Add VibeVoice CoreML models

.gitignore

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+*.DS_Store

acoustic_connector.mlmodelc/analytics/coremldata.bin

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acoustic_connector.mlmodelc/coremldata.bin

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acoustic_connector.mlmodelc/model.mil

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+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3405.2.1"}, {"coremlc-version", "3405.2.1"}, {"coremltools-component-milinternal", ""}, {"coremltools-version", "9.0"}})]
+{
+    func main<ios18>(tensor<fp16, [1, 64, 1]> input_x) {
+            string x_1_pad_type_0 = const()[name = string("x_1_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> x_1_strides_0 = const()[name = string("x_1_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> x_1_pad_0 = const()[name = string("x_1_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> x_1_dilations_0 = const()[name = string("x_1_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 x_1_groups_0 = const()[name = string("x_1_groups_0"), val = int32(1)];
+            tensor<fp16, [896, 64, 1]> var_9_to_fp16 = const()[name = string("op_9_to_fp16"), val = tensor<fp16, [896, 64, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp16, [896]> fc1_bias_to_fp16 = const()[name = string("fc1_bias_to_fp16"), val = tensor<fp16, [896]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114816)))];
+            tensor<fp16, [1, 896, 1]> x_1_cast_fp16 = conv(bias = fc1_bias_to_fp16, dilations = x_1_dilations_0, groups = x_1_groups_0, pad = x_1_pad_0, pad_type = x_1_pad_type_0, strides = x_1_strides_0, weight = var_9_to_fp16, x = input_x)[name = string("x_1_cast_fp16")];
+            int32 var_20 = const()[name = string("op_20"), val = int32(1)];
+            tensor<int32, [1]> x_3_axes_0 = const()[name = string("x_3_axes_0"), val = tensor<int32, [1]>([-2])];
+            tensor<fp16, [1, 896, 1, 1]> x_3_cast_fp16 = expand_dims(axes = x_3_axes_0, x = x_1_cast_fp16)[name = string("x_3_cast_fp16")];
+            fp16 const_0_promoted_to_fp16 = const()[name = string("const_0_promoted_to_fp16"), val = fp16(-0x1p+0)];
+            tensor<fp16, [1, 896, 1, 1]> var_25_cast_fp16 = mul(x = x_3_cast_fp16, y = const_0_promoted_to_fp16)[name = string("op_25_cast_fp16")];
+            bool x_5_interleave_0 = const()[name = string("x_5_interleave_0"), val = bool(false)];
+            tensor<fp16, [1, 1792, 1, 1]> x_5_cast_fp16 = concat(axis = var_20, interleave = x_5_interleave_0, values = (x_3_cast_fp16, var_25_cast_fp16))[name = string("x_5_cast_fp16")];
+            tensor<int32, [1]> out_1_axes_0 = const()[name = string("out_1_axes_0"), val = tensor<int32, [1]>([1])];
+            fp16 var_35_to_fp16 = const()[name = string("op_35_to_fp16"), val = fp16(0x1.1p-20)];
+            tensor<fp16, [1, 1792, 1, 1]> out_1_cast_fp16 = layer_norm(axes = out_1_axes_0, epsilon = var_35_to_fp16, x = x_5_cast_fp16)[name = string("out_1_cast_fp16")];
+            tensor<fp16, [1, 1792, 1, 1]> norm_weight_to_fp16 = const()[name = string("norm_weight_to_fp16"), val = tensor<fp16, [1, 1792, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(116672)))];
+            tensor<fp16, [1, 1792, 1, 1]> out_3_cast_fp16 = mul(x = out_1_cast_fp16, y = norm_weight_to_fp16)[name = string("out_3_cast_fp16")];
+            tensor<int32, [2]> var_41_split_sizes_0 = const()[name = string("op_41_split_sizes_0"), val = tensor<int32, [2]>([896, 896])];
+            int32 var_41_axis_0 = const()[name = string("op_41_axis_0"), val = int32(1)];
+            tensor<fp16, [1, 896, 1, 1]> var_41_cast_fp16_0, tensor<fp16, [1, 896, 1, 1]> var_41_cast_fp16_1 = split(axis = var_41_axis_0, split_sizes = var_41_split_sizes_0, x = out_3_cast_fp16)[name = string("op_41_cast_fp16")];
+            tensor<int32, [1]> x_axes_0 = const()[name = string("x_axes_0"), val = tensor<int32, [1]>([-2])];
+            tensor<fp16, [1, 896, 1]> x_cast_fp16 = squeeze(axes = x_axes_0, x = var_41_cast_fp16_0)[name = string("x_cast_fp16")];
+            string var_55_pad_type_0 = const()[name = string("op_55_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> var_55_strides_0 = const()[name = string("op_55_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_55_pad_0 = const()[name = string("op_55_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_55_dilations_0 = const()[name = string("op_55_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 var_55_groups_0 = const()[name = string("op_55_groups_0"), val = int32(1)];
+            tensor<fp16, [896, 896, 1]> var_49_to_fp16 = const()[name = string("op_49_to_fp16"), val = tensor<fp16, [896, 896, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(120320)))];
+            tensor<fp16, [896]> fc2_bias_to_fp16 = const()[name = string("fc2_bias_to_fp16"), val = tensor<fp16, [896]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1726016)))];
+            tensor<fp16, [1, 896, 1]> output = conv(bias = fc2_bias_to_fp16, dilations = var_55_dilations_0, groups = var_55_groups_0, pad = var_55_pad_0, pad_type = var_55_pad_type_0, strides = var_55_strides_0, weight = var_49_to_fp16, x = x_cast_fp16)[name = string("op_55_cast_fp16")];
+        } -> (output);
+}

acoustic_connector.mlmodelc/weights/weight.bin

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acoustic_connector.mlpackage/Data/com.apple.CoreML/model.mlmodel

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acoustic_connector.mlpackage/Data/com.apple.CoreML/weights/weight.bin

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acoustic_connector.mlpackage/Manifest.json

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+{
+    "fileFormatVersion": "1.0.0",
+    "itemInfoEntries": {
+        "966eb60d-af76-4b59-ab8e-302dfe8a26ac": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Weights",
+            "name": "weights",
+            "path": "com.apple.CoreML/weights"
+        },
+        "c3a8450e-db98-4818-b49d-61a61abf029a": {
+            "author": "com.apple.CoreML",
+            "description": "CoreML Model Specification",
+            "name": "model.mlmodel",
+            "path": "com.apple.CoreML/model.mlmodel"
+        }
+    },
+    "rootModelIdentifier": "c3a8450e-db98-4818-b49d-61a61abf029a"
+}

decoder_coreml_12_ne.mlmodelc/analytics/coremldata.bin

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decoder_coreml_12_ne.mlmodelc/coremldata.bin

