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README.md

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----
-title: Math Solver Api
-emoji: 💻
-colorFrom: gray
-colorTo: indigo
-sdk: gradio
-sdk_version: 6.2.0
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Math Solver API
+emoji: 🧮
+colorFrom: purple
+colorTo: blue
+sdk: gradio
+sdk_version: 4.44.0
+app_file: app.py
+pinned: false
+license: apache-2.0
+---
+
+# 数学问题求解 API 后端
+
+这是一个基于 Gradio 的 API 后端服务,用于为数学问题求解提供模型推理能力。
+
+## 模型信息
+
+- **基础模型**: meta-llama/Llama-3.2-1B-Instruct
+- **微调适配器**: zhman/llama-SFT-GRPO
+- **训练方法**: SFT + GRPO
+- **准确率**: 97%
+
+## API 使用
+
+### 端点
+`POST /api/predict`
+
+### 请求格式
+```json
+{
+  "data": [
+    "你的数学问题",
+    1024,
+    0.7
+  ]
+}
+```
+
+### 响应格式
+```json
+{
+  "data": [
+    "推理过程...",
+    "提取的答案"
+  ]
+}
+```
+
+### JavaScript 示例
+```javascript
+const response = await fetch('https://YOUR_SPACE_URL/api/predict', {
+    method: 'POST',
+    headers: {
+        'Content-Type': 'application/json'
+    },
+    body: JSON.stringify({
+        data: [
+            "Find the positive integer n such that 10^n cubic centimeters is the same as 1 cubic kilometer.",
+            1024,
+            0.7
+        ]
+    })
+});
+
+const result = await response.json();
+console.log('推理过程:', result.data[0]);
+console.log('答案:', result.data[1]);
+```
+
+## 部署说明
+
+1. 创建新的 Gradio Space
+2. 上传 `app.py` 和 `requirements.txt`
+3. 等待模型加载(首次约1-2分钟)
+4. Space URL 即为 API 基础地址
+
+## 注意事项
+
+- Space 长时间不使用会休眠
+- 休眠后首次调用会唤醒(约10-20秒)
+- 推荐使用 GPU 硬件加速

app.py

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+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+import torch
+import re
+import os
+
+# 模型配置
+MODEL_ID = "meta-llama/Llama-3.2-1B-Instruct"
+ADAPTER_ID = "zhman/llama-SFT-GRPO"
+
+# 全局变量存储模型和tokenizer
+model = None
+tokenizer = None
+
+def load_model():
+    """加载模型和tokenizer"""
+    global model, tokenizer
+    
+    print("正在加载模型...")
+    
+    # 加载tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+    
+    # 加载基础模型
+    base_model = AutoModelForCausalLM.from_pretrained(
+        MODEL_ID,
+        torch_dtype=torch.bfloat16,
+        device_map="auto",
+        trust_remote_code=True
+    )
+    
+    # 加载 LoRA 适配器
+    model = PeftModel.from_pretrained(base_model, ADAPTER_ID)
+    model.eval()
+    
+    print("模型加载完成!")
+    return model, tokenizer
+
+def extract_boxed_answer(text):
+    """提取 \\boxed{} 格式的答案"""
+    # 查找 \boxed{} 格式
+    boxed_pattern = r'\\boxed\{([^}]+)\}'
+    matches = re.findall(boxed_pattern, text)
+    
+    if matches:
+        return matches[-1].strip()
+    
+    # 尝试其他格式
+    patterns = [
+        r'答案[:：]\s*([^\n]+)',
+        r'Answer[:：]\s*([^\n]+)',
+        r'= *([^\n]+)',
+        r'因此[:：]\s*([^\n]+)',
+        r'所以[:：]\s*([^\n]+)',
+    ]
+    
+    for pattern in patterns:
+        matches = re.findall(pattern, text)
+        if matches:
+            return matches[-1].strip()
+    
+    return None
+
+def predict(question, max_new_tokens=1024, temperature=0.7):
+    """
+    模型推理函数
+    
+    Args:
+        question: 数学问题
+        max_new_tokens: 最大生成token数
+        temperature: 温度参数
+    
+    Returns:
+        (完整输出, 提取的答案)
+    """
+    global model, tokenizer
+    
+    # 首次调用时加载模型
+    if model is None or tokenizer is None:
+        load_model()
+    
+    # 构建prompt
+    prompt = f"User: {question}\nPlease reason step by step.\nAssistant:"
+    
+    # Tokenize
+    inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=1024)
+    
+    # 生成
+    with torch.no_grad():
+        outputs = model.generate(
+            inputs.input_ids.to(model.device),
+            max_new_tokens=max_new_tokens,
+            temperature=min(temperature, 0.01) if temperature <= 0 else temperature,
+            do_sample=temperature > 0,
+            pad_token_id=tokenizer.pad_token_id,
+            eos_token_id=tokenizer.eos_token_id
+        )
+    
+    # 解码
+    full_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    response = full_output.replace(prompt, "").strip()
+    
+    # 提取答案
+    answer = extract_boxed_answer(response)
+    
+    return response, answer if answer else "未能提取到答案"
+
+# 创建 Gradio 界面
+with gr.Blocks(title="数学问题求解 API") as demo:
+    gr.Markdown("# 🧮 数学问题求解 API 后端")
+    gr.Markdown("基于 Llama-3.2-1B + SFT + GRPO 微调模型")
+    
+    with gr.Row():
+        with gr.Column():
+            question_input = gr.Textbox(
+                label="数学问题",
+                placeholder="例如: 求解方程 x^2 + 5x + 6 = 0",
+                lines=5
+            )
+            with gr.Row():
+                max_tokens = gr.Slider(
+                    minimum=128,
+                    maximum=2048,
+                    value=1024,
+                    step=128,
+                    label="最大生成长度"
+                )
+                temp = gr.Slider(
+                    minimum=0.1,
+                    maximum=1.5,
+                    value=0.7,
+                    step=0.1,
+                    label="Temperature"
+                )
+            submit_btn = gr.Button("求解", variant="primary")
+        
+        with gr.Column():
+            reasoning_output = gr.Textbox(
+                label="推理过程",
+                lines=15,
+                max_lines=20
+            )
+            answer_output = gr.Textbox(
+                label="提取的答案",
+                lines=2
+            )
+    
+    # 示例
+    gr.Examples(
+        examples=[
+            ["Find the positive integer n such that 10^n cubic centimeters is the same as 1 cubic kilometer."],
+            ["求解方程 3×5 等于多少？"],
+        ],
+        inputs=question_input
+    )
+    
+    submit_btn.click(
+        fn=predict,
+        inputs=[question_input, max_tokens, temp],
+        outputs=[reasoning_output, answer_output],
+        api_name="predict"  # 重要: 启用 API 访问
+    )
+    
+    gr.Markdown("---")
+    gr.Markdown("### API 使用说明")
+    gr.Markdown("""
+    **API 端点**: `/api/predict`
+    
+    **POST 请求示例**:
+    ```python
+    import requests
+    
+    response = requests.post(
+        "https://YOUR_SPACE_URL/api/predict",
+        json={
+            "data": [
+                "你的数学问题",  # question
+                1024,           # max_new_tokens
+                0.7             # temperature
+            ]
+        }
+    )
+    result = response.json()
+    reasoning = result["data"][0]  # 推理过程
+    answer = result["data"][1]     # 提取的答案
+    ```
+    """)
+
+# 启动应用
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio>=4.0.0
+transformers>=4.36.0
+torch>=2.0.0
+peft>=0.7.0
+accelerate>=0.25.0
+bitsandbytes>=0.41.0