README.md

---
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
datasets: simone-papicchio/bird
library_name: transformers
tags:
- generated_from_trainer
- open-r1
- Text2SQL
- Reasoning
licence: apache-2.0
---

# 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)


This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) on the [simone-papicchio/bird](https://huggingface.co/datasets/simone-papicchio/bird) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl). 



## Quick start

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=30_000,
    temperature=0.7,
    top_p=0.95
)
print(outputs[0]["generated_text"][-1])
```

## Training procedure

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/spapicchio-politecnico-di-torino/deep-thinking/runs/d93m41pq) 


This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).

### Framework versions

- TRL: 0.17.0.dev0
- Transformers: 4.51.0
- Pytorch: 2.5.1
- Datasets: 3.5.0
- Tokenizers: 0.21.1

## Citations

```bibtex
@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}, 
}
```

```bibtex
@inproceedings{papicchio2023qatch,
  title={QATCH: benchmarking SQL-centric tasks with table representation learning models on your data},
  author={Papicchio, Simone and Papotti, Paolo and Cagliero, Luca},
  booktitle={Proceedings of the 37th International Conference on Neural Information Processing Systems},
  pages={30898--30917},
  year={2023}
}
```