Update README.md

README.md

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----
-license: apache-2.0
----
+---
+pipeline_tag: text-generation
+library_name: transformers
+license: cc-by-nc-4.0
+tags:
+- text-to-sql
+- reinforcement-learning
+---
+
+
+# SLM-SQL: An Exploration of Small Language Models for Text-to-SQL
+
+### Important Links
+
+📖[Arxiv Paper](https://arxiv.org/abs/2507.22478) |
+🤗[HuggingFace](https://huggingface.co/collections/cycloneboy/slm-sql-688b02f99f958d7a417658dc) |
+🤖[ModelScope](https://modelscope.cn/collections/SLM-SQL-624bb6a60e9643) |
+
+## News
+
++ `July 31, 2025`: Upload model to modelscope and huggingface.
++ `July 30, 2025`: Publish the paper to arxiv
+
+## Introduction
+
+> Large language models (LLMs) have demonstrated strong performance in translating natural language questions into SQL
+> queries (Text-to-SQL). In contrast, small language models (SLMs) ranging from 0.5B to 1.5B parameters currently
+> underperform on Text-to-SQL tasks due to their limited logical reasoning capabilities. However, SLMs offer inherent
+> advantages in inference speed and suitability for edge deployment. To explore their potential in Text-to-SQL
+> applications, we leverage recent advancements in post-training techniques. Specifically, we used the open-source
+> SynSQL-2.5M dataset to construct two derived datasets: SynSQL-Think-916K for SQL generation and
+> SynSQL-Merge-Think-310K
+> for SQL merge revision. We then applied supervised fine-tuning and reinforcement learning-based post-training to the
+> SLM, followed by inference using a corrective self-consistency approach. Experimental results validate the
+> effectiveness
+> and generalizability of our method, SLM-SQL. On the BIRD development set, the five evaluated models achieved an
+> average
+> improvement of 31.4 points. Notably, the 0.5B model reached 56.87\% execution accuracy (EX), while the 1.5B model
+> achieved 67.08\% EX. We will release our dataset, model, and code to github: https://github.com/CycloneBoy/slm_sql.
+
+### Framework
+
+<img src="https://raw.githubusercontent.com/CycloneBoy/slm_sql/main/data/image/slmsql_framework.png"  height="500" alt="slmsql_framework">
+
+### Main Results
+
+<img src="https://raw.githubusercontent.com/CycloneBoy/slm_sql/main/data/image/slmsql_bird_result.png"  height="500" alt="slm_sql_result">
+
+
+<img src="https://raw.githubusercontent.com/CycloneBoy/slm_sql/main/data/image/slmsql_bird_main.png"  height="500" alt="slmsql_bird_main">
+
+<img src="https://raw.githubusercontent.com/CycloneBoy/slm_sql/main/data/image/slmsql_spider_main.png"  height="500" alt="slmsql_spider_main">
+
+Performance Comparison of different Text-to-SQL methods on BIRD dev and test dataset.
