), val = int32(1)];
            bool doubled_129_interleave_0 = const()[name = string("doubled_129_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_129_cast_fp16 = concat(axis = var_6043, interleave = doubled_129_interleave_0, values = (hidden_states_159_cast_fp16, var_6045_cast_fp16))[name = string("doubled_129_cast_fp16")];
            tensor<int32, [1]> out_65_axes_0 = const()[name = string("out_65_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_65_gamma_0_to_fp16 = const()[name = string("out_65_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433274816)))];
            fp16 var_6055_to_fp16 = const()[name = string("op_6055_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_65_cast_fp16 = layer_norm(axes = out_65_axes_0, epsilon = var_6055_to_fp16, gamma = out_65_gamma_0_to_fp16, x = doubled_129_cast_fp16)[name = string("out_65_cast_fp16")];
            tensor<int32, [2]> var_6066_split_sizes_0 = const()[name = string("op_6066_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6066_axis_0 = const()[name = string("op_6066_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_6066_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_6066_cast_fp16_1 = split(axis = var_6066_axis_0, split_sizes = var_6066_split_sizes_0, x = out_65_cast_fp16)[name = string("op_6066_cast_fp16")];
            tensor<int32, [2]> query_states_97_strides_0 = const()[name = string("query_states_97_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_97_pad_type_0 = const()[name = string("query_states_97_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_97_pad_0 = const()[name = string("query_states_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_97_dilations_0 = const()[name = string("query_states_97_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_97_groups_0 = const()[name = string("query_states_97_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_97_cast_fp16 = conv(dilations = query_states_97_dilations_0, groups = query_states_97_groups_0, pad = query_states_97_pad_0, pad_type = query_states_97_pad_type_0, strides = query_states_97_strides_0, weight = layers_16_self_attn_q_proj_weight_cast_fp16, x = var_6066_cast_fp16_0)[name = string("query_states_97_cast_fp16")];
            tensor<int32, [2]> key_states_161_strides_0 = const()[name = string("key_states_161_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_161_pad_type_0 = const()[name = string("key_states_161_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_161_pad_0 = const()[name = string("key_states_161_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_161_dilations_0 = const()[name = string("key_states_161_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_161_groups_0 = const()[name = string("key_states_161_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_161_cast_fp16 = conv(dilations = key_states_161_dilations_0, groups = key_states_161_groups_0, pad = key_states_161_pad_0, pad_type = key_states_161_pad_type_0, strides = key_states_161_strides_0, weight = layers_16_self_attn_k_proj_weight_cast_fp16, x = var_6066_cast_fp16_0)[name = string("key_states_161_cast_fp16")];
            tensor<int32, [2]> value_states_97_strides_0 = const()[name = string("value_states_97_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_97_pad_type_0 = const()[name = string("value_states_97_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_97_pad_0 = const()[name = string("value_states_97_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_97_dilations_0 = const()[name = string("value_states_97_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_97_groups_0 = const()[name = string("value_states_97_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_97_cast_fp16 = conv(dilations = value_states_97_dilations_0, groups = value_states_97_groups_0, pad = value_states_97_pad_0, pad_type = value_states_97_pad_type_0, strides = value_states_97_strides_0, weight = layers_16_self_attn_v_proj_weight_cast_fp16, x = var_6066_cast_fp16_0)[name = string("value_states_97_cast_fp16")];
            tensor<int32, [4]> concat_192x = const()[name = string("concat_192x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_161_cast_fp16 = reshape(shape = concat_192x, x = query_states_97_cast_fp16)[name = string("x_161_cast_fp16")];
            tensor<int32, [4]> concat_193x = const()[name = string("concat_193x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_6123_cast_fp16 = reshape(shape = concat_193x, x = key_states_161_cast_fp16)[name = string("op_6123_cast_fp16")];
            tensor<int32, [4]> concat_194x = const()[name = string("concat_194x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_6130_cast_fp16 = reshape(shape = concat_194x, x = value_states_97_cast_fp16)[name = string("op_6130_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_6134_cast_fp16 = mul(x = x_161_cast_fp16, y = var_859_cast_fp16)[name = string("op_6134_cast_fp16")];
            tensor<int32, [2]> var_6135_split_sizes_0 = const()[name = string("op_6135_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6135_axis_0 = const()[name = string("op_6135_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_6135_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_6135_cast_fp16_1 = split(axis = var_6135_axis_0, split_sizes = var_6135_split_sizes_0, x = x_161_cast_fp16)[name = string("op_6135_cast_fp16")];
            fp16 const_164_promoted_to_fp16 = const()[name = string("const_164_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_6137_cast_fp16 = mul(x = var_6135_cast_fp16_1, y = const_164_promoted_to_fp16)[name = string("op_6137_cast_fp16")];
            int32 var_6139 = const()[name = string("op_6139"), val = int32(-2)];
            bool var_6140_interleave_0 = const()[name = string("op_6140_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_6140_cast_fp16 = concat(axis = var_6139, interleave = var_6140_interleave_0, values = (var_6137_cast_fp16, var_6135_cast_fp16_0))[name = string("op_6140_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_6141_cast_fp16 = mul(x = var_6140_cast_fp16, y = var_866_cast_fp16)[name = string("op_6141_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_99_cast_fp16 = add(x = var_6134_cast_fp16, y = var_6141_cast_fp16)[name = string("query_states_99_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_6147_cast_fp16 = mul(x = var_6123_cast_fp16, y = var_859_cast_fp16)[name = string("op_6147_cast_fp16")];
            tensor<int32, [2]> var_6148_split_sizes_0 = const()[name = string("op_6148_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6148_axis_0 = const()[name = string("op_6148_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_6148_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_6148_cast_fp16_1 = split(axis = var_6148_axis_0, split_sizes = var_6148_split_sizes_0, x = var_6123_cast_fp16)[name = string("op_6148_cast_fp16")];
            fp16 const_165_promoted_to_fp16 = const()[name = string("const_165_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_6150_cast_fp16 = mul(x = var_6148_cast_fp16_1, y = const_165_promoted_to_fp16)[name = string("op_6150_cast_fp16")];
            int32 var_6152 = const()[name = string("op_6152"), val = int32(-2)];
            bool var_6153_interleave_0 = const()[name = string("op_6153_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_6153_cast_fp16 = concat(axis = var_6152, interleave = var_6153_interleave_0, values = (var_6150_cast_fp16, var_6148_cast_fp16_0))[name = string("op_6153_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_6154_cast_fp16 = mul(x = var_6153_cast_fp16, y = var_866_cast_fp16)[name = string("op_6154_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_165_cast_fp16 = add(x = var_6147_cast_fp16, y = var_6154_cast_fp16)[name = string("key_states_165_cast_fp16")];
            tensor<int32, [1]> expand_dims_192 = const()[name = string("expand_dims_192"), val = tensor<int32, [1]>([16])];
            tensor<int32, [1]> expand_dims_193 = const()[name = string("expand_dims_193"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_195 = const()[name = string("expand_dims_195"), val = tensor<int32, [1]>([0])];
            int32 concat_197_axis_0 = const()[name = string("concat_197_axis_0"), val = int32(0)];
            bool concat_197_interleave_0 = const()[name = string("concat_197_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_197 = concat(axis = concat_197_axis_0, interleave = concat_197_interleave_0, values = (expand_dims_192, expand_dims_193, position_id, expand_dims_195))[name = string("concat_197")];
            tensor<int32, [1]> expand_dims_196 = const()[name = string("expand_dims_196"), val = tensor<int32, [1]>([17])];
            tensor<int32, [1]> concat_198_values1_0 = const()[name = string("concat_198_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_198_values3_0 = const()[name = string("concat_198_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_198_axis_0 = const()[name = string("concat_198_axis_0"), val = int32(0)];
            bool concat_198_interleave_0 = const()[name = string("concat_198_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_198 = concat(axis = concat_198_axis_0, interleave = concat_198_interleave_0, values = (expand_dims_196, concat_198_values1_0, cache_position_end, concat_198_values3_0))[name = string("concat_198")];
            tensor<int32, [4]> key_states_167_perm_0 = const()[name = string("key_states_167_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_17_stride_0 = const()[name = string("key_cache_internal_tensor_assign_17_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_17_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_17_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_17_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_17_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_17_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_17_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_167_cast_fp16 = transpose(perm = key_states_167_perm_0, x = key_states_165_cast_fp16)[name = string("transpose_35")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_17_cast_fp16 = slice_update(begin = concat_197, begin_mask = key_cache_internal_tensor_assign_17_begin_mask_0, end = concat_198, end_mask = key_cache_internal_tensor_assign_17_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_17_squeeze_mask_0, stride = key_cache_internal_tensor_assign_17_stride_0, update = key_states_167_cast_fp16, x = coreml_update_state_30)[name = string("key_cache_internal_tensor_assign_17_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_17_cast_fp16, input = key_cache)[name = string("coreml_update_state_32_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_32 = read_state(input = key_cache)[name = string("coreml_update_state_32")];
            tensor<int32, [4]> value_states_99_perm_0 = const()[name = string("value_states_99_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_17_stride_0 = const()[name = string("value_cache_internal_tensor_assign_17_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_17_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_17_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_17_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_17_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_17_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_17_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_99_cast_fp16 = transpose(perm = value_states_99_perm_0, x = var_6130_cast_fp16)[name = string("transpose_34")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_17_cast_fp16 = slice_update(begin = concat_197, begin_mask = value_cache_internal_tensor_assign_17_begin_mask_0, end = concat_198, end_mask = value_cache_internal_tensor_assign_17_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_17_squeeze_mask_0, stride = value_cache_internal_tensor_assign_17_stride_0, update = value_states_99_cast_fp16, x = coreml_update_state_31)[name = string("value_cache_internal_tensor_assign_17_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_17_cast_fp16, input = value_cache)[name = string("coreml_update_state_33_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_33 = read_state(input = value_cache)[name = string("coreml_update_state_33")];
            tensor<int32, [4]> var_6224_begin_0 = const()[name = string("op_6224_begin_0"), val = tensor<int32, [4]>([16, 0, 0, 0])];
            tensor<int32, [4]> var_6224_end_0 = const()[name = string("op_6224_end_0"), val = tensor<int32, [4]>([17, 2, 2048, 128])];
            tensor<bool, [4]> var_6224_end_mask_0 = const()[name = string("op_6224_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6224_cast_fp16 = slice_by_index(begin = var_6224_begin_0, end = var_6224_end_0, end_mask = var_6224_end_mask_0, x = coreml_update_state_32)[name = string("op_6224_cast_fp16")];
            tensor<int32, [2]> tile_32 = const()[name = string("tile_32"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6227_axis_0 = const()[name = string("op_6227_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6227_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6227_cast_fp16_1 = split(axis = var_6227_axis_0, split_sizes = tile_32, x = var_6224_cast_fp16)[name = string("op_6227_cast_fp16")];
            tensor<int32, [4]> var_6234_begin_0 = const()[name = string("op_6234_begin_0"), val = tensor<int32, [4]>([16, 0, 0, 0])];
            tensor<int32, [4]> var_6234_end_0 = const()[name = string("op_6234_end_0"), val = tensor<int32, [4]>([17, 2, 2048, 128])];
            tensor<bool, [4]> var_6234_end_mask_0 = const()[name = string("op_6234_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6234_cast_fp16 = slice_by_index(begin = var_6234_begin_0, end = var_6234_end_0, end_mask = var_6234_end_mask_0, x = coreml_update_state_33)[name = string("op_6234_cast_fp16")];
            tensor<int32, [2]> tile_33 = const()[name = string("tile_33"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6237_axis_0 = const()[name = string("op_6237_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6237_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6237_cast_fp16_1 = split(axis = var_6237_axis_0, split_sizes = tile_33, x = var_6234_cast_fp16)[name = string("op_6237_cast_fp16")];
            tensor<int32, [2]> var_6240_split_sizes_0 = const()[name = string("op_6240_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_6240_axis_0 = const()[name = string("op_6240_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_6240_0, tensor<fp16, [1, 8, 128, 1]> var_6240_1 = split(axis = var_6240_axis_0, split_sizes = var_6240_split_sizes_0, x = query_states_99_cast_fp16)[name = string("op_6240")];
            bool attn_weights_257_transpose_x_0 = const()[name = string("attn_weights_257_transpose_x_0"), val = bool(false)];
            bool attn_weights_257_transpose_y_0 = const()[name = string("attn_weights_257_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_257_cast_fp16 = matmul(transpose_x = attn_weights_257_transpose_x_0, transpose_y = attn_weights_257_transpose_y_0, x = var_6227_cast_fp16_0, y = var_6240_0)[name = string("attn_weights_257_cast_fp16")];
            fp16 var_6243_to_fp16 = const()[name = string("op_6243_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_259_cast_fp16 = mul(x = attn_weights_257_cast_fp16, y = var_6243_to_fp16)[name = string("attn_weights_259_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_261_cast_fp16 = add(x = attn_weights_259_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_261_cast_fp16")];
            int32 var_6247 = const()[name = string("op_6247"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_263_cast_fp16 = softmax(axis = var_6247, x = attn_weights_261_cast_fp16)[name = string("attn_weights_263_cast_fp16")];
            bool var_6253_transpose_x_1 = const()[name = string("op_6253_transpose_x_1"), val = bool(true)];
            bool var_6253_transpose_y_1 = const()[name = string("op_6253_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_6253_cast_fp16 = matmul(transpose_x = var_6253_transpose_x_1, transpose_y = var_6253_transpose_y_1, x = attn_weights_263_cast_fp16, y = var_6237_cast_fp16_0)[name = string("op_6253_cast_fp16")];
            bool attn_weights_265_transpose_x_0 = const()[name = string("attn_weights_265_transpose_x_0"), val = bool(false)];
            bool attn_weights_265_transpose_y_0 = const()[name = string("attn_weights_265_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_265_cast_fp16 = matmul(transpose_x = attn_weights_265_transpose_x_0, transpose_y = attn_weights_265_transpose_y_0, x = var_6227_cast_fp16_1, y = var_6240_1)[name = string("attn_weights_265_cast_fp16")];
            fp16 var_6255_to_fp16 = const()[name = string("op_6255_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_267_cast_fp16 = mul(x = attn_weights_265_cast_fp16, y = var_6255_to_fp16)[name = string("attn_weights_267_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_269_cast_fp16 = add(x = attn_weights_267_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_269_cast_fp16")];
            int32 var_6259 = const()[name = string("op_6259"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_271_cast_fp16 = softmax(axis = var_6259, x = attn_weights_269_cast_fp16)[name = string("attn_weights_271_cast_fp16")];
            bool attn_output_129_transpose_x_1 = const()[name = string("attn_output_129_transpose_x_1"), val = bool(true)];
            bool attn_output_129_transpose_y_1 = const()[name = string("attn_output_129_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_129_cast_fp16 = matmul(transpose_x = attn_output_129_transpose_x_1, transpose_y = attn_output_129_transpose_y_1, x = attn_weights_271_cast_fp16, y = var_6237_cast_fp16_1)[name = string("attn_output_129_cast_fp16")];
            int32 var_6267 = const()[name = string("op_6267"), val = int32(1)];
            bool attn_output_131_interleave_0 = const()[name = string("attn_output_131_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_131_cast_fp16 = concat(axis = var_6267, interleave = attn_output_131_interleave_0, values = (var_6253_cast_fp16, attn_output_129_cast_fp16))[name = string("attn_output_131_cast_fp16")];
            tensor<int32, [4]> var_6271_perm_0 = const()[name = string("op_6271_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_203x = const()[name = string("concat_203x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_6271_cast_fp16 = transpose(perm = var_6271_perm_0, x = attn_output_131_cast_fp16)[name = string("transpose_33")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_135_cast_fp16 = reshape(shape = concat_203x, x = var_6271_cast_fp16)[name = string("attn_output_135_cast_fp16")];
            tensor<int32, [2]> hidden_states_163_strides_0 = const()[name = string("hidden_states_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_163_pad_type_0 = const()[name = string("hidden_states_163_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_163_pad_0 = const()[name = string("hidden_states_163_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_163_dilations_0 = const()[name = string("hidden_states_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_163_groups_0 = const()[name = string("hidden_states_163_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_163_cast_fp16 = conv(dilations = hidden_states_163_dilations_0, groups = hidden_states_163_groups_0, pad = hidden_states_163_pad_0, pad_type = hidden_states_163_pad_type_0, strides = hidden_states_163_strides_0, weight = layers_16_self_attn_o_proj_weight_cast_fp16, x = attn_output_135_cast_fp16)[name = string("hidden_states_163_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_165_cast_fp16 = add(x = hidden_states_159_cast_fp16, y = hidden_states_163_cast_fp16)[name = string("hidden_states_165_cast_fp16")];
            fp16 const_170_promoted_to_fp16 = const()[name = string("const_170_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_6304_cast_fp16 = mul(x = hidden_states_165_cast_fp16, y = const_170_promoted_to_fp16)[name = string("op_6304_cast_fp16")];
            int32 var_6302 = const()[name = string("op_6302"), val = int32(1)];
            bool doubled_133_interleave_0 = const()[name = string("doubled_133_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_133_cast_fp16 = concat(axis = var_6302, interleave = doubled_133_interleave_0, values = (hidden_states_165_cast_fp16, var_6304_cast_fp16))[name = string("doubled_133_cast_fp16")];
            tensor<int32, [1]> out_67_axes_0 = const()[name = string("out_67_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_67_gamma_0_to_fp16 = const()[name = string("out_67_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433283072)))];
            fp16 var_6314_to_fp16 = const()[name = string("op_6314_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_67_cast_fp16 = layer_norm(axes = out_67_axes_0, epsilon = var_6314_to_fp16, gamma = out_67_gamma_0_to_fp16, x = doubled_133_cast_fp16)[name = string("out_67_cast_fp16")];
            tensor<int32, [2]> var_6325_split_sizes_0 = const()[name = string("op_6325_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6325_axis_0 = const()[name = string("op_6325_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_6325_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_6325_cast_fp16_1 = split(axis = var_6325_axis_0, split_sizes = var_6325_split_sizes_0, x = out_67_cast_fp16)[name = string("op_6325_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_16_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_16_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1433291328)))];
            tensor<int32, [2]> input_33_strides_0 = const()[name = string("input_33_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_33_pad_type_0 = const()[name = string("input_33_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_33_pad_0 = const()[name = string("input_33_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_33_dilations_0 = const()[name = string("input_33_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_33_groups_0 = const()[name = string("input_33_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_33_cast_fp16 = conv(dilations = input_33_dilations_0, groups = input_33_groups_0, pad = input_33_pad_0, pad_type = input_33_pad_type_0, strides = input_33_strides_0, weight = layers_16_mlp_gate_proj_weight_to_fp16, x = var_6325_cast_fp16_0)[name = string("input_33_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_6342_cast_fp16 = silu(x = input_33_cast_fp16)[name = string("op_6342_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_16_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_16_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1458457216)))];
            tensor<int32, [2]> var_6348_strides_0 = const()[name = string("op_6348_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6348_pad_type_0 = const()[name = string("op_6348_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6348_pad_0 = const()[name = string("op_6348_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6348_dilations_0 = const()[name = string("op_6348_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6348_groups_0 = const()[name = string("op_6348_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_6348_cast_fp16 = conv(dilations = var_6348_dilations_0, groups = var_6348_groups_0, pad = var_6348_pad_0, pad_type = var_6348_pad_type_0, strides = var_6348_strides_0, weight = layers_16_mlp_up_proj_weight_to_fp16, x = var_6325_cast_fp16_0)[name = string("op_6348_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_169_cast_fp16 = mul(x = var_6342_cast_fp16, y = var_6348_cast_fp16)[name = string("x_169_cast_fp16")];
            tensor<int32, [2]> hidden_states_167_strides_0 = const()[name = string("hidden_states_167_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_167_pad_type_0 = const()[name = string("hidden_states_167_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_167_pad_0 = const()[name = string("hidden_states_167_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_167_dilations_0 = const()[name = string("hidden_states_167_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_167_groups_0 = const()[name = string("hidden_states_167_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_167_cast_fp16 = conv(dilations = hidden_states_167_dilations_0, groups = hidden_states_167_groups_0, pad = hidden_states_167_pad_0, pad_type = hidden_states_167_pad_type_0, strides = hidden_states_167_strides_0, weight = layers_16_mlp_down_proj_weight_cast_fp16, x = x_169_cast_fp16)[name = string("hidden_states_167_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_169_cast_fp16 = add(x = hidden_states_165_cast_fp16, y = hidden_states_167_cast_fp16)[name = string("hidden_states_169_cast_fp16")];
            fp16 const_172_promoted_to_fp16 = const()[name = string("const_172_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_6366_cast_fp16 = mul(x = hidden_states_169_cast_fp16, y = const_172_promoted_to_fp16)[name = string("op_6366_cast_fp16")];
            int32 var_6364 = const()[name = string("op_6364"), val = int32(1)];
            bool doubled_137_interleave_0 = const()[name = string("doubled_137_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_137_cast_fp16 = concat(axis = var_6364, interleave = doubled_137_interleave_0, values = (hidden_states_169_cast_fp16, var_6366_cast_fp16))[name = string("doubled_137_cast_fp16")];
            tensor<int32, [1]> out_69_axes_0 = const()[name = string("out_69_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_69_gamma_0_to_fp16 = const()[name = string("out_69_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483623104)))];
            fp16 var_6376_to_fp16 = const()[name = string("op_6376_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_69_cast_fp16 = layer_norm(axes = out_69_axes_0, epsilon = var_6376_to_fp16, gamma = out_69_gamma_0_to_fp16, x = doubled_137_cast_fp16)[name = string("out_69_cast_fp16")];
            tensor<int32, [2]> var_6387_split_sizes_0 = const()[name = string("op_6387_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6387_axis_0 = const()[name = string("op_6387_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_6387_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_6387_cast_fp16_1 = split(axis = var_6387_axis_0, split_sizes = var_6387_split_sizes_0, x = out_69_cast_fp16)[name = string("op_6387_cast_fp16")];
            tensor<int32, [2]> query_states_103_strides_0 = const()[name = string("query_states_103_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_103_pad_type_0 = const()[name = string("query_states_103_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_103_pad_0 = const()[name = string("query_states_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_103_dilations_0 = const()[name = string("query_states_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_103_groups_0 = const()[name = string("query_states_103_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_103_cast_fp16 = conv(dilations = query_states_103_dilations_0, groups = query_states_103_groups_0, pad = query_states_103_pad_0, pad_type = query_states_103_pad_type_0, strides = query_states_103_strides_0, weight = layers_17_self_attn_q_proj_weight_cast_fp16, x = var_6387_cast_fp16_0)[name = string("query_states_103_cast_fp16")];
            tensor<int32, [2]> key_states_171_strides_0 = const()[name = string("key_states_171_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_171_pad_type_0 = const()[name = string("key_states_171_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_171_pad_0 = const()[name = string("key_states_171_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_171_dilations_0 = const()[name = string("key_states_171_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_171_groups_0 = const()[name = string("key_states_171_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_171_cast_fp16 = conv(dilations = key_states_171_dilations_0, groups = key_states_171_groups_0, pad = key_states_171_pad_0, pad_type = key_states_171_pad_type_0, strides = key_states_171_strides_0, weight = layers_17_self_attn_k_proj_weight_cast_fp16, x = var_6387_cast_fp16_0)[name = string("key_states_171_cast_fp16")];
            tensor<int32, [2]> value_states_103_strides_0 = const()[name = string("value_states_103_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_103_pad_type_0 = const()[name = string("value_states_103_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_103_pad_0 = const()[name = string("value_states_103_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_103_dilations_0 = const()[name = string("value_states_103_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_103_groups_0 = const()[name = string("value_states_103_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_103_cast_fp16 = conv(dilations = value_states_103_dilations_0, groups = value_states_103_groups_0, pad = value_states_103_pad_0, pad_type = value_states_103_pad_type_0, strides = value_states_103_strides_0, weight = layers_17_self_attn_v_proj_weight_cast_fp16, x = var_6387_cast_fp16_0)[name = string("value_states_103_cast_fp16")];
            tensor<int32, [4]> concat_204x = const()[name = string("concat_204x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_171_cast_fp16 = reshape(shape = concat_204x, x = query_states_103_cast_fp16)[name = string("x_171_cast_fp16")];
            tensor<int32, [4]> concat_205x = const()[name = string("concat_205x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_6444_cast_fp16 = reshape(shape = concat_205x, x = key_states_171_cast_fp16)[name = string("op_6444_cast_fp16")];
            tensor<int32, [4]> concat_206x = const()[name = string("concat_206x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_6451_cast_fp16 = reshape(shape = concat_206x, x = value_states_103_cast_fp16)[name = string("op_6451_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_6455_cast_fp16 = mul(x = x_171_cast_fp16, y = var_859_cast_fp16)[name = string("op_6455_cast_fp16")];
            tensor<int32, [2]> var_6456_split_sizes_0 = const()[name = string("op_6456_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6456_axis_0 = const()[name = string("op_6456_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_6456_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_6456_cast_fp16_1 = split(axis = var_6456_axis_0, split_sizes = var_6456_split_sizes_0, x = x_171_cast_fp16)[name = string("op_6456_cast_fp16")];
            fp16 const_174_promoted_to_fp16 = const()[name = string("const_174_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_6458_cast_fp16 = mul(x = var_6456_cast_fp16_1, y = const_174_promoted_to_fp16)[name = string("op_6458_cast_fp16")];
            int32 var_6460 = const()[name = string("op_6460"), val = int32(-2)];
            bool var_6461_interleave_0 = const()[name = string("op_6461_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_6461_cast_fp16 = concat(axis = var_6460, interleave = var_6461_interleave_0, values = (var_6458_cast_fp16, var_6456_cast_fp16_0))[name = string("op_6461_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_6462_cast_fp16 = mul(x = var_6461_cast_fp16, y = var_866_cast_fp16)[name = string("op_6462_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_105_cast_fp16 = add(x = var_6455_cast_fp16, y = var_6462_cast_fp16)[name = string("query_states_105_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_6468_cast_fp16 = mul(x = var_6444_cast_fp16, y = var_859_cast_fp16)[name = string("op_6468_cast_fp16")];
            tensor<int32, [2]> var_6469_split_sizes_0 = const()[name = string("op_6469_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6469_axis_0 = const()[name = string("op_6469_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_6469_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_6469_cast_fp16_1 = split(axis = var_6469_axis_0, split_sizes = var_6469_split_sizes_0, x = var_6444_cast_fp16)[name = string("op_6469_cast_fp16")];
            fp16 const_175_promoted_to_fp16 = const()[name = string("const_175_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_6471_cast_fp16 = mul(x = var_6469_cast_fp16_1, y = const_175_promoted_to_fp16)[name = string("op_6471_cast_fp16")];
            int32 var_6473 = const()[name = string("op_6473"), val = int32(-2)];
            bool var_6474_interleave_0 = const()[name = string("op_6474_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_6474_cast_fp16 = concat(axis = var_6473, interleave = var_6474_interleave_0, values = (var_6471_cast_fp16, var_6469_cast_fp16_0))[name = string("op_6474_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_6475_cast_fp16 = mul(x = var_6474_cast_fp16, y = var_866_cast_fp16)[name = string("op_6475_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_175_cast_fp16 = add(x = var_6468_cast_fp16, y = var_6475_cast_fp16)[name = string("key_states_175_cast_fp16")];
            tensor<int32, [1]> expand_dims_204 = const()[name = string("expand_dims_204"), val = tensor<int32, [1]>([17])];
            tensor<int32, [1]> expand_dims_205 = const()[name = string("expand_dims_205"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_207 = const()[name = string("expand_dims_207"), val = tensor<int32, [1]>([0])];
            int32 concat_209_axis_0 = const()[name = string("concat_209_axis_0"), val = int32(0)];
            bool concat_209_interleave_0 = const()[name = string("concat_209_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_209 = concat(axis = concat_209_axis_0, interleave = concat_209_interleave_0, values = (expand_dims_204, expand_dims_205, position_id, expand_dims_207))[name = string("concat_209")];
            tensor<int32, [1]> expand_dims_208 = const()[name = string("expand_dims_208"), val = tensor<int32, [1]>([18])];
            tensor<int32, [1]> concat_210_values1_0 = const()[name = string("concat_210_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_210_values3_0 = const()[name = string("concat_210_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_210_axis_0 = const()[name = string("concat_210_axis_0"), val = int32(0)];
            bool concat_210_interleave_0 = const()[name = string("concat_210_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_210 = concat(axis = concat_210_axis_0, interleave = concat_210_interleave_0, values = (expand_dims_208, concat_210_values1_0, cache_position_end, concat_210_values3_0))[name = string("concat_210")];
            tensor<int32, [4]> key_states_177_perm_0 = const()[name = string("key_states_177_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_18_stride_0 = const()[name = string("key_cache_internal_tensor_assign_18_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_18_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_18_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_18_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_18_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_18_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_18_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_177_cast_fp16 = transpose(perm = key_states_177_perm_0, x = key_states_175_cast_fp16)[name = string("transpose_32")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_18_cast_fp16 = slice_update(begin = concat_209, begin_mask = key_cache_internal_tensor_assign_18_begin_mask_0, end = concat_210, end_mask = key_cache_internal_tensor_assign_18_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_18_squeeze_mask_0, stride = key_cache_internal_tensor_assign_18_stride_0, update = key_states_177_cast_fp16, x = coreml_update_state_32)[name = string("key_cache_internal_tensor_assign_18_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_18_cast_fp16, input = key_cache)[name = string("coreml_update_state_34_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_34 = read_state(input = key_cache)[name = string("coreml_update_state_34")];
            tensor<int32, [4]> value_states_105_perm_0 = const()[name = string("value_states_105_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_18_stride_0 = const()[name = string("value_cache_internal_tensor_assign_18_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_18_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_18_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_18_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_18_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_18_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_18_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_105_cast_fp16 = transpose(perm = value_states_105_perm_0, x = var_6451_cast_fp16)[name = string("transpose_31")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_18_cast_fp16 = slice_update(begin = concat_209, begin_mask = value_cache_internal_tensor_assign_18_begin_mask_0, end = concat_210, end_mask = value_cache_internal_tensor_assign_18_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_18_squeeze_mask_0, stride = value_cache_internal_tensor_assign_18_stride_0, update = value_states_105_cast_fp16, x = coreml_update_state_33)[name = string("value_cache_internal_tensor_assign_18_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_18_cast_fp16, input = value_cache)[name = string("coreml_update_state_35_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_35 = read_state(input = value_cache)[name = string("coreml_update_state_35")];
            tensor<int32, [4]> var_6545_begin_0 = const()[name = string("op_6545_begin_0"), val = tensor<int32, [4]>([17, 0, 0, 0])];
            tensor<int32, [4]> var_6545_end_0 = const()[name = string("op_6545_end_0"), val = tensor<int32, [4]>([18, 2, 2048, 128])];
            tensor<bool, [4]> var_6545_end_mask_0 = const()[name = string("op_6545_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6545_cast_fp16 = slice_by_index(begin = var_6545_begin_0, end = var_6545_end_0, end_mask = var_6545_end_mask_0, x = coreml_update_state_34)[name = string("op_6545_cast_fp16")];
