embedding.mlmodelc/model.mil

program(1.3)
[buildInfo = dict<string, string>({{"coremlc-component-MIL", "3520.4.1"}, {"coremlc-version", "3520.5.1"}})]
{
    func main<ios18>(tensor<int32, [1, 1]> token_id) {
            int32 var_6_axis_0 = const()[name = string("op_6_axis_0"), val = int32(0)];
            int32 var_6_batch_dims_0 = const()[name = string("op_6_batch_dims_0"), val = int32(0)];
            bool var_6_validate_indices_0 = const()[name = string("op_6_validate_indices_0"), val = bool(false)];
            tensor<fp16, [151936, 1024]> embedding_weight_to_fp16_palettized = constexpr_lut_to_dense(indices = tensor<uint8, [151936, 1024]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), lut = tensor<fp16, [1, 1, 256, 1]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(155582592))))[name = string("embedding_weight_to_fp16_palettized")];
            tensor<fp16, [1, 1, 1024]> embedding = gather(axis = var_6_axis_0, batch_dims = var_6_batch_dims_0, indices = token_id, validate_indices = var_6_validate_indices_0, x = embedding_weight_to_fp16_palettized)[name = string("op_6_cast_fp16")];
        } -> (embedding);
}