Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# 模型名称（官方仓库）
+model_name = "Qwen/Qwen2.5-0.5B-Instruct"
+
+# 加载分词器
+tokenizer = AutoTokenizer.from_pretrained(
+    model_name,
+    trust_remote_code=True
+)
+
+# 加载模型到 CPU
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    trust_remote_code=True,
+).to("cpu")   # 显式移至CPU
+
+# 简易对话函数
+def predict(query, history=None):
+    if history is None:
+        history = []
+        
+    # 编码输入
+    inputs = tokenizer(query, return_tensors="pt")
+    # 放到CPU张量上
+    input_ids = inputs["input_ids"].to("cpu")
+    attention_mask = inputs["attention_mask"].to("cpu")
+    
+    # 推理
+    with torch.no_grad():
+        output_ids = model.generate(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            max_new_tokens=128,
+            do_sample=True,
+            top_p=0.9,
+            temperature=0.8
+        )
+    
+    # 解码
+    output_text = tokenizer.decode(
+        output_ids[0][inputs["input_ids"].shape[1]:],
+        skip_special_tokens=True
+    )
+    
+    # 更新对话历史
+    history.append((query, output_text))
+    return history, history
+
+# 搭建 Gradio 界面
+with gr.Blocks() as demo:
+    gr.Markdown("## Qwen2.5-0.5B-Instruct (CPU) 测试 Demo")
+    chatbot = gr.Chatbot(label="Qwen Chatbot")
+    msg = gr.Textbox(label="输入你的问题或对话")
+    state = gr.State([])
+
+    submit = gr.Button("发送")
+    submit.click(
+        fn=predict,
+        inputs=[msg, state],
+        outputs=[chatbot, state]
+    )
+
+# 启动服务
+demo.launch(server_name="0.0.0.0", server_port=7860)