            tensor<int32, [2]> tile_34 = const()[name = string("tile_34"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6548_axis_0 = const()[name = string("op_6548_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6548_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6548_cast_fp16_1 = split(axis = var_6548_axis_0, split_sizes = tile_34, x = var_6545_cast_fp16)[name = string("op_6548_cast_fp16")];
            tensor<int32, [4]> var_6555_begin_0 = const()[name = string("op_6555_begin_0"), val = tensor<int32, [4]>([17, 0, 0, 0])];
            tensor<int32, [4]> var_6555_end_0 = const()[name = string("op_6555_end_0"), val = tensor<int32, [4]>([18, 2, 2048, 128])];
            tensor<bool, [4]> var_6555_end_mask_0 = const()[name = string("op_6555_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6555_cast_fp16 = slice_by_index(begin = var_6555_begin_0, end = var_6555_end_0, end_mask = var_6555_end_mask_0, x = coreml_update_state_35)[name = string("op_6555_cast_fp16")];
            tensor<int32, [2]> tile_35 = const()[name = string("tile_35"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6558_axis_0 = const()[name = string("op_6558_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6558_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6558_cast_fp16_1 = split(axis = var_6558_axis_0, split_sizes = tile_35, x = var_6555_cast_fp16)[name = string("op_6558_cast_fp16")];
            tensor<int32, [2]> var_6561_split_sizes_0 = const()[name = string("op_6561_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_6561_axis_0 = const()[name = string("op_6561_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_6561_0, tensor<fp16, [1, 8, 128, 1]> var_6561_1 = split(axis = var_6561_axis_0, split_sizes = var_6561_split_sizes_0, x = query_states_105_cast_fp16)[name = string("op_6561")];
            bool attn_weights_273_transpose_x_0 = const()[name = string("attn_weights_273_transpose_x_0"), val = bool(false)];
            bool attn_weights_273_transpose_y_0 = const()[name = string("attn_weights_273_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_273_cast_fp16 = matmul(transpose_x = attn_weights_273_transpose_x_0, transpose_y = attn_weights_273_transpose_y_0, x = var_6548_cast_fp16_0, y = var_6561_0)[name = string("attn_weights_273_cast_fp16")];
            fp16 var_6564_to_fp16 = const()[name = string("op_6564_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_275_cast_fp16 = mul(x = attn_weights_273_cast_fp16, y = var_6564_to_fp16)[name = string("attn_weights_275_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_277_cast_fp16 = add(x = attn_weights_275_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_277_cast_fp16")];
            int32 var_6568 = const()[name = string("op_6568"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_279_cast_fp16 = softmax(axis = var_6568, x = attn_weights_277_cast_fp16)[name = string("attn_weights_279_cast_fp16")];
            bool var_6574_transpose_x_1 = const()[name = string("op_6574_transpose_x_1"), val = bool(true)];
            bool var_6574_transpose_y_1 = const()[name = string("op_6574_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_6574_cast_fp16 = matmul(transpose_x = var_6574_transpose_x_1, transpose_y = var_6574_transpose_y_1, x = attn_weights_279_cast_fp16, y = var_6558_cast_fp16_0)[name = string("op_6574_cast_fp16")];
            bool attn_weights_281_transpose_x_0 = const()[name = string("attn_weights_281_transpose_x_0"), val = bool(false)];
            bool attn_weights_281_transpose_y_0 = const()[name = string("attn_weights_281_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_281_cast_fp16 = matmul(transpose_x = attn_weights_281_transpose_x_0, transpose_y = attn_weights_281_transpose_y_0, x = var_6548_cast_fp16_1, y = var_6561_1)[name = string("attn_weights_281_cast_fp16")];
            fp16 var_6576_to_fp16 = const()[name = string("op_6576_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_283_cast_fp16 = mul(x = attn_weights_281_cast_fp16, y = var_6576_to_fp16)[name = string("attn_weights_283_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_285_cast_fp16 = add(x = attn_weights_283_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_285_cast_fp16")];
            int32 var_6580 = const()[name = string("op_6580"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_287_cast_fp16 = softmax(axis = var_6580, x = attn_weights_285_cast_fp16)[name = string("attn_weights_287_cast_fp16")];
            bool attn_output_137_transpose_x_1 = const()[name = string("attn_output_137_transpose_x_1"), val = bool(true)];
            bool attn_output_137_transpose_y_1 = const()[name = string("attn_output_137_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_137_cast_fp16 = matmul(transpose_x = attn_output_137_transpose_x_1, transpose_y = attn_output_137_transpose_y_1, x = attn_weights_287_cast_fp16, y = var_6558_cast_fp16_1)[name = string("attn_output_137_cast_fp16")];
            int32 var_6588 = const()[name = string("op_6588"), val = int32(1)];
            bool attn_output_139_interleave_0 = const()[name = string("attn_output_139_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_139_cast_fp16 = concat(axis = var_6588, interleave = attn_output_139_interleave_0, values = (var_6574_cast_fp16, attn_output_137_cast_fp16))[name = string("attn_output_139_cast_fp16")];
            tensor<int32, [4]> var_6592_perm_0 = const()[name = string("op_6592_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_215x = const()[name = string("concat_215x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_6592_cast_fp16 = transpose(perm = var_6592_perm_0, x = attn_output_139_cast_fp16)[name = string("transpose_30")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_143_cast_fp16 = reshape(shape = concat_215x, x = var_6592_cast_fp16)[name = string("attn_output_143_cast_fp16")];
            tensor<int32, [2]> hidden_states_173_strides_0 = const()[name = string("hidden_states_173_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_173_pad_type_0 = const()[name = string("hidden_states_173_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_173_pad_0 = const()[name = string("hidden_states_173_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_173_dilations_0 = const()[name = string("hidden_states_173_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_173_groups_0 = const()[name = string("hidden_states_173_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_173_cast_fp16 = conv(dilations = hidden_states_173_dilations_0, groups = hidden_states_173_groups_0, pad = hidden_states_173_pad_0, pad_type = hidden_states_173_pad_type_0, strides = hidden_states_173_strides_0, weight = layers_17_self_attn_o_proj_weight_cast_fp16, x = attn_output_143_cast_fp16)[name = string("hidden_states_173_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_175_cast_fp16 = add(x = hidden_states_169_cast_fp16, y = hidden_states_173_cast_fp16)[name = string("hidden_states_175_cast_fp16")];
            fp16 const_180_promoted_to_fp16 = const()[name = string("const_180_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_6625_cast_fp16 = mul(x = hidden_states_175_cast_fp16, y = const_180_promoted_to_fp16)[name = string("op_6625_cast_fp16")];
            int32 var_6623 = const()[name = string("op_6623"), val = int32(1)];
            bool doubled_141_interleave_0 = const()[name = string("doubled_141_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_141_cast_fp16 = concat(axis = var_6623, interleave = doubled_141_interleave_0, values = (hidden_states_175_cast_fp16, var_6625_cast_fp16))[name = string("doubled_141_cast_fp16")];
            tensor<int32, [1]> out_71_axes_0 = const()[name = string("out_71_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_71_gamma_0_to_fp16 = const()[name = string("out_71_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483631360)))];
            fp16 var_6635_to_fp16 = const()[name = string("op_6635_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_71_cast_fp16 = layer_norm(axes = out_71_axes_0, epsilon = var_6635_to_fp16, gamma = out_71_gamma_0_to_fp16, x = doubled_141_cast_fp16)[name = string("out_71_cast_fp16")];
            tensor<int32, [2]> var_6646_split_sizes_0 = const()[name = string("op_6646_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6646_axis_0 = const()[name = string("op_6646_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_6646_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_6646_cast_fp16_1 = split(axis = var_6646_axis_0, split_sizes = var_6646_split_sizes_0, x = out_71_cast_fp16)[name = string("op_6646_cast_fp16")];
            tensor<int32, [2]> input_35_strides_0 = const()[name = string("input_35_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_35_pad_type_0 = const()[name = string("input_35_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_35_pad_0 = const()[name = string("input_35_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_35_dilations_0 = const()[name = string("input_35_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_35_groups_0 = const()[name = string("input_35_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_35_cast_fp16 = conv(dilations = input_35_dilations_0, groups = input_35_groups_0, pad = input_35_pad_0, pad_type = input_35_pad_type_0, strides = input_35_strides_0, weight = layers_17_mlp_gate_proj_weight_cast_fp16, x = var_6646_cast_fp16_0)[name = string("input_35_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_6663_cast_fp16 = silu(x = input_35_cast_fp16)[name = string("op_6663_cast_fp16")];
            tensor<int32, [2]> var_6669_strides_0 = const()[name = string("op_6669_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6669_pad_type_0 = const()[name = string("op_6669_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6669_pad_0 = const()[name = string("op_6669_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6669_dilations_0 = const()[name = string("op_6669_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6669_groups_0 = const()[name = string("op_6669_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_6669_cast_fp16 = conv(dilations = var_6669_dilations_0, groups = var_6669_groups_0, pad = var_6669_pad_0, pad_type = var_6669_pad_type_0, strides = var_6669_strides_0, weight = layers_17_mlp_up_proj_weight_cast_fp16, x = var_6646_cast_fp16_0)[name = string("op_6669_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_179_cast_fp16 = mul(x = var_6663_cast_fp16, y = var_6669_cast_fp16)[name = string("x_179_cast_fp16")];
            tensor<int32, [2]> hidden_states_177_strides_0 = const()[name = string("hidden_states_177_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_177_pad_type_0 = const()[name = string("hidden_states_177_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_177_pad_0 = const()[name = string("hidden_states_177_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_177_dilations_0 = const()[name = string("hidden_states_177_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_177_groups_0 = const()[name = string("hidden_states_177_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_177_cast_fp16 = conv(dilations = hidden_states_177_dilations_0, groups = hidden_states_177_groups_0, pad = hidden_states_177_pad_0, pad_type = hidden_states_177_pad_type_0, strides = hidden_states_177_strides_0, weight = layers_17_mlp_down_proj_weight_cast_fp16, x = x_179_cast_fp16)[name = string("hidden_states_177_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_179_cast_fp16 = add(x = hidden_states_175_cast_fp16, y = hidden_states_177_cast_fp16)[name = string("hidden_states_179_cast_fp16")];
            fp16 const_182_promoted_to_fp16 = const()[name = string("const_182_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_6687_cast_fp16 = mul(x = hidden_states_179_cast_fp16, y = const_182_promoted_to_fp16)[name = string("op_6687_cast_fp16")];
            int32 var_6685 = const()[name = string("op_6685"), val = int32(1)];
            bool doubled_145_interleave_0 = const()[name = string("doubled_145_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_145_cast_fp16 = concat(axis = var_6685, interleave = doubled_145_interleave_0, values = (hidden_states_179_cast_fp16, var_6687_cast_fp16))[name = string("doubled_145_cast_fp16")];
            tensor<int32, [1]> out_73_axes_0 = const()[name = string("out_73_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_73_gamma_0_to_fp16 = const()[name = string("out_73_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483639616)))];
            fp16 var_6697_to_fp16 = const()[name = string("op_6697_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_73_cast_fp16 = layer_norm(axes = out_73_axes_0, epsilon = var_6697_to_fp16, gamma = out_73_gamma_0_to_fp16, x = doubled_145_cast_fp16)[name = string("out_73_cast_fp16")];
            tensor<int32, [2]> var_6708_split_sizes_0 = const()[name = string("op_6708_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6708_axis_0 = const()[name = string("op_6708_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_6708_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_6708_cast_fp16_1 = split(axis = var_6708_axis_0, split_sizes = var_6708_split_sizes_0, x = out_73_cast_fp16)[name = string("op_6708_cast_fp16")];
            tensor<int32, [2]> query_states_109_strides_0 = const()[name = string("query_states_109_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_109_pad_type_0 = const()[name = string("query_states_109_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_109_pad_0 = const()[name = string("query_states_109_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_109_dilations_0 = const()[name = string("query_states_109_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_109_groups_0 = const()[name = string("query_states_109_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_109_cast_fp16 = conv(dilations = query_states_109_dilations_0, groups = query_states_109_groups_0, pad = query_states_109_pad_0, pad_type = query_states_109_pad_type_0, strides = query_states_109_strides_0, weight = layers_18_self_attn_q_proj_weight_cast_fp16, x = var_6708_cast_fp16_0)[name = string("query_states_109_cast_fp16")];
            tensor<int32, [2]> key_states_181_strides_0 = const()[name = string("key_states_181_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_181_pad_type_0 = const()[name = string("key_states_181_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_181_pad_0 = const()[name = string("key_states_181_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_181_dilations_0 = const()[name = string("key_states_181_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_181_groups_0 = const()[name = string("key_states_181_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_181_cast_fp16 = conv(dilations = key_states_181_dilations_0, groups = key_states_181_groups_0, pad = key_states_181_pad_0, pad_type = key_states_181_pad_type_0, strides = key_states_181_strides_0, weight = layers_18_self_attn_k_proj_weight_cast_fp16, x = var_6708_cast_fp16_0)[name = string("key_states_181_cast_fp16")];
            tensor<int32, [2]> value_states_109_strides_0 = const()[name = string("value_states_109_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_109_pad_type_0 = const()[name = string("value_states_109_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_109_pad_0 = const()[name = string("value_states_109_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_109_dilations_0 = const()[name = string("value_states_109_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_109_groups_0 = const()[name = string("value_states_109_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_109_cast_fp16 = conv(dilations = value_states_109_dilations_0, groups = value_states_109_groups_0, pad = value_states_109_pad_0, pad_type = value_states_109_pad_type_0, strides = value_states_109_strides_0, weight = layers_18_self_attn_v_proj_weight_cast_fp16, x = var_6708_cast_fp16_0)[name = string("value_states_109_cast_fp16")];
            tensor<int32, [4]> concat_216x = const()[name = string("concat_216x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_181_cast_fp16 = reshape(shape = concat_216x, x = query_states_109_cast_fp16)[name = string("x_181_cast_fp16")];
            tensor<int32, [4]> concat_217x = const()[name = string("concat_217x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_6765_cast_fp16 = reshape(shape = concat_217x, x = key_states_181_cast_fp16)[name = string("op_6765_cast_fp16")];
            tensor<int32, [4]> concat_218x = const()[name = string("concat_218x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_6772_cast_fp16 = reshape(shape = concat_218x, x = value_states_109_cast_fp16)[name = string("op_6772_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_6776_cast_fp16 = mul(x = x_181_cast_fp16, y = var_859_cast_fp16)[name = string("op_6776_cast_fp16")];
            tensor<int32, [2]> var_6777_split_sizes_0 = const()[name = string("op_6777_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6777_axis_0 = const()[name = string("op_6777_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_6777_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_6777_cast_fp16_1 = split(axis = var_6777_axis_0, split_sizes = var_6777_split_sizes_0, x = x_181_cast_fp16)[name = string("op_6777_cast_fp16")];
            fp16 const_184_promoted_to_fp16 = const()[name = string("const_184_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_6779_cast_fp16 = mul(x = var_6777_cast_fp16_1, y = const_184_promoted_to_fp16)[name = string("op_6779_cast_fp16")];
            int32 var_6781 = const()[name = string("op_6781"), val = int32(-2)];
            bool var_6782_interleave_0 = const()[name = string("op_6782_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_6782_cast_fp16 = concat(axis = var_6781, interleave = var_6782_interleave_0, values = (var_6779_cast_fp16, var_6777_cast_fp16_0))[name = string("op_6782_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_6783_cast_fp16 = mul(x = var_6782_cast_fp16, y = var_866_cast_fp16)[name = string("op_6783_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_111_cast_fp16 = add(x = var_6776_cast_fp16, y = var_6783_cast_fp16)[name = string("query_states_111_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_6789_cast_fp16 = mul(x = var_6765_cast_fp16, y = var_859_cast_fp16)[name = string("op_6789_cast_fp16")];
            tensor<int32, [2]> var_6790_split_sizes_0 = const()[name = string("op_6790_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_6790_axis_0 = const()[name = string("op_6790_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_6790_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_6790_cast_fp16_1 = split(axis = var_6790_axis_0, split_sizes = var_6790_split_sizes_0, x = var_6765_cast_fp16)[name = string("op_6790_cast_fp16")];
            fp16 const_185_promoted_to_fp16 = const()[name = string("const_185_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_6792_cast_fp16 = mul(x = var_6790_cast_fp16_1, y = const_185_promoted_to_fp16)[name = string("op_6792_cast_fp16")];
            int32 var_6794 = const()[name = string("op_6794"), val = int32(-2)];
            bool var_6795_interleave_0 = const()[name = string("op_6795_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_6795_cast_fp16 = concat(axis = var_6794, interleave = var_6795_interleave_0, values = (var_6792_cast_fp16, var_6790_cast_fp16_0))[name = string("op_6795_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_6796_cast_fp16 = mul(x = var_6795_cast_fp16, y = var_866_cast_fp16)[name = string("op_6796_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_185_cast_fp16 = add(x = var_6789_cast_fp16, y = var_6796_cast_fp16)[name = string("key_states_185_cast_fp16")];
            tensor<int32, [1]> expand_dims_216 = const()[name = string("expand_dims_216"), val = tensor<int32, [1]>([18])];
            tensor<int32, [1]> expand_dims_217 = const()[name = string("expand_dims_217"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_219 = const()[name = string("expand_dims_219"), val = tensor<int32, [1]>([0])];
            int32 concat_221_axis_0 = const()[name = string("concat_221_axis_0"), val = int32(0)];
            bool concat_221_interleave_0 = const()[name = string("concat_221_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_221 = concat(axis = concat_221_axis_0, interleave = concat_221_interleave_0, values = (expand_dims_216, expand_dims_217, position_id, expand_dims_219))[name = string("concat_221")];
            tensor<int32, [1]> expand_dims_220 = const()[name = string("expand_dims_220"), val = tensor<int32, [1]>([19])];
            tensor<int32, [1]> concat_222_values1_0 = const()[name = string("concat_222_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_222_values3_0 = const()[name = string("concat_222_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_222_axis_0 = const()[name = string("concat_222_axis_0"), val = int32(0)];
            bool concat_222_interleave_0 = const()[name = string("concat_222_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_222 = concat(axis = concat_222_axis_0, interleave = concat_222_interleave_0, values = (expand_dims_220, concat_222_values1_0, cache_position_end, concat_222_values3_0))[name = string("concat_222")];
            tensor<int32, [4]> key_states_187_perm_0 = const()[name = string("key_states_187_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_19_stride_0 = const()[name = string("key_cache_internal_tensor_assign_19_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_19_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_19_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_19_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_19_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_19_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_19_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_187_cast_fp16 = transpose(perm = key_states_187_perm_0, x = key_states_185_cast_fp16)[name = string("transpose_29")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_19_cast_fp16 = slice_update(begin = concat_221, begin_mask = key_cache_internal_tensor_assign_19_begin_mask_0, end = concat_222, end_mask = key_cache_internal_tensor_assign_19_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_19_squeeze_mask_0, stride = key_cache_internal_tensor_assign_19_stride_0, update = key_states_187_cast_fp16, x = coreml_update_state_34)[name = string("key_cache_internal_tensor_assign_19_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_19_cast_fp16, input = key_cache)[name = string("coreml_update_state_36_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_36 = read_state(input = key_cache)[name = string("coreml_update_state_36")];
            tensor<int32, [4]> value_states_111_perm_0 = const()[name = string("value_states_111_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_19_stride_0 = const()[name = string("value_cache_internal_tensor_assign_19_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_19_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_19_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_19_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_19_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_19_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_19_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_111_cast_fp16 = transpose(perm = value_states_111_perm_0, x = var_6772_cast_fp16)[name = string("transpose_28")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_19_cast_fp16 = slice_update(begin = concat_221, begin_mask = value_cache_internal_tensor_assign_19_begin_mask_0, end = concat_222, end_mask = value_cache_internal_tensor_assign_19_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_19_squeeze_mask_0, stride = value_cache_internal_tensor_assign_19_stride_0, update = value_states_111_cast_fp16, x = coreml_update_state_35)[name = string("value_cache_internal_tensor_assign_19_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_19_cast_fp16, input = value_cache)[name = string("coreml_update_state_37_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_37 = read_state(input = value_cache)[name = string("coreml_update_state_37")];
            tensor<int32, [4]> var_6866_begin_0 = const()[name = string("op_6866_begin_0"), val = tensor<int32, [4]>([18, 0, 0, 0])];
            tensor<int32, [4]> var_6866_end_0 = const()[name = string("op_6866_end_0"), val = tensor<int32, [4]>([19, 2, 2048, 128])];
            tensor<bool, [4]> var_6866_end_mask_0 = const()[name = string("op_6866_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6866_cast_fp16 = slice_by_index(begin = var_6866_begin_0, end = var_6866_end_0, end_mask = var_6866_end_mask_0, x = coreml_update_state_36)[name = string("op_6866_cast_fp16")];
            tensor<int32, [2]> tile_36 = const()[name = string("tile_36"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6869_axis_0 = const()[name = string("op_6869_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6869_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6869_cast_fp16_1 = split(axis = var_6869_axis_0, split_sizes = tile_36, x = var_6866_cast_fp16)[name = string("op_6869_cast_fp16")];
            tensor<int32, [4]> var_6876_begin_0 = const()[name = string("op_6876_begin_0"), val = tensor<int32, [4]>([18, 0, 0, 0])];
            tensor<int32, [4]> var_6876_end_0 = const()[name = string("op_6876_end_0"), val = tensor<int32, [4]>([19, 2, 2048, 128])];
            tensor<bool, [4]> var_6876_end_mask_0 = const()[name = string("op_6876_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_6876_cast_fp16 = slice_by_index(begin = var_6876_begin_0, end = var_6876_end_0, end_mask = var_6876_end_mask_0, x = coreml_update_state_37)[name = string("op_6876_cast_fp16")];
            tensor<int32, [2]> tile_37 = const()[name = string("tile_37"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6879_axis_0 = const()[name = string("op_6879_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_6879_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_6879_cast_fp16_1 = split(axis = var_6879_axis_0, split_sizes = tile_37, x = var_6876_cast_fp16)[name = string("op_6879_cast_fp16")];
            tensor<int32, [2]> var_6882_split_sizes_0 = const()[name = string("op_6882_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_6882_axis_0 = const()[name = string("op_6882_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_6882_0, tensor<fp16, [1, 8, 128, 1]> var_6882_1 = split(axis = var_6882_axis_0, split_sizes = var_6882_split_sizes_0, x = query_states_111_cast_fp16)[name = string("op_6882")];
            bool attn_weights_289_transpose_x_0 = const()[name = string("attn_weights_289_transpose_x_0"), val = bool(false)];
            bool attn_weights_289_transpose_y_0 = const()[name = string("attn_weights_289_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_289_cast_fp16 = matmul(transpose_x = attn_weights_289_transpose_x_0, transpose_y = attn_weights_289_transpose_y_0, x = var_6869_cast_fp16_0, y = var_6882_0)[name = string("attn_weights_289_cast_fp16")];
            fp16 var_6885_to_fp16 = const()[name = string("op_6885_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_291_cast_fp16 = mul(x = attn_weights_289_cast_fp16, y = var_6885_to_fp16)[name = string("attn_weights_291_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_293_cast_fp16 = add(x = attn_weights_291_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_293_cast_fp16")];
            int32 var_6889 = const()[name = string("op_6889"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_295_cast_fp16 = softmax(axis = var_6889, x = attn_weights_293_cast_fp16)[name = string("attn_weights_295_cast_fp16")];
            bool var_6895_transpose_x_1 = const()[name = string("op_6895_transpose_x_1"), val = bool(true)];
            bool var_6895_transpose_y_1 = const()[name = string("op_6895_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_6895_cast_fp16 = matmul(transpose_x = var_6895_transpose_x_1, transpose_y = var_6895_transpose_y_1, x = attn_weights_295_cast_fp16, y = var_6879_cast_fp16_0)[name = string("op_6895_cast_fp16")];
            bool attn_weights_297_transpose_x_0 = const()[name = string("attn_weights_297_transpose_x_0"), val = bool(false)];
            bool attn_weights_297_transpose_y_0 = const()[name = string("attn_weights_297_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_297_cast_fp16 = matmul(transpose_x = attn_weights_297_transpose_x_0, transpose_y = attn_weights_297_transpose_y_0, x = var_6869_cast_fp16_1, y = var_6882_1)[name = string("attn_weights_297_cast_fp16")];
            fp16 var_6897_to_fp16 = const()[name = string("op_6897_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_299_cast_fp16 = mul(x = attn_weights_297_cast_fp16, y = var_6897_to_fp16)[name = string("attn_weights_299_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_301_cast_fp16 = add(x = attn_weights_299_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_301_cast_fp16")];
            int32 var_6901 = const()[name = string("op_6901"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_303_cast_fp16 = softmax(axis = var_6901, x = attn_weights_301_cast_fp16)[name = string("attn_weights_303_cast_fp16")];
            bool attn_output_145_transpose_x_1 = const()[name = string("attn_output_145_transpose_x_1"), val = bool(true)];
            bool attn_output_145_transpose_y_1 = const()[name = string("attn_output_145_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_145_cast_fp16 = matmul(transpose_x = attn_output_145_transpose_x_1, transpose_y = attn_output_145_transpose_y_1, x = attn_weights_303_cast_fp16, y = var_6879_cast_fp16_1)[name = string("attn_output_145_cast_fp16")];
            int32 var_6909 = const()[name = string("op_6909"), val = int32(1)];
            bool attn_output_147_interleave_0 = const()[name = string("attn_output_147_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_147_cast_fp16 = concat(axis = var_6909, interleave = attn_output_147_interleave_0, values = (var_6895_cast_fp16, attn_output_145_cast_fp16))[name = string("attn_output_147_cast_fp16")];
            tensor<int32, [4]> var_6913_perm_0 = const()[name = string("op_6913_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_227x = const()[name = string("concat_227x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_6913_cast_fp16 = transpose(perm = var_6913_perm_0, x = attn_output_147_cast_fp16)[name = string("transpose_27")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_151_cast_fp16 = reshape(shape = concat_227x, x = var_6913_cast_fp16)[name = string("attn_output_151_cast_fp16")];
            tensor<int32, [2]> hidden_states_183_strides_0 = const()[name = string("hidden_states_183_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_183_pad_type_0 = const()[name = string("hidden_states_183_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_183_pad_0 = const()[name = string("hidden_states_183_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_183_dilations_0 = const()[name = string("hidden_states_183_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_183_groups_0 = const()[name = string("hidden_states_183_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_183_cast_fp16 = conv(dilations = hidden_states_183_dilations_0, groups = hidden_states_183_groups_0, pad = hidden_states_183_pad_0, pad_type = hidden_states_183_pad_type_0, strides = hidden_states_183_strides_0, weight = layers_18_self_attn_o_proj_weight_cast_fp16, x = attn_output_151_cast_fp16)[name = string("hidden_states_183_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_185_cast_fp16 = add(x = hidden_states_179_cast_fp16, y = hidden_states_183_cast_fp16)[name = string("hidden_states_185_cast_fp16")];
            fp16 const_190_promoted_to_fp16 = const()[name = string("const_190_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_6946_cast_fp16 = mul(x = hidden_states_185_cast_fp16, y = const_190_promoted_to_fp16)[name = string("op_6946_cast_fp16")];
            int32 var_6944 = const()[name = string("op_6944"), val = int32(1)];
            bool doubled_149_interleave_0 = const()[name = string("doubled_149_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_149_cast_fp16 = concat(axis = var_6944, interleave = doubled_149_interleave_0, values = (hidden_states_185_cast_fp16, var_6946_cast_fp16))[name = string("doubled_149_cast_fp16")];
            tensor<int32, [1]> out_75_axes_0 = const()[name = string("out_75_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_75_gamma_0_to_fp16 = const()[name = string("out_75_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483647872)))];
            fp16 var_6956_to_fp16 = const()[name = string("op_6956_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_75_cast_fp16 = layer_norm(axes = out_75_axes_0, epsilon = var_6956_to_fp16, gamma = out_75_gamma_0_to_fp16, x = doubled_149_cast_fp16)[name = string("out_75_cast_fp16")];
            tensor<int32, [2]> var_6967_split_sizes_0 = const()[name = string("op_6967_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_6967_axis_0 = const()[name = string("op_6967_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_6967_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_6967_cast_fp16_1 = split(axis = var_6967_axis_0, split_sizes = var_6967_split_sizes_0, x = out_75_cast_fp16)[name = string("op_6967_cast_fp16")];
            tensor<int32, [2]> input_37_strides_0 = const()[name = string("input_37_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_37_pad_type_0 = const()[name = string("input_37_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_37_pad_0 = const()[name = string("input_37_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_37_dilations_0 = const()[name = string("input_37_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_37_groups_0 = const()[name = string("input_37_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_37_cast_fp16 = conv(dilations = input_37_dilations_0, groups = input_37_groups_0, pad = input_37_pad_0, pad_type = input_37_pad_type_0, strides = input_37_strides_0, weight = layers_18_mlp_gate_proj_weight_cast_fp16, x = var_6967_cast_fp16_0)[name = string("input_37_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_6984_cast_fp16 = silu(x = input_37_cast_fp16)[name = string("op_6984_cast_fp16")];
            tensor<int32, [2]> var_6990_strides_0 = const()[name = string("op_6990_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_6990_pad_type_0 = const()[name = string("op_6990_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_6990_pad_0 = const()[name = string("op_6990_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_6990_dilations_0 = const()[name = string("op_6990_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_6990_groups_0 = const()[name = string("op_6990_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_6990_cast_fp16 = conv(dilations = var_6990_dilations_0, groups = var_6990_groups_0, pad = var_6990_pad_0, pad_type = var_6990_pad_type_0, strides = var_6990_strides_0, weight = layers_18_mlp_up_proj_weight_cast_fp16, x = var_6967_cast_fp16_0)[name = string("op_6990_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_189_cast_fp16 = mul(x = var_6984_cast_fp16, y = var_6990_cast_fp16)[name = string("x_189_cast_fp16")];
            tensor<int32, [2]> hidden_states_187_strides_0 = const()[name = string("hidden_states_187_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_187_pad_type_0 = const()[name = string("hidden_states_187_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_187_pad_0 = const()[name = string("hidden_states_187_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_187_dilations_0 = const()[name = string("hidden_states_187_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_187_groups_0 = const()[name = string("hidden_states_187_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_187_cast_fp16 = conv(dilations = hidden_states_187_dilations_0, groups = hidden_states_187_groups_0, pad = hidden_states_187_pad_0, pad_type = hidden_states_187_pad_type_0, strides = hidden_states_187_strides_0, weight = layers_18_mlp_down_proj_weight_cast_fp16, x = x_189_cast_fp16)[name = string("hidden_states_187_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_189_cast_fp16 = add(x = hidden_states_185_cast_fp16, y = hidden_states_187_cast_fp16)[name = string("hidden_states_189_cast_fp16")];
            fp16 const_192_promoted_to_fp16 = const()[name = string("const_192_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_7008_cast_fp16 = mul(x = hidden_states_189_cast_fp16, y = const_192_promoted_to_fp16)[name = string("op_7008_cast_fp16")];
            int32 var_7006 = const()[name = string("op_7006"), val = int32(1)];
            bool doubled_153_interleave_0 = const()[name = string("doubled_153_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_153_cast_fp16 = concat(axis = var_7006, interleave = doubled_153_interleave_0, values = (hidden_states_189_cast_fp16, var_7008_cast_fp16))[name = string("doubled_153_cast_fp16")];
            tensor<int32, [1]> out_77_axes_0 = const()[name = string("out_77_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_77_gamma_0_to_fp16 = const()[name = string("out_77_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483656128)))];
            fp16 var_7018_to_fp16 = const()[name = string("op_7018_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_77_cast_fp16 = layer_norm(axes = out_77_axes_0, epsilon = var_7018_to_fp16, gamma = out_77_gamma_0_to_fp16, x = doubled_153_cast_fp16)[name = string("out_77_cast_fp16")];
            tensor<int32, [2]> var_7029_split_sizes_0 = const()[name = string("op_7029_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7029_axis_0 = const()[name = string("op_7029_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_7029_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_7029_cast_fp16_1 = split(axis = var_7029_axis_0, split_sizes = var_7029_split_sizes_0, x = out_77_cast_fp16)[name = string("op_7029_cast_fp16")];
