Create app.py

app.py

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+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# 加载模型（使用量化版本节省显存）
+model_name = "Qwen/Qwen2.5-7B-Instruct-GPTQ-Int8"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    device_map="auto",  # 自动分配GPU/CPU
+    torch_dtype="auto"
+)
+
+# 定义生成函数
+def generate_response(message, history):
+    # 格式化对话历史
+    messages = [{"role": "user", "content": message}]
+    text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    inputs = tokenizer(text, return_tensors="pt").to(model.device)
+    
+    # 生成回复
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=512,
+        temperature=0.7
+    )
+    response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
+    return response
+
+# 启动Gradio界面
+gr.ChatInterface(
+    fn=generate_response,
+    title="Qwen2.5-7B大模型在线演示",
+    description="输入问题后按回车开始对话"
+).launch()