| base_model: | |
| - griffith-bigdata/Qwen-2.5-Coder-3B-SQL-Writer | |
| # FINER-SQL-3B-BIRD | |
| Trained from [`griffith-bigdata/Qwen-2.5-Coder-3B-SQL-Writer`](https://huggingface.co/griffith-bigdata/Qwen-2.5-Coder-3B-SQL-Writer) using GRPO with two dense rewards from the FINER-SQL paper: | |
| 🧠 Memory Reward — aligns reasoning with verified traces | |
| ⚙️ Atomic Reward — measures operation-level SQL overlap | |
| ✅ 67.5% EX on BIRD when training only on BIRD train dataset; infer on a single 12-24 GB GPU | |
| 📄 See other models: https://huggingface.co/collections/griffith-bigdata/finer-sql | |
| 📄 Github: https://github.com/thanhdath/finer-sql/tree/main | |
| ## Citation | |
| ```bibtex | |
| @inproceedings{finersql, | |
| author = {Thanh Dat Hoang and Thanh Trung Huynh and Matthias Weidlich and Thanh Tam Nguyen and Tong Chen and Hongzhi Yin and Quoc Viet Hung Nguyen}, | |
| title = {Boosting Small Language Models for Text-to-SQL with Fine-Grained Execution Feedback and Cost-Efficient Rewards}, | |
| booktitle = {ICDE}, | |
| publisher = {IEEE}, | |
| year = {2026}, | |
| } | |
| ``` | |