            tensor<int32, [2]> query_states_115_strides_0 = const()[name = string("query_states_115_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_115_pad_type_0 = const()[name = string("query_states_115_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_115_pad_0 = const()[name = string("query_states_115_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_115_dilations_0 = const()[name = string("query_states_115_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_115_groups_0 = const()[name = string("query_states_115_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_115_cast_fp16 = conv(dilations = query_states_115_dilations_0, groups = query_states_115_groups_0, pad = query_states_115_pad_0, pad_type = query_states_115_pad_type_0, strides = query_states_115_strides_0, weight = layers_19_self_attn_q_proj_weight_cast_fp16, x = var_7029_cast_fp16_0)[name = string("query_states_115_cast_fp16")];
            tensor<int32, [2]> key_states_191_strides_0 = const()[name = string("key_states_191_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_191_pad_type_0 = const()[name = string("key_states_191_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_191_pad_0 = const()[name = string("key_states_191_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_191_dilations_0 = const()[name = string("key_states_191_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_191_groups_0 = const()[name = string("key_states_191_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_191_cast_fp16 = conv(dilations = key_states_191_dilations_0, groups = key_states_191_groups_0, pad = key_states_191_pad_0, pad_type = key_states_191_pad_type_0, strides = key_states_191_strides_0, weight = layers_19_self_attn_k_proj_weight_cast_fp16, x = var_7029_cast_fp16_0)[name = string("key_states_191_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_19_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_19_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1483664384)))];
            tensor<int32, [2]> value_states_115_strides_0 = const()[name = string("value_states_115_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_115_pad_type_0 = const()[name = string("value_states_115_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_115_pad_0 = const()[name = string("value_states_115_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_115_dilations_0 = const()[name = string("value_states_115_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_115_groups_0 = const()[name = string("value_states_115_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_115_cast_fp16 = conv(dilations = value_states_115_dilations_0, groups = value_states_115_groups_0, pad = value_states_115_pad_0, pad_type = value_states_115_pad_type_0, strides = value_states_115_strides_0, weight = layers_19_self_attn_v_proj_weight_to_fp16, x = var_7029_cast_fp16_0)[name = string("value_states_115_cast_fp16")];
            tensor<int32, [4]> concat_228x = const()[name = string("concat_228x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_191_cast_fp16 = reshape(shape = concat_228x, x = query_states_115_cast_fp16)[name = string("x_191_cast_fp16")];
            tensor<int32, [4]> concat_229x = const()[name = string("concat_229x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_7086_cast_fp16 = reshape(shape = concat_229x, x = key_states_191_cast_fp16)[name = string("op_7086_cast_fp16")];
            tensor<int32, [4]> concat_230x = const()[name = string("concat_230x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_7093_cast_fp16 = reshape(shape = concat_230x, x = value_states_115_cast_fp16)[name = string("op_7093_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_7097_cast_fp16 = mul(x = x_191_cast_fp16, y = var_859_cast_fp16)[name = string("op_7097_cast_fp16")];
            tensor<int32, [2]> var_7098_split_sizes_0 = const()[name = string("op_7098_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7098_axis_0 = const()[name = string("op_7098_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_7098_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_7098_cast_fp16_1 = split(axis = var_7098_axis_0, split_sizes = var_7098_split_sizes_0, x = x_191_cast_fp16)[name = string("op_7098_cast_fp16")];
            fp16 const_194_promoted_to_fp16 = const()[name = string("const_194_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_7100_cast_fp16 = mul(x = var_7098_cast_fp16_1, y = const_194_promoted_to_fp16)[name = string("op_7100_cast_fp16")];
            int32 var_7102 = const()[name = string("op_7102"), val = int32(-2)];
            bool var_7103_interleave_0 = const()[name = string("op_7103_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_7103_cast_fp16 = concat(axis = var_7102, interleave = var_7103_interleave_0, values = (var_7100_cast_fp16, var_7098_cast_fp16_0))[name = string("op_7103_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_7104_cast_fp16 = mul(x = var_7103_cast_fp16, y = var_866_cast_fp16)[name = string("op_7104_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_117_cast_fp16 = add(x = var_7097_cast_fp16, y = var_7104_cast_fp16)[name = string("query_states_117_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_7110_cast_fp16 = mul(x = var_7086_cast_fp16, y = var_859_cast_fp16)[name = string("op_7110_cast_fp16")];
            tensor<int32, [2]> var_7111_split_sizes_0 = const()[name = string("op_7111_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7111_axis_0 = const()[name = string("op_7111_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_7111_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_7111_cast_fp16_1 = split(axis = var_7111_axis_0, split_sizes = var_7111_split_sizes_0, x = var_7086_cast_fp16)[name = string("op_7111_cast_fp16")];
            fp16 const_195_promoted_to_fp16 = const()[name = string("const_195_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_7113_cast_fp16 = mul(x = var_7111_cast_fp16_1, y = const_195_promoted_to_fp16)[name = string("op_7113_cast_fp16")];
            int32 var_7115 = const()[name = string("op_7115"), val = int32(-2)];
            bool var_7116_interleave_0 = const()[name = string("op_7116_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_7116_cast_fp16 = concat(axis = var_7115, interleave = var_7116_interleave_0, values = (var_7113_cast_fp16, var_7111_cast_fp16_0))[name = string("op_7116_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_7117_cast_fp16 = mul(x = var_7116_cast_fp16, y = var_866_cast_fp16)[name = string("op_7117_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_195_cast_fp16 = add(x = var_7110_cast_fp16, y = var_7117_cast_fp16)[name = string("key_states_195_cast_fp16")];
            tensor<int32, [1]> expand_dims_228 = const()[name = string("expand_dims_228"), val = tensor<int32, [1]>([19])];
            tensor<int32, [1]> expand_dims_229 = const()[name = string("expand_dims_229"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_231 = const()[name = string("expand_dims_231"), val = tensor<int32, [1]>([0])];
            int32 concat_233_axis_0 = const()[name = string("concat_233_axis_0"), val = int32(0)];
            bool concat_233_interleave_0 = const()[name = string("concat_233_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_233 = concat(axis = concat_233_axis_0, interleave = concat_233_interleave_0, values = (expand_dims_228, expand_dims_229, position_id, expand_dims_231))[name = string("concat_233")];
            tensor<int32, [1]> expand_dims_232 = const()[name = string("expand_dims_232"), val = tensor<int32, [1]>([20])];
            tensor<int32, [1]> concat_234_values1_0 = const()[name = string("concat_234_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_234_values3_0 = const()[name = string("concat_234_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_234_axis_0 = const()[name = string("concat_234_axis_0"), val = int32(0)];
            bool concat_234_interleave_0 = const()[name = string("concat_234_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_234 = concat(axis = concat_234_axis_0, interleave = concat_234_interleave_0, values = (expand_dims_232, concat_234_values1_0, cache_position_end, concat_234_values3_0))[name = string("concat_234")];
            tensor<int32, [4]> key_states_197_perm_0 = const()[name = string("key_states_197_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_20_stride_0 = const()[name = string("key_cache_internal_tensor_assign_20_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_20_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_20_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_20_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_20_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_20_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_20_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_197_cast_fp16 = transpose(perm = key_states_197_perm_0, x = key_states_195_cast_fp16)[name = string("transpose_26")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_20_cast_fp16 = slice_update(begin = concat_233, begin_mask = key_cache_internal_tensor_assign_20_begin_mask_0, end = concat_234, end_mask = key_cache_internal_tensor_assign_20_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_20_squeeze_mask_0, stride = key_cache_internal_tensor_assign_20_stride_0, update = key_states_197_cast_fp16, x = coreml_update_state_36)[name = string("key_cache_internal_tensor_assign_20_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_20_cast_fp16, input = key_cache)[name = string("coreml_update_state_38_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_38 = read_state(input = key_cache)[name = string("coreml_update_state_38")];
            tensor<int32, [4]> value_states_117_perm_0 = const()[name = string("value_states_117_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_20_stride_0 = const()[name = string("value_cache_internal_tensor_assign_20_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_20_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_20_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_20_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_20_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_20_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_20_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_117_cast_fp16 = transpose(perm = value_states_117_perm_0, x = var_7093_cast_fp16)[name = string("transpose_25")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_20_cast_fp16 = slice_update(begin = concat_233, begin_mask = value_cache_internal_tensor_assign_20_begin_mask_0, end = concat_234, end_mask = value_cache_internal_tensor_assign_20_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_20_squeeze_mask_0, stride = value_cache_internal_tensor_assign_20_stride_0, update = value_states_117_cast_fp16, x = coreml_update_state_37)[name = string("value_cache_internal_tensor_assign_20_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_20_cast_fp16, input = value_cache)[name = string("coreml_update_state_39_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_39 = read_state(input = value_cache)[name = string("coreml_update_state_39")];
            tensor<int32, [4]> var_7187_begin_0 = const()[name = string("op_7187_begin_0"), val = tensor<int32, [4]>([19, 0, 0, 0])];
            tensor<int32, [4]> var_7187_end_0 = const()[name = string("op_7187_end_0"), val = tensor<int32, [4]>([20, 2, 2048, 128])];
            tensor<bool, [4]> var_7187_end_mask_0 = const()[name = string("op_7187_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7187_cast_fp16 = slice_by_index(begin = var_7187_begin_0, end = var_7187_end_0, end_mask = var_7187_end_mask_0, x = coreml_update_state_38)[name = string("op_7187_cast_fp16")];
            tensor<int32, [2]> tile_38 = const()[name = string("tile_38"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7190_axis_0 = const()[name = string("op_7190_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7190_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7190_cast_fp16_1 = split(axis = var_7190_axis_0, split_sizes = tile_38, x = var_7187_cast_fp16)[name = string("op_7190_cast_fp16")];
            tensor<int32, [4]> var_7197_begin_0 = const()[name = string("op_7197_begin_0"), val = tensor<int32, [4]>([19, 0, 0, 0])];
            tensor<int32, [4]> var_7197_end_0 = const()[name = string("op_7197_end_0"), val = tensor<int32, [4]>([20, 2, 2048, 128])];
            tensor<bool, [4]> var_7197_end_mask_0 = const()[name = string("op_7197_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7197_cast_fp16 = slice_by_index(begin = var_7197_begin_0, end = var_7197_end_0, end_mask = var_7197_end_mask_0, x = coreml_update_state_39)[name = string("op_7197_cast_fp16")];
            tensor<int32, [2]> tile_39 = const()[name = string("tile_39"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7200_axis_0 = const()[name = string("op_7200_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7200_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7200_cast_fp16_1 = split(axis = var_7200_axis_0, split_sizes = tile_39, x = var_7197_cast_fp16)[name = string("op_7200_cast_fp16")];
            tensor<int32, [2]> var_7203_split_sizes_0 = const()[name = string("op_7203_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_7203_axis_0 = const()[name = string("op_7203_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_7203_0, tensor<fp16, [1, 8, 128, 1]> var_7203_1 = split(axis = var_7203_axis_0, split_sizes = var_7203_split_sizes_0, x = query_states_117_cast_fp16)[name = string("op_7203")];
            bool attn_weights_305_transpose_x_0 = const()[name = string("attn_weights_305_transpose_x_0"), val = bool(false)];
            bool attn_weights_305_transpose_y_0 = const()[name = string("attn_weights_305_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_305_cast_fp16 = matmul(transpose_x = attn_weights_305_transpose_x_0, transpose_y = attn_weights_305_transpose_y_0, x = var_7190_cast_fp16_0, y = var_7203_0)[name = string("attn_weights_305_cast_fp16")];
            fp16 var_7206_to_fp16 = const()[name = string("op_7206_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_307_cast_fp16 = mul(x = attn_weights_305_cast_fp16, y = var_7206_to_fp16)[name = string("attn_weights_307_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_309_cast_fp16 = add(x = attn_weights_307_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_309_cast_fp16")];
            int32 var_7210 = const()[name = string("op_7210"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_311_cast_fp16 = softmax(axis = var_7210, x = attn_weights_309_cast_fp16)[name = string("attn_weights_311_cast_fp16")];
            bool var_7216_transpose_x_1 = const()[name = string("op_7216_transpose_x_1"), val = bool(true)];
            bool var_7216_transpose_y_1 = const()[name = string("op_7216_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_7216_cast_fp16 = matmul(transpose_x = var_7216_transpose_x_1, transpose_y = var_7216_transpose_y_1, x = attn_weights_311_cast_fp16, y = var_7200_cast_fp16_0)[name = string("op_7216_cast_fp16")];
            bool attn_weights_313_transpose_x_0 = const()[name = string("attn_weights_313_transpose_x_0"), val = bool(false)];
            bool attn_weights_313_transpose_y_0 = const()[name = string("attn_weights_313_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_313_cast_fp16 = matmul(transpose_x = attn_weights_313_transpose_x_0, transpose_y = attn_weights_313_transpose_y_0, x = var_7190_cast_fp16_1, y = var_7203_1)[name = string("attn_weights_313_cast_fp16")];
            fp16 var_7218_to_fp16 = const()[name = string("op_7218_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_315_cast_fp16 = mul(x = attn_weights_313_cast_fp16, y = var_7218_to_fp16)[name = string("attn_weights_315_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_317_cast_fp16 = add(x = attn_weights_315_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_317_cast_fp16")];
            int32 var_7222 = const()[name = string("op_7222"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_319_cast_fp16 = softmax(axis = var_7222, x = attn_weights_317_cast_fp16)[name = string("attn_weights_319_cast_fp16")];
            bool attn_output_153_transpose_x_1 = const()[name = string("attn_output_153_transpose_x_1"), val = bool(true)];
            bool attn_output_153_transpose_y_1 = const()[name = string("attn_output_153_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_153_cast_fp16 = matmul(transpose_x = attn_output_153_transpose_x_1, transpose_y = attn_output_153_transpose_y_1, x = attn_weights_319_cast_fp16, y = var_7200_cast_fp16_1)[name = string("attn_output_153_cast_fp16")];
            int32 var_7230 = const()[name = string("op_7230"), val = int32(1)];
            bool attn_output_155_interleave_0 = const()[name = string("attn_output_155_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_155_cast_fp16 = concat(axis = var_7230, interleave = attn_output_155_interleave_0, values = (var_7216_cast_fp16, attn_output_153_cast_fp16))[name = string("attn_output_155_cast_fp16")];
            tensor<int32, [4]> var_7234_perm_0 = const()[name = string("op_7234_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_239x = const()[name = string("concat_239x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_7234_cast_fp16 = transpose(perm = var_7234_perm_0, x = attn_output_155_cast_fp16)[name = string("transpose_24")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_159_cast_fp16 = reshape(shape = concat_239x, x = var_7234_cast_fp16)[name = string("attn_output_159_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_19_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_19_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1484713024)))];
            tensor<int32, [2]> hidden_states_193_strides_0 = const()[name = string("hidden_states_193_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_193_pad_type_0 = const()[name = string("hidden_states_193_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_193_pad_0 = const()[name = string("hidden_states_193_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_193_dilations_0 = const()[name = string("hidden_states_193_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_193_groups_0 = const()[name = string("hidden_states_193_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_193_cast_fp16 = conv(dilations = hidden_states_193_dilations_0, groups = hidden_states_193_groups_0, pad = hidden_states_193_pad_0, pad_type = hidden_states_193_pad_type_0, strides = hidden_states_193_strides_0, weight = layers_19_self_attn_o_proj_weight_to_fp16, x = attn_output_159_cast_fp16)[name = string("hidden_states_193_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_195_cast_fp16 = add(x = hidden_states_189_cast_fp16, y = hidden_states_193_cast_fp16)[name = string("hidden_states_195_cast_fp16")];
            fp16 const_200_promoted_to_fp16 = const()[name = string("const_200_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_7267_cast_fp16 = mul(x = hidden_states_195_cast_fp16, y = const_200_promoted_to_fp16)[name = string("op_7267_cast_fp16")];
            int32 var_7265 = const()[name = string("op_7265"), val = int32(1)];
            bool doubled_157_interleave_0 = const()[name = string("doubled_157_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_157_cast_fp16 = concat(axis = var_7265, interleave = doubled_157_interleave_0, values = (hidden_states_195_cast_fp16, var_7267_cast_fp16))[name = string("doubled_157_cast_fp16")];
            tensor<int32, [1]> out_79_axes_0 = const()[name = string("out_79_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_79_gamma_0_to_fp16 = const()[name = string("out_79_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1493101696)))];
            fp16 var_7277_to_fp16 = const()[name = string("op_7277_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_79_cast_fp16 = layer_norm(axes = out_79_axes_0, epsilon = var_7277_to_fp16, gamma = out_79_gamma_0_to_fp16, x = doubled_157_cast_fp16)[name = string("out_79_cast_fp16")];
            tensor<int32, [2]> var_7288_split_sizes_0 = const()[name = string("op_7288_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7288_axis_0 = const()[name = string("op_7288_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_7288_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_7288_cast_fp16_1 = split(axis = var_7288_axis_0, split_sizes = var_7288_split_sizes_0, x = out_79_cast_fp16)[name = string("op_7288_cast_fp16")];
            tensor<int32, [2]> input_39_strides_0 = const()[name = string("input_39_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_39_pad_type_0 = const()[name = string("input_39_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_39_pad_0 = const()[name = string("input_39_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_39_dilations_0 = const()[name = string("input_39_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_39_groups_0 = const()[name = string("input_39_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_39_cast_fp16 = conv(dilations = input_39_dilations_0, groups = input_39_groups_0, pad = input_39_pad_0, pad_type = input_39_pad_type_0, strides = input_39_strides_0, weight = layers_19_mlp_gate_proj_weight_cast_fp16, x = var_7288_cast_fp16_0)[name = string("input_39_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_7305_cast_fp16 = silu(x = input_39_cast_fp16)[name = string("op_7305_cast_fp16")];
            tensor<int32, [2]> var_7311_strides_0 = const()[name = string("op_7311_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_7311_pad_type_0 = const()[name = string("op_7311_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_7311_pad_0 = const()[name = string("op_7311_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_7311_dilations_0 = const()[name = string("op_7311_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7311_groups_0 = const()[name = string("op_7311_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_7311_cast_fp16 = conv(dilations = var_7311_dilations_0, groups = var_7311_groups_0, pad = var_7311_pad_0, pad_type = var_7311_pad_type_0, strides = var_7311_strides_0, weight = layers_19_mlp_up_proj_weight_cast_fp16, x = var_7288_cast_fp16_0)[name = string("op_7311_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_199_cast_fp16 = mul(x = var_7305_cast_fp16, y = var_7311_cast_fp16)[name = string("x_199_cast_fp16")];
            tensor<int32, [2]> hidden_states_197_strides_0 = const()[name = string("hidden_states_197_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_197_pad_type_0 = const()[name = string("hidden_states_197_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_197_pad_0 = const()[name = string("hidden_states_197_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_197_dilations_0 = const()[name = string("hidden_states_197_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_197_groups_0 = const()[name = string("hidden_states_197_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_197_cast_fp16 = conv(dilations = hidden_states_197_dilations_0, groups = hidden_states_197_groups_0, pad = hidden_states_197_pad_0, pad_type = hidden_states_197_pad_type_0, strides = hidden_states_197_strides_0, weight = layers_19_mlp_down_proj_weight_cast_fp16, x = x_199_cast_fp16)[name = string("hidden_states_197_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_199_cast_fp16 = add(x = hidden_states_195_cast_fp16, y = hidden_states_197_cast_fp16)[name = string("hidden_states_199_cast_fp16")];
            fp16 const_202_promoted_to_fp16 = const()[name = string("const_202_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_7329_cast_fp16 = mul(x = hidden_states_199_cast_fp16, y = const_202_promoted_to_fp16)[name = string("op_7329_cast_fp16")];
            int32 var_7327 = const()[name = string("op_7327"), val = int32(1)];
            bool doubled_161_interleave_0 = const()[name = string("doubled_161_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_161_cast_fp16 = concat(axis = var_7327, interleave = doubled_161_interleave_0, values = (hidden_states_199_cast_fp16, var_7329_cast_fp16))[name = string("doubled_161_cast_fp16")];
            tensor<int32, [1]> out_81_axes_0 = const()[name = string("out_81_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_81_gamma_0_to_fp16 = const()[name = string("out_81_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1493109952)))];
            fp16 var_7339_to_fp16 = const()[name = string("op_7339_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_81_cast_fp16 = layer_norm(axes = out_81_axes_0, epsilon = var_7339_to_fp16, gamma = out_81_gamma_0_to_fp16, x = doubled_161_cast_fp16)[name = string("out_81_cast_fp16")];
            tensor<int32, [2]> var_7350_split_sizes_0 = const()[name = string("op_7350_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7350_axis_0 = const()[name = string("op_7350_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_7350_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_7350_cast_fp16_1 = split(axis = var_7350_axis_0, split_sizes = var_7350_split_sizes_0, x = out_81_cast_fp16)[name = string("op_7350_cast_fp16")];
            tensor<int32, [2]> query_states_121_strides_0 = const()[name = string("query_states_121_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_121_pad_type_0 = const()[name = string("query_states_121_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_121_pad_0 = const()[name = string("query_states_121_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_121_dilations_0 = const()[name = string("query_states_121_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_121_groups_0 = const()[name = string("query_states_121_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_121_cast_fp16 = conv(dilations = query_states_121_dilations_0, groups = query_states_121_groups_0, pad = query_states_121_pad_0, pad_type = query_states_121_pad_type_0, strides = query_states_121_strides_0, weight = layers_20_self_attn_q_proj_weight_cast_fp16, x = var_7350_cast_fp16_0)[name = string("query_states_121_cast_fp16")];
            tensor<int32, [2]> key_states_201_strides_0 = const()[name = string("key_states_201_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_201_pad_type_0 = const()[name = string("key_states_201_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_201_pad_0 = const()[name = string("key_states_201_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_201_dilations_0 = const()[name = string("key_states_201_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_201_groups_0 = const()[name = string("key_states_201_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_201_cast_fp16 = conv(dilations = key_states_201_dilations_0, groups = key_states_201_groups_0, pad = key_states_201_pad_0, pad_type = key_states_201_pad_type_0, strides = key_states_201_strides_0, weight = layers_20_self_attn_k_proj_weight_cast_fp16, x = var_7350_cast_fp16_0)[name = string("key_states_201_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_20_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_20_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1493118208)))];
            tensor<int32, [2]> value_states_121_strides_0 = const()[name = string("value_states_121_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_121_pad_type_0 = const()[name = string("value_states_121_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_121_pad_0 = const()[name = string("value_states_121_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_121_dilations_0 = const()[name = string("value_states_121_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_121_groups_0 = const()[name = string("value_states_121_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_121_cast_fp16 = conv(dilations = value_states_121_dilations_0, groups = value_states_121_groups_0, pad = value_states_121_pad_0, pad_type = value_states_121_pad_type_0, strides = value_states_121_strides_0, weight = layers_20_self_attn_v_proj_weight_to_fp16, x = var_7350_cast_fp16_0)[name = string("value_states_121_cast_fp16")];
            tensor<int32, [4]> concat_240x = const()[name = string("concat_240x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_201_cast_fp16 = reshape(shape = concat_240x, x = query_states_121_cast_fp16)[name = string("x_201_cast_fp16")];
            tensor<int32, [4]> concat_241x = const()[name = string("concat_241x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_7407_cast_fp16 = reshape(shape = concat_241x, x = key_states_201_cast_fp16)[name = string("op_7407_cast_fp16")];
            tensor<int32, [4]> concat_242x = const()[name = string("concat_242x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_7414_cast_fp16 = reshape(shape = concat_242x, x = value_states_121_cast_fp16)[name = string("op_7414_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_7418_cast_fp16 = mul(x = x_201_cast_fp16, y = var_859_cast_fp16)[name = string("op_7418_cast_fp16")];
            tensor<int32, [2]> var_7419_split_sizes_0 = const()[name = string("op_7419_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7419_axis_0 = const()[name = string("op_7419_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_7419_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_7419_cast_fp16_1 = split(axis = var_7419_axis_0, split_sizes = var_7419_split_sizes_0, x = x_201_cast_fp16)[name = string("op_7419_cast_fp16")];
            fp16 const_204_promoted_to_fp16 = const()[name = string("const_204_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_7421_cast_fp16 = mul(x = var_7419_cast_fp16_1, y = const_204_promoted_to_fp16)[name = string("op_7421_cast_fp16")];
            int32 var_7423 = const()[name = string("op_7423"), val = int32(-2)];
            bool var_7424_interleave_0 = const()[name = string("op_7424_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_7424_cast_fp16 = concat(axis = var_7423, interleave = var_7424_interleave_0, values = (var_7421_cast_fp16, var_7419_cast_fp16_0))[name = string("op_7424_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_7425_cast_fp16 = mul(x = var_7424_cast_fp16, y = var_866_cast_fp16)[name = string("op_7425_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_123_cast_fp16 = add(x = var_7418_cast_fp16, y = var_7425_cast_fp16)[name = string("query_states_123_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_7431_cast_fp16 = mul(x = var_7407_cast_fp16, y = var_859_cast_fp16)[name = string("op_7431_cast_fp16")];
            tensor<int32, [2]> var_7432_split_sizes_0 = const()[name = string("op_7432_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7432_axis_0 = const()[name = string("op_7432_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_7432_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_7432_cast_fp16_1 = split(axis = var_7432_axis_0, split_sizes = var_7432_split_sizes_0, x = var_7407_cast_fp16)[name = string("op_7432_cast_fp16")];
            fp16 const_205_promoted_to_fp16 = const()[name = string("const_205_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_7434_cast_fp16 = mul(x = var_7432_cast_fp16_1, y = const_205_promoted_to_fp16)[name = string("op_7434_cast_fp16")];
            int32 var_7436 = const()[name = string("op_7436"), val = int32(-2)];
            bool var_7437_interleave_0 = const()[name = string("op_7437_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_7437_cast_fp16 = concat(axis = var_7436, interleave = var_7437_interleave_0, values = (var_7434_cast_fp16, var_7432_cast_fp16_0))[name = string("op_7437_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_7438_cast_fp16 = mul(x = var_7437_cast_fp16, y = var_866_cast_fp16)[name = string("op_7438_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_205_cast_fp16 = add(x = var_7431_cast_fp16, y = var_7438_cast_fp16)[name = string("key_states_205_cast_fp16")];
            tensor<int32, [1]> expand_dims_240 = const()[name = string("expand_dims_240"), val = tensor<int32, [1]>([20])];
            tensor<int32, [1]> expand_dims_241 = const()[name = string("expand_dims_241"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_243 = const()[name = string("expand_dims_243"), val = tensor<int32, [1]>([0])];
            int32 concat_245_axis_0 = const()[name = string("concat_245_axis_0"), val = int32(0)];
            bool concat_245_interleave_0 = const()[name = string("concat_245_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_245 = concat(axis = concat_245_axis_0, interleave = concat_245_interleave_0, values = (expand_dims_240, expand_dims_241, position_id, expand_dims_243))[name = string("concat_245")];
            tensor<int32, [1]> expand_dims_244 = const()[name = string("expand_dims_244"), val = tensor<int32, [1]>([21])];
            tensor<int32, [1]> concat_246_values1_0 = const()[name = string("concat_246_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_246_values3_0 = const()[name = string("concat_246_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_246_axis_0 = const()[name = string("concat_246_axis_0"), val = int32(0)];
            bool concat_246_interleave_0 = const()[name = string("concat_246_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_246 = concat(axis = concat_246_axis_0, interleave = concat_246_interleave_0, values = (expand_dims_244, concat_246_values1_0, cache_position_end, concat_246_values3_0))[name = string("concat_246")];
            tensor<int32, [4]> key_states_207_perm_0 = const()[name = string("key_states_207_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_21_stride_0 = const()[name = string("key_cache_internal_tensor_assign_21_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_21_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_21_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_21_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_21_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_21_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_21_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_207_cast_fp16 = transpose(perm = key_states_207_perm_0, x = key_states_205_cast_fp16)[name = string("transpose_23")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_21_cast_fp16 = slice_update(begin = concat_245, begin_mask = key_cache_internal_tensor_assign_21_begin_mask_0, end = concat_246, end_mask = key_cache_internal_tensor_assign_21_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_21_squeeze_mask_0, stride = key_cache_internal_tensor_assign_21_stride_0, update = key_states_207_cast_fp16, x = coreml_update_state_38)[name = string("key_cache_internal_tensor_assign_21_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_21_cast_fp16, input = key_cache)[name = string("coreml_update_state_40_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_40 = read_state(input = key_cache)[name = string("coreml_update_state_40")];
            tensor<int32, [4]> value_states_123_perm_0 = const()[name = string("value_states_123_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_21_stride_0 = const()[name = string("value_cache_internal_tensor_assign_21_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_21_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_21_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_21_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_21_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_21_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_21_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_123_cast_fp16 = transpose(perm = value_states_123_perm_0, x = var_7414_cast_fp16)[name = string("transpose_22")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_21_cast_fp16 = slice_update(begin = concat_245, begin_mask = value_cache_internal_tensor_assign_21_begin_mask_0, end = concat_246, end_mask = value_cache_internal_tensor_assign_21_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_21_squeeze_mask_0, stride = value_cache_internal_tensor_assign_21_stride_0, update = value_states_123_cast_fp16, x = coreml_update_state_39)[name = string("value_cache_internal_tensor_assign_21_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_21_cast_fp16, input = value_cache)[name = string("coreml_update_state_41_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_41 = read_state(input = value_cache)[name = string("coreml_update_state_41")];
            tensor<int32, [4]> var_7508_begin_0 = const()[name = string("op_7508_begin_0"), val = tensor<int32, [4]>([20, 0, 0, 0])];
            tensor<int32, [4]> var_7508_end_0 = const()[name = string("op_7508_end_0"), val = tensor<int32, [4]>([21, 2, 2048, 128])];
            tensor<bool, [4]> var_7508_end_mask_0 = const()[name = string("op_7508_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7508_cast_fp16 = slice_by_index(begin = var_7508_begin_0, end = var_7508_end_0, end_mask = var_7508_end_mask_0, x = coreml_update_state_40)[name = string("op_7508_cast_fp16")];
            tensor<int32, [2]> tile_40 = const()[name = string("tile_40"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7511_axis_0 = const()[name = string("op_7511_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7511_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7511_cast_fp16_1 = split(axis = var_7511_axis_0, split_sizes = tile_40, x = var_7508_cast_fp16)[name = string("op_7511_cast_fp16")];
            tensor<int32, [4]> var_7518_begin_0 = const()[name = string("op_7518_begin_0"), val = tensor<int32, [4]>([20, 0, 0, 0])];
            tensor<int32, [4]> var_7518_end_0 = const()[name = string("op_7518_end_0"), val = tensor<int32, [4]>([21, 2, 2048, 128])];
            tensor<bool, [4]> var_7518_end_mask_0 = const()[name = string("op_7518_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7518_cast_fp16 = slice_by_index(begin = var_7518_begin_0, end = var_7518_end_0, end_mask = var_7518_end_mask_0, x = coreml_update_state_41)[name = string("op_7518_cast_fp16")];
            tensor<int32, [2]> tile_41 = const()[name = string("tile_41"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7521_axis_0 = const()[name = string("op_7521_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7521_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7521_cast_fp16_1 = split(axis = var_7521_axis_0, split_sizes = tile_41, x = var_7518_cast_fp16)[name = string("op_7521_cast_fp16")];