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decoder_coreml_12_ne.mlmodelc/weights/weight.bin

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diffusion_head_model.mlmodelc/analytics/coremldata.bin

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diffusion_head_model.mlmodelc/coremldata.bin

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diffusion_head_model.mlmodelc/model.mil

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+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3405.2.1"}, {"coremlc-version", "3405.2.1"}, {"coremltools-component-torch", "2.8.0"}, {"coremltools-source-dialect", "TorchScript"}, {"coremltools-version", "9.0"}})]
+{
+    func main<ios18>(tensor<fp16, [2, 896, 1, 1]> condition, tensor<fp16, [2, 64, 1, 1]> noisy_images, tensor<fp16, [256, 1, 1]> timesteps) {
+            string x_5_pad_type_0 = const()[name = string("x_5_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> x_5_strides_0 = const()[name = string("x_5_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> x_5_pad_0 = const()[name = string("x_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> x_5_dilations_0 = const()[name = string("x_5_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 x_5_groups_0 = const()[name = string("x_5_groups_0"), val = int32(1)];
+            tensor<fp16, [896, 64, 1, 1]> var_21_to_fp16 = const()[name = string("op_21_to_fp16"), val = tensor<fp16, [896, 64, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp16, [2, 896, 1, 1]> x_5_cast_fp16 = conv(dilations = x_5_dilations_0, groups = x_5_groups_0, pad = x_5_pad_0, pad_type = x_5_pad_type_0, strides = x_5_strides_0, weight = var_21_to_fp16, x = noisy_images)[name = string("x_5_cast_fp16")];
+            tensor<int32, [1]> var_34_axes_0 = const()[name = string("op_34_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 256, 1, 1]> var_34_cast_fp16 = expand_dims(axes = var_34_axes_0, x = timesteps)[name = string("op_34_cast_fp16")];
+            string var_39_pad_type_0 = const()[name = string("op_39_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> var_39_strides_0 = const()[name = string("op_39_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> var_39_pad_0 = const()[name = string("op_39_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> var_39_dilations_0 = const()[name = string("op_39_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 var_39_groups_0 = const()[name = string("op_39_groups_0"), val = int32(1)];
+            tensor<fp16, [896, 256, 1, 1]> var_29_to_fp16 = const()[name = string("op_29_to_fp16"), val = tensor<fp16, [896, 256, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(114816)))];
+            tensor<fp16, [1, 896, 1, 1]> var_39_cast_fp16 = conv(dilations = var_39_dilations_0, groups = var_39_groups_0, pad = var_39_pad_0, pad_type = var_39_pad_type_0, strides = var_39_strides_0, weight = var_29_to_fp16, x = var_34_cast_fp16)[name = string("op_39_cast_fp16")];
+            tensor<int32, [1]> input_1_axes_0 = const()[name = string("input_1_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [896, 1, 1]> input_1_cast_fp16 = squeeze(axes = input_1_axes_0, x = var_39_cast_fp16)[name = string("input_1_cast_fp16")];
+            tensor<fp16, [896, 1, 1]> x_1_cast_fp16 = silu(x = input_1_cast_fp16)[name = string("x_1_cast_fp16")];
+            tensor<int32, [1]> var_42_axes_0 = const()[name = string("op_42_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [1, 896, 1, 1]> var_42_cast_fp16 = expand_dims(axes = var_42_axes_0, x = x_1_cast_fp16)[name = string("op_42_cast_fp16")];
+            string var_47_pad_type_0 = const()[name = string("op_47_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> var_47_strides_0 = const()[name = string("op_47_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> var_47_pad_0 = const()[name = string("op_47_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> var_47_dilations_0 = const()[name = string("op_47_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 var_47_groups_0 = const()[name = string("op_47_groups_0"), val = int32(1)];
+            tensor<fp16, [896, 896, 1, 1]> var_27_to_fp16 = const()[name = string("op_27_to_fp16"), val = tensor<fp16, [896, 896, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(573632)))];
+            tensor<fp16, [1, 896, 1, 1]> var_47_cast_fp16 = conv(dilations = var_47_dilations_0, groups = var_47_groups_0, pad = var_47_pad_0, pad_type = var_47_pad_type_0, strides = var_47_strides_0, weight = var_27_to_fp16, x = var_42_cast_fp16)[name = string("op_47_cast_fp16")];
+            tensor<int32, [1]> t_axes_0 = const()[name = string("t_axes_0"), val = tensor<int32, [1]>([0])];
+            tensor<fp16, [896, 1, 1]> t_cast_fp16 = squeeze(axes = t_axes_0, x = var_47_cast_fp16)[name = string("t_cast_fp16")];
+            string condition_pad_type_0 = const()[name = string("condition_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> condition_strides_0 = const()[name = string("condition_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> condition_pad_0 = const()[name = string("condition_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> condition_dilations_0 = const()[name = string("condition_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 condition_groups_0 = const()[name = string("condition_groups_0"), val = int32(1)];
+            tensor<fp16, [896, 896, 1, 1]> var_54_to_fp16 = const()[name = string("op_54_to_fp16"), val = tensor<fp16, [896, 896, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(2179328)))];
+            tensor<fp16, [2, 896, 1, 1]> condition_cast_fp16 = conv(dilations = condition_dilations_0, groups = condition_groups_0, pad = condition_pad_0, pad_type = condition_pad_type_0, strides = condition_strides_0, weight = var_54_to_fp16, x = condition)[name = string("condition_cast_fp16")];
+            tensor<fp16, [2, 896, 1, 1]> input_3_cast_fp16 = add(x = condition_cast_fp16, y = t_cast_fp16)[name = string("input_3_cast_fp16")];
+            int32 var_69 = const()[name = string("op_69"), val = int32(1)];
+            tensor<fp16, [2, 896, 1, 1]> x_3_cast_fp16 = silu(x = input_3_cast_fp16)[name = string("x_3_cast_fp16")];
+            string var_79_pad_type_0 = const()[name = string("op_79_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> var_79_strides_0 = const()[name = string("op_79_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> var_79_pad_0 = const()[name = string("op_79_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> var_79_dilations_0 = const()[name = string("op_79_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 var_79_groups_0 = const()[name = string("op_79_groups_0"), val = int32(1)];
