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Model: simone-papicchio/Think2SQL-7B
Source: Original Platform

README.md

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+---
+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}
+}
+```