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‎LICENSE‎

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‎README.md‎

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</div>
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## 🚀 Coming Soon Checklist
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## 📚 Citations
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- [x] 📊 Release model weights on HuggingFace
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- [ ] 🔧 Open source training code
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- [ ] 📝 Detailed documentation
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- [ ] 🛠️ Environment setup guide
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```bibtex
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@article{ma2025sql,
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title={SQL-R1: Training Natural Language to SQL Reasoning Model By Reinforcement Learning},
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author={Ma, Peixian and Zhuang, Xialie and Xu, Chengjin and Jiang, Xuhui and Chen, Ran and Guo, Jian},
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journal={arXiv preprint arXiv:2504.08600},
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year={2025}
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}
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```
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## 📰 News
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- **[2024.05]** 🎉 We have released our model weights on Hugging Face! Check out the [Model Weights](#-model-weights) section below.
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- **[2024.04]** 📑 Our paper is now available on [arXiv](https://arxiv.org/abs/2504.08600).
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- **[2024.05.27]** 🎉 We have released the full code of SQL-R1.
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- **[2024.05.21]** 🎉 We have released our model weights on Hugging Face! Check out the [Model Weights](#-model-weights) section below.
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- **[2024.04.11]** 📑 Our paper is now available on [arXiv](https://arxiv.org/abs/2504.08600).
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## 🚀 Coming Soon Checklist
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- [x] 📊 Release model weights on HuggingFace
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- [x] 🔧 Open source training code
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- [x] 📝 Detailed documentation
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- [x] 🛠️ Environment setup guide
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## 🤖 Model Weights
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| SQL-R1 (14B) | 14B | [🤗 Download](https://huggingface.co/MPX0222forHF/SQL-R1-14B) |
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## 📑 Documentation Structure
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## 📚 Citations
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This repository is organized as follows:
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```bibtex
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@article{ma2025sql,
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title={SQL-R1: Training Natural Language to SQL Reasoning Model By Reinforcement Learning},
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author={Ma, Peixian and Zhuang, Xialie and Xu, Chengjin and Jiang, Xuhui and Chen, Ran and Guo, Jian},
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journal={arXiv preprint arXiv:2504.08600},
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year={2025}
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}
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```
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```
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SQL-R1/
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├── data/ # Data processing scripts and datasets
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│ ├── Spider/
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│ └── BIRD/
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├── models/ # Base models
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│ ├── Qwen2.5-Coder-3B-Instruct/
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│ └── Qwen2.5-Coder-7B-Instruct/
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├── sh/ # Scripts for training, inference and evaluation
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├── src/ # Source code
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└── verl/ # Verl framework
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```
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## 🛠️ Environment Setup
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### Prerequisites
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- Python 3.9+
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- CUDA 12.0+
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- 8 x 80GB+ GPU (for training) / 2 x 40GB GPU (for inference)
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### Installation
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1. Clone the repository:
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```bash
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git clone https://github.com/MPX0222/SQL-R1.git
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cd SQL-R1
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```
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2. Create and activate a virtual environment (recommended):
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```bash
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conda create -n sqlr1 python=3.9
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```
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3. Install dependencies:
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```bash
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pip install torch==2.4.0 --index-url https://download.pytorch.org/whl/cu121
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pip install vllm==0.6.3 ray
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pip install flash-attn --no-build-isolation
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pip install -e . # For verl integration
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pip install wandb IPython matplotlib sqlparse func_timeout
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```
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4. Download the model weights from Hugging Face and put them in the `models/` directory
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5. Copy the database information in the `db_info` directory to the related dataset (`data/Spider/`, `data/BIRD/`) directory.
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## 🚀 Quick Start
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> Note: Please set the related data paths and params before running the scripts.
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1. Run training:
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```bash
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sh sh/train_sqlr1.sh
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```
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2. Run inference:
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```bash
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sh sh/inference.sh"
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```
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3. Run evaluation:
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```bash
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# evaluate spider
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sh sh/eval_spider.sh
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# evaluate bird
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sh sh/eval_bird.sh
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```
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## Thanks for
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We thank [OmniSQL](https://github.com/GIST-IRR/OmniSQL) and follow their evaluation code and database information retrieval code. We have adapted and modified their evaluation scripts for our project.

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