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 ...
在Reward model的训练中,我们需要人的参与,human labelers给policy模型生成的文本打分,这个分数作为reward model学习的标签 Reward mode训练好后,那么在训练policy model时,Reward model便可以完全取代human labeler打分,分数作为信号传给policy model,再利用OpenAI默认的策略优化算法PPO来训练 Learning to summarize with...
float = 1.05 @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[...
@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=...
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 = '/root/autodl-tmp/qwen/Qwen2-7B-Instruct/' model_path = '/root/autodl-tmp/qwen/Qwen2-7B-Instruct/' lora_path = 'lora_path' # 加载tokenizer tokenizer = AutoTokenizer.from_pretrained(mode_path) tokenizer = AutoTokenizer.from_pretrained(model_path) # 加载模型 model = AutoModel...
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_...