| --- |
| license: apache-2.0 |
| base_model: |
| - Qwen/Qwen2.5-Coder-7B-Instruct |
| --- |
| |
| ## Model Information |
| This model is the reasoning model for Text2SQL task introduced in [Think2SQL: Reinforce LLM Reasoning Capabilities for Text2SQL](https://arxiv.org/abs/2504.15077) |
|
|
| ## Intended use |
| The best model performance are given with its System and User prompt. |
| The model is intended to use with three input: question, evidence and the database schema. |
|
|
|
|
| Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function. |
|
|
| Make sure to update your transformers installation via `pip install --upgrade transformers`. |
|
|
| ```python |
| import transformers |
| import torch |
| model_id = "simone-papicchio/Think2SQL-7B" |
| pipeline = transformers.pipeline( |
| "text-generation", |
| model=model_id, |
| model_kwargs={"torch_dtype": torch.bfloat16}, |
| device_map="auto", |
| ) |
| |
| system_message = ( |
| "You are a helpful AI Assistant that provides well-reasoned and detailed responses. " |
| "You first think about the reasoning process as an internal monologue and then provide the user with the answer. " |
| "Respond in the following format: <think>\n...\n</think>\n<answer>\n...\n</answer>" |
| ).strip() |
| |
| user_message = ( |
| "Answer the following question with the SQL code. Use the piece of evidence and base your answer on the database schema. " |
| "Given the question, the evidence and the database schema, return in the <answer> tags only the SQL script that addresses the question.\n" |
| "Question:\n{question}\n\n" |
| "Evidence:\n{evidence}\n\n" |
| "Database Schema:\n{schema}\n\n" |
| "Return only the SQL script enclosed in <answer> tags." |
| ).strip() |
| |
| messages = [ |
| {"role": "system", "content": system_message}, |
| {"role": "user", "content": user_message}, |
| ] |
| |
| outputs = pipeline( |
| messages, |
| max_new_tokens=256, |
| ) |
| print(outputs[0]["generated_text"][-1]) |
| ``` |
|
|
|
|
| ## Citation |
| ```bitex |
| @misc{papicchio2025think2sqlreinforcellmreasoning, |
| title={Think2SQL: Reinforce LLM Reasoning Capabilities for Text2SQL}, |
| author={Simone Papicchio and Simone Rossi and Luca Cagliero and Paolo Papotti}, |
| year={2025}, |
| eprint={2504.15077}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.LG}, |
| url={https://arxiv.org/abs/2504.15077}, |
| } |
| ``` |
|
|
|
|
