model = AutoModelForCausalLM.from_pretrained(mode_path, device_map="auto",torch_dtype=torch.bfloat16) model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto",torch_dtype=torch.bfloat16) # 加载lora权重 model = PeftModel.from_pretrained(model, model_id=lora_path, config=...
Set the model to evaluation mode (model.eval()) to merge the weights. This triggers the LoRALinear.train() method, and making the merging process. Remove any LoRA parameters from the state dict Save the merged checkpoint You can use the following script to do the conversion, remember to ...
@torch.inference_mode() def generate_interactive( model, tokenizer, prompt, generation_config: Optional[GenerationConfig] = None, logits_processor: Optional[LogitsProcessorList] = None, stopping_criteria: Optional[StoppingCriteriaList] = None, prefix_allowed_tokens_fn: Optional[Callable[[int, torch....
model = AutoModelForCausalLM.from_pretrained(mode_path, device_map="auto",torch_dtype=torch.bfloat16) model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto",torch_dtype=torch.bfloat16) # 加载lora权重 model = PeftModel.from_pretrained(model, model_id=lora_path, config=...
model = AutoModelForCausalLM.from_pretrained(mode_path, device_map="auto",torch_dtype=torch.bfloat16) model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto",torch_dtype=torch.bfloat16) # 加载lora权重 model = PeftModel.from_pretrained(model, model_id=lora_path, config=...
mode_path = './qwen/Qwen1.5-7B-Chat/' model_path = './qwen/Qwen1.5-7B-Chat/' lora_path = 'lora_path' # 加载tokenizer tokenizer = AutoTokenizer.from_pretrained(mode_path) tokenizer = AutoTokenizer.from_pretrained(model_path) # 加载模型 model = AutoModelForCausalLM.from_pretrained(mode_...
model = AutoModelForCausalLM.from_pretrained(mode_path, device_map="auto",torch_dtype=torch.bfloat16) model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto",torch_dtype=torch.bfloat16) # 加载lora权重 model = PeftModel.from_pretrained(model, model_id=lora_path, config=...
model=AutoModelForCausalLM.from_pretrained(mode_path,device_map="auto",torch_dtype=torch.bfloat16) model=AutoModelForCausalLM.from_pretrained(model_path,device_map="auto",torch_dtype=torch.bfloat16) #加载lora权重 model=PeftModel.from_pretrained(model,model_id=lora_path,config=config) ...