| --- |
| tags: |
| - text-to-sql |
| - qwen |
| - tencent-trac3 |
| - fine-tuned |
| license: apache-2.0 |
| --- |
| |
| # wexhi/trac3_sql |
| |
| ## 模型描述 |
| |
| 这是一个基于 **Qwen** 微调的**全量模型**，专门用于 SQL 生成任务（Text-to-SQL）。 |
| |
| 训练数据来自 Tencent TRAC3 数据集，采用**记忆化训练策略**，目标是在训练集上达到 100% 准确率。 |
| |
| ## 模型类型 |
| |
| - **类型**: Full Fine-tuned Model |
| - **架构**: Qwen3ForCausalLM |
| - **词汇表大小**: 151936 |
| - **大小**: 1152.06 MB |
| |
| ## 使用方法 |
| |
| ### 1. 安装依赖 |
| |
| ```bash |
| pip install transformers torch |
| ``` |
| |
| ### 2. 加载模型 |
| |
| ```python |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| |
| model = AutoModelForCausalLM.from_pretrained( |
| "wexhi/trac3_sql", |
| torch_dtype="auto", |
| device_map="auto", |
| trust_remote_code=True, |
| ) |
| |
| tokenizer = AutoTokenizer.from_pretrained( |
| "wexhi/trac3_sql", |
| trust_remote_code=True, |
| ) |
| ``` |
| |
| ### 3. 生成 SQL |
|
|
| ```python |
| messages = [ |
| {"role": "system", "content": "You are a SQL generator. Generate SQL in this format:\n```sql\n...\n```"}, |
| {"role": "user", "content": "ID: 1\n\nQuestion:\nWhat is the total revenue?"} |
| ] |
| |
| prompt = tokenizer.apply_chat_template( |
| messages, |
| tokenize=False, |
| add_generation_prompt=True, |
| enable_thinking=False, |
| ) |
| |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
| outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.0) |
| response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True) |
| print(response) |
| ``` |
|
|
| ### 4. 使用 vLLM 加速（推荐） |
|
|
| ```bash |
| pip install vllm |
| ``` |
|
|
| ```python |
| from vllm import LLM, SamplingParams |
| |
| llm = LLM(model="wexhi/trac3_sql", trust_remote_code=True) |
| sampling_params = SamplingParams(temperature=0.0, max_tokens=512) |
| |
| prompts = [...] # 批量 prompts |
| outputs = llm.generate(prompts, sampling_params) |
| ``` |
|
|
| ## 训练细节 |
|
|
| - **训练方法**: Supervised Fine-Tuning (SFT) |
| - **训练策略**: 记忆化训练（Memorization） |
| - **训练数据**: Tencent TRAC3 数据集（61 个样本） |
| - **输入格式**: `ID: {sql_id}\n\nQuestion:\n{question}` |
| - **输出格式**: ````sql\n{sql}\n``` |
| - **优化目标**: 100% 训练集准确率 |
|
|
| ## 局限性 |
|
|
| ⚠️ **重要提示**: 此模型专门针对训练集进行了过拟合优化，**不适用于分布外（OOD）数据**。 |
|
|
| - ✅ 对于训练集中的问题，能够准确生成 SQL |
| - ❌ 对于未见过的问题，可能无法正确泛化 |
|
|
| ## License |
|
|
| Apache 2.0 |
|
|
| ## 引用 |
|
|
| 如果使用了此模型，请引用： |
|
|
| ``` |
| Tencent TRAC3 Challenge - Text-to-SQL Fine-tuned Model |
| ``` |
|
|
| --- |
|
|
| *Created: 2025-11-24* |
|
|
