prefix_encoder.float() model = model.eval() #tokenizer = AutoTokenizer.from_pretrained("./chatglm2-6b-int4", revision="v1.0", trust_remote_code=True) #model = AutoModel.from_pretrained("./chatglm2-6b-int4", revision="v1.0", trust_remote_code=True).half().cuda() # 多显卡支持,...
re.sub( f"^ {prefix} " , "" , text) # format query query_format = lambda query: f"问: {query} \n" # format result result_format = lambda result: f"答: {result} \n\n" # format history list returned by gradio def history_format ( history: list ) -> str: messages = "" ...
(BaseModel): model: str messages: List[ChatMessage] temperature: Optional[float] = None top_p: Optional[float] = None max_length: Optional[int] = None stream: Optional[bool] = False class ChatCompletionResponseChoice(BaseModel): index: int message: ChatMessage finish_reason: Literal["stop"...
你需要在下描代码中修改你本地的模型路径,修改这个 MODELPATH = os.environ.get('MODELPATH', 'D:\LLM-GPU\chatglm3-6b') D:\LLM-GPU\chatglm3-6b 是我本地的路径,修改为你自己的。 api_server.py importosimporttimeimporttiktokenimporttorchimportuvicornfromfastapiimportFastAPI,HTTPException,Responsefromf...
@@ -23,6 +24,8 @@ def initialize_llm_chain(messages: list): max_tokens=8096, prefix_messages=messages, top_p=0.9, streaming=True, # Set to True for streaming completions ) return LLMChain(prompt=prompt, llm=llm) 0 comments on commit 6e00a6c Please sign in to comment. Footer...
# # 设置此次请求中的messages # messages = req_messages.copy() # # 检查vision # for msg in messages: # if 'content' in msg and isinstance(msg["content"], list): # for me in msg["content"]: # if me["type"] == "image_url": # me["image_url"]['url'] = await self.get_...
('ENDPOINT_URL')max_tokens=int(os.environ.get("MAX_TOKENS",1024))top_p=float(os.environ.get("TOP_P",0.9))ifnotendpoint_url:raiseValueError('endpoint_url not right,url:{}'.format(endpoint_url))llm=ChatGLM3(endpoint_url=endpoint_url,max_tokens=max_tokens,top_p=top_p,prefix_messages=...
Prefix Tuning 启动命令参考 python run_finetune.py ./config/llama/pt_argument.json 我使用的是LoRA 精调模型 LoRA: 低秩适配(Low-Rank Adaptation)是最常用参数高效微调(PEFT,Parameter-Efficient Fine-Tuning)技术。它不是重新训练整个模型,而是冻结权重,并在每个目标线性层引入低秩矩阵。这使得 LoRA 所需训...
if len(request.messages) < 1 or request.messages[-1].role == "assistant": raise HTTPException(status_code=400, detail="Invalid request") gen_params = dict( messages=request.messages, temperature=request.temperature, top_p=request.top_p, ...
+ ZhipuAI API 微调数据集中的 `messages` 字段在本仓库为 `conversation` 字段。+ ZhipuAI API 中的微调文件为 `jsonl`, 在本仓库,需要简单的将文件名改为 `json`。## 参考文献 ``` @inproceedings{liu2022p, title={P-tuning: Prompt tuning can be comparable to fine-tuning across s...