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