            tensor<int32, [2]> var_7524_split_sizes_0 = const()[name = string("op_7524_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_7524_axis_0 = const()[name = string("op_7524_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_7524_0, tensor<fp16, [1, 8, 128, 1]> var_7524_1 = split(axis = var_7524_axis_0, split_sizes = var_7524_split_sizes_0, x = query_states_123_cast_fp16)[name = string("op_7524")];
            bool attn_weights_321_transpose_x_0 = const()[name = string("attn_weights_321_transpose_x_0"), val = bool(false)];
            bool attn_weights_321_transpose_y_0 = const()[name = string("attn_weights_321_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_321_cast_fp16 = matmul(transpose_x = attn_weights_321_transpose_x_0, transpose_y = attn_weights_321_transpose_y_0, x = var_7511_cast_fp16_0, y = var_7524_0)[name = string("attn_weights_321_cast_fp16")];
            fp16 var_7527_to_fp16 = const()[name = string("op_7527_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_323_cast_fp16 = mul(x = attn_weights_321_cast_fp16, y = var_7527_to_fp16)[name = string("attn_weights_323_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_325_cast_fp16 = add(x = attn_weights_323_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_325_cast_fp16")];
            int32 var_7531 = const()[name = string("op_7531"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_327_cast_fp16 = softmax(axis = var_7531, x = attn_weights_325_cast_fp16)[name = string("attn_weights_327_cast_fp16")];
            bool var_7537_transpose_x_1 = const()[name = string("op_7537_transpose_x_1"), val = bool(true)];
            bool var_7537_transpose_y_1 = const()[name = string("op_7537_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_7537_cast_fp16 = matmul(transpose_x = var_7537_transpose_x_1, transpose_y = var_7537_transpose_y_1, x = attn_weights_327_cast_fp16, y = var_7521_cast_fp16_0)[name = string("op_7537_cast_fp16")];
            bool attn_weights_329_transpose_x_0 = const()[name = string("attn_weights_329_transpose_x_0"), val = bool(false)];
            bool attn_weights_329_transpose_y_0 = const()[name = string("attn_weights_329_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_329_cast_fp16 = matmul(transpose_x = attn_weights_329_transpose_x_0, transpose_y = attn_weights_329_transpose_y_0, x = var_7511_cast_fp16_1, y = var_7524_1)[name = string("attn_weights_329_cast_fp16")];
            fp16 var_7539_to_fp16 = const()[name = string("op_7539_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_331_cast_fp16 = mul(x = attn_weights_329_cast_fp16, y = var_7539_to_fp16)[name = string("attn_weights_331_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_333_cast_fp16 = add(x = attn_weights_331_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_333_cast_fp16")];
            int32 var_7543 = const()[name = string("op_7543"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_335_cast_fp16 = softmax(axis = var_7543, x = attn_weights_333_cast_fp16)[name = string("attn_weights_335_cast_fp16")];
            bool attn_output_161_transpose_x_1 = const()[name = string("attn_output_161_transpose_x_1"), val = bool(true)];
            bool attn_output_161_transpose_y_1 = const()[name = string("attn_output_161_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_161_cast_fp16 = matmul(transpose_x = attn_output_161_transpose_x_1, transpose_y = attn_output_161_transpose_y_1, x = attn_weights_335_cast_fp16, y = var_7521_cast_fp16_1)[name = string("attn_output_161_cast_fp16")];
            int32 var_7551 = const()[name = string("op_7551"), val = int32(1)];
            bool attn_output_163_interleave_0 = const()[name = string("attn_output_163_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_163_cast_fp16 = concat(axis = var_7551, interleave = attn_output_163_interleave_0, values = (var_7537_cast_fp16, attn_output_161_cast_fp16))[name = string("attn_output_163_cast_fp16")];
            tensor<int32, [4]> var_7555_perm_0 = const()[name = string("op_7555_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_251x = const()[name = string("concat_251x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_7555_cast_fp16 = transpose(perm = var_7555_perm_0, x = attn_output_163_cast_fp16)[name = string("transpose_21")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_167_cast_fp16 = reshape(shape = concat_251x, x = var_7555_cast_fp16)[name = string("attn_output_167_cast_fp16")];
            tensor<int32, [2]> hidden_states_203_strides_0 = const()[name = string("hidden_states_203_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_203_pad_type_0 = const()[name = string("hidden_states_203_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_203_pad_0 = const()[name = string("hidden_states_203_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_203_dilations_0 = const()[name = string("hidden_states_203_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_203_groups_0 = const()[name = string("hidden_states_203_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_203_cast_fp16 = conv(dilations = hidden_states_203_dilations_0, groups = hidden_states_203_groups_0, pad = hidden_states_203_pad_0, pad_type = hidden_states_203_pad_type_0, strides = hidden_states_203_strides_0, weight = layers_20_self_attn_o_proj_weight_cast_fp16, x = attn_output_167_cast_fp16)[name = string("hidden_states_203_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_205_cast_fp16 = add(x = hidden_states_199_cast_fp16, y = hidden_states_203_cast_fp16)[name = string("hidden_states_205_cast_fp16")];
            fp16 const_210_promoted_to_fp16 = const()[name = string("const_210_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_7588_cast_fp16 = mul(x = hidden_states_205_cast_fp16, y = const_210_promoted_to_fp16)[name = string("op_7588_cast_fp16")];
            int32 var_7586 = const()[name = string("op_7586"), val = int32(1)];
            bool doubled_165_interleave_0 = const()[name = string("doubled_165_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_165_cast_fp16 = concat(axis = var_7586, interleave = doubled_165_interleave_0, values = (hidden_states_205_cast_fp16, var_7588_cast_fp16))[name = string("doubled_165_cast_fp16")];
            tensor<int32, [1]> out_83_axes_0 = const()[name = string("out_83_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_83_gamma_0_to_fp16 = const()[name = string("out_83_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1494166848)))];
            fp16 var_7598_to_fp16 = const()[name = string("op_7598_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_83_cast_fp16 = layer_norm(axes = out_83_axes_0, epsilon = var_7598_to_fp16, gamma = out_83_gamma_0_to_fp16, x = doubled_165_cast_fp16)[name = string("out_83_cast_fp16")];
            tensor<int32, [2]> var_7609_split_sizes_0 = const()[name = string("op_7609_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7609_axis_0 = const()[name = string("op_7609_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_7609_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_7609_cast_fp16_1 = split(axis = var_7609_axis_0, split_sizes = var_7609_split_sizes_0, x = out_83_cast_fp16)[name = string("op_7609_cast_fp16")];
            tensor<int32, [2]> input_41_strides_0 = const()[name = string("input_41_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_41_pad_type_0 = const()[name = string("input_41_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_41_pad_0 = const()[name = string("input_41_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_41_dilations_0 = const()[name = string("input_41_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_41_groups_0 = const()[name = string("input_41_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_41_cast_fp16 = conv(dilations = input_41_dilations_0, groups = input_41_groups_0, pad = input_41_pad_0, pad_type = input_41_pad_type_0, strides = input_41_strides_0, weight = layers_20_mlp_gate_proj_weight_cast_fp16, x = var_7609_cast_fp16_0)[name = string("input_41_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_7626_cast_fp16 = silu(x = input_41_cast_fp16)[name = string("op_7626_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_20_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_20_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1494175104)))];
            tensor<int32, [2]> var_7632_strides_0 = const()[name = string("op_7632_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_7632_pad_type_0 = const()[name = string("op_7632_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_7632_pad_0 = const()[name = string("op_7632_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_7632_dilations_0 = const()[name = string("op_7632_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7632_groups_0 = const()[name = string("op_7632_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_7632_cast_fp16 = conv(dilations = var_7632_dilations_0, groups = var_7632_groups_0, pad = var_7632_pad_0, pad_type = var_7632_pad_type_0, strides = var_7632_strides_0, weight = layers_20_mlp_up_proj_weight_to_fp16, x = var_7609_cast_fp16_0)[name = string("op_7632_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_209_cast_fp16 = mul(x = var_7626_cast_fp16, y = var_7632_cast_fp16)[name = string("x_209_cast_fp16")];
            tensor<int32, [2]> hidden_states_207_strides_0 = const()[name = string("hidden_states_207_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_207_pad_type_0 = const()[name = string("hidden_states_207_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_207_pad_0 = const()[name = string("hidden_states_207_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_207_dilations_0 = const()[name = string("hidden_states_207_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_207_groups_0 = const()[name = string("hidden_states_207_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_207_cast_fp16 = conv(dilations = hidden_states_207_dilations_0, groups = hidden_states_207_groups_0, pad = hidden_states_207_pad_0, pad_type = hidden_states_207_pad_type_0, strides = hidden_states_207_strides_0, weight = layers_20_mlp_down_proj_weight_cast_fp16, x = x_209_cast_fp16)[name = string("hidden_states_207_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_209_cast_fp16 = add(x = hidden_states_205_cast_fp16, y = hidden_states_207_cast_fp16)[name = string("hidden_states_209_cast_fp16")];
            fp16 const_212_promoted_to_fp16 = const()[name = string("const_212_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_7650_cast_fp16 = mul(x = hidden_states_209_cast_fp16, y = const_212_promoted_to_fp16)[name = string("op_7650_cast_fp16")];
            int32 var_7648 = const()[name = string("op_7648"), val = int32(1)];
            bool doubled_169_interleave_0 = const()[name = string("doubled_169_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_169_cast_fp16 = concat(axis = var_7648, interleave = doubled_169_interleave_0, values = (hidden_states_209_cast_fp16, var_7650_cast_fp16))[name = string("doubled_169_cast_fp16")];
            tensor<int32, [1]> out_85_axes_0 = const()[name = string("out_85_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_85_gamma_0_to_fp16 = const()[name = string("out_85_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1519340992)))];
            fp16 var_7660_to_fp16 = const()[name = string("op_7660_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_85_cast_fp16 = layer_norm(axes = out_85_axes_0, epsilon = var_7660_to_fp16, gamma = out_85_gamma_0_to_fp16, x = doubled_169_cast_fp16)[name = string("out_85_cast_fp16")];
            tensor<int32, [2]> var_7671_split_sizes_0 = const()[name = string("op_7671_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7671_axis_0 = const()[name = string("op_7671_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_7671_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_7671_cast_fp16_1 = split(axis = var_7671_axis_0, split_sizes = var_7671_split_sizes_0, x = out_85_cast_fp16)[name = string("op_7671_cast_fp16")];
            tensor<int32, [2]> query_states_127_strides_0 = const()[name = string("query_states_127_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_127_pad_type_0 = const()[name = string("query_states_127_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_127_pad_0 = const()[name = string("query_states_127_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_127_dilations_0 = const()[name = string("query_states_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_127_groups_0 = const()[name = string("query_states_127_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_127_cast_fp16 = conv(dilations = query_states_127_dilations_0, groups = query_states_127_groups_0, pad = query_states_127_pad_0, pad_type = query_states_127_pad_type_0, strides = query_states_127_strides_0, weight = layers_21_self_attn_q_proj_weight_cast_fp16, x = var_7671_cast_fp16_0)[name = string("query_states_127_cast_fp16")];
            tensor<int32, [2]> key_states_211_strides_0 = const()[name = string("key_states_211_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_211_pad_type_0 = const()[name = string("key_states_211_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_211_pad_0 = const()[name = string("key_states_211_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_211_dilations_0 = const()[name = string("key_states_211_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_211_groups_0 = const()[name = string("key_states_211_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_211_cast_fp16 = conv(dilations = key_states_211_dilations_0, groups = key_states_211_groups_0, pad = key_states_211_pad_0, pad_type = key_states_211_pad_type_0, strides = key_states_211_strides_0, weight = layers_21_self_attn_k_proj_weight_cast_fp16, x = var_7671_cast_fp16_0)[name = string("key_states_211_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_21_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_21_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1519349248)))];
            tensor<int32, [2]> value_states_127_strides_0 = const()[name = string("value_states_127_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_127_pad_type_0 = const()[name = string("value_states_127_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_127_pad_0 = const()[name = string("value_states_127_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_127_dilations_0 = const()[name = string("value_states_127_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_127_groups_0 = const()[name = string("value_states_127_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_127_cast_fp16 = conv(dilations = value_states_127_dilations_0, groups = value_states_127_groups_0, pad = value_states_127_pad_0, pad_type = value_states_127_pad_type_0, strides = value_states_127_strides_0, weight = layers_21_self_attn_v_proj_weight_to_fp16, x = var_7671_cast_fp16_0)[name = string("value_states_127_cast_fp16")];
            tensor<int32, [4]> concat_252x = const()[name = string("concat_252x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_211_cast_fp16 = reshape(shape = concat_252x, x = query_states_127_cast_fp16)[name = string("x_211_cast_fp16")];
            tensor<int32, [4]> concat_253x = const()[name = string("concat_253x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_7728_cast_fp16 = reshape(shape = concat_253x, x = key_states_211_cast_fp16)[name = string("op_7728_cast_fp16")];
            tensor<int32, [4]> concat_254x = const()[name = string("concat_254x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_7735_cast_fp16 = reshape(shape = concat_254x, x = value_states_127_cast_fp16)[name = string("op_7735_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_7739_cast_fp16 = mul(x = x_211_cast_fp16, y = var_859_cast_fp16)[name = string("op_7739_cast_fp16")];
            tensor<int32, [2]> var_7740_split_sizes_0 = const()[name = string("op_7740_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7740_axis_0 = const()[name = string("op_7740_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_7740_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_7740_cast_fp16_1 = split(axis = var_7740_axis_0, split_sizes = var_7740_split_sizes_0, x = x_211_cast_fp16)[name = string("op_7740_cast_fp16")];
            fp16 const_214_promoted_to_fp16 = const()[name = string("const_214_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_7742_cast_fp16 = mul(x = var_7740_cast_fp16_1, y = const_214_promoted_to_fp16)[name = string("op_7742_cast_fp16")];
            int32 var_7744 = const()[name = string("op_7744"), val = int32(-2)];
            bool var_7745_interleave_0 = const()[name = string("op_7745_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_7745_cast_fp16 = concat(axis = var_7744, interleave = var_7745_interleave_0, values = (var_7742_cast_fp16, var_7740_cast_fp16_0))[name = string("op_7745_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_7746_cast_fp16 = mul(x = var_7745_cast_fp16, y = var_866_cast_fp16)[name = string("op_7746_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_129_cast_fp16 = add(x = var_7739_cast_fp16, y = var_7746_cast_fp16)[name = string("query_states_129_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_7752_cast_fp16 = mul(x = var_7728_cast_fp16, y = var_859_cast_fp16)[name = string("op_7752_cast_fp16")];
            tensor<int32, [2]> var_7753_split_sizes_0 = const()[name = string("op_7753_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_7753_axis_0 = const()[name = string("op_7753_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_7753_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_7753_cast_fp16_1 = split(axis = var_7753_axis_0, split_sizes = var_7753_split_sizes_0, x = var_7728_cast_fp16)[name = string("op_7753_cast_fp16")];
            fp16 const_215_promoted_to_fp16 = const()[name = string("const_215_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_7755_cast_fp16 = mul(x = var_7753_cast_fp16_1, y = const_215_promoted_to_fp16)[name = string("op_7755_cast_fp16")];
            int32 var_7757 = const()[name = string("op_7757"), val = int32(-2)];
            bool var_7758_interleave_0 = const()[name = string("op_7758_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_7758_cast_fp16 = concat(axis = var_7757, interleave = var_7758_interleave_0, values = (var_7755_cast_fp16, var_7753_cast_fp16_0))[name = string("op_7758_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_7759_cast_fp16 = mul(x = var_7758_cast_fp16, y = var_866_cast_fp16)[name = string("op_7759_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_215_cast_fp16 = add(x = var_7752_cast_fp16, y = var_7759_cast_fp16)[name = string("key_states_215_cast_fp16")];
            tensor<int32, [1]> expand_dims_252 = const()[name = string("expand_dims_252"), val = tensor<int32, [1]>([21])];
            tensor<int32, [1]> expand_dims_253 = const()[name = string("expand_dims_253"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_255 = const()[name = string("expand_dims_255"), val = tensor<int32, [1]>([0])];
            int32 concat_257_axis_0 = const()[name = string("concat_257_axis_0"), val = int32(0)];
            bool concat_257_interleave_0 = const()[name = string("concat_257_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_257 = concat(axis = concat_257_axis_0, interleave = concat_257_interleave_0, values = (expand_dims_252, expand_dims_253, position_id, expand_dims_255))[name = string("concat_257")];
            tensor<int32, [1]> expand_dims_256 = const()[name = string("expand_dims_256"), val = tensor<int32, [1]>([22])];
            tensor<int32, [1]> concat_258_values1_0 = const()[name = string("concat_258_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_258_values3_0 = const()[name = string("concat_258_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_258_axis_0 = const()[name = string("concat_258_axis_0"), val = int32(0)];
            bool concat_258_interleave_0 = const()[name = string("concat_258_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_258 = concat(axis = concat_258_axis_0, interleave = concat_258_interleave_0, values = (expand_dims_256, concat_258_values1_0, cache_position_end, concat_258_values3_0))[name = string("concat_258")];
            tensor<int32, [4]> key_states_217_perm_0 = const()[name = string("key_states_217_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_22_stride_0 = const()[name = string("key_cache_internal_tensor_assign_22_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_22_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_22_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_22_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_22_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_22_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_22_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_217_cast_fp16 = transpose(perm = key_states_217_perm_0, x = key_states_215_cast_fp16)[name = string("transpose_20")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_22_cast_fp16 = slice_update(begin = concat_257, begin_mask = key_cache_internal_tensor_assign_22_begin_mask_0, end = concat_258, end_mask = key_cache_internal_tensor_assign_22_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_22_squeeze_mask_0, stride = key_cache_internal_tensor_assign_22_stride_0, update = key_states_217_cast_fp16, x = coreml_update_state_40)[name = string("key_cache_internal_tensor_assign_22_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_22_cast_fp16, input = key_cache)[name = string("coreml_update_state_42_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_42 = read_state(input = key_cache)[name = string("coreml_update_state_42")];
            tensor<int32, [4]> value_states_129_perm_0 = const()[name = string("value_states_129_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_22_stride_0 = const()[name = string("value_cache_internal_tensor_assign_22_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_22_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_22_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_22_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_22_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_22_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_22_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_129_cast_fp16 = transpose(perm = value_states_129_perm_0, x = var_7735_cast_fp16)[name = string("transpose_19")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_22_cast_fp16 = slice_update(begin = concat_257, begin_mask = value_cache_internal_tensor_assign_22_begin_mask_0, end = concat_258, end_mask = value_cache_internal_tensor_assign_22_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_22_squeeze_mask_0, stride = value_cache_internal_tensor_assign_22_stride_0, update = value_states_129_cast_fp16, x = coreml_update_state_41)[name = string("value_cache_internal_tensor_assign_22_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_22_cast_fp16, input = value_cache)[name = string("coreml_update_state_43_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_43 = read_state(input = value_cache)[name = string("coreml_update_state_43")];
            tensor<int32, [4]> var_7829_begin_0 = const()[name = string("op_7829_begin_0"), val = tensor<int32, [4]>([21, 0, 0, 0])];
            tensor<int32, [4]> var_7829_end_0 = const()[name = string("op_7829_end_0"), val = tensor<int32, [4]>([22, 2, 2048, 128])];
            tensor<bool, [4]> var_7829_end_mask_0 = const()[name = string("op_7829_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7829_cast_fp16 = slice_by_index(begin = var_7829_begin_0, end = var_7829_end_0, end_mask = var_7829_end_mask_0, x = coreml_update_state_42)[name = string("op_7829_cast_fp16")];
            tensor<int32, [2]> tile_42 = const()[name = string("tile_42"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7832_axis_0 = const()[name = string("op_7832_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7832_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7832_cast_fp16_1 = split(axis = var_7832_axis_0, split_sizes = tile_42, x = var_7829_cast_fp16)[name = string("op_7832_cast_fp16")];
            tensor<int32, [4]> var_7839_begin_0 = const()[name = string("op_7839_begin_0"), val = tensor<int32, [4]>([21, 0, 0, 0])];
            tensor<int32, [4]> var_7839_end_0 = const()[name = string("op_7839_end_0"), val = tensor<int32, [4]>([22, 2, 2048, 128])];
            tensor<bool, [4]> var_7839_end_mask_0 = const()[name = string("op_7839_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_7839_cast_fp16 = slice_by_index(begin = var_7839_begin_0, end = var_7839_end_0, end_mask = var_7839_end_mask_0, x = coreml_update_state_43)[name = string("op_7839_cast_fp16")];
            tensor<int32, [2]> tile_43 = const()[name = string("tile_43"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7842_axis_0 = const()[name = string("op_7842_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_7842_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_7842_cast_fp16_1 = split(axis = var_7842_axis_0, split_sizes = tile_43, x = var_7839_cast_fp16)[name = string("op_7842_cast_fp16")];
            tensor<int32, [2]> var_7845_split_sizes_0 = const()[name = string("op_7845_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_7845_axis_0 = const()[name = string("op_7845_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_7845_0, tensor<fp16, [1, 8, 128, 1]> var_7845_1 = split(axis = var_7845_axis_0, split_sizes = var_7845_split_sizes_0, x = query_states_129_cast_fp16)[name = string("op_7845")];
            bool attn_weights_337_transpose_x_0 = const()[name = string("attn_weights_337_transpose_x_0"), val = bool(false)];
            bool attn_weights_337_transpose_y_0 = const()[name = string("attn_weights_337_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_337_cast_fp16 = matmul(transpose_x = attn_weights_337_transpose_x_0, transpose_y = attn_weights_337_transpose_y_0, x = var_7832_cast_fp16_0, y = var_7845_0)[name = string("attn_weights_337_cast_fp16")];
            fp16 var_7848_to_fp16 = const()[name = string("op_7848_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_339_cast_fp16 = mul(x = attn_weights_337_cast_fp16, y = var_7848_to_fp16)[name = string("attn_weights_339_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_341_cast_fp16 = add(x = attn_weights_339_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_341_cast_fp16")];
            int32 var_7852 = const()[name = string("op_7852"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_343_cast_fp16 = softmax(axis = var_7852, x = attn_weights_341_cast_fp16)[name = string("attn_weights_343_cast_fp16")];
            bool var_7858_transpose_x_1 = const()[name = string("op_7858_transpose_x_1"), val = bool(true)];
            bool var_7858_transpose_y_1 = const()[name = string("op_7858_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_7858_cast_fp16 = matmul(transpose_x = var_7858_transpose_x_1, transpose_y = var_7858_transpose_y_1, x = attn_weights_343_cast_fp16, y = var_7842_cast_fp16_0)[name = string("op_7858_cast_fp16")];
            bool attn_weights_345_transpose_x_0 = const()[name = string("attn_weights_345_transpose_x_0"), val = bool(false)];
            bool attn_weights_345_transpose_y_0 = const()[name = string("attn_weights_345_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_345_cast_fp16 = matmul(transpose_x = attn_weights_345_transpose_x_0, transpose_y = attn_weights_345_transpose_y_0, x = var_7832_cast_fp16_1, y = var_7845_1)[name = string("attn_weights_345_cast_fp16")];
            fp16 var_7860_to_fp16 = const()[name = string("op_7860_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_347_cast_fp16 = mul(x = attn_weights_345_cast_fp16, y = var_7860_to_fp16)[name = string("attn_weights_347_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_349_cast_fp16 = add(x = attn_weights_347_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_349_cast_fp16")];
            int32 var_7864 = const()[name = string("op_7864"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_351_cast_fp16 = softmax(axis = var_7864, x = attn_weights_349_cast_fp16)[name = string("attn_weights_351_cast_fp16")];
            bool attn_output_169_transpose_x_1 = const()[name = string("attn_output_169_transpose_x_1"), val = bool(true)];
            bool attn_output_169_transpose_y_1 = const()[name = string("attn_output_169_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_169_cast_fp16 = matmul(transpose_x = attn_output_169_transpose_x_1, transpose_y = attn_output_169_transpose_y_1, x = attn_weights_351_cast_fp16, y = var_7842_cast_fp16_1)[name = string("attn_output_169_cast_fp16")];
            int32 var_7872 = const()[name = string("op_7872"), val = int32(1)];
            bool attn_output_171_interleave_0 = const()[name = string("attn_output_171_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_171_cast_fp16 = concat(axis = var_7872, interleave = attn_output_171_interleave_0, values = (var_7858_cast_fp16, attn_output_169_cast_fp16))[name = string("attn_output_171_cast_fp16")];
            tensor<int32, [4]> var_7876_perm_0 = const()[name = string("op_7876_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_263x = const()[name = string("concat_263x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_7876_cast_fp16 = transpose(perm = var_7876_perm_0, x = attn_output_171_cast_fp16)[name = string("transpose_18")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_175_cast_fp16 = reshape(shape = concat_263x, x = var_7876_cast_fp16)[name = string("attn_output_175_cast_fp16")];
            tensor<int32, [2]> hidden_states_213_strides_0 = const()[name = string("hidden_states_213_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_213_pad_type_0 = const()[name = string("hidden_states_213_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_213_pad_0 = const()[name = string("hidden_states_213_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_213_dilations_0 = const()[name = string("hidden_states_213_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_213_groups_0 = const()[name = string("hidden_states_213_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_213_cast_fp16 = conv(dilations = hidden_states_213_dilations_0, groups = hidden_states_213_groups_0, pad = hidden_states_213_pad_0, pad_type = hidden_states_213_pad_type_0, strides = hidden_states_213_strides_0, weight = layers_21_self_attn_o_proj_weight_cast_fp16, x = attn_output_175_cast_fp16)[name = string("hidden_states_213_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_215_cast_fp16 = add(x = hidden_states_209_cast_fp16, y = hidden_states_213_cast_fp16)[name = string("hidden_states_215_cast_fp16")];
            fp16 const_220_promoted_to_fp16 = const()[name = string("const_220_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_7909_cast_fp16 = mul(x = hidden_states_215_cast_fp16, y = const_220_promoted_to_fp16)[name = string("op_7909_cast_fp16")];
            int32 var_7907 = const()[name = string("op_7907"), val = int32(1)];
            bool doubled_173_interleave_0 = const()[name = string("doubled_173_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_173_cast_fp16 = concat(axis = var_7907, interleave = doubled_173_interleave_0, values = (hidden_states_215_cast_fp16, var_7909_cast_fp16))[name = string("doubled_173_cast_fp16")];
            tensor<int32, [1]> out_87_axes_0 = const()[name = string("out_87_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_87_gamma_0_to_fp16 = const()[name = string("out_87_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1520397888)))];
            fp16 var_7919_to_fp16 = const()[name = string("op_7919_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_87_cast_fp16 = layer_norm(axes = out_87_axes_0, epsilon = var_7919_to_fp16, gamma = out_87_gamma_0_to_fp16, x = doubled_173_cast_fp16)[name = string("out_87_cast_fp16")];
            tensor<int32, [2]> var_7930_split_sizes_0 = const()[name = string("op_7930_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7930_axis_0 = const()[name = string("op_7930_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_7930_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_7930_cast_fp16_1 = split(axis = var_7930_axis_0, split_sizes = var_7930_split_sizes_0, x = out_87_cast_fp16)[name = string("op_7930_cast_fp16")];
            tensor<int32, [2]> input_43_strides_0 = const()[name = string("input_43_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_43_pad_type_0 = const()[name = string("input_43_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_43_pad_0 = const()[name = string("input_43_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_43_dilations_0 = const()[name = string("input_43_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_43_groups_0 = const()[name = string("input_43_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_43_cast_fp16 = conv(dilations = input_43_dilations_0, groups = input_43_groups_0, pad = input_43_pad_0, pad_type = input_43_pad_type_0, strides = input_43_strides_0, weight = layers_21_mlp_gate_proj_weight_cast_fp16, x = var_7930_cast_fp16_0)[name = string("input_43_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_7947_cast_fp16 = silu(x = input_43_cast_fp16)[name = string("op_7947_cast_fp16")];
            tensor<int32, [2]> var_7953_strides_0 = const()[name = string("op_7953_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_7953_pad_type_0 = const()[name = string("op_7953_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_7953_pad_0 = const()[name = string("op_7953_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_7953_dilations_0 = const()[name = string("op_7953_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_7953_groups_0 = const()[name = string("op_7953_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_7953_cast_fp16 = conv(dilations = var_7953_dilations_0, groups = var_7953_groups_0, pad = var_7953_pad_0, pad_type = var_7953_pad_type_0, strides = var_7953_strides_0, weight = layers_21_mlp_up_proj_weight_cast_fp16, x = var_7930_cast_fp16_0)[name = string("op_7953_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_219_cast_fp16 = mul(x = var_7947_cast_fp16, y = var_7953_cast_fp16)[name = string("x_219_cast_fp16")];
            tensor<int32, [2]> hidden_states_217_strides_0 = const()[name = string("hidden_states_217_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_217_pad_type_0 = const()[name = string("hidden_states_217_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_217_pad_0 = const()[name = string("hidden_states_217_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_217_dilations_0 = const()[name = string("hidden_states_217_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_217_groups_0 = const()[name = string("hidden_states_217_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_217_cast_fp16 = conv(dilations = hidden_states_217_dilations_0, groups = hidden_states_217_groups_0, pad = hidden_states_217_pad_0, pad_type = hidden_states_217_pad_type_0, strides = hidden_states_217_strides_0, weight = layers_21_mlp_down_proj_weight_cast_fp16, x = x_219_cast_fp16)[name = string("hidden_states_217_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_219_cast_fp16 = add(x = hidden_states_215_cast_fp16, y = hidden_states_217_cast_fp16)[name = string("hidden_states_219_cast_fp16")];
            fp16 const_222_promoted_to_fp16 = const()[name = string("const_222_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_7971_cast_fp16 = mul(x = hidden_states_219_cast_fp16, y = const_222_promoted_to_fp16)[name = string("op_7971_cast_fp16")];
            int32 var_7969 = const()[name = string("op_7969"), val = int32(1)];
            bool doubled_177_interleave_0 = const()[name = string("doubled_177_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_177_cast_fp16 = concat(axis = var_7969, interleave = doubled_177_interleave_0, values = (hidden_states_219_cast_fp16, var_7971_cast_fp16))[name = string("doubled_177_cast_fp16")];
            tensor<int32, [1]> out_89_axes_0 = const()[name = string("out_89_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_89_gamma_0_to_fp16 = const()[name = string("out_89_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1520406144)))];
            fp16 var_7981_to_fp16 = const()[name = string("op_7981_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_89_cast_fp16 = layer_norm(axes = out_89_axes_0, epsilon = var_7981_to_fp16, gamma = out_89_gamma_0_to_fp16, x = doubled_177_cast_fp16)[name = string("out_89_cast_fp16")];
            tensor<int32, [2]> var_7992_split_sizes_0 = const()[name = string("op_7992_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_7992_axis_0 = const()[name = string("op_7992_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_7992_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_7992_cast_fp16_1 = split(axis = var_7992_axis_0, split_sizes = var_7992_split_sizes_0, x = out_89_cast_fp16)[name = string("op_7992_cast_fp16")];
            tensor<int32, [2]> query_states_133_strides_0 = const()[name = string("query_states_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_133_pad_type_0 = const()[name = string("query_states_133_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_133_pad_0 = const()[name = string("query_states_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_133_dilations_0 = const()[name = string("query_states_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_133_groups_0 = const()[name = string("query_states_133_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_133_cast_fp16 = conv(dilations = query_states_133_dilations_0, groups = query_states_133_groups_0, pad = query_states_133_pad_0, pad_type = query_states_133_pad_type_0, strides = query_states_133_strides_0, weight = layers_22_self_attn_q_proj_weight_cast_fp16, x = var_7992_cast_fp16_0)[name = string("query_states_133_cast_fp16")];
