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SaT.mlmodelc/analytics/coremldata.bin CHANGED
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SaT.mlmodelc/metadata.json CHANGED
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  "name" : "MLModelType_mlProgram"
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  },
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  "userDefinedMetadata" : {
 
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  "com.github.apple.coremltools.source_dialect" : "TorchScript",
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  "name" : "MLModelType_mlProgram"
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SaT.mlmodelc/model.mil CHANGED
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  program(1.3)
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- [buildInfo = dict<string, string>({{"coremlc-component-MIL", "3500.14.1"}, {"coremlc-version", "3500.32.1"}})]
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  {
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  func main<ios18>(tensor<int32, [1, 512]> attention_mask, tensor<int32, [1, 512]> input_ids) {
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  tensor<fp32, [250002, 768]> base_model_roberta_embeddings_word_embeddings_weight_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [250002, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor<fp32, [250002, 24]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192001664))))[name = string("base_model_roberta_embeddings_word_embeddings_weight_quantized")];
 
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  {
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  func main<ios18>(tensor<int32, [1, 512]> attention_mask, tensor<int32, [1, 512]> input_ids) {
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  tensor<fp32, [250002, 768]> base_model_roberta_embeddings_word_embeddings_weight_quantized = constexpr_blockwise_shift_scale(data = tensor<int8, [250002, 768]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(64))), scale = tensor<fp32, [250002, 24]>(BLOBFILE(path = string("@model_path/weights/weight.bin"), offset = uint64(192001664))))[name = string("base_model_roberta_embeddings_word_embeddings_weight_quantized")];
SaT.mlpackage/Data/com.apple.CoreML/SaT.mlmodel ADDED
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SaT.mlpackage/Manifest.json ADDED
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+ "author": "com.apple.CoreML",
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+ "description": "CoreML Model Weights",
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+ "name": "weights",
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+ "419EF5EF-7790-4402-A426-FD90BD52869F": {
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+ "author": "com.apple.CoreML",
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+ "description": "CoreML Model Specification",
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+ "name": "SaT.mlmodel",
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+ "path": "com.apple.CoreML/SaT.mlmodel"
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