适配远程LLM调用
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@ -69,12 +69,23 @@ llm_model_dict = {
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"local_model_path": None,
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"provides": "LLamaLLM"
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},
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"fastChatOpenAI": {
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"fast-chat-chatglm-6b": {
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"name": "FastChatOpenAI",
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"pretrained_model_name": "FastChatOpenAI",
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"local_model_path": None,
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"provides": "FastChatOpenAILLM"
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}
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"provides": "FastChatOpenAILLM",
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"api_base_url": "http://localhost:8000/v1",
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"model_name": "chatglm-6b"
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},
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"fast-chat-vicuna-13b-hf": {
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"name": "FastChatOpenAI",
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"pretrained_model_name": "vicuna-13b-hf",
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"local_model_path": None,
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"provides": "FastChatOpenAILLM",
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"api_base_url": "http://localhost:8000/v1",
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"model_name": "vicuna-13b-hf"
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},
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}
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# LLM 名称
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@ -111,9 +111,9 @@ class FastChatOpenAILLM(RemoteRpcModel, LLM, ABC):
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messages=self.build_message_list(prompt)
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)
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self.history += [[prompt, completion.choices[0].message.content]]
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history += [[prompt, completion.choices[0].message.content]]
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answer_result = AnswerResult()
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answer_result.history = self.history
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answer_result.history = history
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answer_result.llm_output = {"answer": completion.choices[0].message.content}
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yield answer_result
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@ -34,11 +34,14 @@ def loaderLLM(llm_model: str = None, no_remote_model: bool = False, use_ptuning_
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loaderCheckPoint.model_path = llm_model_info["local_model_path"]
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if 'FastChat' in loaderCheckPoint.model_name:
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if 'FastChatOpenAILLM' in llm_model_info["local_model_path"]:
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loaderCheckPoint.unload_model()
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else:
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loaderCheckPoint.reload_model()
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provides_class = getattr(sys.modules['models'], llm_model_info['provides'])
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modelInsLLM = provides_class(checkPoint=loaderCheckPoint)
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if 'FastChatOpenAILLM' in llm_model_info["provides"]:
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modelInsLLM.set_api_base_url(llm_model_info['api_base_url'])
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modelInsLLM.call_model_name(llm_model_info['model_name'])
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return modelInsLLM
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@ -18,14 +18,13 @@ async def dispatch(args: Namespace):
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shared.loaderCheckPoint = LoaderCheckPoint(args_dict)
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llm_model_ins = shared.loaderLLM()
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llm_model_ins.set_api_base_url("http://localhost:8000/v1")
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llm_model_ins.call_model_name("chatglm-6b")
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history = [
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("which city is this?", "tokyo"),
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("why?", "she's japanese"),
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]
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for answer_result in llm_model_ins.generatorAnswer(prompt="她在做什么? ", history=history,
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for answer_result in llm_model_ins.generatorAnswer(prompt="你好? ", history=history,
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streaming=False):
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resp = answer_result.llm_output["answer"]
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