).to(device).eval() gen_kwargs = {"max_length": 2500, "do_sample": True, "top_k": 1} with torch.no_grad(): outputs = model.generate(**inputs, **gen_kwargs) outputs = outputs[:, inputs['input_ids'].shape[1]:] print(tokenizer.decode(outputs[0])注意: GLM-4V-9B 暂不支...
).to(device).eval() gen_kwargs = {"max_length": 2500, "do_sample": True, "top_k": 1} with torch.no_grad(): outputs = model.generate(**inputs, **gen_kwargs) outputs = outputs[:, inputs['input_ids'].shape[1]:] print(tokenizer.decode(outputs[0], skip_special_tokens=True)...
gen_kwargs = {"max_length": 2500, "do_sample": True, "top_k": 1} with torch.no_grad(): outputs = model.generate(**inputs, **gen_kwargs) outputs = outputs[:, inputs['input_ids'].shape[1]:] print(tokenizer.decode(outputs[0], skip_special_tokens=True)) 1. 2. 3. 4. 5...
def completions(self, **kwargs) -> Any | None: return self.client.chat.completions.create(**kwargs) async def async_completions(self, **kwargs) -> Any: loop = asyncio.get_running_loop() partial_func = partial(self.client.chat.completions.create, **kwargs) response = await ...
message_dict["name"] = message.additional_kwargs["name"]returnmessage_dict defconvert_dict_to_message(_dict: Mapping[str, Any])->BaseMessage:"""Convert a dictionary to a LangChain message. Args: _dict: The dictionary. Returns: The LangChain message. ...
logging.info(f"messages:{messages}")inputs=inputs.to(device)gen_kwargs={"max_length":max_length,"do_sample":True,"top_p":top_p,"temperature":temperature}# 这个不是真流式,能同时兼容 glm4 和 qwen2defgenerate_response():withtorch.no_grad():outputs=model.generate(**inputs,**gen_kwarg...
trust_remote_code=True ).to(device).eval() gen_kwargs = {"max_length": 2500, "do_sample": True, "top_k": 1} with torch.no_grad(): outputs = model.generate(**inputs, **gen_kwargs) outputs = outputs[:, inputs['input_ids'].shape[1]:] print(tokenizer.decode(outputs[0], ...
**gen_kwargs): if return_past_key_values: outputs, past_key_values = outputs outputs = outputs.tolist()[0][ len(batch_input["input_ids"] [0]):-1] # Exclude the last token if it's EOS response = tokenizer.decode(outputs) if response and response[-1] != "�": response, new...
trust_remote_code=True ).to(device).eval() gen_kwargs = {"max_length": 2500, "do_sample": True, "top_k": 1} with torch.no_grad(): outputs = model.generate(**inputs, **gen_kwargs) outputs = outputs[:, inputs['input_ids'].shape[1]:] print(tokenizer.decode(outputs[0], ...
73 + "do_sample": self.gen_kwargs.get("do_sample"), 74 + "top_k": self.gen_kwargs.get("top_k"), 75 + } 76 + 77 + @property 78 + def _llm_type(self) -> str: 79 + return "glm-4-9b-chat" 80 + ``` 81 + 在上述类定义中,我们分别重写了构造函数和 _call...