+            tensor<fp16, [2688, 896, 1, 1]> var_71_to_fp16 = const()[name = string("op_71_to_fp16"), val = tensor<fp16, [2688, 896, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(3785024)))];
+            tensor<fp16, [2, 2688, 1, 1]> var_79_cast_fp16 = conv(dilations = var_79_dilations_0, groups = var_79_groups_0, pad = var_79_pad_0, pad_type = var_79_pad_type_0, strides = var_79_strides_0, weight = var_71_to_fp16, x = x_3_cast_fp16)[name = string("op_79_cast_fp16")];
+            tensor<int32, [3]> var_80_split_sizes_0 = const()[name = string("op_80_split_sizes_0"), val = tensor<int32, [3]>([896, 896, 896])];
+            int32 var_80_axis_0 = const()[name = string("op_80_axis_0"), val = int32(1)];
+            tensor<fp16, [2, 896, 1, 1]> var_80_cast_fp16_0, tensor<fp16, [2, 896, 1, 1]> var_80_cast_fp16_1, tensor<fp16, [2, 896, 1, 1]> var_80_cast_fp16_2 = split(axis = var_80_axis_0, split_sizes = var_80_split_sizes_0, x = var_79_cast_fp16)[name = string("op_80_cast_fp16")];
+            fp16 const_0_promoted_to_fp16 = const()[name = string("const_0_promoted_to_fp16"), val = fp16(-0x1p+0)];
+            tensor<fp16, [2, 896, 1, 1]> var_85_cast_fp16 = mul(x = x_5_cast_fp16, y = const_0_promoted_to_fp16)[name = string("op_85_cast_fp16")];
+            bool x_7_interleave_0 = const()[name = string("x_7_interleave_0"), val = bool(false)];
+            tensor<fp16, [2, 1792, 1, 1]> x_7_cast_fp16 = concat(axis = var_69, interleave = x_7_interleave_0, values = (x_5_cast_fp16, var_85_cast_fp16))[name = string("x_7_cast_fp16")];
+            tensor<int32, [1]> out_1_axes_0 = const()[name = string("out_1_axes_0"), val = tensor<int32, [1]>([1])];
+            fp16 var_95_to_fp16 = const()[name = string("op_95_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [2, 1792, 1, 1]> out_1_cast_fp16 = layer_norm(axes = out_1_axes_0, epsilon = var_95_to_fp16, x = x_7_cast_fp16)[name = string("out_1_cast_fp16")];
+            tensor<fp16, [1, 1792, 1, 1]> layer_layers_0_layer_norm_weight_to_fp16 = const()[name = string("layer_layers_0_layer_norm_weight_to_fp16"), val = tensor<fp16, [1, 1792, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8601984)))];
+            tensor<fp16, [2, 1792, 1, 1]> out_3_cast_fp16 = mul(x = out_1_cast_fp16, y = layer_layers_0_layer_norm_weight_to_fp16)[name = string("out_3_cast_fp16")];
+            tensor<int32, [2]> var_101_split_sizes_0 = const()[name = string("op_101_split_sizes_0"), val = tensor<int32, [2]>([896, 896])];
+            int32 var_101_axis_0 = const()[name = string("op_101_axis_0"), val = int32(1)];
+            tensor<fp16, [2, 896, 1, 1]> var_101_cast_fp16_0, tensor<fp16, [2, 896, 1, 1]> var_101_cast_fp16_1 = split(axis = var_101_axis_0, split_sizes = var_101_split_sizes_0, x = out_3_cast_fp16)[name = string("op_101_cast_fp16")];
+            fp16 var_104_promoted_to_fp16 = const()[name = string("op_104_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [2, 896, 1, 1]> var_105_cast_fp16 = add(x = var_80_cast_fp16_1, y = var_104_promoted_to_fp16)[name = string("op_105_cast_fp16")];
+            tensor<fp16, [2, 896, 1, 1]> var_106_cast_fp16 = mul(x = var_101_cast_fp16_0, y = var_105_cast_fp16)[name = string("op_106_cast_fp16")];
+            tensor<fp16, [2, 896, 1, 1]> x_13_cast_fp16 = add(x = var_106_cast_fp16, y = var_80_cast_fp16_0)[name = string("x_13_cast_fp16")];
+            string input_5_pad_type_0 = const()[name = string("input_5_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_5_strides_0 = const()[name = string("input_5_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            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, [2688, 896, 1, 1]> var_62_to_fp16 = const()[name = string("op_62_to_fp16"), val = tensor<fp16, [2688, 896, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(8605632)))];
+            tensor<fp16, [2, 2688, 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 = var_62_to_fp16, x = x_13_cast_fp16)[name = string("input_5_cast_fp16")];
+            string up_1_pad_type_0 = const()[name = string("up_1_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> up_1_strides_0 = const()[name = string("up_1_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> up_1_pad_0 = const()[name = string("up_1_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> up_1_dilations_0 = const()[name = string("up_1_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 up_1_groups_0 = const()[name = string("up_1_groups_0"), val = int32(1)];
+            tensor<fp16, [2688, 896, 1, 1]> var_63_to_fp16 = const()[name = string("op_63_to_fp16"), val = tensor<fp16, [2688, 896, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(13422592)))];
+            tensor<fp16, [2, 2688, 1, 1]> up_1_cast_fp16 = conv(dilations = up_1_dilations_0, groups = up_1_groups_0, pad = up_1_pad_0, pad_type = up_1_pad_type_0, strides = up_1_strides_0, weight = var_63_to_fp16, x = x_13_cast_fp16)[name = string("up_1_cast_fp16")];
+            tensor<fp16, [2, 2688, 1, 1]> gate_1_cast_fp16 = silu(x = input_5_cast_fp16)[name = string("gate_1_cast_fp16")];
+            tensor<fp16, [2, 2688, 1, 1]> x_15_cast_fp16 = mul(x = gate_1_cast_fp16, y = up_1_cast_fp16)[name = string("x_15_cast_fp16")];
+            string var_124_pad_type_0 = const()[name = string("op_124_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> var_124_strides_0 = const()[name = string("op_124_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> var_124_pad_0 = const()[name = string("op_124_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> var_124_dilations_0 = const()[name = string("op_124_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 var_124_groups_0 = const()[name = string("op_124_groups_0"), val = int32(1)];
+            tensor<fp16, [896, 2688, 1, 1]> var_64_to_fp16 = const()[name = string("op_64_to_fp16"), val = tensor<fp16, [896, 2688, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(18239552)))];
+            tensor<fp16, [2, 896, 1, 1]> var_124_cast_fp16 = conv(dilations = var_124_dilations_0, groups = var_124_groups_0, pad = var_124_pad_0, pad_type = var_124_pad_type_0, strides = var_124_strides_0, weight = var_64_to_fp16, x = x_15_cast_fp16)[name = string("op_124_cast_fp16")];
+            tensor<fp16, [2, 896, 1, 1]> var_125_cast_fp16 = mul(x = var_80_cast_fp16_2, y = var_124_cast_fp16)[name = string("op_125_cast_fp16")];
