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AutoGPTQ breakmodel
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@@ -350,7 +350,7 @@ class model_backend(HFTorchInferenceModel):
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auto_gptq.modeling._base.AutoConfig = hf_bleeding_edge.AutoConfig
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auto_gptq.modeling._base.AutoConfig = hf_bleeding_edge.AutoConfig
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auto_gptq.modeling._base.AutoModelForCausalLM = hf_bleeding_edge.AutoModelForCausalLM
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auto_gptq.modeling._base.AutoModelForCausalLM = hf_bleeding_edge.AutoModelForCausalLM
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model = AutoGPTQForCausalLM.from_quantized(location, model_basename=Path(gptq_file).stem, use_safetensors=gptq_file.endswith(".safetensors"))
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model = AutoGPTQForCausalLM.from_quantized(location, model_basename=Path(gptq_file).stem, use_safetensors=gptq_file.endswith(".safetensors"), device_map=device_map)
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# Patch in embeddings function
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# Patch in embeddings function
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def get_input_embeddings(self):
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def get_input_embeddings(self):
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