            tensor<int32, [2]> key_states_221_strides_0 = const()[name = string("key_states_221_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_221_pad_type_0 = const()[name = string("key_states_221_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_221_pad_0 = const()[name = string("key_states_221_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_221_dilations_0 = const()[name = string("key_states_221_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_221_groups_0 = const()[name = string("key_states_221_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_221_cast_fp16 = conv(dilations = key_states_221_dilations_0, groups = key_states_221_groups_0, pad = key_states_221_pad_0, pad_type = key_states_221_pad_type_0, strides = key_states_221_strides_0, weight = layers_22_self_attn_k_proj_weight_cast_fp16, x = var_7992_cast_fp16_0)[name = string("key_states_221_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_22_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_22_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1520414400)))];
            tensor<int32, [2]> value_states_133_strides_0 = const()[name = string("value_states_133_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_133_pad_type_0 = const()[name = string("value_states_133_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_133_pad_0 = const()[name = string("value_states_133_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_133_dilations_0 = const()[name = string("value_states_133_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_133_groups_0 = const()[name = string("value_states_133_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_133_cast_fp16 = conv(dilations = value_states_133_dilations_0, groups = value_states_133_groups_0, pad = value_states_133_pad_0, pad_type = value_states_133_pad_type_0, strides = value_states_133_strides_0, weight = layers_22_self_attn_v_proj_weight_to_fp16, x = var_7992_cast_fp16_0)[name = string("value_states_133_cast_fp16")];
            tensor<int32, [4]> concat_264x = const()[name = string("concat_264x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_221_cast_fp16 = reshape(shape = concat_264x, x = query_states_133_cast_fp16)[name = string("x_221_cast_fp16")];
            tensor<int32, [4]> concat_265x = const()[name = string("concat_265x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_8049_cast_fp16 = reshape(shape = concat_265x, x = key_states_221_cast_fp16)[name = string("op_8049_cast_fp16")];
            tensor<int32, [4]> concat_266x = const()[name = string("concat_266x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_8056_cast_fp16 = reshape(shape = concat_266x, x = value_states_133_cast_fp16)[name = string("op_8056_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_8060_cast_fp16 = mul(x = x_221_cast_fp16, y = var_859_cast_fp16)[name = string("op_8060_cast_fp16")];
            tensor<int32, [2]> var_8061_split_sizes_0 = const()[name = string("op_8061_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8061_axis_0 = const()[name = string("op_8061_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_8061_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_8061_cast_fp16_1 = split(axis = var_8061_axis_0, split_sizes = var_8061_split_sizes_0, x = x_221_cast_fp16)[name = string("op_8061_cast_fp16")];
            fp16 const_224_promoted_to_fp16 = const()[name = string("const_224_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_8063_cast_fp16 = mul(x = var_8061_cast_fp16_1, y = const_224_promoted_to_fp16)[name = string("op_8063_cast_fp16")];
            int32 var_8065 = const()[name = string("op_8065"), val = int32(-2)];
            bool var_8066_interleave_0 = const()[name = string("op_8066_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_8066_cast_fp16 = concat(axis = var_8065, interleave = var_8066_interleave_0, values = (var_8063_cast_fp16, var_8061_cast_fp16_0))[name = string("op_8066_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_8067_cast_fp16 = mul(x = var_8066_cast_fp16, y = var_866_cast_fp16)[name = string("op_8067_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_135_cast_fp16 = add(x = var_8060_cast_fp16, y = var_8067_cast_fp16)[name = string("query_states_135_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_8073_cast_fp16 = mul(x = var_8049_cast_fp16, y = var_859_cast_fp16)[name = string("op_8073_cast_fp16")];
            tensor<int32, [2]> var_8074_split_sizes_0 = const()[name = string("op_8074_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8074_axis_0 = const()[name = string("op_8074_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_8074_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_8074_cast_fp16_1 = split(axis = var_8074_axis_0, split_sizes = var_8074_split_sizes_0, x = var_8049_cast_fp16)[name = string("op_8074_cast_fp16")];
            fp16 const_225_promoted_to_fp16 = const()[name = string("const_225_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_8076_cast_fp16 = mul(x = var_8074_cast_fp16_1, y = const_225_promoted_to_fp16)[name = string("op_8076_cast_fp16")];
            int32 var_8078 = const()[name = string("op_8078"), val = int32(-2)];
            bool var_8079_interleave_0 = const()[name = string("op_8079_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_8079_cast_fp16 = concat(axis = var_8078, interleave = var_8079_interleave_0, values = (var_8076_cast_fp16, var_8074_cast_fp16_0))[name = string("op_8079_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_8080_cast_fp16 = mul(x = var_8079_cast_fp16, y = var_866_cast_fp16)[name = string("op_8080_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_225_cast_fp16 = add(x = var_8073_cast_fp16, y = var_8080_cast_fp16)[name = string("key_states_225_cast_fp16")];
            tensor<int32, [1]> expand_dims_264 = const()[name = string("expand_dims_264"), val = tensor<int32, [1]>([22])];
            tensor<int32, [1]> expand_dims_265 = const()[name = string("expand_dims_265"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_267 = const()[name = string("expand_dims_267"), val = tensor<int32, [1]>([0])];
            int32 concat_269_axis_0 = const()[name = string("concat_269_axis_0"), val = int32(0)];
            bool concat_269_interleave_0 = const()[name = string("concat_269_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_269 = concat(axis = concat_269_axis_0, interleave = concat_269_interleave_0, values = (expand_dims_264, expand_dims_265, position_id, expand_dims_267))[name = string("concat_269")];
            tensor<int32, [1]> expand_dims_268 = const()[name = string("expand_dims_268"), val = tensor<int32, [1]>([23])];
            tensor<int32, [1]> concat_270_values1_0 = const()[name = string("concat_270_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_270_values3_0 = const()[name = string("concat_270_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_270_axis_0 = const()[name = string("concat_270_axis_0"), val = int32(0)];
            bool concat_270_interleave_0 = const()[name = string("concat_270_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_270 = concat(axis = concat_270_axis_0, interleave = concat_270_interleave_0, values = (expand_dims_268, concat_270_values1_0, cache_position_end, concat_270_values3_0))[name = string("concat_270")];
            tensor<int32, [4]> key_states_227_perm_0 = const()[name = string("key_states_227_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_23_stride_0 = const()[name = string("key_cache_internal_tensor_assign_23_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_23_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_23_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_23_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_23_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_23_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_23_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_227_cast_fp16 = transpose(perm = key_states_227_perm_0, x = key_states_225_cast_fp16)[name = string("transpose_17")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_23_cast_fp16 = slice_update(begin = concat_269, begin_mask = key_cache_internal_tensor_assign_23_begin_mask_0, end = concat_270, end_mask = key_cache_internal_tensor_assign_23_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_23_squeeze_mask_0, stride = key_cache_internal_tensor_assign_23_stride_0, update = key_states_227_cast_fp16, x = coreml_update_state_42)[name = string("key_cache_internal_tensor_assign_23_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_23_cast_fp16, input = key_cache)[name = string("coreml_update_state_44_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_44 = read_state(input = key_cache)[name = string("coreml_update_state_44")];
            tensor<int32, [4]> value_states_135_perm_0 = const()[name = string("value_states_135_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_23_stride_0 = const()[name = string("value_cache_internal_tensor_assign_23_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_23_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_23_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_23_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_23_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_23_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_23_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_135_cast_fp16 = transpose(perm = value_states_135_perm_0, x = var_8056_cast_fp16)[name = string("transpose_16")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_23_cast_fp16 = slice_update(begin = concat_269, begin_mask = value_cache_internal_tensor_assign_23_begin_mask_0, end = concat_270, end_mask = value_cache_internal_tensor_assign_23_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_23_squeeze_mask_0, stride = value_cache_internal_tensor_assign_23_stride_0, update = value_states_135_cast_fp16, x = coreml_update_state_43)[name = string("value_cache_internal_tensor_assign_23_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_23_cast_fp16, input = value_cache)[name = string("coreml_update_state_45_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_45 = read_state(input = value_cache)[name = string("coreml_update_state_45")];
            tensor<int32, [4]> var_8150_begin_0 = const()[name = string("op_8150_begin_0"), val = tensor<int32, [4]>([22, 0, 0, 0])];
            tensor<int32, [4]> var_8150_end_0 = const()[name = string("op_8150_end_0"), val = tensor<int32, [4]>([23, 2, 2048, 128])];
            tensor<bool, [4]> var_8150_end_mask_0 = const()[name = string("op_8150_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8150_cast_fp16 = slice_by_index(begin = var_8150_begin_0, end = var_8150_end_0, end_mask = var_8150_end_mask_0, x = coreml_update_state_44)[name = string("op_8150_cast_fp16")];
            tensor<int32, [2]> tile_44 = const()[name = string("tile_44"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8153_axis_0 = const()[name = string("op_8153_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8153_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8153_cast_fp16_1 = split(axis = var_8153_axis_0, split_sizes = tile_44, x = var_8150_cast_fp16)[name = string("op_8153_cast_fp16")];
            tensor<int32, [4]> var_8160_begin_0 = const()[name = string("op_8160_begin_0"), val = tensor<int32, [4]>([22, 0, 0, 0])];
            tensor<int32, [4]> var_8160_end_0 = const()[name = string("op_8160_end_0"), val = tensor<int32, [4]>([23, 2, 2048, 128])];
            tensor<bool, [4]> var_8160_end_mask_0 = const()[name = string("op_8160_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8160_cast_fp16 = slice_by_index(begin = var_8160_begin_0, end = var_8160_end_0, end_mask = var_8160_end_mask_0, x = coreml_update_state_45)[name = string("op_8160_cast_fp16")];
            tensor<int32, [2]> tile_45 = const()[name = string("tile_45"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8163_axis_0 = const()[name = string("op_8163_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8163_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8163_cast_fp16_1 = split(axis = var_8163_axis_0, split_sizes = tile_45, x = var_8160_cast_fp16)[name = string("op_8163_cast_fp16")];
            tensor<int32, [2]> var_8166_split_sizes_0 = const()[name = string("op_8166_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_8166_axis_0 = const()[name = string("op_8166_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_8166_0, tensor<fp16, [1, 8, 128, 1]> var_8166_1 = split(axis = var_8166_axis_0, split_sizes = var_8166_split_sizes_0, x = query_states_135_cast_fp16)[name = string("op_8166")];
            bool attn_weights_353_transpose_x_0 = const()[name = string("attn_weights_353_transpose_x_0"), val = bool(false)];
            bool attn_weights_353_transpose_y_0 = const()[name = string("attn_weights_353_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_353_cast_fp16 = matmul(transpose_x = attn_weights_353_transpose_x_0, transpose_y = attn_weights_353_transpose_y_0, x = var_8153_cast_fp16_0, y = var_8166_0)[name = string("attn_weights_353_cast_fp16")];
            fp16 var_8169_to_fp16 = const()[name = string("op_8169_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_355_cast_fp16 = mul(x = attn_weights_353_cast_fp16, y = var_8169_to_fp16)[name = string("attn_weights_355_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_357_cast_fp16 = add(x = attn_weights_355_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_357_cast_fp16")];
            int32 var_8173 = const()[name = string("op_8173"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_359_cast_fp16 = softmax(axis = var_8173, x = attn_weights_357_cast_fp16)[name = string("attn_weights_359_cast_fp16")];
            bool var_8179_transpose_x_1 = const()[name = string("op_8179_transpose_x_1"), val = bool(true)];
            bool var_8179_transpose_y_1 = const()[name = string("op_8179_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_8179_cast_fp16 = matmul(transpose_x = var_8179_transpose_x_1, transpose_y = var_8179_transpose_y_1, x = attn_weights_359_cast_fp16, y = var_8163_cast_fp16_0)[name = string("op_8179_cast_fp16")];
            bool attn_weights_361_transpose_x_0 = const()[name = string("attn_weights_361_transpose_x_0"), val = bool(false)];
            bool attn_weights_361_transpose_y_0 = const()[name = string("attn_weights_361_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_361_cast_fp16 = matmul(transpose_x = attn_weights_361_transpose_x_0, transpose_y = attn_weights_361_transpose_y_0, x = var_8153_cast_fp16_1, y = var_8166_1)[name = string("attn_weights_361_cast_fp16")];
            fp16 var_8181_to_fp16 = const()[name = string("op_8181_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_363_cast_fp16 = mul(x = attn_weights_361_cast_fp16, y = var_8181_to_fp16)[name = string("attn_weights_363_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_365_cast_fp16 = add(x = attn_weights_363_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_365_cast_fp16")];
            int32 var_8185 = const()[name = string("op_8185"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_367_cast_fp16 = softmax(axis = var_8185, x = attn_weights_365_cast_fp16)[name = string("attn_weights_367_cast_fp16")];
            bool attn_output_177_transpose_x_1 = const()[name = string("attn_output_177_transpose_x_1"), val = bool(true)];
            bool attn_output_177_transpose_y_1 = const()[name = string("attn_output_177_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_177_cast_fp16 = matmul(transpose_x = attn_output_177_transpose_x_1, transpose_y = attn_output_177_transpose_y_1, x = attn_weights_367_cast_fp16, y = var_8163_cast_fp16_1)[name = string("attn_output_177_cast_fp16")];
            int32 var_8193 = const()[name = string("op_8193"), val = int32(1)];
            bool attn_output_179_interleave_0 = const()[name = string("attn_output_179_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_179_cast_fp16 = concat(axis = var_8193, interleave = attn_output_179_interleave_0, values = (var_8179_cast_fp16, attn_output_177_cast_fp16))[name = string("attn_output_179_cast_fp16")];
            tensor<int32, [4]> var_8197_perm_0 = const()[name = string("op_8197_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_275x = const()[name = string("concat_275x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_8197_cast_fp16 = transpose(perm = var_8197_perm_0, x = attn_output_179_cast_fp16)[name = string("transpose_15")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_183_cast_fp16 = reshape(shape = concat_275x, x = var_8197_cast_fp16)[name = string("attn_output_183_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_22_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_22_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1521463040)))];
            tensor<int32, [2]> hidden_states_223_strides_0 = const()[name = string("hidden_states_223_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_223_pad_type_0 = const()[name = string("hidden_states_223_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_223_pad_0 = const()[name = string("hidden_states_223_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_223_dilations_0 = const()[name = string("hidden_states_223_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_223_groups_0 = const()[name = string("hidden_states_223_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_223_cast_fp16 = conv(dilations = hidden_states_223_dilations_0, groups = hidden_states_223_groups_0, pad = hidden_states_223_pad_0, pad_type = hidden_states_223_pad_type_0, strides = hidden_states_223_strides_0, weight = layers_22_self_attn_o_proj_weight_to_fp16, x = attn_output_183_cast_fp16)[name = string("hidden_states_223_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_225_cast_fp16 = add(x = hidden_states_219_cast_fp16, y = hidden_states_223_cast_fp16)[name = string("hidden_states_225_cast_fp16")];
            fp16 const_230_promoted_to_fp16 = const()[name = string("const_230_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_8230_cast_fp16 = mul(x = hidden_states_225_cast_fp16, y = const_230_promoted_to_fp16)[name = string("op_8230_cast_fp16")];
            int32 var_8228 = const()[name = string("op_8228"), val = int32(1)];
            bool doubled_181_interleave_0 = const()[name = string("doubled_181_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_181_cast_fp16 = concat(axis = var_8228, interleave = doubled_181_interleave_0, values = (hidden_states_225_cast_fp16, var_8230_cast_fp16))[name = string("doubled_181_cast_fp16")];
            tensor<int32, [1]> out_91_axes_0 = const()[name = string("out_91_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_91_gamma_0_to_fp16 = const()[name = string("out_91_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1529851712)))];
            fp16 var_8240_to_fp16 = const()[name = string("op_8240_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_91_cast_fp16 = layer_norm(axes = out_91_axes_0, epsilon = var_8240_to_fp16, gamma = out_91_gamma_0_to_fp16, x = doubled_181_cast_fp16)[name = string("out_91_cast_fp16")];
            tensor<int32, [2]> var_8251_split_sizes_0 = const()[name = string("op_8251_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8251_axis_0 = const()[name = string("op_8251_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_8251_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_8251_cast_fp16_1 = split(axis = var_8251_axis_0, split_sizes = var_8251_split_sizes_0, x = out_91_cast_fp16)[name = string("op_8251_cast_fp16")];
            tensor<int32, [2]> input_45_strides_0 = const()[name = string("input_45_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_45_pad_type_0 = const()[name = string("input_45_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_45_pad_0 = const()[name = string("input_45_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_45_dilations_0 = const()[name = string("input_45_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_45_groups_0 = const()[name = string("input_45_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_45_cast_fp16 = conv(dilations = input_45_dilations_0, groups = input_45_groups_0, pad = input_45_pad_0, pad_type = input_45_pad_type_0, strides = input_45_strides_0, weight = layers_22_mlp_gate_proj_weight_cast_fp16, x = var_8251_cast_fp16_0)[name = string("input_45_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_8268_cast_fp16 = silu(x = input_45_cast_fp16)[name = string("op_8268_cast_fp16")];
            tensor<int32, [2]> var_8274_strides_0 = const()[name = string("op_8274_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_8274_pad_type_0 = const()[name = string("op_8274_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_8274_pad_0 = const()[name = string("op_8274_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_8274_dilations_0 = const()[name = string("op_8274_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8274_groups_0 = const()[name = string("op_8274_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_8274_cast_fp16 = conv(dilations = var_8274_dilations_0, groups = var_8274_groups_0, pad = var_8274_pad_0, pad_type = var_8274_pad_type_0, strides = var_8274_strides_0, weight = layers_22_mlp_up_proj_weight_cast_fp16, x = var_8251_cast_fp16_0)[name = string("op_8274_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_229_cast_fp16 = mul(x = var_8268_cast_fp16, y = var_8274_cast_fp16)[name = string("x_229_cast_fp16")];
            tensor<int32, [2]> hidden_states_227_strides_0 = const()[name = string("hidden_states_227_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_227_pad_type_0 = const()[name = string("hidden_states_227_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_227_pad_0 = const()[name = string("hidden_states_227_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_227_dilations_0 = const()[name = string("hidden_states_227_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_227_groups_0 = const()[name = string("hidden_states_227_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_227_cast_fp16 = conv(dilations = hidden_states_227_dilations_0, groups = hidden_states_227_groups_0, pad = hidden_states_227_pad_0, pad_type = hidden_states_227_pad_type_0, strides = hidden_states_227_strides_0, weight = layers_22_mlp_down_proj_weight_cast_fp16, x = x_229_cast_fp16)[name = string("hidden_states_227_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_229_cast_fp16 = add(x = hidden_states_225_cast_fp16, y = hidden_states_227_cast_fp16)[name = string("hidden_states_229_cast_fp16")];
            fp16 const_232_promoted_to_fp16 = const()[name = string("const_232_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_8292_cast_fp16 = mul(x = hidden_states_229_cast_fp16, y = const_232_promoted_to_fp16)[name = string("op_8292_cast_fp16")];
            int32 var_8290 = const()[name = string("op_8290"), val = int32(1)];
            bool doubled_185_interleave_0 = const()[name = string("doubled_185_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_185_cast_fp16 = concat(axis = var_8290, interleave = doubled_185_interleave_0, values = (hidden_states_229_cast_fp16, var_8292_cast_fp16))[name = string("doubled_185_cast_fp16")];
            tensor<int32, [1]> out_93_axes_0 = const()[name = string("out_93_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_93_gamma_0_to_fp16 = const()[name = string("out_93_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1529859968)))];
            fp16 var_8302_to_fp16 = const()[name = string("op_8302_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_93_cast_fp16 = layer_norm(axes = out_93_axes_0, epsilon = var_8302_to_fp16, gamma = out_93_gamma_0_to_fp16, x = doubled_185_cast_fp16)[name = string("out_93_cast_fp16")];
            tensor<int32, [2]> var_8313_split_sizes_0 = const()[name = string("op_8313_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8313_axis_0 = const()[name = string("op_8313_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_8313_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_8313_cast_fp16_1 = split(axis = var_8313_axis_0, split_sizes = var_8313_split_sizes_0, x = out_93_cast_fp16)[name = string("op_8313_cast_fp16")];
            tensor<int32, [2]> query_states_139_strides_0 = const()[name = string("query_states_139_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_139_pad_type_0 = const()[name = string("query_states_139_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_139_pad_0 = const()[name = string("query_states_139_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_139_dilations_0 = const()[name = string("query_states_139_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_139_groups_0 = const()[name = string("query_states_139_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_139_cast_fp16 = conv(dilations = query_states_139_dilations_0, groups = query_states_139_groups_0, pad = query_states_139_pad_0, pad_type = query_states_139_pad_type_0, strides = query_states_139_strides_0, weight = layers_23_self_attn_q_proj_weight_cast_fp16, x = var_8313_cast_fp16_0)[name = string("query_states_139_cast_fp16")];
            tensor<int32, [2]> key_states_231_strides_0 = const()[name = string("key_states_231_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_231_pad_type_0 = const()[name = string("key_states_231_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_231_pad_0 = const()[name = string("key_states_231_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_231_dilations_0 = const()[name = string("key_states_231_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_231_groups_0 = const()[name = string("key_states_231_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_231_cast_fp16 = conv(dilations = key_states_231_dilations_0, groups = key_states_231_groups_0, pad = key_states_231_pad_0, pad_type = key_states_231_pad_type_0, strides = key_states_231_strides_0, weight = layers_23_self_attn_k_proj_weight_cast_fp16, x = var_8313_cast_fp16_0)[name = string("key_states_231_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_23_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_23_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1529868224)))];
            tensor<int32, [2]> value_states_139_strides_0 = const()[name = string("value_states_139_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_139_pad_type_0 = const()[name = string("value_states_139_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_139_pad_0 = const()[name = string("value_states_139_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_139_dilations_0 = const()[name = string("value_states_139_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_139_groups_0 = const()[name = string("value_states_139_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_139_cast_fp16 = conv(dilations = value_states_139_dilations_0, groups = value_states_139_groups_0, pad = value_states_139_pad_0, pad_type = value_states_139_pad_type_0, strides = value_states_139_strides_0, weight = layers_23_self_attn_v_proj_weight_to_fp16, x = var_8313_cast_fp16_0)[name = string("value_states_139_cast_fp16")];
            tensor<int32, [4]> concat_276x = const()[name = string("concat_276x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_231_cast_fp16 = reshape(shape = concat_276x, x = query_states_139_cast_fp16)[name = string("x_231_cast_fp16")];
            tensor<int32, [4]> concat_277x = const()[name = string("concat_277x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_8370_cast_fp16 = reshape(shape = concat_277x, x = key_states_231_cast_fp16)[name = string("op_8370_cast_fp16")];
            tensor<int32, [4]> concat_278x = const()[name = string("concat_278x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_8377_cast_fp16 = reshape(shape = concat_278x, x = value_states_139_cast_fp16)[name = string("op_8377_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_8381_cast_fp16 = mul(x = x_231_cast_fp16, y = var_859_cast_fp16)[name = string("op_8381_cast_fp16")];
            tensor<int32, [2]> var_8382_split_sizes_0 = const()[name = string("op_8382_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8382_axis_0 = const()[name = string("op_8382_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_8382_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_8382_cast_fp16_1 = split(axis = var_8382_axis_0, split_sizes = var_8382_split_sizes_0, x = x_231_cast_fp16)[name = string("op_8382_cast_fp16")];
            fp16 const_234_promoted_to_fp16 = const()[name = string("const_234_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_8384_cast_fp16 = mul(x = var_8382_cast_fp16_1, y = const_234_promoted_to_fp16)[name = string("op_8384_cast_fp16")];
            int32 var_8386 = const()[name = string("op_8386"), val = int32(-2)];
            bool var_8387_interleave_0 = const()[name = string("op_8387_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_8387_cast_fp16 = concat(axis = var_8386, interleave = var_8387_interleave_0, values = (var_8384_cast_fp16, var_8382_cast_fp16_0))[name = string("op_8387_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_8388_cast_fp16 = mul(x = var_8387_cast_fp16, y = var_866_cast_fp16)[name = string("op_8388_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_141_cast_fp16 = add(x = var_8381_cast_fp16, y = var_8388_cast_fp16)[name = string("query_states_141_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_8394_cast_fp16 = mul(x = var_8370_cast_fp16, y = var_859_cast_fp16)[name = string("op_8394_cast_fp16")];
            tensor<int32, [2]> var_8395_split_sizes_0 = const()[name = string("op_8395_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8395_axis_0 = const()[name = string("op_8395_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_8395_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_8395_cast_fp16_1 = split(axis = var_8395_axis_0, split_sizes = var_8395_split_sizes_0, x = var_8370_cast_fp16)[name = string("op_8395_cast_fp16")];
            fp16 const_235_promoted_to_fp16 = const()[name = string("const_235_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_8397_cast_fp16 = mul(x = var_8395_cast_fp16_1, y = const_235_promoted_to_fp16)[name = string("op_8397_cast_fp16")];
            int32 var_8399 = const()[name = string("op_8399"), val = int32(-2)];
            bool var_8400_interleave_0 = const()[name = string("op_8400_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_8400_cast_fp16 = concat(axis = var_8399, interleave = var_8400_interleave_0, values = (var_8397_cast_fp16, var_8395_cast_fp16_0))[name = string("op_8400_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_8401_cast_fp16 = mul(x = var_8400_cast_fp16, y = var_866_cast_fp16)[name = string("op_8401_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_235_cast_fp16 = add(x = var_8394_cast_fp16, y = var_8401_cast_fp16)[name = string("key_states_235_cast_fp16")];
            tensor<int32, [1]> expand_dims_276 = const()[name = string("expand_dims_276"), val = tensor<int32, [1]>([23])];
            tensor<int32, [1]> expand_dims_277 = const()[name = string("expand_dims_277"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_279 = const()[name = string("expand_dims_279"), val = tensor<int32, [1]>([0])];
            int32 concat_281_axis_0 = const()[name = string("concat_281_axis_0"), val = int32(0)];
            bool concat_281_interleave_0 = const()[name = string("concat_281_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_281 = concat(axis = concat_281_axis_0, interleave = concat_281_interleave_0, values = (expand_dims_276, expand_dims_277, position_id, expand_dims_279))[name = string("concat_281")];
            tensor<int32, [1]> expand_dims_280 = const()[name = string("expand_dims_280"), val = tensor<int32, [1]>([24])];
            tensor<int32, [1]> concat_282_values1_0 = const()[name = string("concat_282_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_282_values3_0 = const()[name = string("concat_282_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_282_axis_0 = const()[name = string("concat_282_axis_0"), val = int32(0)];
            bool concat_282_interleave_0 = const()[name = string("concat_282_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_282 = concat(axis = concat_282_axis_0, interleave = concat_282_interleave_0, values = (expand_dims_280, concat_282_values1_0, cache_position_end, concat_282_values3_0))[name = string("concat_282")];
            tensor<int32, [4]> key_states_237_perm_0 = const()[name = string("key_states_237_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_24_stride_0 = const()[name = string("key_cache_internal_tensor_assign_24_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_24_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_24_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_24_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_24_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_24_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_24_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_237_cast_fp16 = transpose(perm = key_states_237_perm_0, x = key_states_235_cast_fp16)[name = string("transpose_14")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_24_cast_fp16 = slice_update(begin = concat_281, begin_mask = key_cache_internal_tensor_assign_24_begin_mask_0, end = concat_282, end_mask = key_cache_internal_tensor_assign_24_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_24_squeeze_mask_0, stride = key_cache_internal_tensor_assign_24_stride_0, update = key_states_237_cast_fp16, x = coreml_update_state_44)[name = string("key_cache_internal_tensor_assign_24_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_24_cast_fp16, input = key_cache)[name = string("coreml_update_state_46_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_46 = read_state(input = key_cache)[name = string("coreml_update_state_46")];
            tensor<int32, [4]> value_states_141_perm_0 = const()[name = string("value_states_141_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_24_stride_0 = const()[name = string("value_cache_internal_tensor_assign_24_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_24_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_24_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_24_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_24_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_24_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_24_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_141_cast_fp16 = transpose(perm = value_states_141_perm_0, x = var_8377_cast_fp16)[name = string("transpose_13")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_24_cast_fp16 = slice_update(begin = concat_281, begin_mask = value_cache_internal_tensor_assign_24_begin_mask_0, end = concat_282, end_mask = value_cache_internal_tensor_assign_24_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_24_squeeze_mask_0, stride = value_cache_internal_tensor_assign_24_stride_0, update = value_states_141_cast_fp16, x = coreml_update_state_45)[name = string("value_cache_internal_tensor_assign_24_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_24_cast_fp16, input = value_cache)[name = string("coreml_update_state_47_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_47 = read_state(input = value_cache)[name = string("coreml_update_state_47")];
            tensor<int32, [4]> var_8471_begin_0 = const()[name = string("op_8471_begin_0"), val = tensor<int32, [4]>([23, 0, 0, 0])];
            tensor<int32, [4]> var_8471_end_0 = const()[name = string("op_8471_end_0"), val = tensor<int32, [4]>([24, 2, 2048, 128])];
            tensor<bool, [4]> var_8471_end_mask_0 = const()[name = string("op_8471_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8471_cast_fp16 = slice_by_index(begin = var_8471_begin_0, end = var_8471_end_0, end_mask = var_8471_end_mask_0, x = coreml_update_state_46)[name = string("op_8471_cast_fp16")];
            tensor<int32, [2]> tile_46 = const()[name = string("tile_46"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8474_axis_0 = const()[name = string("op_8474_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8474_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8474_cast_fp16_1 = split(axis = var_8474_axis_0, split_sizes = tile_46, x = var_8471_cast_fp16)[name = string("op_8474_cast_fp16")];
            tensor<int32, [4]> var_8481_begin_0 = const()[name = string("op_8481_begin_0"), val = tensor<int32, [4]>([23, 0, 0, 0])];
            tensor<int32, [4]> var_8481_end_0 = const()[name = string("op_8481_end_0"), val = tensor<int32, [4]>([24, 2, 2048, 128])];
            tensor<bool, [4]> var_8481_end_mask_0 = const()[name = string("op_8481_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8481_cast_fp16 = slice_by_index(begin = var_8481_begin_0, end = var_8481_end_0, end_mask = var_8481_end_mask_0, x = coreml_update_state_47)[name = string("op_8481_cast_fp16")];
            tensor<int32, [2]> tile_47 = const()[name = string("tile_47"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8484_axis_0 = const()[name = string("op_8484_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8484_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8484_cast_fp16_1 = split(axis = var_8484_axis_0, split_sizes = tile_47, x = var_8481_cast_fp16)[name = string("op_8484_cast_fp16")];
            tensor<int32, [2]> var_8487_split_sizes_0 = const()[name = string("op_8487_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_8487_axis_0 = const()[name = string("op_8487_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_8487_0, tensor<fp16, [1, 8, 128, 1]> var_8487_1 = split(axis = var_8487_axis_0, split_sizes = var_8487_split_sizes_0, x = query_states_141_cast_fp16)[name = string("op_8487")];
            bool attn_weights_369_transpose_x_0 = const()[name = string("attn_weights_369_transpose_x_0"), val = bool(false)];
            bool attn_weights_369_transpose_y_0 = const()[name = string("attn_weights_369_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_369_cast_fp16 = matmul(transpose_x = attn_weights_369_transpose_x_0, transpose_y = attn_weights_369_transpose_y_0, x = var_8474_cast_fp16_0, y = var_8487_0)[name = string("attn_weights_369_cast_fp16")];