+            tensor<fp16, [2, 896, 1, 1]> x_19_cast_fp16 = add(x = x_5_cast_fp16, y = var_125_cast_fp16)[name = string("x_19_cast_fp16")];
+            int32 var_134 = const()[name = string("op_134"), val = int32(1)];
+            string var_144_pad_type_0 = const()[name = string("op_144_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> var_144_strides_0 = const()[name = string("op_144_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> var_144_pad_0 = const()[name = string("op_144_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> var_144_dilations_0 = const()[name = string("op_144_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 var_144_groups_0 = const()[name = string("op_144_groups_0"), val = int32(1)];
+            tensor<fp16, [2688, 896, 1, 1]> var_136_to_fp16 = const()[name = string("op_136_to_fp16"), val = tensor<fp16, [2688, 896, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(23056512)))];
+            tensor<fp16, [2, 2688, 1, 1]> var_144_cast_fp16 = conv(dilations = var_144_dilations_0, groups = var_144_groups_0, pad = var_144_pad_0, pad_type = var_144_pad_type_0, strides = var_144_strides_0, weight = var_136_to_fp16, x = x_3_cast_fp16)[name = string("op_144_cast_fp16")];
+            tensor<int32, [3]> var_145_split_sizes_0 = const()[name = string("op_145_split_sizes_0"), val = tensor<int32, [3]>([896, 896, 896])];
+            int32 var_145_axis_0 = const()[name = string("op_145_axis_0"), val = int32(1)];
+            tensor<fp16, [2, 896, 1, 1]> var_145_cast_fp16_0, tensor<fp16, [2, 896, 1, 1]> var_145_cast_fp16_1, tensor<fp16, [2, 896, 1, 1]> var_145_cast_fp16_2 = split(axis = var_145_axis_0, split_sizes = var_145_split_sizes_0, x = var_144_cast_fp16)[name = string("op_145_cast_fp16")];
+            fp16 const_1_promoted_to_fp16 = const()[name = string("const_1_promoted_to_fp16"), val = fp16(-0x1p+0)];
+            tensor<fp16, [2, 896, 1, 1]> var_150_cast_fp16 = mul(x = x_19_cast_fp16, y = const_1_promoted_to_fp16)[name = string("op_150_cast_fp16")];
+            bool x_21_interleave_0 = const()[name = string("x_21_interleave_0"), val = bool(false)];
+            tensor<fp16, [2, 1792, 1, 1]> x_21_cast_fp16 = concat(axis = var_134, interleave = x_21_interleave_0, values = (x_19_cast_fp16, var_150_cast_fp16))[name = string("x_21_cast_fp16")];
+            tensor<int32, [1]> out_7_axes_0 = const()[name = string("out_7_axes_0"), val = tensor<int32, [1]>([1])];
+            fp16 var_160_to_fp16 = const()[name = string("op_160_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [2, 1792, 1, 1]> out_7_cast_fp16 = layer_norm(axes = out_7_axes_0, epsilon = var_160_to_fp16, x = x_21_cast_fp16)[name = string("out_7_cast_fp16")];
+            tensor<fp16, [1, 1792, 1, 1]> layer_layers_1_layer_norm_weight_to_fp16 = const()[name = string("layer_layers_1_layer_norm_weight_to_fp16"), val = tensor<fp16, [1, 1792, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27873472)))];
+            tensor<fp16, [2, 1792, 1, 1]> out_9_cast_fp16 = mul(x = out_7_cast_fp16, y = layer_layers_1_layer_norm_weight_to_fp16)[name = string("out_9_cast_fp16")];
+            tensor<int32, [2]> var_166_split_sizes_0 = const()[name = string("op_166_split_sizes_0"), val = tensor<int32, [2]>([896, 896])];
+            int32 var_166_axis_0 = const()[name = string("op_166_axis_0"), val = int32(1)];
+            tensor<fp16, [2, 896, 1, 1]> var_166_cast_fp16_0, tensor<fp16, [2, 896, 1, 1]> var_166_cast_fp16_1 = split(axis = var_166_axis_0, split_sizes = var_166_split_sizes_0, x = out_9_cast_fp16)[name = string("op_166_cast_fp16")];
+            fp16 var_169_promoted_to_fp16 = const()[name = string("op_169_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [2, 896, 1, 1]> var_170_cast_fp16 = add(x = var_145_cast_fp16_1, y = var_169_promoted_to_fp16)[name = string("op_170_cast_fp16")];
+            tensor<fp16, [2, 896, 1, 1]> var_171_cast_fp16 = mul(x = var_166_cast_fp16_0, y = var_170_cast_fp16)[name = string("op_171_cast_fp16")];
+            tensor<fp16, [2, 896, 1, 1]> x_27_cast_fp16 = add(x = var_171_cast_fp16, y = var_145_cast_fp16_0)[name = string("x_27_cast_fp16")];
+            string input_7_pad_type_0 = const()[name = string("input_7_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_7_strides_0 = const()[name = string("input_7_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            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, [2688, 896, 1, 1]> var_127_to_fp16 = const()[name = string("op_127_to_fp16"), val = tensor<fp16, [2688, 896, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(27877120)))];
+            tensor<fp16, [2, 2688, 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 = var_127_to_fp16, x = x_27_cast_fp16)[name = string("input_7_cast_fp16")];
+            string up_3_pad_type_0 = const()[name = string("up_3_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> up_3_strides_0 = const()[name = string("up_3_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> up_3_pad_0 = const()[name = string("up_3_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> up_3_dilations_0 = const()[name = string("up_3_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 up_3_groups_0 = const()[name = string("up_3_groups_0"), val = int32(1)];
+            tensor<fp16, [2688, 896, 1, 1]> var_128_to_fp16 = const()[name = string("op_128_to_fp16"), val = tensor<fp16, [2688, 896, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(32694080)))];
+            tensor<fp16, [2, 2688, 1, 1]> up_3_cast_fp16 = conv(dilations = up_3_dilations_0, groups = up_3_groups_0, pad = up_3_pad_0, pad_type = up_3_pad_type_0, strides = up_3_strides_0, weight = var_128_to_fp16, x = x_27_cast_fp16)[name = string("up_3_cast_fp16")];
+            tensor<fp16, [2, 2688, 1, 1]> gate_3_cast_fp16 = silu(x = input_7_cast_fp16)[name = string("gate_3_cast_fp16")];
+            tensor<fp16, [2, 2688, 1, 1]> x_29_cast_fp16 = mul(x = gate_3_cast_fp16, y = up_3_cast_fp16)[name = string("x_29_cast_fp16")];
+            string var_189_pad_type_0 = const()[name = string("op_189_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> var_189_strides_0 = const()[name = string("op_189_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> var_189_pad_0 = const()[name = string("op_189_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> var_189_dilations_0 = const()[name = string("op_189_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 var_189_groups_0 = const()[name = string("op_189_groups_0"), val = int32(1)];
