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import os |
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import torch |
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import requests |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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os.environ['CUDA_LAUNCH_BLOCKING'] = '1' |
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class Qwen: |
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def __init__(self, mode='offline', model_path="Qwen/Qwen-1_8B-Chat", prefix_prompt = '''请用少于25个字回答以下问题\n\n'''): |
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'''暂时不写api版本,与Linly-api相类似,感兴趣可以实现一下''' |
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self.url = "http://ip:port" |
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self.headers = { |
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"Content-Type": "application/json" |
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} |
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self.data = { |
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"question": "北京有什么好玩的地方?" |
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} |
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self.prefix_prompt = prefix_prompt |
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self.mode = mode |
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self.model, self.tokenizer = self.init_model(model_path) |
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self.history = None |
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def init_model(self, path = "Qwen/Qwen-1_8B-Chat"): |
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model = AutoModelForCausalLM.from_pretrained(path, |
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device_map="auto", |
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trust_remote_code=True).eval() |
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tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True) |
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return model, tokenizer |
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def generate(self, question, system_prompt=""): |
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if self.mode != 'api': |
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self.data["question"] = self.prefix_prompt + question |
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try: |
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response, self.history = self.model.chat(self.tokenizer, self.data["question"], history=self.history, system = system_prompt) |
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return response |
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except Exception as e: |
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print(e) |
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return "对不起,你的请求出错了,请再次尝试。\nSorry, your request has encountered an error. Please try again.\n" |
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else: |
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return self.predict_api(question) |
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def predict_api(self, question): |
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'''暂时不写api版本,与Linly-api相类似,感兴趣可以实现一下''' |
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pass |
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def chat(self, system_prompt, message, history): |
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response = self.generate(message, system_prompt) |
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history.append((message, response)) |
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return response, history |
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def clear_history(self): |
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self.history = [] |
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def test(): |
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llm = Qwen(mode='offline', model_path="../Qwen/Qwen-1_8B-Chat") |
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answer = llm.generate("如何应对压力?") |
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print(answer) |
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if __name__ == '__main__': |
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test() |
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