            fp16 var_8490_to_fp16 = const()[name = string("op_8490_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_371_cast_fp16 = mul(x = attn_weights_369_cast_fp16, y = var_8490_to_fp16)[name = string("attn_weights_371_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_373_cast_fp16 = add(x = attn_weights_371_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_373_cast_fp16")];
            int32 var_8494 = const()[name = string("op_8494"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_375_cast_fp16 = softmax(axis = var_8494, x = attn_weights_373_cast_fp16)[name = string("attn_weights_375_cast_fp16")];
            bool var_8500_transpose_x_1 = const()[name = string("op_8500_transpose_x_1"), val = bool(true)];
            bool var_8500_transpose_y_1 = const()[name = string("op_8500_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_8500_cast_fp16 = matmul(transpose_x = var_8500_transpose_x_1, transpose_y = var_8500_transpose_y_1, x = attn_weights_375_cast_fp16, y = var_8484_cast_fp16_0)[name = string("op_8500_cast_fp16")];
            bool attn_weights_377_transpose_x_0 = const()[name = string("attn_weights_377_transpose_x_0"), val = bool(false)];
            bool attn_weights_377_transpose_y_0 = const()[name = string("attn_weights_377_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_377_cast_fp16 = matmul(transpose_x = attn_weights_377_transpose_x_0, transpose_y = attn_weights_377_transpose_y_0, x = var_8474_cast_fp16_1, y = var_8487_1)[name = string("attn_weights_377_cast_fp16")];
            fp16 var_8502_to_fp16 = const()[name = string("op_8502_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_379_cast_fp16 = mul(x = attn_weights_377_cast_fp16, y = var_8502_to_fp16)[name = string("attn_weights_379_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_381_cast_fp16 = add(x = attn_weights_379_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_381_cast_fp16")];
            int32 var_8506 = const()[name = string("op_8506"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_383_cast_fp16 = softmax(axis = var_8506, x = attn_weights_381_cast_fp16)[name = string("attn_weights_383_cast_fp16")];
            bool attn_output_185_transpose_x_1 = const()[name = string("attn_output_185_transpose_x_1"), val = bool(true)];
            bool attn_output_185_transpose_y_1 = const()[name = string("attn_output_185_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_185_cast_fp16 = matmul(transpose_x = attn_output_185_transpose_x_1, transpose_y = attn_output_185_transpose_y_1, x = attn_weights_383_cast_fp16, y = var_8484_cast_fp16_1)[name = string("attn_output_185_cast_fp16")];
            int32 var_8514 = const()[name = string("op_8514"), val = int32(1)];
            bool attn_output_187_interleave_0 = const()[name = string("attn_output_187_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_187_cast_fp16 = concat(axis = var_8514, interleave = attn_output_187_interleave_0, values = (var_8500_cast_fp16, attn_output_185_cast_fp16))[name = string("attn_output_187_cast_fp16")];
            tensor<int32, [4]> var_8518_perm_0 = const()[name = string("op_8518_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_287x = const()[name = string("concat_287x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_8518_cast_fp16 = transpose(perm = var_8518_perm_0, x = attn_output_187_cast_fp16)[name = string("transpose_12")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_191_cast_fp16 = reshape(shape = concat_287x, x = var_8518_cast_fp16)[name = string("attn_output_191_cast_fp16")];
            tensor<int32, [2]> hidden_states_233_strides_0 = const()[name = string("hidden_states_233_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_233_pad_type_0 = const()[name = string("hidden_states_233_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_233_pad_0 = const()[name = string("hidden_states_233_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_233_dilations_0 = const()[name = string("hidden_states_233_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_233_groups_0 = const()[name = string("hidden_states_233_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_233_cast_fp16 = conv(dilations = hidden_states_233_dilations_0, groups = hidden_states_233_groups_0, pad = hidden_states_233_pad_0, pad_type = hidden_states_233_pad_type_0, strides = hidden_states_233_strides_0, weight = layers_23_self_attn_o_proj_weight_cast_fp16, x = attn_output_191_cast_fp16)[name = string("hidden_states_233_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_235_cast_fp16 = add(x = hidden_states_229_cast_fp16, y = hidden_states_233_cast_fp16)[name = string("hidden_states_235_cast_fp16")];
            fp16 const_240_promoted_to_fp16 = const()[name = string("const_240_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_8551_cast_fp16 = mul(x = hidden_states_235_cast_fp16, y = const_240_promoted_to_fp16)[name = string("op_8551_cast_fp16")];
            int32 var_8549 = const()[name = string("op_8549"), val = int32(1)];
            bool doubled_189_interleave_0 = const()[name = string("doubled_189_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_189_cast_fp16 = concat(axis = var_8549, interleave = doubled_189_interleave_0, values = (hidden_states_235_cast_fp16, var_8551_cast_fp16))[name = string("doubled_189_cast_fp16")];
            tensor<int32, [1]> out_95_axes_0 = const()[name = string("out_95_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_95_gamma_0_to_fp16 = const()[name = string("out_95_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1530916864)))];
            fp16 var_8561_to_fp16 = const()[name = string("op_8561_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_95_cast_fp16 = layer_norm(axes = out_95_axes_0, epsilon = var_8561_to_fp16, gamma = out_95_gamma_0_to_fp16, x = doubled_189_cast_fp16)[name = string("out_95_cast_fp16")];
            tensor<int32, [2]> var_8572_split_sizes_0 = const()[name = string("op_8572_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8572_axis_0 = const()[name = string("op_8572_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_8572_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_8572_cast_fp16_1 = split(axis = var_8572_axis_0, split_sizes = var_8572_split_sizes_0, x = out_95_cast_fp16)[name = string("op_8572_cast_fp16")];
            tensor<int32, [2]> input_47_strides_0 = const()[name = string("input_47_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_47_pad_type_0 = const()[name = string("input_47_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_47_pad_0 = const()[name = string("input_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_47_dilations_0 = const()[name = string("input_47_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_47_groups_0 = const()[name = string("input_47_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_47_cast_fp16 = conv(dilations = input_47_dilations_0, groups = input_47_groups_0, pad = input_47_pad_0, pad_type = input_47_pad_type_0, strides = input_47_strides_0, weight = layers_23_mlp_gate_proj_weight_cast_fp16, x = var_8572_cast_fp16_0)[name = string("input_47_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_8589_cast_fp16 = silu(x = input_47_cast_fp16)[name = string("op_8589_cast_fp16")];
            tensor<int32, [2]> var_8595_strides_0 = const()[name = string("op_8595_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_8595_pad_type_0 = const()[name = string("op_8595_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_8595_pad_0 = const()[name = string("op_8595_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_8595_dilations_0 = const()[name = string("op_8595_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8595_groups_0 = const()[name = string("op_8595_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_8595_cast_fp16 = conv(dilations = var_8595_dilations_0, groups = var_8595_groups_0, pad = var_8595_pad_0, pad_type = var_8595_pad_type_0, strides = var_8595_strides_0, weight = layers_23_mlp_up_proj_weight_cast_fp16, x = var_8572_cast_fp16_0)[name = string("op_8595_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_239_cast_fp16 = mul(x = var_8589_cast_fp16, y = var_8595_cast_fp16)[name = string("x_239_cast_fp16")];
            tensor<int32, [2]> hidden_states_237_strides_0 = const()[name = string("hidden_states_237_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_237_pad_type_0 = const()[name = string("hidden_states_237_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_237_pad_0 = const()[name = string("hidden_states_237_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_237_dilations_0 = const()[name = string("hidden_states_237_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_237_groups_0 = const()[name = string("hidden_states_237_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_237_cast_fp16 = conv(dilations = hidden_states_237_dilations_0, groups = hidden_states_237_groups_0, pad = hidden_states_237_pad_0, pad_type = hidden_states_237_pad_type_0, strides = hidden_states_237_strides_0, weight = layers_23_mlp_down_proj_weight_cast_fp16, x = x_239_cast_fp16)[name = string("hidden_states_237_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_239_cast_fp16 = add(x = hidden_states_235_cast_fp16, y = hidden_states_237_cast_fp16)[name = string("hidden_states_239_cast_fp16")];
            fp16 const_242_promoted_to_fp16 = const()[name = string("const_242_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_8613_cast_fp16 = mul(x = hidden_states_239_cast_fp16, y = const_242_promoted_to_fp16)[name = string("op_8613_cast_fp16")];
            int32 var_8611 = const()[name = string("op_8611"), val = int32(1)];
            bool doubled_193_interleave_0 = const()[name = string("doubled_193_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_193_cast_fp16 = concat(axis = var_8611, interleave = doubled_193_interleave_0, values = (hidden_states_239_cast_fp16, var_8613_cast_fp16))[name = string("doubled_193_cast_fp16")];
            tensor<int32, [1]> out_97_axes_0 = const()[name = string("out_97_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_97_gamma_0_to_fp16 = const()[name = string("out_97_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1530925120)))];
            fp16 var_8623_to_fp16 = const()[name = string("op_8623_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_97_cast_fp16 = layer_norm(axes = out_97_axes_0, epsilon = var_8623_to_fp16, gamma = out_97_gamma_0_to_fp16, x = doubled_193_cast_fp16)[name = string("out_97_cast_fp16")];
            tensor<int32, [2]> var_8634_split_sizes_0 = const()[name = string("op_8634_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8634_axis_0 = const()[name = string("op_8634_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_8634_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_8634_cast_fp16_1 = split(axis = var_8634_axis_0, split_sizes = var_8634_split_sizes_0, x = out_97_cast_fp16)[name = string("op_8634_cast_fp16")];
            tensor<int32, [2]> query_states_145_strides_0 = const()[name = string("query_states_145_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_145_pad_type_0 = const()[name = string("query_states_145_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_145_pad_0 = const()[name = string("query_states_145_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_145_dilations_0 = const()[name = string("query_states_145_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_145_groups_0 = const()[name = string("query_states_145_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_145_cast_fp16 = conv(dilations = query_states_145_dilations_0, groups = query_states_145_groups_0, pad = query_states_145_pad_0, pad_type = query_states_145_pad_type_0, strides = query_states_145_strides_0, weight = layers_24_self_attn_q_proj_weight_cast_fp16, x = var_8634_cast_fp16_0)[name = string("query_states_145_cast_fp16")];
            tensor<int32, [2]> key_states_241_strides_0 = const()[name = string("key_states_241_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_241_pad_type_0 = const()[name = string("key_states_241_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_241_pad_0 = const()[name = string("key_states_241_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_241_dilations_0 = const()[name = string("key_states_241_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_241_groups_0 = const()[name = string("key_states_241_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_241_cast_fp16 = conv(dilations = key_states_241_dilations_0, groups = key_states_241_groups_0, pad = key_states_241_pad_0, pad_type = key_states_241_pad_type_0, strides = key_states_241_strides_0, weight = layers_24_self_attn_k_proj_weight_cast_fp16, x = var_8634_cast_fp16_0)[name = string("key_states_241_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_24_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_24_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1530933376)))];
            tensor<int32, [2]> value_states_145_strides_0 = const()[name = string("value_states_145_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_145_pad_type_0 = const()[name = string("value_states_145_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_145_pad_0 = const()[name = string("value_states_145_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_145_dilations_0 = const()[name = string("value_states_145_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_145_groups_0 = const()[name = string("value_states_145_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_145_cast_fp16 = conv(dilations = value_states_145_dilations_0, groups = value_states_145_groups_0, pad = value_states_145_pad_0, pad_type = value_states_145_pad_type_0, strides = value_states_145_strides_0, weight = layers_24_self_attn_v_proj_weight_to_fp16, x = var_8634_cast_fp16_0)[name = string("value_states_145_cast_fp16")];
            tensor<int32, [4]> concat_288x = const()[name = string("concat_288x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_241_cast_fp16 = reshape(shape = concat_288x, x = query_states_145_cast_fp16)[name = string("x_241_cast_fp16")];
            tensor<int32, [4]> concat_289x = const()[name = string("concat_289x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_8691_cast_fp16 = reshape(shape = concat_289x, x = key_states_241_cast_fp16)[name = string("op_8691_cast_fp16")];
            tensor<int32, [4]> concat_290x = const()[name = string("concat_290x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_8698_cast_fp16 = reshape(shape = concat_290x, x = value_states_145_cast_fp16)[name = string("op_8698_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_8702_cast_fp16 = mul(x = x_241_cast_fp16, y = var_859_cast_fp16)[name = string("op_8702_cast_fp16")];
            tensor<int32, [2]> var_8703_split_sizes_0 = const()[name = string("op_8703_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8703_axis_0 = const()[name = string("op_8703_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_8703_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_8703_cast_fp16_1 = split(axis = var_8703_axis_0, split_sizes = var_8703_split_sizes_0, x = x_241_cast_fp16)[name = string("op_8703_cast_fp16")];
            fp16 const_244_promoted_to_fp16 = const()[name = string("const_244_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_8705_cast_fp16 = mul(x = var_8703_cast_fp16_1, y = const_244_promoted_to_fp16)[name = string("op_8705_cast_fp16")];
            int32 var_8707 = const()[name = string("op_8707"), val = int32(-2)];
            bool var_8708_interleave_0 = const()[name = string("op_8708_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_8708_cast_fp16 = concat(axis = var_8707, interleave = var_8708_interleave_0, values = (var_8705_cast_fp16, var_8703_cast_fp16_0))[name = string("op_8708_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_8709_cast_fp16 = mul(x = var_8708_cast_fp16, y = var_866_cast_fp16)[name = string("op_8709_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_147_cast_fp16 = add(x = var_8702_cast_fp16, y = var_8709_cast_fp16)[name = string("query_states_147_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_8715_cast_fp16 = mul(x = var_8691_cast_fp16, y = var_859_cast_fp16)[name = string("op_8715_cast_fp16")];
            tensor<int32, [2]> var_8716_split_sizes_0 = const()[name = string("op_8716_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_8716_axis_0 = const()[name = string("op_8716_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_8716_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_8716_cast_fp16_1 = split(axis = var_8716_axis_0, split_sizes = var_8716_split_sizes_0, x = var_8691_cast_fp16)[name = string("op_8716_cast_fp16")];
            fp16 const_245_promoted_to_fp16 = const()[name = string("const_245_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_8718_cast_fp16 = mul(x = var_8716_cast_fp16_1, y = const_245_promoted_to_fp16)[name = string("op_8718_cast_fp16")];
            int32 var_8720 = const()[name = string("op_8720"), val = int32(-2)];
            bool var_8721_interleave_0 = const()[name = string("op_8721_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_8721_cast_fp16 = concat(axis = var_8720, interleave = var_8721_interleave_0, values = (var_8718_cast_fp16, var_8716_cast_fp16_0))[name = string("op_8721_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_8722_cast_fp16 = mul(x = var_8721_cast_fp16, y = var_866_cast_fp16)[name = string("op_8722_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_245_cast_fp16 = add(x = var_8715_cast_fp16, y = var_8722_cast_fp16)[name = string("key_states_245_cast_fp16")];
            tensor<int32, [1]> expand_dims_288 = const()[name = string("expand_dims_288"), val = tensor<int32, [1]>([24])];
            tensor<int32, [1]> expand_dims_289 = const()[name = string("expand_dims_289"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_291 = const()[name = string("expand_dims_291"), val = tensor<int32, [1]>([0])];
            int32 concat_293_axis_0 = const()[name = string("concat_293_axis_0"), val = int32(0)];
            bool concat_293_interleave_0 = const()[name = string("concat_293_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_293 = concat(axis = concat_293_axis_0, interleave = concat_293_interleave_0, values = (expand_dims_288, expand_dims_289, position_id, expand_dims_291))[name = string("concat_293")];
            tensor<int32, [1]> expand_dims_292 = const()[name = string("expand_dims_292"), val = tensor<int32, [1]>([25])];
            tensor<int32, [1]> concat_294_values1_0 = const()[name = string("concat_294_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_294_values3_0 = const()[name = string("concat_294_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_294_axis_0 = const()[name = string("concat_294_axis_0"), val = int32(0)];
            bool concat_294_interleave_0 = const()[name = string("concat_294_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_294 = concat(axis = concat_294_axis_0, interleave = concat_294_interleave_0, values = (expand_dims_292, concat_294_values1_0, cache_position_end, concat_294_values3_0))[name = string("concat_294")];
            tensor<int32, [4]> key_states_247_perm_0 = const()[name = string("key_states_247_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_25_stride_0 = const()[name = string("key_cache_internal_tensor_assign_25_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_25_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_25_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_25_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_25_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_25_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_25_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_247_cast_fp16 = transpose(perm = key_states_247_perm_0, x = key_states_245_cast_fp16)[name = string("transpose_11")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_25_cast_fp16 = slice_update(begin = concat_293, begin_mask = key_cache_internal_tensor_assign_25_begin_mask_0, end = concat_294, end_mask = key_cache_internal_tensor_assign_25_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_25_squeeze_mask_0, stride = key_cache_internal_tensor_assign_25_stride_0, update = key_states_247_cast_fp16, x = coreml_update_state_46)[name = string("key_cache_internal_tensor_assign_25_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_25_cast_fp16, input = key_cache)[name = string("coreml_update_state_48_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_48 = read_state(input = key_cache)[name = string("coreml_update_state_48")];
            tensor<int32, [4]> value_states_147_perm_0 = const()[name = string("value_states_147_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_25_stride_0 = const()[name = string("value_cache_internal_tensor_assign_25_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_25_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_25_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_25_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_25_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_25_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_25_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_147_cast_fp16 = transpose(perm = value_states_147_perm_0, x = var_8698_cast_fp16)[name = string("transpose_10")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_25_cast_fp16 = slice_update(begin = concat_293, begin_mask = value_cache_internal_tensor_assign_25_begin_mask_0, end = concat_294, end_mask = value_cache_internal_tensor_assign_25_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_25_squeeze_mask_0, stride = value_cache_internal_tensor_assign_25_stride_0, update = value_states_147_cast_fp16, x = coreml_update_state_47)[name = string("value_cache_internal_tensor_assign_25_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_25_cast_fp16, input = value_cache)[name = string("coreml_update_state_49_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_49 = read_state(input = value_cache)[name = string("coreml_update_state_49")];
            tensor<int32, [4]> var_8792_begin_0 = const()[name = string("op_8792_begin_0"), val = tensor<int32, [4]>([24, 0, 0, 0])];
            tensor<int32, [4]> var_8792_end_0 = const()[name = string("op_8792_end_0"), val = tensor<int32, [4]>([25, 2, 2048, 128])];
            tensor<bool, [4]> var_8792_end_mask_0 = const()[name = string("op_8792_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8792_cast_fp16 = slice_by_index(begin = var_8792_begin_0, end = var_8792_end_0, end_mask = var_8792_end_mask_0, x = coreml_update_state_48)[name = string("op_8792_cast_fp16")];
            tensor<int32, [2]> tile_48 = const()[name = string("tile_48"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8795_axis_0 = const()[name = string("op_8795_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8795_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8795_cast_fp16_1 = split(axis = var_8795_axis_0, split_sizes = tile_48, x = var_8792_cast_fp16)[name = string("op_8795_cast_fp16")];
            tensor<int32, [4]> var_8802_begin_0 = const()[name = string("op_8802_begin_0"), val = tensor<int32, [4]>([24, 0, 0, 0])];
            tensor<int32, [4]> var_8802_end_0 = const()[name = string("op_8802_end_0"), val = tensor<int32, [4]>([25, 2, 2048, 128])];
            tensor<bool, [4]> var_8802_end_mask_0 = const()[name = string("op_8802_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_8802_cast_fp16 = slice_by_index(begin = var_8802_begin_0, end = var_8802_end_0, end_mask = var_8802_end_mask_0, x = coreml_update_state_49)[name = string("op_8802_cast_fp16")];
            tensor<int32, [2]> tile_49 = const()[name = string("tile_49"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8805_axis_0 = const()[name = string("op_8805_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_8805_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_8805_cast_fp16_1 = split(axis = var_8805_axis_0, split_sizes = tile_49, x = var_8802_cast_fp16)[name = string("op_8805_cast_fp16")];
            tensor<int32, [2]> var_8808_split_sizes_0 = const()[name = string("op_8808_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_8808_axis_0 = const()[name = string("op_8808_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_8808_0, tensor<fp16, [1, 8, 128, 1]> var_8808_1 = split(axis = var_8808_axis_0, split_sizes = var_8808_split_sizes_0, x = query_states_147_cast_fp16)[name = string("op_8808")];
            bool attn_weights_385_transpose_x_0 = const()[name = string("attn_weights_385_transpose_x_0"), val = bool(false)];
            bool attn_weights_385_transpose_y_0 = const()[name = string("attn_weights_385_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_385_cast_fp16 = matmul(transpose_x = attn_weights_385_transpose_x_0, transpose_y = attn_weights_385_transpose_y_0, x = var_8795_cast_fp16_0, y = var_8808_0)[name = string("attn_weights_385_cast_fp16")];
            fp16 var_8811_to_fp16 = const()[name = string("op_8811_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_387_cast_fp16 = mul(x = attn_weights_385_cast_fp16, y = var_8811_to_fp16)[name = string("attn_weights_387_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_389_cast_fp16 = add(x = attn_weights_387_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_389_cast_fp16")];
            int32 var_8815 = const()[name = string("op_8815"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_391_cast_fp16 = softmax(axis = var_8815, x = attn_weights_389_cast_fp16)[name = string("attn_weights_391_cast_fp16")];
            bool var_8821_transpose_x_1 = const()[name = string("op_8821_transpose_x_1"), val = bool(true)];
            bool var_8821_transpose_y_1 = const()[name = string("op_8821_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_8821_cast_fp16 = matmul(transpose_x = var_8821_transpose_x_1, transpose_y = var_8821_transpose_y_1, x = attn_weights_391_cast_fp16, y = var_8805_cast_fp16_0)[name = string("op_8821_cast_fp16")];
            bool attn_weights_393_transpose_x_0 = const()[name = string("attn_weights_393_transpose_x_0"), val = bool(false)];
            bool attn_weights_393_transpose_y_0 = const()[name = string("attn_weights_393_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_393_cast_fp16 = matmul(transpose_x = attn_weights_393_transpose_x_0, transpose_y = attn_weights_393_transpose_y_0, x = var_8795_cast_fp16_1, y = var_8808_1)[name = string("attn_weights_393_cast_fp16")];
            fp16 var_8823_to_fp16 = const()[name = string("op_8823_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_395_cast_fp16 = mul(x = attn_weights_393_cast_fp16, y = var_8823_to_fp16)[name = string("attn_weights_395_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_397_cast_fp16 = add(x = attn_weights_395_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_397_cast_fp16")];
            int32 var_8827 = const()[name = string("op_8827"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_399_cast_fp16 = softmax(axis = var_8827, x = attn_weights_397_cast_fp16)[name = string("attn_weights_399_cast_fp16")];
            bool attn_output_193_transpose_x_1 = const()[name = string("attn_output_193_transpose_x_1"), val = bool(true)];
            bool attn_output_193_transpose_y_1 = const()[name = string("attn_output_193_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_193_cast_fp16 = matmul(transpose_x = attn_output_193_transpose_x_1, transpose_y = attn_output_193_transpose_y_1, x = attn_weights_399_cast_fp16, y = var_8805_cast_fp16_1)[name = string("attn_output_193_cast_fp16")];
            int32 var_8835 = const()[name = string("op_8835"), val = int32(1)];
            bool attn_output_195_interleave_0 = const()[name = string("attn_output_195_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_195_cast_fp16 = concat(axis = var_8835, interleave = attn_output_195_interleave_0, values = (var_8821_cast_fp16, attn_output_193_cast_fp16))[name = string("attn_output_195_cast_fp16")];
            tensor<int32, [4]> var_8839_perm_0 = const()[name = string("op_8839_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_299x = const()[name = string("concat_299x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_8839_cast_fp16 = transpose(perm = var_8839_perm_0, x = attn_output_195_cast_fp16)[name = string("transpose_9")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_199_cast_fp16 = reshape(shape = concat_299x, x = var_8839_cast_fp16)[name = string("attn_output_199_cast_fp16")];
            tensor<int32, [2]> hidden_states_243_strides_0 = const()[name = string("hidden_states_243_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_243_pad_type_0 = const()[name = string("hidden_states_243_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_243_pad_0 = const()[name = string("hidden_states_243_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_243_dilations_0 = const()[name = string("hidden_states_243_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_243_groups_0 = const()[name = string("hidden_states_243_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_243_cast_fp16 = conv(dilations = hidden_states_243_dilations_0, groups = hidden_states_243_groups_0, pad = hidden_states_243_pad_0, pad_type = hidden_states_243_pad_type_0, strides = hidden_states_243_strides_0, weight = layers_24_self_attn_o_proj_weight_cast_fp16, x = attn_output_199_cast_fp16)[name = string("hidden_states_243_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_245_cast_fp16 = add(x = hidden_states_239_cast_fp16, y = hidden_states_243_cast_fp16)[name = string("hidden_states_245_cast_fp16")];
            fp16 const_250_promoted_to_fp16 = const()[name = string("const_250_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_8872_cast_fp16 = mul(x = hidden_states_245_cast_fp16, y = const_250_promoted_to_fp16)[name = string("op_8872_cast_fp16")];
            int32 var_8870 = const()[name = string("op_8870"), val = int32(1)];
            bool doubled_197_interleave_0 = const()[name = string("doubled_197_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_197_cast_fp16 = concat(axis = var_8870, interleave = doubled_197_interleave_0, values = (hidden_states_245_cast_fp16, var_8872_cast_fp16))[name = string("doubled_197_cast_fp16")];
            tensor<int32, [1]> out_99_axes_0 = const()[name = string("out_99_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_99_gamma_0_to_fp16 = const()[name = string("out_99_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1531982016)))];
            fp16 var_8882_to_fp16 = const()[name = string("op_8882_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_99_cast_fp16 = layer_norm(axes = out_99_axes_0, epsilon = var_8882_to_fp16, gamma = out_99_gamma_0_to_fp16, x = doubled_197_cast_fp16)[name = string("out_99_cast_fp16")];
            tensor<int32, [2]> var_8893_split_sizes_0 = const()[name = string("op_8893_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8893_axis_0 = const()[name = string("op_8893_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_8893_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_8893_cast_fp16_1 = split(axis = var_8893_axis_0, split_sizes = var_8893_split_sizes_0, x = out_99_cast_fp16)[name = string("op_8893_cast_fp16")];
            tensor<int32, [2]> input_49_strides_0 = const()[name = string("input_49_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_49_pad_type_0 = const()[name = string("input_49_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_49_pad_0 = const()[name = string("input_49_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_49_dilations_0 = const()[name = string("input_49_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_49_groups_0 = const()[name = string("input_49_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_49_cast_fp16 = conv(dilations = input_49_dilations_0, groups = input_49_groups_0, pad = input_49_pad_0, pad_type = input_49_pad_type_0, strides = input_49_strides_0, weight = layers_24_mlp_gate_proj_weight_cast_fp16, x = var_8893_cast_fp16_0)[name = string("input_49_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_8910_cast_fp16 = silu(x = input_49_cast_fp16)[name = string("op_8910_cast_fp16")];
            tensor<int32, [2]> var_8916_strides_0 = const()[name = string("op_8916_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_8916_pad_type_0 = const()[name = string("op_8916_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_8916_pad_0 = const()[name = string("op_8916_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_8916_dilations_0 = const()[name = string("op_8916_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_8916_groups_0 = const()[name = string("op_8916_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_8916_cast_fp16 = conv(dilations = var_8916_dilations_0, groups = var_8916_groups_0, pad = var_8916_pad_0, pad_type = var_8916_pad_type_0, strides = var_8916_strides_0, weight = layers_24_mlp_up_proj_weight_cast_fp16, x = var_8893_cast_fp16_0)[name = string("op_8916_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_249_cast_fp16 = mul(x = var_8910_cast_fp16, y = var_8916_cast_fp16)[name = string("x_249_cast_fp16")];
            tensor<int32, [2]> hidden_states_247_strides_0 = const()[name = string("hidden_states_247_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_247_pad_type_0 = const()[name = string("hidden_states_247_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_247_pad_0 = const()[name = string("hidden_states_247_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_247_dilations_0 = const()[name = string("hidden_states_247_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_247_groups_0 = const()[name = string("hidden_states_247_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_247_cast_fp16 = conv(dilations = hidden_states_247_dilations_0, groups = hidden_states_247_groups_0, pad = hidden_states_247_pad_0, pad_type = hidden_states_247_pad_type_0, strides = hidden_states_247_strides_0, weight = layers_24_mlp_down_proj_weight_cast_fp16, x = x_249_cast_fp16)[name = string("hidden_states_247_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_249_cast_fp16 = add(x = hidden_states_245_cast_fp16, y = hidden_states_247_cast_fp16)[name = string("hidden_states_249_cast_fp16")];
            fp16 const_252_promoted_to_fp16 = const()[name = string("const_252_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_8934_cast_fp16 = mul(x = hidden_states_249_cast_fp16, y = const_252_promoted_to_fp16)[name = string("op_8934_cast_fp16")];
            int32 var_8932 = const()[name = string("op_8932"), val = int32(1)];
            bool doubled_201_interleave_0 = const()[name = string("doubled_201_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_201_cast_fp16 = concat(axis = var_8932, interleave = doubled_201_interleave_0, values = (hidden_states_249_cast_fp16, var_8934_cast_fp16))[name = string("doubled_201_cast_fp16")];
            tensor<int32, [1]> out_101_axes_0 = const()[name = string("out_101_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_101_gamma_0_to_fp16 = const()[name = string("out_101_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1531990272)))];
            fp16 var_8944_to_fp16 = const()[name = string("op_8944_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_101_cast_fp16 = layer_norm(axes = out_101_axes_0, epsilon = var_8944_to_fp16, gamma = out_101_gamma_0_to_fp16, x = doubled_201_cast_fp16)[name = string("out_101_cast_fp16")];
            tensor<int32, [2]> var_8955_split_sizes_0 = const()[name = string("op_8955_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_8955_axis_0 = const()[name = string("op_8955_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_8955_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_8955_cast_fp16_1 = split(axis = var_8955_axis_0, split_sizes = var_8955_split_sizes_0, x = out_101_cast_fp16)[name = string("op_8955_cast_fp16")];
            tensor<int32, [2]> query_states_151_strides_0 = const()[name = string("query_states_151_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_151_pad_type_0 = const()[name = string("query_states_151_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_151_pad_0 = const()[name = string("query_states_151_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_151_dilations_0 = const()[name = string("query_states_151_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_151_groups_0 = const()[name = string("query_states_151_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_151_cast_fp16 = conv(dilations = query_states_151_dilations_0, groups = query_states_151_groups_0, pad = query_states_151_pad_0, pad_type = query_states_151_pad_type_0, strides = query_states_151_strides_0, weight = layers_25_self_attn_q_proj_weight_cast_fp16, x = var_8955_cast_fp16_0)[name = string("query_states_151_cast_fp16")];
            tensor<int32, [2]> key_states_251_strides_0 = const()[name = string("key_states_251_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_251_pad_type_0 = const()[name = string("key_states_251_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_251_pad_0 = const()[name = string("key_states_251_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_251_dilations_0 = const()[name = string("key_states_251_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_251_groups_0 = const()[name = string("key_states_251_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_251_cast_fp16 = conv(dilations = key_states_251_dilations_0, groups = key_states_251_groups_0, pad = key_states_251_pad_0, pad_type = key_states_251_pad_type_0, strides = key_states_251_strides_0, weight = layers_25_self_attn_k_proj_weight_cast_fp16, x = var_8955_cast_fp16_0)[name = string("key_states_251_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_25_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_25_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1531998528)))];