+            tensor<fp16, [896, 2688, 1, 1]> var_129_to_fp16 = const()[name = string("op_129_to_fp16"), val = tensor<fp16, [896, 2688, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(37511040)))];
+            tensor<fp16, [2, 896, 1, 1]> var_189_cast_fp16 = conv(dilations = var_189_dilations_0, groups = var_189_groups_0, pad = var_189_pad_0, pad_type = var_189_pad_type_0, strides = var_189_strides_0, weight = var_129_to_fp16, x = x_29_cast_fp16)[name = string("op_189_cast_fp16")];
+            tensor<fp16, [2, 896, 1, 1]> var_190_cast_fp16 = mul(x = var_145_cast_fp16_2, y = var_189_cast_fp16)[name = string("op_190_cast_fp16")];
+            tensor<fp16, [2, 896, 1, 1]> x_33_cast_fp16 = add(x = x_19_cast_fp16, y = var_190_cast_fp16)[name = string("x_33_cast_fp16")];
+            int32 var_199 = const()[name = string("op_199"), val = int32(1)];
+            string var_209_pad_type_0 = const()[name = string("op_209_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> var_209_strides_0 = const()[name = string("op_209_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> var_209_pad_0 = const()[name = string("op_209_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> var_209_dilations_0 = const()[name = string("op_209_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 var_209_groups_0 = const()[name = string("op_209_groups_0"), val = int32(1)];
+            tensor<fp16, [2688, 896, 1, 1]> var_201_to_fp16 = const()[name = string("op_201_to_fp16"), val = tensor<fp16, [2688, 896, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(42328000)))];
+            tensor<fp16, [2, 2688, 1, 1]> var_209_cast_fp16 = conv(dilations = var_209_dilations_0, groups = var_209_groups_0, pad = var_209_pad_0, pad_type = var_209_pad_type_0, strides = var_209_strides_0, weight = var_201_to_fp16, x = x_3_cast_fp16)[name = string("op_209_cast_fp16")];
+            tensor<int32, [3]> var_210_split_sizes_0 = const()[name = string("op_210_split_sizes_0"), val = tensor<int32, [3]>([896, 896, 896])];
+            int32 var_210_axis_0 = const()[name = string("op_210_axis_0"), val = int32(1)];
+            tensor<fp16, [2, 896, 1, 1]> var_210_cast_fp16_0, tensor<fp16, [2, 896, 1, 1]> var_210_cast_fp16_1, tensor<fp16, [2, 896, 1, 1]> var_210_cast_fp16_2 = split(axis = var_210_axis_0, split_sizes = var_210_split_sizes_0, x = var_209_cast_fp16)[name = string("op_210_cast_fp16")];
+            fp16 const_2_promoted_to_fp16 = const()[name = string("const_2_promoted_to_fp16"), val = fp16(-0x1p+0)];
+            tensor<fp16, [2, 896, 1, 1]> var_215_cast_fp16 = mul(x = x_33_cast_fp16, y = const_2_promoted_to_fp16)[name = string("op_215_cast_fp16")];
+            bool x_35_interleave_0 = const()[name = string("x_35_interleave_0"), val = bool(false)];
+            tensor<fp16, [2, 1792, 1, 1]> x_35_cast_fp16 = concat(axis = var_199, interleave = x_35_interleave_0, values = (x_33_cast_fp16, var_215_cast_fp16))[name = string("x_35_cast_fp16")];
+            tensor<int32, [1]> out_13_axes_0 = const()[name = string("out_13_axes_0"), val = tensor<int32, [1]>([1])];
+            fp16 var_225_to_fp16 = const()[name = string("op_225_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [2, 1792, 1, 1]> out_13_cast_fp16 = layer_norm(axes = out_13_axes_0, epsilon = var_225_to_fp16, x = x_35_cast_fp16)[name = string("out_13_cast_fp16")];
+            tensor<fp16, [1, 1792, 1, 1]> layer_layers_2_layer_norm_weight_to_fp16 = const()[name = string("layer_layers_2_layer_norm_weight_to_fp16"), val = tensor<fp16, [1, 1792, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47144960)))];
+            tensor<fp16, [2, 1792, 1, 1]> out_15_cast_fp16 = mul(x = out_13_cast_fp16, y = layer_layers_2_layer_norm_weight_to_fp16)[name = string("out_15_cast_fp16")];
+            tensor<int32, [2]> var_231_split_sizes_0 = const()[name = string("op_231_split_sizes_0"), val = tensor<int32, [2]>([896, 896])];
+            int32 var_231_axis_0 = const()[name = string("op_231_axis_0"), val = int32(1)];
+            tensor<fp16, [2, 896, 1, 1]> var_231_cast_fp16_0, tensor<fp16, [2, 896, 1, 1]> var_231_cast_fp16_1 = split(axis = var_231_axis_0, split_sizes = var_231_split_sizes_0, x = out_15_cast_fp16)[name = string("op_231_cast_fp16")];
+            fp16 var_234_promoted_to_fp16 = const()[name = string("op_234_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [2, 896, 1, 1]> var_235_cast_fp16 = add(x = var_210_cast_fp16_1, y = var_234_promoted_to_fp16)[name = string("op_235_cast_fp16")];
+            tensor<fp16, [2, 896, 1, 1]> var_236_cast_fp16 = mul(x = var_231_cast_fp16_0, y = var_235_cast_fp16)[name = string("op_236_cast_fp16")];
+            tensor<fp16, [2, 896, 1, 1]> x_41_cast_fp16 = add(x = var_236_cast_fp16, y = var_210_cast_fp16_0)[name = string("x_41_cast_fp16")];
+            string input_9_pad_type_0 = const()[name = string("input_9_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_9_strides_0 = const()[name = string("input_9_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            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, [2688, 896, 1, 1]> var_192_to_fp16 = const()[name = string("op_192_to_fp16"), val = tensor<fp16, [2688, 896, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(47148608)))];
+            tensor<fp16, [2, 2688, 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 = var_192_to_fp16, x = x_41_cast_fp16)[name = string("input_9_cast_fp16")];
+            string up_5_pad_type_0 = const()[name = string("up_5_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> up_5_strides_0 = const()[name = string("up_5_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> up_5_pad_0 = const()[name = string("up_5_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> up_5_dilations_0 = const()[name = string("up_5_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 up_5_groups_0 = const()[name = string("up_5_groups_0"), val = int32(1)];
+            tensor<fp16, [2688, 896, 1, 1]> var_193_to_fp16 = const()[name = string("op_193_to_fp16"), val = tensor<fp16, [2688, 896, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(51965568)))];
+            tensor<fp16, [2, 2688, 1, 1]> up_5_cast_fp16 = conv(dilations = up_5_dilations_0, groups = up_5_groups_0, pad = up_5_pad_0, pad_type = up_5_pad_type_0, strides = up_5_strides_0, weight = var_193_to_fp16, x = x_41_cast_fp16)[name = string("up_5_cast_fp16")];
+            tensor<fp16, [2, 2688, 1, 1]> gate_5_cast_fp16 = silu(x = input_9_cast_fp16)[name = string("gate_5_cast_fp16")];
+            tensor<fp16, [2, 2688, 1, 1]> x_43_cast_fp16 = mul(x = gate_5_cast_fp16, y = up_5_cast_fp16)[name = string("x_43_cast_fp16")];
