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import os
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import platform
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import signal
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from transformers.generation import GenerationConfig
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tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-7B-Chat", trust_remote_code=True)
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# We recommend checking the support of BF16 first. Run the command below:
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# import torch
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# torch.cuda.is_bf16_supported()
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# use bf16
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# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B-Chat", device_map="auto", trust_remote_code=True, bf16=True).eval()
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# use fp16
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# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B-Chat", device_map="auto", trust_remote_code=True, fp16=True).eval()
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# use cpu only
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# model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B-Chat", device_map="cpu", trust_remote_code=True).eval()
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# use fp32
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model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B-Chat", device_map="auto", trust_remote_code=True).eval()
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model.generation_config = GenerationConfig.from_pretrained("Qwen/Qwen-7B-Chat",
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trust_remote_code=True) # 可指定不同的生成长度、top_p等相关超参
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stop_stream = False
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def signal_handler(signal, frame):
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global stop_stream
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stop_stream = True
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def clear_screen():
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if platform.system() == "Windows":
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os.system("cls")
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else:
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os.system("clear")
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def print_history(history):
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for pair in history:
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print(f"\nUser：{pair[0]}\nQwen-7B：{pair[1]}")
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def main():
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history, response = [], ''
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global stop_stream
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clear_screen()
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print("欢迎使用 Qwen-7B 模型，输入内容即可进行对话，clear 清空对话历史，stop 终止程序")
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while True:
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query = input("\nUser：")
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if query.strip() == "stop":
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break
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if query.strip() == "clear":
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history = []
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clear_screen()
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print("欢迎使用 Qwen-7B 模型，输入内容即可进行对话，clear 清空对话历史，stop 终止程序")
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continue
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for response in model.chat(tokenizer, query, history=history, stream=True):
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if stop_stream:
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stop_stream = False
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break
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else:
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clear_screen()
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print_history(history)
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print(f"\nUser: {query}")
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print("\nQwen-7B：", end="")
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print(response)
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history.append((query, response))
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if __name__ == "__main__":
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main()
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