初始化项目，由ModelHub XC社区提供模型

Model: jaring/qwen-medical
Source: Original Platform

chat_with_model.py

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