+            string var_254_pad_type_0 = const()[name = string("op_254_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> var_254_strides_0 = const()[name = string("op_254_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> var_254_pad_0 = const()[name = string("op_254_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> var_254_dilations_0 = const()[name = string("op_254_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 var_254_groups_0 = const()[name = string("op_254_groups_0"), val = int32(1)];
+            tensor<fp16, [896, 2688, 1, 1]> var_194_to_fp16 = const()[name = string("op_194_to_fp16"), val = tensor<fp16, [896, 2688, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(56782528)))];
+            tensor<fp16, [2, 896, 1, 1]> var_254_cast_fp16 = conv(dilations = var_254_dilations_0, groups = var_254_groups_0, pad = var_254_pad_0, pad_type = var_254_pad_type_0, strides = var_254_strides_0, weight = var_194_to_fp16, x = x_43_cast_fp16)[name = string("op_254_cast_fp16")];
+            tensor<fp16, [2, 896, 1, 1]> var_255_cast_fp16 = mul(x = var_210_cast_fp16_2, y = var_254_cast_fp16)[name = string("op_255_cast_fp16")];
+            tensor<fp16, [2, 896, 1, 1]> x_47_cast_fp16 = add(x = x_33_cast_fp16, y = var_255_cast_fp16)[name = string("x_47_cast_fp16")];
+            int32 var_264 = const()[name = string("op_264"), val = int32(1)];
+            string var_274_pad_type_0 = const()[name = string("op_274_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> var_274_strides_0 = const()[name = string("op_274_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> var_274_pad_0 = const()[name = string("op_274_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> var_274_dilations_0 = const()[name = string("op_274_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 var_274_groups_0 = const()[name = string("op_274_groups_0"), val = int32(1)];
+            tensor<fp16, [2688, 896, 1, 1]> var_266_to_fp16 = const()[name = string("op_266_to_fp16"), val = tensor<fp16, [2688, 896, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(61599488)))];
+            tensor<fp16, [2, 2688, 1, 1]> var_274_cast_fp16 = conv(dilations = var_274_dilations_0, groups = var_274_groups_0, pad = var_274_pad_0, pad_type = var_274_pad_type_0, strides = var_274_strides_0, weight = var_266_to_fp16, x = x_3_cast_fp16)[name = string("op_274_cast_fp16")];
+            tensor<int32, [3]> var_275_split_sizes_0 = const()[name = string("op_275_split_sizes_0"), val = tensor<int32, [3]>([896, 896, 896])];
+            int32 var_275_axis_0 = const()[name = string("op_275_axis_0"), val = int32(1)];
+            tensor<fp16, [2, 896, 1, 1]> var_275_cast_fp16_0, tensor<fp16, [2, 896, 1, 1]> var_275_cast_fp16_1, tensor<fp16, [2, 896, 1, 1]> var_275_cast_fp16_2 = split(axis = var_275_axis_0, split_sizes = var_275_split_sizes_0, x = var_274_cast_fp16)[name = string("op_275_cast_fp16")];
+            fp16 const_3_promoted_to_fp16 = const()[name = string("const_3_promoted_to_fp16"), val = fp16(-0x1p+0)];
+            tensor<fp16, [2, 896, 1, 1]> var_280_cast_fp16 = mul(x = x_47_cast_fp16, y = const_3_promoted_to_fp16)[name = string("op_280_cast_fp16")];
+            bool x_49_interleave_0 = const()[name = string("x_49_interleave_0"), val = bool(false)];
+            tensor<fp16, [2, 1792, 1, 1]> x_49_cast_fp16 = concat(axis = var_264, interleave = x_49_interleave_0, values = (x_47_cast_fp16, var_280_cast_fp16))[name = string("x_49_cast_fp16")];
+            tensor<int32, [1]> out_19_axes_0 = const()[name = string("out_19_axes_0"), val = tensor<int32, [1]>([1])];
+            fp16 var_290_to_fp16 = const()[name = string("op_290_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [2, 1792, 1, 1]> out_19_cast_fp16 = layer_norm(axes = out_19_axes_0, epsilon = var_290_to_fp16, x = x_49_cast_fp16)[name = string("out_19_cast_fp16")];
+            tensor<fp16, [1, 1792, 1, 1]> layer_layers_3_layer_norm_weight_to_fp16 = const()[name = string("layer_layers_3_layer_norm_weight_to_fp16"), val = tensor<fp16, [1, 1792, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66416448)))];
+            tensor<fp16, [2, 1792, 1, 1]> out_21_cast_fp16 = mul(x = out_19_cast_fp16, y = layer_layers_3_layer_norm_weight_to_fp16)[name = string("out_21_cast_fp16")];
+            tensor<int32, [2]> var_296_split_sizes_0 = const()[name = string("op_296_split_sizes_0"), val = tensor<int32, [2]>([896, 896])];
+            int32 var_296_axis_0 = const()[name = string("op_296_axis_0"), val = int32(1)];
+            tensor<fp16, [2, 896, 1, 1]> var_296_cast_fp16_0, tensor<fp16, [2, 896, 1, 1]> var_296_cast_fp16_1 = split(axis = var_296_axis_0, split_sizes = var_296_split_sizes_0, x = out_21_cast_fp16)[name = string("op_296_cast_fp16")];
+            fp16 var_299_promoted_to_fp16 = const()[name = string("op_299_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [2, 896, 1, 1]> var_300_cast_fp16 = add(x = var_275_cast_fp16_1, y = var_299_promoted_to_fp16)[name = string("op_300_cast_fp16")];
+            tensor<fp16, [2, 896, 1, 1]> var_301_cast_fp16 = mul(x = var_296_cast_fp16_0, y = var_300_cast_fp16)[name = string("op_301_cast_fp16")];
+            tensor<fp16, [2, 896, 1, 1]> x_55_cast_fp16 = add(x = var_301_cast_fp16, y = var_275_cast_fp16_0)[name = string("x_55_cast_fp16")];
+            string input_pad_type_0 = const()[name = string("input_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> input_strides_0 = const()[name = string("input_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            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, [2688, 896, 1, 1]> var_257_to_fp16 = const()[name = string("op_257_to_fp16"), val = tensor<fp16, [2688, 896, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(66420096)))];
+            tensor<fp16, [2, 2688, 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 = var_257_to_fp16, x = x_55_cast_fp16)[name = string("input_cast_fp16")];
+            string up_pad_type_0 = const()[name = string("up_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> up_strides_0 = const()[name = string("up_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> up_pad_0 = const()[name = string("up_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> up_dilations_0 = const()[name = string("up_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 up_groups_0 = const()[name = string("up_groups_0"), val = int32(1)];
+            tensor<fp16, [2688, 896, 1, 1]> var_258_to_fp16 = const()[name = string("op_258_to_fp16"), val = tensor<fp16, [2688, 896, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(71237056)))];
