85 lines
2.5 KiB
Python
85 lines
2.5 KiB
Python
import os
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# 设置环境变量禁用 TensorFlow
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os.environ["USE_TORCH"] = "1"
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os.environ["USE_TF"] = "0"
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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def load_model(model_path):
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"""
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加载模型和分词器
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"""
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print(f"正在从 {model_path} 加载模型...")
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# 检查 CUDA 是否可用
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if torch.cuda.is_available():
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device = torch.device("cuda")
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print("使用 CUDA 后端加速")
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# 检查 MPS 是否可用
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elif torch.backends.mps.is_available():
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device = torch.device("mps")
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print("使用 MPS 后端加速")
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else:
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device = torch.device("cpu")
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print("CUDA 和 MPS 均不可用，使用 CPU")
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_path,
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torch_dtype=torch.float16,
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trust_remote_code=True
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).to(device)
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print("模型加载完成！")
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return model, tokenizer, device
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def chat_with_model(model, tokenizer, device):
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"""
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与模型进行对话
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"""
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history = []
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print("开始对话，输入 'exit' 退出")
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while True:
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user_input = input("\n用户: ")
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if user_input.lower() == 'exit':
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print("对话结束")
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break
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# 为 Qwen2 模型构建对话格式
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if not history:
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messages = [{"role": "user", "content": user_input}]
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else:
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messages = []
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for i, (user_msg, assistant_msg) in enumerate(history):
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messages.append({"role": "user", "content": user_msg})
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messages.append({"role": "assistant", "content": assistant_msg})
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messages.append({"role": "user", "content": user_input})
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# 使用 tokenizer 处理对话
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inputs = tokenizer.apply_chat_template(
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messages,
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return_tensors="pt"
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).to(device)
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# 生成回复
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outputs = model.generate(
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inputs,
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max_new_tokens=2048,
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do_sample=True,
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temperature=0.7,
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top_p=0.9,
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)
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# 解码回复
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response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)
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print(f"\n助手: {response}")
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# 更新历史记录
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history.append((user_input, response))
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if __name__ == "__main__":
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model_path = "../qwen-medical"
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model, tokenizer, device = load_model(model_path)
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chat_with_model(model, tokenizer, device) |