I recently found that when fine-tuning usingalpaca-lora,model.save_pretrained()will save aadapter_model.binthat is only 443 B. This seems to be happening after peft@75808eb2a6e7b4c3ed8aec003b6eeb30a2db1495. Nor
model(input_data)loss=criterion(outputs,target_data)loss.backward()optimizer.step()# 训练后保存 LoRA 权重lora_model.save_pretrained('linear_lora_model')# 方法 1:先使用 get_peft_model,再加载 LoRA 权重model1=PeftModel.from_pretrained(get_peft_model(deepcopy(original_model),config),'linear_lora...
os.makedirs(output_merged_dir, exist_ok=True) model.save_pretrained(output_merged_dir, safe_serialization=True) # save tokenizer for easy inference tokenizer = AutoTokenizer.from_pretrained(qlora_path) tokenizer.save_pretrained(output_merged_dir) Right on that first line, I get this error: Run...
low_cpu_mem_usage=True, return_dict=True, torch_dtype=torch.float16, device_map=device_map, )# Merge LoRA and base modelmerged_model = model.merge_and_unload()# Save the merged modelmerged_model.save_pretrained("merged_model",safe_serialization=True)tokenizer...
pytorch 在使用AutoPeftModelForCausal LM时，使用DPOTrainer训练并保存、加载错误后，下面是保存模型的正确...
save_pretrained('linear_lora_model') # Method 1: Use get_peft_model before loading LoRA weights model1 = PeftModel.from_pretrained(get_peft_model(deepcopy(original_model), config), 'linear_lora_model') # Method 2: Directly load LoRA weights model2 = PeftModel.from_pretrained(deepcopy(...
save_pretrained("your-name/opt-350m-lora") # push to Hub lora_model.push_to_hub("your-name/opt-350m-lora") To load a [PeftModel] for inference, you'll need to provide the [PeftConfig] used to create it and the base model it was trained from. from peft import PeftModel, Peft...
save_pretrained(output_dir) if peft_config.task_type is None: # deal with auto mapping base_model_class = self._get_base_model_class( is_prompt_tuning=isinstance(peft_config, PromptLearningConfig) ) parent_library = base_model_class.__module__ auto_mapping_dict = { "base_model_class":...
- A path to a directory containing a PEFT configuration file saved using the `save_pretrained` method (`./my_peft_config_directory/`). adapter_name (`str`, *optional*, defaults to `"default"`): The name of the adapter to be loaded. This is useful for loading multiple adapters. is_...
@@ -132,7 +132,7 @@ def save_pretrained(self, save_directory, **kwargs): 132 132 peft_config.inference_mode = inference_mode 133 133 134 134 @classmethod 135 - def from_pretrained(cls, model, model_id, adapter_name="default", **kwargs): 135 + def from_pretrained(cls, mod...