+            tensor<fp16, [2, 2688, 1, 1]> up_cast_fp16 = conv(dilations = up_dilations_0, groups = up_groups_0, pad = up_pad_0, pad_type = up_pad_type_0, strides = up_strides_0, weight = var_258_to_fp16, x = x_55_cast_fp16)[name = string("up_cast_fp16")];
+            tensor<fp16, [2, 2688, 1, 1]> gate_cast_fp16 = silu(x = input_cast_fp16)[name = string("gate_cast_fp16")];
+            tensor<fp16, [2, 2688, 1, 1]> x_57_cast_fp16 = mul(x = gate_cast_fp16, y = up_cast_fp16)[name = string("x_57_cast_fp16")];
+            string var_319_pad_type_0 = const()[name = string("op_319_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> var_319_strides_0 = const()[name = string("op_319_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> var_319_pad_0 = const()[name = string("op_319_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> var_319_dilations_0 = const()[name = string("op_319_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 var_319_groups_0 = const()[name = string("op_319_groups_0"), val = int32(1)];
+            tensor<fp16, [896, 2688, 1, 1]> var_259_to_fp16 = const()[name = string("op_259_to_fp16"), val = tensor<fp16, [896, 2688, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(76054016)))];
+            tensor<fp16, [2, 896, 1, 1]> var_319_cast_fp16 = conv(dilations = var_319_dilations_0, groups = var_319_groups_0, pad = var_319_pad_0, pad_type = var_319_pad_type_0, strides = var_319_strides_0, weight = var_259_to_fp16, x = x_57_cast_fp16)[name = string("op_319_cast_fp16")];
+            tensor<fp16, [2, 896, 1, 1]> var_320_cast_fp16 = mul(x = var_275_cast_fp16_2, y = var_319_cast_fp16)[name = string("op_320_cast_fp16")];
+            tensor<fp16, [2, 896, 1, 1]> x_61_cast_fp16 = add(x = x_47_cast_fp16, y = var_320_cast_fp16)[name = string("x_61_cast_fp16")];
+            int32 var_327 = const()[name = string("op_327"), val = int32(1)];
+            string var_335_pad_type_0 = const()[name = string("op_335_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> var_335_strides_0 = const()[name = string("op_335_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> var_335_pad_0 = const()[name = string("op_335_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> var_335_dilations_0 = const()[name = string("op_335_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 var_335_groups_0 = const()[name = string("op_335_groups_0"), val = int32(1)];
+            tensor<fp16, [1792, 896, 1, 1]> var_329_to_fp16 = const()[name = string("op_329_to_fp16"), val = tensor<fp16, [1792, 896, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(80870976)))];
+            tensor<fp16, [2, 1792, 1, 1]> var_335_cast_fp16 = conv(dilations = var_335_dilations_0, groups = var_335_groups_0, pad = var_335_pad_0, pad_type = var_335_pad_type_0, strides = var_335_strides_0, weight = var_329_to_fp16, x = x_3_cast_fp16)[name = string("op_335_cast_fp16")];
+            tensor<int32, [2]> var_336_split_sizes_0 = const()[name = string("op_336_split_sizes_0"), val = tensor<int32, [2]>([896, 896])];
+            int32 var_336_axis_0 = const()[name = string("op_336_axis_0"), val = int32(1)];
+            tensor<fp16, [2, 896, 1, 1]> var_336_cast_fp16_0, tensor<fp16, [2, 896, 1, 1]> var_336_cast_fp16_1 = split(axis = var_336_axis_0, split_sizes = var_336_split_sizes_0, x = var_335_cast_fp16)[name = string("op_336_cast_fp16")];
+            fp16 const_4_promoted_to_fp16 = const()[name = string("const_4_promoted_to_fp16"), val = fp16(-0x1p+0)];
+            tensor<fp16, [2, 896, 1, 1]> var_338_cast_fp16 = mul(x = x_61_cast_fp16, y = const_4_promoted_to_fp16)[name = string("op_338_cast_fp16")];
+            bool x_63_interleave_0 = const()[name = string("x_63_interleave_0"), val = bool(false)];
+            tensor<fp16, [2, 1792, 1, 1]> x_63_cast_fp16 = concat(axis = var_327, interleave = x_63_interleave_0, values = (x_61_cast_fp16, var_338_cast_fp16))[name = string("x_63_cast_fp16")];
+            tensor<int32, [1]> out_25_axes_0 = const()[name = string("out_25_axes_0"), val = tensor<int32, [1]>([1])];
+            fp16 var_348_to_fp16 = const()[name = string("op_348_to_fp16"), val = fp16(0x1.5p-17)];
+            tensor<fp16, [2, 1792, 1, 1]> out_25_cast_fp16 = layer_norm(axes = out_25_axes_0, epsilon = var_348_to_fp16, x = x_63_cast_fp16)[name = string("out_25_cast_fp16")];
+            tensor<int32, [2]> var_352_split_sizes_0 = const()[name = string("op_352_split_sizes_0"), val = tensor<int32, [2]>([896, 896])];
+            int32 var_352_axis_0 = const()[name = string("op_352_axis_0"), val = int32(1)];
+            tensor<fp16, [2, 896, 1, 1]> var_352_cast_fp16_0, tensor<fp16, [2, 896, 1, 1]> var_352_cast_fp16_1 = split(axis = var_352_axis_0, split_sizes = var_352_split_sizes_0, x = out_25_cast_fp16)[name = string("op_352_cast_fp16")];
+            fp16 var_355_promoted_to_fp16 = const()[name = string("op_355_promoted_to_fp16"), val = fp16(0x1p+0)];
+            tensor<fp16, [2, 896, 1, 1]> var_356_cast_fp16 = add(x = var_336_cast_fp16_1, y = var_355_promoted_to_fp16)[name = string("op_356_cast_fp16")];
+            tensor<fp16, [2, 896, 1, 1]> var_357_cast_fp16 = mul(x = var_352_cast_fp16_0, y = var_356_cast_fp16)[name = string("op_357_cast_fp16")];
+            tensor<fp16, [2, 896, 1, 1]> x_cast_fp16 = add(x = var_357_cast_fp16, y = var_336_cast_fp16_0)[name = string("x_cast_fp16")];
+            string var_363_pad_type_0 = const()[name = string("op_363_pad_type_0"), val = string("valid")];
+            tensor<int32, [2]> var_363_strides_0 = const()[name = string("op_363_strides_0"), val = tensor<int32, [2]>([1, 1])];
+            tensor<int32, [4]> var_363_pad_0 = const()[name = string("op_363_pad_0"), val = tensor<int32, [4]>([0, 0, 0, 0])];
+            tensor<int32, [2]> var_363_dilations_0 = const()[name = string("op_363_dilations_0"), val = tensor<int32, [2]>([1, 1])];
+            int32 var_363_groups_0 = const()[name = string("op_363_groups_0"), val = int32(1)];
+            tensor<fp16, [64, 896, 1, 1]> var_322_to_fp16 = const()[name = string("op_322_to_fp16"), val = tensor<fp16, [64, 896, 1, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(84082304)))];
+            tensor<fp16, [2, 64, 1, 1]> predicted_noise = conv(dilations = var_363_dilations_0, groups = var_363_groups_0, pad = var_363_pad_0, pad_type = var_363_pad_type_0, strides = var_363_strides_0, weight = var_322_to_fp16, x = x_cast_fp16)[name = string("op_363_cast_fp16")];
+        } -> (predicted_noise);
+}