+
+<img src="https://raw.githubusercontent.com/CycloneBoy/slm_sql/main/data/image/slmsql_ablation_study.png"  height="300" alt="slmsql_ablation_study">
+
+## Model
+
+| **Model**                                | Base Model                   | Train Method | Modelscope                                                                                        | HuggingFace                                                                                  |
+|------------------------------------------|------------------------------|--------------|---------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------|
+| SLM-SQL-Base-0.5B                        | Qwen2.5-Coder-0.5B-Instruct  | SFT          | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-Base-0.5B)                        | [🤗 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-Base-0.5B)                        |
+| SLM-SQL-0.5B                             | Qwen2.5-Coder-0.5B-Instruct  | SFT + GRPO   | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-0.5B)                             | [🤗 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-0.5B)                             |
+| CscSQL-Merge-Qwen2.5-Coder-0.5B-Instruct | Qwen2.5-Coder-0.5B-Instruct  | SFT + GRPO   | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/CscSQL-Merge-Qwen2.5-Coder-0.5B-Instruct) | [🤗 HuggingFace](https://huggingface.co/cycloneboy/CscSQL-Merge-Qwen2.5-Coder-0.5B-Instruct) |
+| SLM-SQL-Base-1.5B                        | Qwen2.5-Coder-1.5B-Instruct  | SFT          | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-Base-1.5B)                        | [🤗 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-Base-1.5B)                        |
+| SLM-SQL-1.5B                             | Qwen2.5-Coder-1.5B-Instruct  | SFT + GRPO   | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-1.5B)                             | [🤗 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-1.5B)                             |
+| CscSQL-Merge-Qwen2.5-Coder-1.5B-Instruct | Qwen2.5-Coder-1.5B-Instruct  | SFT + GRPO   | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/CscSQL-Merge-Qwen2.5-Coder-1.5B-Instruct) | [🤗 HuggingFace](https://huggingface.co/cycloneboy/CscSQL-Merge-Qwen2.5-Coder-1.5B-Instruct) |
+| SLM-SQL-Base-0.6B                        | Qwen3-0.6B                   | SFT          | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-Base-0.6B)                        | [🤗 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-Base-0.6B)                        |
+| SLM-SQL-0.6B                             | Qwen3-0.6B                   | SFT + GRPO   | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-0.6B)                             | [🤗 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-0.6B)                             |
+| SLM-SQL-Base-1.3B                        | deepseek-coder-1.3b-instruct | SFT          | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-Base-1.3B )                       | [🤗 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-Base-1.3B )                       |
+| SLM-SQL-1.3B                             | deepseek-coder-1.3b-instruct | SFT + GRPO   | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-1.3B )                            | [🤗 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-1.3B )                            |
+| SLM-SQL-Base-1B                          | Llama-3.2-1B-Instruct        | SFT          | [🤖 Modelscope](https://modelscope.cn/models/cycloneboy/SLM-SQL-Base-1B )                         | [🤗 HuggingFace](https://huggingface.co/cycloneboy/SLM-SQL-Base-1B )                         |
+
+## Dataset
+
+| **Dataset**                | Modelscope                                                                         | HuggingFace                                                                          |
+|----------------------------|------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------|
+| SynsQL-Think-916k          | [🤖 Modelscope](https://modelscope.cn/datasets/cycloneboy/SynsQL-Think-916k)       | [🤗 HuggingFace](https://huggingface.co/datasets/cycloneboy/SynsQL-Think-916k)       |
+| SynsQL-Merge-Think-310k    | [🤖 Modelscope](https://modelscope.cn/datasets/cycloneboy/SynsQL-Merge-Think-310k) | [🤗 HuggingFace](https://huggingface.co/datasets/cycloneboy/SynsQL-Merge-Think-310k) |
+| bird train and dev dataset | [🤖 Modelscope](https://modelscope.cn/datasets/cycloneboy/bird_train)              | [🤗 HuggingFace](https://huggingface.co/datasets/cycloneboy/bird_train)              |
+
+## TODO
+
+- [ ] Release inference code
+- [ ] Upload Model
+- [ ] Release training code
+- [ ] Fix bug
+- [ ] Update doc
+
+## Thanks to the following projects
+
+- [csc_sql](https://github.com/CycloneBoy/csc_sql)
+- [open-r1](https://github.com/huggingface/open-r1)
+- [OmniSQL](https://github.com/RUCKBReasoning/OmniSQL)
+
+## Citation
+
+```bibtex
+
+@misc{sheng2025slmsqlexplorationsmalllanguage,
+      title={SLM-SQL: An Exploration of Small Language Models for Text-to-SQL}, 
+      author={Lei Sheng and Shuai-Shuai Xu},
+      year={2025},
+      eprint={2507.22478},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2507.22478}, 
+}
+
+@misc{sheng2025cscsqlcorrectiveselfconsistencytexttosql,
+      title={CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning}, 
+      author={Lei Sheng and Shuai-Shuai Xu},
+      year={2025},
+      eprint={2505.13271},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2505.13271}, 
+}
+```