            tensor<int32, [2]> value_states_151_strides_0 = const()[name = string("value_states_151_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_151_pad_type_0 = const()[name = string("value_states_151_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_151_pad_0 = const()[name = string("value_states_151_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_151_dilations_0 = const()[name = string("value_states_151_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_151_groups_0 = const()[name = string("value_states_151_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_151_cast_fp16 = conv(dilations = value_states_151_dilations_0, groups = value_states_151_groups_0, pad = value_states_151_pad_0, pad_type = value_states_151_pad_type_0, strides = value_states_151_strides_0, weight = layers_25_self_attn_v_proj_weight_to_fp16, x = var_8955_cast_fp16_0)[name = string("value_states_151_cast_fp16")];
            tensor<int32, [4]> concat_300x = const()[name = string("concat_300x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_251_cast_fp16 = reshape(shape = concat_300x, x = query_states_151_cast_fp16)[name = string("x_251_cast_fp16")];
            tensor<int32, [4]> concat_301x = const()[name = string("concat_301x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_9012_cast_fp16 = reshape(shape = concat_301x, x = key_states_251_cast_fp16)[name = string("op_9012_cast_fp16")];
            tensor<int32, [4]> concat_302x = const()[name = string("concat_302x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_9019_cast_fp16 = reshape(shape = concat_302x, x = value_states_151_cast_fp16)[name = string("op_9019_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_9023_cast_fp16 = mul(x = x_251_cast_fp16, y = var_859_cast_fp16)[name = string("op_9023_cast_fp16")];
            tensor<int32, [2]> var_9024_split_sizes_0 = const()[name = string("op_9024_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9024_axis_0 = const()[name = string("op_9024_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_9024_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_9024_cast_fp16_1 = split(axis = var_9024_axis_0, split_sizes = var_9024_split_sizes_0, x = x_251_cast_fp16)[name = string("op_9024_cast_fp16")];
            fp16 const_254_promoted_to_fp16 = const()[name = string("const_254_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_9026_cast_fp16 = mul(x = var_9024_cast_fp16_1, y = const_254_promoted_to_fp16)[name = string("op_9026_cast_fp16")];
            int32 var_9028 = const()[name = string("op_9028"), val = int32(-2)];
            bool var_9029_interleave_0 = const()[name = string("op_9029_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_9029_cast_fp16 = concat(axis = var_9028, interleave = var_9029_interleave_0, values = (var_9026_cast_fp16, var_9024_cast_fp16_0))[name = string("op_9029_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_9030_cast_fp16 = mul(x = var_9029_cast_fp16, y = var_866_cast_fp16)[name = string("op_9030_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_153_cast_fp16 = add(x = var_9023_cast_fp16, y = var_9030_cast_fp16)[name = string("query_states_153_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_9036_cast_fp16 = mul(x = var_9012_cast_fp16, y = var_859_cast_fp16)[name = string("op_9036_cast_fp16")];
            tensor<int32, [2]> var_9037_split_sizes_0 = const()[name = string("op_9037_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9037_axis_0 = const()[name = string("op_9037_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_9037_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_9037_cast_fp16_1 = split(axis = var_9037_axis_0, split_sizes = var_9037_split_sizes_0, x = var_9012_cast_fp16)[name = string("op_9037_cast_fp16")];
            fp16 const_255_promoted_to_fp16 = const()[name = string("const_255_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_9039_cast_fp16 = mul(x = var_9037_cast_fp16_1, y = const_255_promoted_to_fp16)[name = string("op_9039_cast_fp16")];
            int32 var_9041 = const()[name = string("op_9041"), val = int32(-2)];
            bool var_9042_interleave_0 = const()[name = string("op_9042_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_9042_cast_fp16 = concat(axis = var_9041, interleave = var_9042_interleave_0, values = (var_9039_cast_fp16, var_9037_cast_fp16_0))[name = string("op_9042_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_9043_cast_fp16 = mul(x = var_9042_cast_fp16, y = var_866_cast_fp16)[name = string("op_9043_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_255_cast_fp16 = add(x = var_9036_cast_fp16, y = var_9043_cast_fp16)[name = string("key_states_255_cast_fp16")];
            tensor<int32, [1]> expand_dims_300 = const()[name = string("expand_dims_300"), val = tensor<int32, [1]>([25])];
            tensor<int32, [1]> expand_dims_301 = const()[name = string("expand_dims_301"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_303 = const()[name = string("expand_dims_303"), val = tensor<int32, [1]>([0])];
            int32 concat_305_axis_0 = const()[name = string("concat_305_axis_0"), val = int32(0)];
            bool concat_305_interleave_0 = const()[name = string("concat_305_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_305 = concat(axis = concat_305_axis_0, interleave = concat_305_interleave_0, values = (expand_dims_300, expand_dims_301, position_id, expand_dims_303))[name = string("concat_305")];
            tensor<int32, [1]> expand_dims_304 = const()[name = string("expand_dims_304"), val = tensor<int32, [1]>([26])];
            tensor<int32, [1]> concat_306_values1_0 = const()[name = string("concat_306_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_306_values3_0 = const()[name = string("concat_306_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_306_axis_0 = const()[name = string("concat_306_axis_0"), val = int32(0)];
            bool concat_306_interleave_0 = const()[name = string("concat_306_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_306 = concat(axis = concat_306_axis_0, interleave = concat_306_interleave_0, values = (expand_dims_304, concat_306_values1_0, cache_position_end, concat_306_values3_0))[name = string("concat_306")];
            tensor<int32, [4]> key_states_257_perm_0 = const()[name = string("key_states_257_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_26_stride_0 = const()[name = string("key_cache_internal_tensor_assign_26_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_26_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_26_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_26_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_26_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_26_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_26_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_257_cast_fp16 = transpose(perm = key_states_257_perm_0, x = key_states_255_cast_fp16)[name = string("transpose_8")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_26_cast_fp16 = slice_update(begin = concat_305, begin_mask = key_cache_internal_tensor_assign_26_begin_mask_0, end = concat_306, end_mask = key_cache_internal_tensor_assign_26_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_26_squeeze_mask_0, stride = key_cache_internal_tensor_assign_26_stride_0, update = key_states_257_cast_fp16, x = coreml_update_state_48)[name = string("key_cache_internal_tensor_assign_26_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_26_cast_fp16, input = key_cache)[name = string("coreml_update_state_50_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_50 = read_state(input = key_cache)[name = string("coreml_update_state_50")];
            tensor<int32, [4]> value_states_153_perm_0 = const()[name = string("value_states_153_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_26_stride_0 = const()[name = string("value_cache_internal_tensor_assign_26_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_26_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_26_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_26_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_26_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_26_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_26_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_153_cast_fp16 = transpose(perm = value_states_153_perm_0, x = var_9019_cast_fp16)[name = string("transpose_7")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_26_cast_fp16 = slice_update(begin = concat_305, begin_mask = value_cache_internal_tensor_assign_26_begin_mask_0, end = concat_306, end_mask = value_cache_internal_tensor_assign_26_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_26_squeeze_mask_0, stride = value_cache_internal_tensor_assign_26_stride_0, update = value_states_153_cast_fp16, x = coreml_update_state_49)[name = string("value_cache_internal_tensor_assign_26_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_26_cast_fp16, input = value_cache)[name = string("coreml_update_state_51_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_51 = read_state(input = value_cache)[name = string("coreml_update_state_51")];
            tensor<int32, [4]> var_9113_begin_0 = const()[name = string("op_9113_begin_0"), val = tensor<int32, [4]>([25, 0, 0, 0])];
            tensor<int32, [4]> var_9113_end_0 = const()[name = string("op_9113_end_0"), val = tensor<int32, [4]>([26, 2, 2048, 128])];
            tensor<bool, [4]> var_9113_end_mask_0 = const()[name = string("op_9113_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9113_cast_fp16 = slice_by_index(begin = var_9113_begin_0, end = var_9113_end_0, end_mask = var_9113_end_mask_0, x = coreml_update_state_50)[name = string("op_9113_cast_fp16")];
            tensor<int32, [2]> tile_50 = const()[name = string("tile_50"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9116_axis_0 = const()[name = string("op_9116_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9116_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9116_cast_fp16_1 = split(axis = var_9116_axis_0, split_sizes = tile_50, x = var_9113_cast_fp16)[name = string("op_9116_cast_fp16")];
            tensor<int32, [4]> var_9123_begin_0 = const()[name = string("op_9123_begin_0"), val = tensor<int32, [4]>([25, 0, 0, 0])];
            tensor<int32, [4]> var_9123_end_0 = const()[name = string("op_9123_end_0"), val = tensor<int32, [4]>([26, 2, 2048, 128])];
            tensor<bool, [4]> var_9123_end_mask_0 = const()[name = string("op_9123_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9123_cast_fp16 = slice_by_index(begin = var_9123_begin_0, end = var_9123_end_0, end_mask = var_9123_end_mask_0, x = coreml_update_state_51)[name = string("op_9123_cast_fp16")];
            tensor<int32, [2]> tile_51 = const()[name = string("tile_51"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9126_axis_0 = const()[name = string("op_9126_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9126_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9126_cast_fp16_1 = split(axis = var_9126_axis_0, split_sizes = tile_51, x = var_9123_cast_fp16)[name = string("op_9126_cast_fp16")];
            tensor<int32, [2]> var_9129_split_sizes_0 = const()[name = string("op_9129_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_9129_axis_0 = const()[name = string("op_9129_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_9129_0, tensor<fp16, [1, 8, 128, 1]> var_9129_1 = split(axis = var_9129_axis_0, split_sizes = var_9129_split_sizes_0, x = query_states_153_cast_fp16)[name = string("op_9129")];
            bool attn_weights_401_transpose_x_0 = const()[name = string("attn_weights_401_transpose_x_0"), val = bool(false)];
            bool attn_weights_401_transpose_y_0 = const()[name = string("attn_weights_401_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_401_cast_fp16 = matmul(transpose_x = attn_weights_401_transpose_x_0, transpose_y = attn_weights_401_transpose_y_0, x = var_9116_cast_fp16_0, y = var_9129_0)[name = string("attn_weights_401_cast_fp16")];
            fp16 var_9132_to_fp16 = const()[name = string("op_9132_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_403_cast_fp16 = mul(x = attn_weights_401_cast_fp16, y = var_9132_to_fp16)[name = string("attn_weights_403_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_405_cast_fp16 = add(x = attn_weights_403_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_405_cast_fp16")];
            int32 var_9136 = const()[name = string("op_9136"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_407_cast_fp16 = softmax(axis = var_9136, x = attn_weights_405_cast_fp16)[name = string("attn_weights_407_cast_fp16")];
            bool var_9142_transpose_x_1 = const()[name = string("op_9142_transpose_x_1"), val = bool(true)];
            bool var_9142_transpose_y_1 = const()[name = string("op_9142_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_9142_cast_fp16 = matmul(transpose_x = var_9142_transpose_x_1, transpose_y = var_9142_transpose_y_1, x = attn_weights_407_cast_fp16, y = var_9126_cast_fp16_0)[name = string("op_9142_cast_fp16")];
            bool attn_weights_409_transpose_x_0 = const()[name = string("attn_weights_409_transpose_x_0"), val = bool(false)];
            bool attn_weights_409_transpose_y_0 = const()[name = string("attn_weights_409_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_409_cast_fp16 = matmul(transpose_x = attn_weights_409_transpose_x_0, transpose_y = attn_weights_409_transpose_y_0, x = var_9116_cast_fp16_1, y = var_9129_1)[name = string("attn_weights_409_cast_fp16")];
            fp16 var_9144_to_fp16 = const()[name = string("op_9144_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_411_cast_fp16 = mul(x = attn_weights_409_cast_fp16, y = var_9144_to_fp16)[name = string("attn_weights_411_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_413_cast_fp16 = add(x = attn_weights_411_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_413_cast_fp16")];
            int32 var_9148 = const()[name = string("op_9148"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_415_cast_fp16 = softmax(axis = var_9148, x = attn_weights_413_cast_fp16)[name = string("attn_weights_415_cast_fp16")];
            bool attn_output_201_transpose_x_1 = const()[name = string("attn_output_201_transpose_x_1"), val = bool(true)];
            bool attn_output_201_transpose_y_1 = const()[name = string("attn_output_201_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_201_cast_fp16 = matmul(transpose_x = attn_output_201_transpose_x_1, transpose_y = attn_output_201_transpose_y_1, x = attn_weights_415_cast_fp16, y = var_9126_cast_fp16_1)[name = string("attn_output_201_cast_fp16")];
            int32 var_9156 = const()[name = string("op_9156"), val = int32(1)];
            bool attn_output_203_interleave_0 = const()[name = string("attn_output_203_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_203_cast_fp16 = concat(axis = var_9156, interleave = attn_output_203_interleave_0, values = (var_9142_cast_fp16, attn_output_201_cast_fp16))[name = string("attn_output_203_cast_fp16")];
            tensor<int32, [4]> var_9160_perm_0 = const()[name = string("op_9160_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_311x = const()[name = string("concat_311x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_9160_cast_fp16 = transpose(perm = var_9160_perm_0, x = attn_output_203_cast_fp16)[name = string("transpose_6")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_207_cast_fp16 = reshape(shape = concat_311x, x = var_9160_cast_fp16)[name = string("attn_output_207_cast_fp16")];
            tensor<int32, [2]> hidden_states_253_strides_0 = const()[name = string("hidden_states_253_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_253_pad_type_0 = const()[name = string("hidden_states_253_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_253_pad_0 = const()[name = string("hidden_states_253_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_253_dilations_0 = const()[name = string("hidden_states_253_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_253_groups_0 = const()[name = string("hidden_states_253_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_253_cast_fp16 = conv(dilations = hidden_states_253_dilations_0, groups = hidden_states_253_groups_0, pad = hidden_states_253_pad_0, pad_type = hidden_states_253_pad_type_0, strides = hidden_states_253_strides_0, weight = layers_25_self_attn_o_proj_weight_cast_fp16, x = attn_output_207_cast_fp16)[name = string("hidden_states_253_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_255_cast_fp16 = add(x = hidden_states_249_cast_fp16, y = hidden_states_253_cast_fp16)[name = string("hidden_states_255_cast_fp16")];
            fp16 const_260_promoted_to_fp16 = const()[name = string("const_260_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_9193_cast_fp16 = mul(x = hidden_states_255_cast_fp16, y = const_260_promoted_to_fp16)[name = string("op_9193_cast_fp16")];
            int32 var_9191 = const()[name = string("op_9191"), val = int32(1)];
            bool doubled_205_interleave_0 = const()[name = string("doubled_205_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_205_cast_fp16 = concat(axis = var_9191, interleave = doubled_205_interleave_0, values = (hidden_states_255_cast_fp16, var_9193_cast_fp16))[name = string("doubled_205_cast_fp16")];
            tensor<int32, [1]> out_103_axes_0 = const()[name = string("out_103_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_103_gamma_0_to_fp16 = const()[name = string("out_103_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1533047168)))];
            fp16 var_9203_to_fp16 = const()[name = string("op_9203_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_103_cast_fp16 = layer_norm(axes = out_103_axes_0, epsilon = var_9203_to_fp16, gamma = out_103_gamma_0_to_fp16, x = doubled_205_cast_fp16)[name = string("out_103_cast_fp16")];
            tensor<int32, [2]> var_9214_split_sizes_0 = const()[name = string("op_9214_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9214_axis_0 = const()[name = string("op_9214_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_9214_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_9214_cast_fp16_1 = split(axis = var_9214_axis_0, split_sizes = var_9214_split_sizes_0, x = out_103_cast_fp16)[name = string("op_9214_cast_fp16")];
            tensor<int32, [2]> input_51_strides_0 = const()[name = string("input_51_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_51_pad_type_0 = const()[name = string("input_51_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_51_pad_0 = const()[name = string("input_51_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_51_dilations_0 = const()[name = string("input_51_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_51_groups_0 = const()[name = string("input_51_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_51_cast_fp16 = conv(dilations = input_51_dilations_0, groups = input_51_groups_0, pad = input_51_pad_0, pad_type = input_51_pad_type_0, strides = input_51_strides_0, weight = layers_25_mlp_gate_proj_weight_cast_fp16, x = var_9214_cast_fp16_0)[name = string("input_51_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_9231_cast_fp16 = silu(x = input_51_cast_fp16)[name = string("op_9231_cast_fp16")];
            tensor<int32, [2]> var_9237_strides_0 = const()[name = string("op_9237_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_9237_pad_type_0 = const()[name = string("op_9237_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_9237_pad_0 = const()[name = string("op_9237_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_9237_dilations_0 = const()[name = string("op_9237_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9237_groups_0 = const()[name = string("op_9237_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_9237_cast_fp16 = conv(dilations = var_9237_dilations_0, groups = var_9237_groups_0, pad = var_9237_pad_0, pad_type = var_9237_pad_type_0, strides = var_9237_strides_0, weight = layers_25_mlp_up_proj_weight_cast_fp16, x = var_9214_cast_fp16_0)[name = string("op_9237_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_259_cast_fp16 = mul(x = var_9231_cast_fp16, y = var_9237_cast_fp16)[name = string("x_259_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_25_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_25_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1533055424)))];
            tensor<int32, [2]> hidden_states_257_strides_0 = const()[name = string("hidden_states_257_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_257_pad_type_0 = const()[name = string("hidden_states_257_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_257_pad_0 = const()[name = string("hidden_states_257_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_257_dilations_0 = const()[name = string("hidden_states_257_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_257_groups_0 = const()[name = string("hidden_states_257_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_257_cast_fp16 = conv(dilations = hidden_states_257_dilations_0, groups = hidden_states_257_groups_0, pad = hidden_states_257_pad_0, pad_type = hidden_states_257_pad_type_0, strides = hidden_states_257_strides_0, weight = layers_25_mlp_down_proj_weight_to_fp16, x = x_259_cast_fp16)[name = string("hidden_states_257_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_259_cast_fp16 = add(x = hidden_states_255_cast_fp16, y = hidden_states_257_cast_fp16)[name = string("hidden_states_259_cast_fp16")];
            fp16 const_262_promoted_to_fp16 = const()[name = string("const_262_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_9255_cast_fp16 = mul(x = hidden_states_259_cast_fp16, y = const_262_promoted_to_fp16)[name = string("op_9255_cast_fp16")];
            int32 var_9253 = const()[name = string("op_9253"), val = int32(1)];
            bool doubled_209_interleave_0 = const()[name = string("doubled_209_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_209_cast_fp16 = concat(axis = var_9253, interleave = doubled_209_interleave_0, values = (hidden_states_259_cast_fp16, var_9255_cast_fp16))[name = string("doubled_209_cast_fp16")];
            tensor<int32, [1]> out_105_axes_0 = const()[name = string("out_105_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_105_gamma_0_to_fp16 = const()[name = string("out_105_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1558221312)))];
            fp16 var_9265_to_fp16 = const()[name = string("op_9265_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_105_cast_fp16 = layer_norm(axes = out_105_axes_0, epsilon = var_9265_to_fp16, gamma = out_105_gamma_0_to_fp16, x = doubled_209_cast_fp16)[name = string("out_105_cast_fp16")];
            tensor<int32, [2]> var_9276_split_sizes_0 = const()[name = string("op_9276_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9276_axis_0 = const()[name = string("op_9276_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_9276_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_9276_cast_fp16_1 = split(axis = var_9276_axis_0, split_sizes = var_9276_split_sizes_0, x = out_105_cast_fp16)[name = string("op_9276_cast_fp16")];
            tensor<int32, [2]> query_states_157_strides_0 = const()[name = string("query_states_157_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_157_pad_type_0 = const()[name = string("query_states_157_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_157_pad_0 = const()[name = string("query_states_157_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_157_dilations_0 = const()[name = string("query_states_157_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_157_groups_0 = const()[name = string("query_states_157_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_157_cast_fp16 = conv(dilations = query_states_157_dilations_0, groups = query_states_157_groups_0, pad = query_states_157_pad_0, pad_type = query_states_157_pad_type_0, strides = query_states_157_strides_0, weight = layers_26_self_attn_q_proj_weight_cast_fp16, x = var_9276_cast_fp16_0)[name = string("query_states_157_cast_fp16")];
            tensor<int32, [2]> key_states_261_strides_0 = const()[name = string("key_states_261_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_261_pad_type_0 = const()[name = string("key_states_261_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_261_pad_0 = const()[name = string("key_states_261_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_261_dilations_0 = const()[name = string("key_states_261_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_261_groups_0 = const()[name = string("key_states_261_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_261_cast_fp16 = conv(dilations = key_states_261_dilations_0, groups = key_states_261_groups_0, pad = key_states_261_pad_0, pad_type = key_states_261_pad_type_0, strides = key_states_261_strides_0, weight = layers_26_self_attn_k_proj_weight_cast_fp16, x = var_9276_cast_fp16_0)[name = string("key_states_261_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_26_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_26_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1558229568)))];
            tensor<int32, [2]> value_states_157_strides_0 = const()[name = string("value_states_157_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_157_pad_type_0 = const()[name = string("value_states_157_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_157_pad_0 = const()[name = string("value_states_157_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_157_dilations_0 = const()[name = string("value_states_157_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_157_groups_0 = const()[name = string("value_states_157_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_157_cast_fp16 = conv(dilations = value_states_157_dilations_0, groups = value_states_157_groups_0, pad = value_states_157_pad_0, pad_type = value_states_157_pad_type_0, strides = value_states_157_strides_0, weight = layers_26_self_attn_v_proj_weight_to_fp16, x = var_9276_cast_fp16_0)[name = string("value_states_157_cast_fp16")];
            tensor<int32, [4]> concat_312x = const()[name = string("concat_312x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_261_cast_fp16 = reshape(shape = concat_312x, x = query_states_157_cast_fp16)[name = string("x_261_cast_fp16")];
            tensor<int32, [4]> concat_313x = const()[name = string("concat_313x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_9333_cast_fp16 = reshape(shape = concat_313x, x = key_states_261_cast_fp16)[name = string("op_9333_cast_fp16")];
            tensor<int32, [4]> concat_314x = const()[name = string("concat_314x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_9340_cast_fp16 = reshape(shape = concat_314x, x = value_states_157_cast_fp16)[name = string("op_9340_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_9344_cast_fp16 = mul(x = x_261_cast_fp16, y = var_859_cast_fp16)[name = string("op_9344_cast_fp16")];
            tensor<int32, [2]> var_9345_split_sizes_0 = const()[name = string("op_9345_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9345_axis_0 = const()[name = string("op_9345_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_9345_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_9345_cast_fp16_1 = split(axis = var_9345_axis_0, split_sizes = var_9345_split_sizes_0, x = x_261_cast_fp16)[name = string("op_9345_cast_fp16")];
            fp16 const_264_promoted_to_fp16 = const()[name = string("const_264_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_9347_cast_fp16 = mul(x = var_9345_cast_fp16_1, y = const_264_promoted_to_fp16)[name = string("op_9347_cast_fp16")];
            int32 var_9349 = const()[name = string("op_9349"), val = int32(-2)];
            bool var_9350_interleave_0 = const()[name = string("op_9350_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_9350_cast_fp16 = concat(axis = var_9349, interleave = var_9350_interleave_0, values = (var_9347_cast_fp16, var_9345_cast_fp16_0))[name = string("op_9350_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_9351_cast_fp16 = mul(x = var_9350_cast_fp16, y = var_866_cast_fp16)[name = string("op_9351_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_159_cast_fp16 = add(x = var_9344_cast_fp16, y = var_9351_cast_fp16)[name = string("query_states_159_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_9357_cast_fp16 = mul(x = var_9333_cast_fp16, y = var_859_cast_fp16)[name = string("op_9357_cast_fp16")];
            tensor<int32, [2]> var_9358_split_sizes_0 = const()[name = string("op_9358_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9358_axis_0 = const()[name = string("op_9358_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_9358_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_9358_cast_fp16_1 = split(axis = var_9358_axis_0, split_sizes = var_9358_split_sizes_0, x = var_9333_cast_fp16)[name = string("op_9358_cast_fp16")];
            fp16 const_265_promoted_to_fp16 = const()[name = string("const_265_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_9360_cast_fp16 = mul(x = var_9358_cast_fp16_1, y = const_265_promoted_to_fp16)[name = string("op_9360_cast_fp16")];
            int32 var_9362 = const()[name = string("op_9362"), val = int32(-2)];
            bool var_9363_interleave_0 = const()[name = string("op_9363_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_9363_cast_fp16 = concat(axis = var_9362, interleave = var_9363_interleave_0, values = (var_9360_cast_fp16, var_9358_cast_fp16_0))[name = string("op_9363_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_9364_cast_fp16 = mul(x = var_9363_cast_fp16, y = var_866_cast_fp16)[name = string("op_9364_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_265_cast_fp16 = add(x = var_9357_cast_fp16, y = var_9364_cast_fp16)[name = string("key_states_265_cast_fp16")];
            tensor<int32, [1]> expand_dims_312 = const()[name = string("expand_dims_312"), val = tensor<int32, [1]>([26])];
            tensor<int32, [1]> expand_dims_313 = const()[name = string("expand_dims_313"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_315 = const()[name = string("expand_dims_315"), val = tensor<int32, [1]>([0])];
            int32 concat_317_axis_0 = const()[name = string("concat_317_axis_0"), val = int32(0)];
            bool concat_317_interleave_0 = const()[name = string("concat_317_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_317 = concat(axis = concat_317_axis_0, interleave = concat_317_interleave_0, values = (expand_dims_312, expand_dims_313, position_id, expand_dims_315))[name = string("concat_317")];
            tensor<int32, [1]> expand_dims_316 = const()[name = string("expand_dims_316"), val = tensor<int32, [1]>([27])];
            tensor<int32, [1]> concat_318_values1_0 = const()[name = string("concat_318_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_318_values3_0 = const()[name = string("concat_318_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_318_axis_0 = const()[name = string("concat_318_axis_0"), val = int32(0)];
            bool concat_318_interleave_0 = const()[name = string("concat_318_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_318 = concat(axis = concat_318_axis_0, interleave = concat_318_interleave_0, values = (expand_dims_316, concat_318_values1_0, cache_position_end, concat_318_values3_0))[name = string("concat_318")];
            tensor<int32, [4]> key_states_267_perm_0 = const()[name = string("key_states_267_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_27_stride_0 = const()[name = string("key_cache_internal_tensor_assign_27_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_27_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_27_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_27_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_27_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_27_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_27_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_267_cast_fp16 = transpose(perm = key_states_267_perm_0, x = key_states_265_cast_fp16)[name = string("transpose_5")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_27_cast_fp16 = slice_update(begin = concat_317, begin_mask = key_cache_internal_tensor_assign_27_begin_mask_0, end = concat_318, end_mask = key_cache_internal_tensor_assign_27_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_27_squeeze_mask_0, stride = key_cache_internal_tensor_assign_27_stride_0, update = key_states_267_cast_fp16, x = coreml_update_state_50)[name = string("key_cache_internal_tensor_assign_27_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_27_cast_fp16, input = key_cache)[name = string("coreml_update_state_52_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_52 = read_state(input = key_cache)[name = string("coreml_update_state_52")];
            tensor<int32, [4]> value_states_159_perm_0 = const()[name = string("value_states_159_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_27_stride_0 = const()[name = string("value_cache_internal_tensor_assign_27_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_27_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_27_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_27_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_27_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_27_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_27_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_159_cast_fp16 = transpose(perm = value_states_159_perm_0, x = var_9340_cast_fp16)[name = string("transpose_4")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_27_cast_fp16 = slice_update(begin = concat_317, begin_mask = value_cache_internal_tensor_assign_27_begin_mask_0, end = concat_318, end_mask = value_cache_internal_tensor_assign_27_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_27_squeeze_mask_0, stride = value_cache_internal_tensor_assign_27_stride_0, update = value_states_159_cast_fp16, x = coreml_update_state_51)[name = string("value_cache_internal_tensor_assign_27_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_27_cast_fp16, input = value_cache)[name = string("coreml_update_state_53_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_53 = read_state(input = value_cache)[name = string("coreml_update_state_53")];
            tensor<int32, [4]> var_9434_begin_0 = const()[name = string("op_9434_begin_0"), val = tensor<int32, [4]>([26, 0, 0, 0])];
            tensor<int32, [4]> var_9434_end_0 = const()[name = string("op_9434_end_0"), val = tensor<int32, [4]>([27, 2, 2048, 128])];
            tensor<bool, [4]> var_9434_end_mask_0 = const()[name = string("op_9434_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9434_cast_fp16 = slice_by_index(begin = var_9434_begin_0, end = var_9434_end_0, end_mask = var_9434_end_mask_0, x = coreml_update_state_52)[name = string("op_9434_cast_fp16")];
            tensor<int32, [2]> tile_52 = const()[name = string("tile_52"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9437_axis_0 = const()[name = string("op_9437_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9437_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9437_cast_fp16_1 = split(axis = var_9437_axis_0, split_sizes = tile_52, x = var_9434_cast_fp16)[name = string("op_9437_cast_fp16")];
            tensor<int32, [4]> var_9444_begin_0 = const()[name = string("op_9444_begin_0"), val = tensor<int32, [4]>([26, 0, 0, 0])];
            tensor<int32, [4]> var_9444_end_0 = const()[name = string("op_9444_end_0"), val = tensor<int32, [4]>([27, 2, 2048, 128])];
            tensor<bool, [4]> var_9444_end_mask_0 = const()[name = string("op_9444_end_mask_0"), val = tensor<bool, [4]>([false, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9444_cast_fp16 = slice_by_index(begin = var_9444_begin_0, end = var_9444_end_0, end_mask = var_9444_end_mask_0, x = coreml_update_state_53)[name = string("op_9444_cast_fp16")];
            tensor<int32, [2]> tile_53 = const()[name = string("tile_53"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9447_axis_0 = const()[name = string("op_9447_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9447_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9447_cast_fp16_1 = split(axis = var_9447_axis_0, split_sizes = tile_53, x = var_9444_cast_fp16)[name = string("op_9447_cast_fp16")];
            tensor<int32, [2]> var_9450_split_sizes_0 = const()[name = string("op_9450_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_9450_axis_0 = const()[name = string("op_9450_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_9450_0, tensor<fp16, [1, 8, 128, 1]> var_9450_1 = split(axis = var_9450_axis_0, split_sizes = var_9450_split_sizes_0, x = query_states_159_cast_fp16)[name = string("op_9450")];
            bool attn_weights_417_transpose_x_0 = const()[name = string("attn_weights_417_transpose_x_0"), val = bool(false)];
            bool attn_weights_417_transpose_y_0 = const()[name = string("attn_weights_417_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_417_cast_fp16 = matmul(transpose_x = attn_weights_417_transpose_x_0, transpose_y = attn_weights_417_transpose_y_0, x = var_9437_cast_fp16_0, y = var_9450_0)[name = string("attn_weights_417_cast_fp16")];
            fp16 var_9453_to_fp16 = const()[name = string("op_9453_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_419_cast_fp16 = mul(x = attn_weights_417_cast_fp16, y = var_9453_to_fp16)[name = string("attn_weights_419_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_421_cast_fp16 = add(x = attn_weights_419_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_421_cast_fp16")];
            int32 var_9457 = const()[name = string("op_9457"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_423_cast_fp16 = softmax(axis = var_9457, x = attn_weights_421_cast_fp16)[name = string("attn_weights_423_cast_fp16")];
            bool var_9463_transpose_x_1 = const()[name = string("op_9463_transpose_x_1"), val = bool(true)];
            bool var_9463_transpose_y_1 = const()[name = string("op_9463_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_9463_cast_fp16 = matmul(transpose_x = var_9463_transpose_x_1, transpose_y = var_9463_transpose_y_1, x = attn_weights_423_cast_fp16, y = var_9447_cast_fp16_0)[name = string("op_9463_cast_fp16")];
            bool attn_weights_425_transpose_x_0 = const()[name = string("attn_weights_425_transpose_x_0"), val = bool(false)];