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tts_eos_classifier.mlmodelc/model.mil

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+program(1.3)
+[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3405.2.1"}, {"coremlc-version", "3405.2.1"}, {"coremltools-component-milinternal", ""}, {"coremltools-version", "9.0"}})]
+{
+    func main<ios18>(tensor<fp16, [1, 896, 1]> input_x) {
+            string var_14_pad_type_0 = const()[name = string("op_14_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> var_14_strides_0 = const()[name = string("op_14_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_14_pad_0 = const()[name = string("op_14_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_14_dilations_0 = const()[name = string("op_14_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 var_14_groups_0 = const()[name = string("op_14_groups_0"), val = int32(1)];
+            tensor<fp16, [896, 896, 1]> var_8_to_fp16 = const()[name = string("op_8_to_fp16"), val = tensor<fp16, [896, 896, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64)))];
+            tensor<fp16, [896]> fc1_bias_to_fp16 = const()[name = string("fc1_bias_to_fp16"), val = tensor<fp16, [896]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1605760)))];
+            tensor<fp16, [1, 896, 1]> var_14_cast_fp16 = conv(bias = fc1_bias_to_fp16, dilations = var_14_dilations_0, groups = var_14_groups_0, pad = var_14_pad_0, pad_type = var_14_pad_type_0, strides = var_14_strides_0, weight = var_8_to_fp16, x = input_x)[name = string("op_14_cast_fp16")];
+            tensor<fp16, [1, 896, 1]> x_cast_fp16 = relu(x = var_14_cast_fp16)[name = string("x_cast_fp16")];
+            string var_26_pad_type_0 = const()[name = string("op_26_pad_type_0"), val = string("valid")];
+            tensor<int32, [1]> var_26_strides_0 = const()[name = string("op_26_strides_0"), val = tensor<int32, [1]>([1])];
+            tensor<int32, [2]> var_26_pad_0 = const()[name = string("op_26_pad_0"), val = tensor<int32, [2]>([0, 0])];
+            tensor<int32, [1]> var_26_dilations_0 = const()[name = string("op_26_dilations_0"), val = tensor<int32, [1]>([1])];
+            int32 var_26_groups_0 = const()[name = string("op_26_groups_0"), val = int32(1)];
+            tensor<fp16, [1, 896, 1]> var_20_to_fp16 = const()[name = string("op_20_to_fp16"), val = tensor<fp16, [1, 896, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(1607616)))];
+            tensor<fp16, [1]> fc2_bias_to_fp16 = const()[name = string("fc2_bias_to_fp16"), val = tensor<fp16, [1]>([-0x1.36p-5])];
+            tensor<fp16, [1, 1, 1]> output = conv(bias = fc2_bias_to_fp16, dilations = var_26_dilations_0, groups = var_26_groups_0, pad = var_26_pad_0, pad_type = var_26_pad_type_0, strides = var_26_strides_0, weight = var_20_to_fp16, x = x_cast_fp16)[name = string("op_26_cast_fp16")];
+        } -> (output);
+}

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vibevoice_embeddings.npy

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voices/streaming_model/de-Spk0_man.npz

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