            bool attn_weights_425_transpose_y_0 = const()[name = string("attn_weights_425_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_425_cast_fp16 = matmul(transpose_x = attn_weights_425_transpose_x_0, transpose_y = attn_weights_425_transpose_y_0, x = var_9437_cast_fp16_1, y = var_9450_1)[name = string("attn_weights_425_cast_fp16")];
            fp16 var_9465_to_fp16 = const()[name = string("op_9465_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_427_cast_fp16 = mul(x = attn_weights_425_cast_fp16, y = var_9465_to_fp16)[name = string("attn_weights_427_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_429_cast_fp16 = add(x = attn_weights_427_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_429_cast_fp16")];
            int32 var_9469 = const()[name = string("op_9469"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_431_cast_fp16 = softmax(axis = var_9469, x = attn_weights_429_cast_fp16)[name = string("attn_weights_431_cast_fp16")];
            bool attn_output_209_transpose_x_1 = const()[name = string("attn_output_209_transpose_x_1"), val = bool(true)];
            bool attn_output_209_transpose_y_1 = const()[name = string("attn_output_209_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_209_cast_fp16 = matmul(transpose_x = attn_output_209_transpose_x_1, transpose_y = attn_output_209_transpose_y_1, x = attn_weights_431_cast_fp16, y = var_9447_cast_fp16_1)[name = string("attn_output_209_cast_fp16")];
            int32 var_9477 = const()[name = string("op_9477"), val = int32(1)];
            bool attn_output_211_interleave_0 = const()[name = string("attn_output_211_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_211_cast_fp16 = concat(axis = var_9477, interleave = attn_output_211_interleave_0, values = (var_9463_cast_fp16, attn_output_209_cast_fp16))[name = string("attn_output_211_cast_fp16")];
            tensor<int32, [4]> var_9481_perm_0 = const()[name = string("op_9481_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_323x = const()[name = string("concat_323x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_9481_cast_fp16 = transpose(perm = var_9481_perm_0, x = attn_output_211_cast_fp16)[name = string("transpose_3")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_215_cast_fp16 = reshape(shape = concat_323x, x = var_9481_cast_fp16)[name = string("attn_output_215_cast_fp16")];
            tensor<int32, [2]> hidden_states_263_strides_0 = const()[name = string("hidden_states_263_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_263_pad_type_0 = const()[name = string("hidden_states_263_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_263_pad_0 = const()[name = string("hidden_states_263_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_263_dilations_0 = const()[name = string("hidden_states_263_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_263_groups_0 = const()[name = string("hidden_states_263_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_263_cast_fp16 = conv(dilations = hidden_states_263_dilations_0, groups = hidden_states_263_groups_0, pad = hidden_states_263_pad_0, pad_type = hidden_states_263_pad_type_0, strides = hidden_states_263_strides_0, weight = layers_26_self_attn_o_proj_weight_cast_fp16, x = attn_output_215_cast_fp16)[name = string("hidden_states_263_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_265_cast_fp16 = add(x = hidden_states_259_cast_fp16, y = hidden_states_263_cast_fp16)[name = string("hidden_states_265_cast_fp16")];
            fp16 const_270_promoted_to_fp16 = const()[name = string("const_270_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_9514_cast_fp16 = mul(x = hidden_states_265_cast_fp16, y = const_270_promoted_to_fp16)[name = string("op_9514_cast_fp16")];
            int32 var_9512 = const()[name = string("op_9512"), val = int32(1)];
            bool doubled_213_interleave_0 = const()[name = string("doubled_213_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_213_cast_fp16 = concat(axis = var_9512, interleave = doubled_213_interleave_0, values = (hidden_states_265_cast_fp16, var_9514_cast_fp16))[name = string("doubled_213_cast_fp16")];
            tensor<int32, [1]> out_107_axes_0 = const()[name = string("out_107_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_107_gamma_0_to_fp16 = const()[name = string("out_107_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1559278208)))];
            fp16 var_9524_to_fp16 = const()[name = string("op_9524_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_107_cast_fp16 = layer_norm(axes = out_107_axes_0, epsilon = var_9524_to_fp16, gamma = out_107_gamma_0_to_fp16, x = doubled_213_cast_fp16)[name = string("out_107_cast_fp16")];
            tensor<int32, [2]> var_9535_split_sizes_0 = const()[name = string("op_9535_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9535_axis_0 = const()[name = string("op_9535_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_9535_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_9535_cast_fp16_1 = split(axis = var_9535_axis_0, split_sizes = var_9535_split_sizes_0, x = out_107_cast_fp16)[name = string("op_9535_cast_fp16")];
            tensor<int32, [2]> input_53_strides_0 = const()[name = string("input_53_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_53_pad_type_0 = const()[name = string("input_53_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_53_pad_0 = const()[name = string("input_53_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_53_dilations_0 = const()[name = string("input_53_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_53_groups_0 = const()[name = string("input_53_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_53_cast_fp16 = conv(dilations = input_53_dilations_0, groups = input_53_groups_0, pad = input_53_pad_0, pad_type = input_53_pad_type_0, strides = input_53_strides_0, weight = layers_26_mlp_gate_proj_weight_cast_fp16, x = var_9535_cast_fp16_0)[name = string("input_53_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_9552_cast_fp16 = silu(x = input_53_cast_fp16)[name = string("op_9552_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_26_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_26_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1559286464)))];
            tensor<int32, [2]> var_9558_strides_0 = const()[name = string("op_9558_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_9558_pad_type_0 = const()[name = string("op_9558_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_9558_pad_0 = const()[name = string("op_9558_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_9558_dilations_0 = const()[name = string("op_9558_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9558_groups_0 = const()[name = string("op_9558_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_9558_cast_fp16 = conv(dilations = var_9558_dilations_0, groups = var_9558_groups_0, pad = var_9558_pad_0, pad_type = var_9558_pad_type_0, strides = var_9558_strides_0, weight = layers_26_mlp_up_proj_weight_to_fp16, x = var_9535_cast_fp16_0)[name = string("op_9558_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_269_cast_fp16 = mul(x = var_9552_cast_fp16, y = var_9558_cast_fp16)[name = string("x_269_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_26_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_26_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1584452352)))];
            tensor<int32, [2]> hidden_states_267_strides_0 = const()[name = string("hidden_states_267_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_267_pad_type_0 = const()[name = string("hidden_states_267_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_267_pad_0 = const()[name = string("hidden_states_267_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_267_dilations_0 = const()[name = string("hidden_states_267_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_267_groups_0 = const()[name = string("hidden_states_267_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_267_cast_fp16 = conv(dilations = hidden_states_267_dilations_0, groups = hidden_states_267_groups_0, pad = hidden_states_267_pad_0, pad_type = hidden_states_267_pad_type_0, strides = hidden_states_267_strides_0, weight = layers_26_mlp_down_proj_weight_to_fp16, x = x_269_cast_fp16)[name = string("hidden_states_267_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_269_cast_fp16 = add(x = hidden_states_265_cast_fp16, y = hidden_states_267_cast_fp16)[name = string("hidden_states_269_cast_fp16")];
            fp16 const_272_promoted_to_fp16 = const()[name = string("const_272_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_9576_cast_fp16 = mul(x = hidden_states_269_cast_fp16, y = const_272_promoted_to_fp16)[name = string("op_9576_cast_fp16")];
            int32 var_9574 = const()[name = string("op_9574"), val = int32(1)];
            bool doubled_217_interleave_0 = const()[name = string("doubled_217_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_217_cast_fp16 = concat(axis = var_9574, interleave = doubled_217_interleave_0, values = (hidden_states_269_cast_fp16, var_9576_cast_fp16))[name = string("doubled_217_cast_fp16")];
            tensor<int32, [1]> out_109_axes_0 = const()[name = string("out_109_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_109_gamma_0_to_fp16 = const()[name = string("out_109_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1609618240)))];
            fp16 var_9586_to_fp16 = const()[name = string("op_9586_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_109_cast_fp16 = layer_norm(axes = out_109_axes_0, epsilon = var_9586_to_fp16, gamma = out_109_gamma_0_to_fp16, x = doubled_217_cast_fp16)[name = string("out_109_cast_fp16")];
            tensor<int32, [2]> var_9597_split_sizes_0 = const()[name = string("op_9597_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9597_axis_0 = const()[name = string("op_9597_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_9597_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_9597_cast_fp16_1 = split(axis = var_9597_axis_0, split_sizes = var_9597_split_sizes_0, x = out_109_cast_fp16)[name = string("op_9597_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_27_self_attn_q_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_q_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1609626496)))];
            tensor<int32, [2]> query_states_163_strides_0 = const()[name = string("query_states_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string query_states_163_pad_type_0 = const()[name = string("query_states_163_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> query_states_163_pad_0 = const()[name = string("query_states_163_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> query_states_163_dilations_0 = const()[name = string("query_states_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 query_states_163_groups_0 = const()[name = string("query_states_163_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> query_states_163_cast_fp16 = conv(dilations = query_states_163_dilations_0, groups = query_states_163_groups_0, pad = query_states_163_pad_0, pad_type = query_states_163_pad_type_0, strides = query_states_163_strides_0, weight = layers_27_self_attn_q_proj_weight_to_fp16, x = var_9597_cast_fp16_0)[name = string("query_states_163_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_27_self_attn_k_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_k_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1618015168)))];
            tensor<int32, [2]> key_states_271_strides_0 = const()[name = string("key_states_271_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string key_states_271_pad_type_0 = const()[name = string("key_states_271_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> key_states_271_pad_0 = const()[name = string("key_states_271_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> key_states_271_dilations_0 = const()[name = string("key_states_271_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 key_states_271_groups_0 = const()[name = string("key_states_271_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> key_states_271_cast_fp16 = conv(dilations = key_states_271_dilations_0, groups = key_states_271_groups_0, pad = key_states_271_pad_0, pad_type = key_states_271_pad_type_0, strides = key_states_271_strides_0, weight = layers_27_self_attn_k_proj_weight_to_fp16, x = var_9597_cast_fp16_0)[name = string("key_states_271_cast_fp16")];
            tensor<fp16, [256, 2048, 1, 1]> layers_27_self_attn_v_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_v_proj_weight_to_fp16"), val = tensor<fp16, [256, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1619063808)))];
            tensor<int32, [2]> value_states_163_strides_0 = const()[name = string("value_states_163_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string value_states_163_pad_type_0 = const()[name = string("value_states_163_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> value_states_163_pad_0 = const()[name = string("value_states_163_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> value_states_163_dilations_0 = const()[name = string("value_states_163_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 value_states_163_groups_0 = const()[name = string("value_states_163_groups_0"), val = int32(1)];
            tensor<fp16, [1, 256, 1, 1]> value_states_163_cast_fp16 = conv(dilations = value_states_163_dilations_0, groups = value_states_163_groups_0, pad = value_states_163_pad_0, pad_type = value_states_163_pad_type_0, strides = value_states_163_strides_0, weight = layers_27_self_attn_v_proj_weight_to_fp16, x = var_9597_cast_fp16_0)[name = string("value_states_163_cast_fp16")];
            tensor<int32, [4]> concat_324x = const()[name = string("concat_324x"), val = tensor<int32, [4]>([1, 16, 128, -1])];
            tensor<fp16, [1, 16, 128, 1]> x_271_cast_fp16 = reshape(shape = concat_324x, x = query_states_163_cast_fp16)[name = string("x_271_cast_fp16")];
            tensor<int32, [4]> concat_325x = const()[name = string("concat_325x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_9654_cast_fp16 = reshape(shape = concat_325x, x = key_states_271_cast_fp16)[name = string("op_9654_cast_fp16")];
            tensor<int32, [4]> concat_326x = const()[name = string("concat_326x"), val = tensor<int32, [4]>([1, 2, 128, -1])];
            tensor<fp16, [1, 2, 128, 1]> var_9661_cast_fp16 = reshape(shape = concat_326x, x = value_states_163_cast_fp16)[name = string("op_9661_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_9665_cast_fp16 = mul(x = x_271_cast_fp16, y = var_859_cast_fp16)[name = string("op_9665_cast_fp16")];
            tensor<int32, [2]> var_9666_split_sizes_0 = const()[name = string("op_9666_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9666_axis_0 = const()[name = string("op_9666_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 16, 64, 1]> var_9666_cast_fp16_0, tensor<fp16, [1, 16, 64, 1]> var_9666_cast_fp16_1 = split(axis = var_9666_axis_0, split_sizes = var_9666_split_sizes_0, x = x_271_cast_fp16)[name = string("op_9666_cast_fp16")];
            fp16 const_274_promoted_to_fp16 = const()[name = string("const_274_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 16, 64, 1]> var_9668_cast_fp16 = mul(x = var_9666_cast_fp16_1, y = const_274_promoted_to_fp16)[name = string("op_9668_cast_fp16")];
            int32 var_9670 = const()[name = string("op_9670"), val = int32(-2)];
            bool var_9671_interleave_0 = const()[name = string("op_9671_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 128, 1]> var_9671_cast_fp16 = concat(axis = var_9670, interleave = var_9671_interleave_0, values = (var_9668_cast_fp16, var_9666_cast_fp16_0))[name = string("op_9671_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> var_9672_cast_fp16 = mul(x = var_9671_cast_fp16, y = var_866_cast_fp16)[name = string("op_9672_cast_fp16")];
            tensor<fp16, [1, 16, 128, 1]> query_states_165_cast_fp16 = add(x = var_9665_cast_fp16, y = var_9672_cast_fp16)[name = string("query_states_165_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_9678_cast_fp16 = mul(x = var_9654_cast_fp16, y = var_859_cast_fp16)[name = string("op_9678_cast_fp16")];
            tensor<int32, [2]> var_9679_split_sizes_0 = const()[name = string("op_9679_split_sizes_0"), val = tensor<int32, [2]>([64, 64])];
            int32 var_9679_axis_0 = const()[name = string("op_9679_axis_0"), val = int32(-2)];
            tensor<fp16, [1, 2, 64, 1]> var_9679_cast_fp16_0, tensor<fp16, [1, 2, 64, 1]> var_9679_cast_fp16_1 = split(axis = var_9679_axis_0, split_sizes = var_9679_split_sizes_0, x = var_9654_cast_fp16)[name = string("op_9679_cast_fp16")];
            fp16 const_275_promoted_to_fp16 = const()[name = string("const_275_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2, 64, 1]> var_9681_cast_fp16 = mul(x = var_9679_cast_fp16_1, y = const_275_promoted_to_fp16)[name = string("op_9681_cast_fp16")];
            int32 var_9683 = const()[name = string("op_9683"), val = int32(-2)];
            bool var_9684_interleave_0 = const()[name = string("op_9684_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 2, 128, 1]> var_9684_cast_fp16 = concat(axis = var_9683, interleave = var_9684_interleave_0, values = (var_9681_cast_fp16, var_9679_cast_fp16_0))[name = string("op_9684_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> var_9685_cast_fp16 = mul(x = var_9684_cast_fp16, y = var_866_cast_fp16)[name = string("op_9685_cast_fp16")];
            tensor<fp16, [1, 2, 128, 1]> key_states_275_cast_fp16 = add(x = var_9678_cast_fp16, y = var_9685_cast_fp16)[name = string("key_states_275_cast_fp16")];
            tensor<int32, [1]> expand_dims_324 = const()[name = string("expand_dims_324"), val = tensor<int32, [1]>([27])];
            tensor<int32, [1]> expand_dims_325 = const()[name = string("expand_dims_325"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> expand_dims_327 = const()[name = string("expand_dims_327"), val = tensor<int32, [1]>([0])];
            int32 concat_329_axis_0 = const()[name = string("concat_329_axis_0"), val = int32(0)];
            bool concat_329_interleave_0 = const()[name = string("concat_329_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_329 = concat(axis = concat_329_axis_0, interleave = concat_329_interleave_0, values = (expand_dims_324, expand_dims_325, position_id, expand_dims_327))[name = string("concat_329")];
            tensor<int32, [1]> expand_dims_328 = const()[name = string("expand_dims_328"), val = tensor<int32, [1]>([28])];
            tensor<int32, [1]> concat_330_values1_0 = const()[name = string("concat_330_values1_0"), val = tensor<int32, [1]>([0])];
            tensor<int32, [1]> concat_330_values3_0 = const()[name = string("concat_330_values3_0"), val = tensor<int32, [1]>([0])];
            int32 concat_330_axis_0 = const()[name = string("concat_330_axis_0"), val = int32(0)];
            bool concat_330_interleave_0 = const()[name = string("concat_330_interleave_0"), val = bool(false)];
            tensor<int32, [4]> concat_330 = concat(axis = concat_330_axis_0, interleave = concat_330_interleave_0, values = (expand_dims_328, concat_330_values1_0, cache_position_end, concat_330_values3_0))[name = string("concat_330")];
            tensor<int32, [4]> key_states_277_perm_0 = const()[name = string("key_states_277_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> key_cache_internal_tensor_assign_28_stride_0 = const()[name = string("key_cache_internal_tensor_assign_28_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_28_begin_mask_0 = const()[name = string("key_cache_internal_tensor_assign_28_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_28_end_mask_0 = const()[name = string("key_cache_internal_tensor_assign_28_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> key_cache_internal_tensor_assign_28_squeeze_mask_0 = const()[name = string("key_cache_internal_tensor_assign_28_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> key_states_277_cast_fp16 = transpose(perm = key_states_277_perm_0, x = key_states_275_cast_fp16)[name = string("transpose_2")];
            tensor<fp16, [28, 2, 2048, 128]> key_cache_internal_tensor_assign_28_cast_fp16 = slice_update(begin = concat_329, begin_mask = key_cache_internal_tensor_assign_28_begin_mask_0, end = concat_330, end_mask = key_cache_internal_tensor_assign_28_end_mask_0, squeeze_mask = key_cache_internal_tensor_assign_28_squeeze_mask_0, stride = key_cache_internal_tensor_assign_28_stride_0, update = key_states_277_cast_fp16, x = coreml_update_state_52)[name = string("key_cache_internal_tensor_assign_28_cast_fp16")];
            write_state(data = key_cache_internal_tensor_assign_28_cast_fp16, input = key_cache)[name = string("coreml_update_state_54_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_54 = read_state(input = key_cache)[name = string("coreml_update_state_54")];
            tensor<int32, [4]> value_states_165_perm_0 = const()[name = string("value_states_165_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> value_cache_internal_tensor_assign_28_stride_0 = const()[name = string("value_cache_internal_tensor_assign_28_stride_0"), val = tensor<int32, [4]>([1, 1, 1, 1])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_28_begin_mask_0 = const()[name = string("value_cache_internal_tensor_assign_28_begin_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_28_end_mask_0 = const()[name = string("value_cache_internal_tensor_assign_28_end_mask_0"), val = tensor<bool, [4]>([false, true, false, true])];
            tensor<bool, [4]> value_cache_internal_tensor_assign_28_squeeze_mask_0 = const()[name = string("value_cache_internal_tensor_assign_28_squeeze_mask_0"), val = tensor<bool, [4]>([false, false, false, false])];
            tensor<fp16, [1, 2, 1, 128]> value_states_165_cast_fp16 = transpose(perm = value_states_165_perm_0, x = var_9661_cast_fp16)[name = string("transpose_1")];
            tensor<fp16, [28, 2, 2048, 128]> value_cache_internal_tensor_assign_28_cast_fp16 = slice_update(begin = concat_329, begin_mask = value_cache_internal_tensor_assign_28_begin_mask_0, end = concat_330, end_mask = value_cache_internal_tensor_assign_28_end_mask_0, squeeze_mask = value_cache_internal_tensor_assign_28_squeeze_mask_0, stride = value_cache_internal_tensor_assign_28_stride_0, update = value_states_165_cast_fp16, x = coreml_update_state_53)[name = string("value_cache_internal_tensor_assign_28_cast_fp16")];
            write_state(data = value_cache_internal_tensor_assign_28_cast_fp16, input = value_cache)[name = string("coreml_update_state_55_write_state")];
            tensor<fp16, [28, 2, 2048, 128]> coreml_update_state_55 = read_state(input = value_cache)[name = string("coreml_update_state_55")];
            tensor<int32, [4]> var_9755_begin_0 = const()[name = string("op_9755_begin_0"), val = tensor<int32, [4]>([27, 0, 0, 0])];
            tensor<int32, [4]> var_9755_end_0 = const()[name = string("op_9755_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_9755_end_mask_0 = const()[name = string("op_9755_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9755_cast_fp16 = slice_by_index(begin = var_9755_begin_0, end = var_9755_end_0, end_mask = var_9755_end_mask_0, x = coreml_update_state_54)[name = string("op_9755_cast_fp16")];
            tensor<int32, [2]> tile_54 = const()[name = string("tile_54"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9758_axis_0 = const()[name = string("op_9758_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9758_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9758_cast_fp16_1 = split(axis = var_9758_axis_0, split_sizes = tile_54, x = var_9755_cast_fp16)[name = string("op_9758_cast_fp16")];
            tensor<int32, [4]> var_9765_begin_0 = const()[name = string("op_9765_begin_0"), val = tensor<int32, [4]>([27, 0, 0, 0])];
            tensor<int32, [4]> var_9765_end_0 = const()[name = string("op_9765_end_0"), val = tensor<int32, [4]>([1, 2, 2048, 128])];
            tensor<bool, [4]> var_9765_end_mask_0 = const()[name = string("op_9765_end_mask_0"), val = tensor<bool, [4]>([true, true, true, true])];
            tensor<fp16, [1, 2, 2048, 128]> var_9765_cast_fp16 = slice_by_index(begin = var_9765_begin_0, end = var_9765_end_0, end_mask = var_9765_end_mask_0, x = coreml_update_state_55)[name = string("op_9765_cast_fp16")];
            tensor<int32, [2]> tile_55 = const()[name = string("tile_55"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9768_axis_0 = const()[name = string("op_9768_axis_0"), val = int32(1)];
            tensor<fp16, [1, 1, 2048, 128]> var_9768_cast_fp16_0, tensor<fp16, [1, 1, 2048, 128]> var_9768_cast_fp16_1 = split(axis = var_9768_axis_0, split_sizes = tile_55, x = var_9765_cast_fp16)[name = string("op_9768_cast_fp16")];
            tensor<int32, [2]> var_9771_split_sizes_0 = const()[name = string("op_9771_split_sizes_0"), val = tensor<int32, [2]>([8, 8])];
            int32 var_9771_axis_0 = const()[name = string("op_9771_axis_0"), val = int32(1)];
            tensor<fp16, [1, 8, 128, 1]> var_9771_0, tensor<fp16, [1, 8, 128, 1]> var_9771_1 = split(axis = var_9771_axis_0, split_sizes = var_9771_split_sizes_0, x = query_states_165_cast_fp16)[name = string("op_9771")];
            bool attn_weights_433_transpose_x_0 = const()[name = string("attn_weights_433_transpose_x_0"), val = bool(false)];
            bool attn_weights_433_transpose_y_0 = const()[name = string("attn_weights_433_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_433_cast_fp16 = matmul(transpose_x = attn_weights_433_transpose_x_0, transpose_y = attn_weights_433_transpose_y_0, x = var_9758_cast_fp16_0, y = var_9771_0)[name = string("attn_weights_433_cast_fp16")];
            fp16 var_9774_to_fp16 = const()[name = string("op_9774_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_435_cast_fp16 = mul(x = attn_weights_433_cast_fp16, y = var_9774_to_fp16)[name = string("attn_weights_435_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_437_cast_fp16 = add(x = attn_weights_435_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_437_cast_fp16")];
            int32 var_9778 = const()[name = string("op_9778"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_439_cast_fp16 = softmax(axis = var_9778, x = attn_weights_437_cast_fp16)[name = string("attn_weights_439_cast_fp16")];
            bool var_9784_transpose_x_1 = const()[name = string("op_9784_transpose_x_1"), val = bool(true)];
            bool var_9784_transpose_y_1 = const()[name = string("op_9784_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> var_9784_cast_fp16 = matmul(transpose_x = var_9784_transpose_x_1, transpose_y = var_9784_transpose_y_1, x = attn_weights_439_cast_fp16, y = var_9768_cast_fp16_0)[name = string("op_9784_cast_fp16")];
            bool attn_weights_441_transpose_x_0 = const()[name = string("attn_weights_441_transpose_x_0"), val = bool(false)];
            bool attn_weights_441_transpose_y_0 = const()[name = string("attn_weights_441_transpose_y_0"), val = bool(false)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_441_cast_fp16 = matmul(transpose_x = attn_weights_441_transpose_x_0, transpose_y = attn_weights_441_transpose_y_0, x = var_9758_cast_fp16_1, y = var_9771_1)[name = string("attn_weights_441_cast_fp16")];
            fp16 var_9786_to_fp16 = const()[name = string("op_9786_to_fp16"), val = fp16(0x1.6ap-4)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_443_cast_fp16 = mul(x = attn_weights_441_cast_fp16, y = var_9786_to_fp16)[name = string("attn_weights_443_cast_fp16")];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_445_cast_fp16 = add(x = attn_weights_443_cast_fp16, y = attn_mask_1_cast_fp16)[name = string("attn_weights_445_cast_fp16")];
            int32 var_9790 = const()[name = string("op_9790"), val = int32(-2)];
            tensor<fp16, [1, 8, 2048, 1]> attn_weights_cast_fp16 = softmax(axis = var_9790, x = attn_weights_445_cast_fp16)[name = string("attn_weights_cast_fp16")];
            bool attn_output_217_transpose_x_1 = const()[name = string("attn_output_217_transpose_x_1"), val = bool(true)];
            bool attn_output_217_transpose_y_1 = const()[name = string("attn_output_217_transpose_y_1"), val = bool(false)];
            tensor<fp16, [1, 8, 1, 128]> attn_output_217_cast_fp16 = matmul(transpose_x = attn_output_217_transpose_x_1, transpose_y = attn_output_217_transpose_y_1, x = attn_weights_cast_fp16, y = var_9768_cast_fp16_1)[name = string("attn_output_217_cast_fp16")];
            int32 var_9798 = const()[name = string("op_9798"), val = int32(1)];
            bool attn_output_219_interleave_0 = const()[name = string("attn_output_219_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 16, 1, 128]> attn_output_219_cast_fp16 = concat(axis = var_9798, interleave = attn_output_219_interleave_0, values = (var_9784_cast_fp16, attn_output_217_cast_fp16))[name = string("attn_output_219_cast_fp16")];
            tensor<int32, [4]> var_9802_perm_0 = const()[name = string("op_9802_perm_0"), val = tensor<int32, [4]>([0, 1, 3, 2])];
            tensor<int32, [4]> concat_335x = const()[name = string("concat_335x"), val = tensor<int32, [4]>([1, 2048, 1, -1])];
            tensor<fp16, [1, 16, 128, 1]> var_9802_cast_fp16 = transpose(perm = var_9802_perm_0, x = attn_output_219_cast_fp16)[name = string("transpose_0")];
            tensor<fp16, [1, 2048, 1, 1]> attn_output_cast_fp16 = reshape(shape = concat_335x, x = var_9802_cast_fp16)[name = string("attn_output_cast_fp16")];
            tensor<fp16, [2048, 2048, 1, 1]> layers_27_self_attn_o_proj_weight_to_fp16 = const()[name = string("layers_27_self_attn_o_proj_weight_to_fp16"), val = tensor<fp16, [2048, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1620112448)))];
            tensor<int32, [2]> hidden_states_273_strides_0 = const()[name = string("hidden_states_273_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_273_pad_type_0 = const()[name = string("hidden_states_273_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_273_pad_0 = const()[name = string("hidden_states_273_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_273_dilations_0 = const()[name = string("hidden_states_273_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_273_groups_0 = const()[name = string("hidden_states_273_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_273_cast_fp16 = conv(dilations = hidden_states_273_dilations_0, groups = hidden_states_273_groups_0, pad = hidden_states_273_pad_0, pad_type = hidden_states_273_pad_type_0, strides = hidden_states_273_strides_0, weight = layers_27_self_attn_o_proj_weight_to_fp16, x = attn_output_cast_fp16)[name = string("hidden_states_273_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_275_cast_fp16 = add(x = hidden_states_269_cast_fp16, y = hidden_states_273_cast_fp16)[name = string("hidden_states_275_cast_fp16")];
            fp16 const_280_promoted_to_fp16 = const()[name = string("const_280_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_9835_cast_fp16 = mul(x = hidden_states_275_cast_fp16, y = const_280_promoted_to_fp16)[name = string("op_9835_cast_fp16")];
            int32 var_9833 = const()[name = string("op_9833"), val = int32(1)];
            bool doubled_221_interleave_0 = const()[name = string("doubled_221_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_221_cast_fp16 = concat(axis = var_9833, interleave = doubled_221_interleave_0, values = (hidden_states_275_cast_fp16, var_9835_cast_fp16))[name = string("doubled_221_cast_fp16")];
            tensor<int32, [1]> out_111_axes_0 = const()[name = string("out_111_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_111_gamma_0_to_fp16 = const()[name = string("out_111_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1628501120)))];
            fp16 var_9845_to_fp16 = const()[name = string("op_9845_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_111_cast_fp16 = layer_norm(axes = out_111_axes_0, epsilon = var_9845_to_fp16, gamma = out_111_gamma_0_to_fp16, x = doubled_221_cast_fp16)[name = string("out_111_cast_fp16")];
            tensor<int32, [2]> var_9856_split_sizes_0 = const()[name = string("op_9856_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9856_axis_0 = const()[name = string("op_9856_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> var_9856_cast_fp16_0, tensor<fp16, [1, 2048, 1, 1]> var_9856_cast_fp16_1 = split(axis = var_9856_axis_0, split_sizes = var_9856_split_sizes_0, x = out_111_cast_fp16)[name = string("op_9856_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_27_mlp_gate_proj_weight_to_fp16 = const()[name = string("layers_27_mlp_gate_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1628509376)))];
            tensor<int32, [2]> input_strides_0 = const()[name = string("input_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string input_pad_type_0 = const()[name = string("input_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> input_pad_0 = const()[name = string("input_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> input_dilations_0 = const()[name = string("input_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 input_groups_0 = const()[name = string("input_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> input_cast_fp16 = conv(dilations = input_dilations_0, groups = input_groups_0, pad = input_pad_0, pad_type = input_pad_type_0, strides = input_strides_0, weight = layers_27_mlp_gate_proj_weight_to_fp16, x = var_9856_cast_fp16_0)[name = string("input_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> var_9873_cast_fp16 = silu(x = input_cast_fp16)[name = string("op_9873_cast_fp16")];
            tensor<fp16, [6144, 2048, 1, 1]> layers_27_mlp_up_proj_weight_to_fp16 = const()[name = string("layers_27_mlp_up_proj_weight_to_fp16"), val = tensor<fp16, [6144, 2048, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1653675264)))];
            tensor<int32, [2]> var_9879_strides_0 = const()[name = string("op_9879_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string var_9879_pad_type_0 = const()[name = string("op_9879_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> var_9879_pad_0 = const()[name = string("op_9879_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> var_9879_dilations_0 = const()[name = string("op_9879_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 var_9879_groups_0 = const()[name = string("op_9879_groups_0"), val = int32(1)];
            tensor<fp16, [1, 6144, 1, 1]> var_9879_cast_fp16 = conv(dilations = var_9879_dilations_0, groups = var_9879_groups_0, pad = var_9879_pad_0, pad_type = var_9879_pad_type_0, strides = var_9879_strides_0, weight = layers_27_mlp_up_proj_weight_to_fp16, x = var_9856_cast_fp16_0)[name = string("op_9879_cast_fp16")];
            tensor<fp16, [1, 6144, 1, 1]> x_cast_fp16 = mul(x = var_9873_cast_fp16, y = var_9879_cast_fp16)[name = string("x_cast_fp16")];
            tensor<fp16, [2048, 6144, 1, 1]> layers_27_mlp_down_proj_weight_to_fp16 = const()[name = string("layers_27_mlp_down_proj_weight_to_fp16"), val = tensor<fp16, [2048, 6144, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1678841152)))];
            tensor<int32, [2]> hidden_states_277_strides_0 = const()[name = string("hidden_states_277_strides_0"), val = tensor<int32, [2]>([1, 1])];
            string hidden_states_277_pad_type_0 = const()[name = string("hidden_states_277_pad_type_0"), val = string("valid")];
            tensor<int32, [4]> hidden_states_277_pad_0 = const()[name = string("hidden_states_277_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
            tensor<int32, [2]> hidden_states_277_dilations_0 = const()[name = string("hidden_states_277_dilations_0"), val = tensor<int32, [2]>([1, 1])];
            int32 hidden_states_277_groups_0 = const()[name = string("hidden_states_277_groups_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_277_cast_fp16 = conv(dilations = hidden_states_277_dilations_0, groups = hidden_states_277_groups_0, pad = hidden_states_277_pad_0, pad_type = hidden_states_277_pad_type_0, strides = hidden_states_277_strides_0, weight = layers_27_mlp_down_proj_weight_to_fp16, x = x_cast_fp16)[name = string("hidden_states_277_cast_fp16")];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states_cast_fp16 = add(x = hidden_states_275_cast_fp16, y = hidden_states_277_cast_fp16)[name = string("hidden_states_cast_fp16")];
            fp16 const_282_promoted_to_fp16 = const()[name = string("const_282_promoted_to_fp16"), val = fp16(-0x1p+0)];
            tensor<fp16, [1, 2048, 1, 1]> var_9897_cast_fp16 = mul(x = hidden_states_cast_fp16, y = const_282_promoted_to_fp16)[name = string("op_9897_cast_fp16")];
            int32 var_9895 = const()[name = string("op_9895"), val = int32(1)];
            bool doubled_225_interleave_0 = const()[name = string("doubled_225_interleave_0"), val = bool(false)];
            tensor<fp16, [1, 4096, 1, 1]> doubled_225_cast_fp16 = concat(axis = var_9895, interleave = doubled_225_interleave_0, values = (hidden_states_cast_fp16, var_9897_cast_fp16))[name = string("doubled_225_cast_fp16")];
            tensor<int32, [1]> out_axes_0 = const()[name = string("out_axes_0"), val = tensor<int32, [1]>([1])];
            tensor<fp16, [4096]> out_gamma_0_to_fp16 = const()[name = string("out_gamma_0_to_fp16"), val = tensor<fp16, [4096]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1704007040)))];
            fp16 var_9907_to_fp16 = const()[name = string("op_9907_to_fp16"), val = fp16(0x1.5p-17)];
            tensor<fp16, [1, 4096, 1, 1]> out_cast_fp16 = layer_norm(axes = out_axes_0, epsilon = var_9907_to_fp16, gamma = out_gamma_0_to_fp16, x = doubled_225_cast_fp16)[name = string("out_cast_fp16")];
            tensor<int32, [2]> var_9918_split_sizes_0 = const()[name = string("op_9918_split_sizes_0"), val = tensor<int32, [2]>([2048, 2048])];
            int32 var_9918_axis_0 = const()[name = string("op_9918_axis_0"), val = int32(1)];
            tensor<fp16, [1, 2048, 1, 1]> hidden_states, tensor<fp16, [1, 2048, 1, 1]> var_9918_cast_fp16_1 = split(axis = var_9918_axis_0, split_sizes = var_9918_split_sizes_0, x = out_cast_fp16)[name = string("op_9918_cast_fp16")];
        } -> (hidden_states);
}