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--- |
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library_name: transformers |
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license: cc-by-nc-4.0 |
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pipeline_tag: text-generation |
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tags: |
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- text-to-sql |
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- reinforcement-learning |
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--- |
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# SLM-SQL: An Exploration of Small Language Models for Text-to-SQL |
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### Important Links |
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📖[Arxiv Paper](https://arxiv.org/abs/2507.22478) | 🤗[Hugging Face Paper](https://huggingface.co/papers/2507.22478) | 🐙[GitHub Repository](https://github.com/CycloneBoy/slm_sql) | 🤗[HuggingFace Collection](https://huggingface.co/collections/cycloneboy/slm-sql-688b02f99f958d7a417658dc) | 🤖[ModelScope Collection](https://modelscope.cn/collections/SLM-SQL-624bb6a60e9643) | |
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## News |
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+ `July 31, 2025`: Upload model to modelscope and huggingface. |
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+ `July 30, 2025`: Publish the paper to arxiv |
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## Abstract |
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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. |
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### Framework |
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<img src="https://raw.githubusercontent.com/CycloneBoy/slm_sql/main/data/image/slmsql_framework.png" height="500" alt="slmsql_framework"> |
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### Main Results |
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<img src="https://raw.githubusercontent.com/CycloneBoy/slm_sql/main/data/image/slmsql_bird_result.png" height="500" alt="slm_sql_result"> |
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<img src="https://raw.githubusercontent.com/CycloneBoy/slm_sql/main/data/image/slmsql_bird_main.png" height="500" alt="slmsql_bird_main"> |
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<img src="https://raw.githubusercontent.com/CycloneBoy/slm_sql/main/data/image/slmsql_spider_main.png" height="500" alt="slmsql_spider_main"> |
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Performance Comparison of different Text-to-SQL methods on BIRD dev and test dataset. |
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<img src="https://raw.githubusercontent.com/CycloneBoy/slm_sql/main/data/image/slmsql_ablation_study.png" height="300" alt="slmsql_ablation_study"> |
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## How to Use |
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You can easily use this model with the Hugging Face `transformers` library. Below is a general example for inference: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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# Load the model and tokenizer |
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model_name = "cycloneboy/SLM-SQL-1.5B" # Example: You can choose other models from the table below |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype=torch.bfloat16, # or torch.float16, adjust based on your GPU |
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device_map="auto" # Automatically map model to available devices |
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) |
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model.eval() |
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# Example prompt for Text-to-SQL |
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# Replace this with your natural language query for a specific database schema |
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prompt = """ |
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[Instruction]: Given the following database schema, generate a SQL query that answers the question. |
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[Schema]: |
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CREATE TABLE Student (StuID INT, Name TEXT, Age INT, Sex TEXT, Major TEXT, Advisor INT, Graduated BOOL); |
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CREATE TABLE Course (CrsID INT, Title TEXT, Dept TEXT, Credits INT); |
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CREATE TABLE Enrollment (StuID INT, CrsID INT, Grade REAL); |
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CREATE TABLE Advisor (AdvID INT, Name TEXT, Dept TEXT); |
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[Question]: What is the average age of students who are taking 'Database' course? |
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""" |
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
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# Generate SQL query |
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outputs = model.generate( |
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**inputs, |
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max_new_tokens=256, |
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num_beams=1, # Adjust for different decoding strategies |
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do_sample=False, |
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temperature=0.0, |
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top_p=1.0, |
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eos_token_id=tokenizer.eos_token_id |
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) |
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generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(generated_text) |
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# The output will contain the prompt and the generated SQL. |
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# You might need to parse the generated_text to extract only the SQL query. |
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``` |
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## Model |
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| **Model** | Base Model | Train Method | Modelscope | HuggingFace | |
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|------------------------------------------|------------------------------|--------------|---------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------| |
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| 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) | |
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| 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) | |
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| 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) | |
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| 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) | |
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| 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) | |
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| 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) | |
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| 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) | |
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| 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) | |
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| 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 ) | |
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| 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 ) | |
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| 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 ) | |
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## Dataset |
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| **Dataset** | Modelscope | HuggingFace | |
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|----------------------------|------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------| |
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| SynsQL-Think-916k | [🤖 Modelscope](https://modelscope.cn/datasets/cycloneboy/SynsQL-Think-916k) | [🤗 HuggingFace](https://huggingface.co/datasets/cycloneboy/SynsQL-Think-916k) | |
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| SynsQL-Merge-Think-310k | [🤖 Modelscope](https://modelscope.cn/datasets/cycloneboy/SynsQL-Merge-Think-310k) | [🤗 HuggingFace](https://huggingface.co/datasets/cycloneboy/SynsQL-Merge-Think-310k) | |
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| bird train and dev dataset | [🤖 Modelscope](https://modelscope.cn/datasets/cycloneboy/bird_train) | [🤗 HuggingFace](https://huggingface.co/datasets/cycloneboy/bird_train) | |
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## TODO |
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- [ ] Release inference code |
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- [ ] Upload Model |
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- [ ] Release training code |
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- [ ] Fix bug |
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- [ ] Update doc |
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## Thanks to the following projects |
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- [csc_sql](https://github.com/CycloneBoy/csc_sql) |
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- [open-r1](https://github.com/huggingface/open-r1) |
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- [OmniSQL](https://github.com/RUCKBReasoning/OmniSQL) |
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## Citation |
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```bibtex |
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@misc{sheng2025slmsqlexplorationsmalllanguage, |
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title={SLM-SQL: An Exploration of Small Language Models for Text-to-SQL}, |
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author={Lei Sheng and Shuai-Shuai Xu}, |
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year={2025}, |
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eprint={2507.22478}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2507.22478}, |
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} |
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@misc{sheng2025cscsqlcorrectiveselfconsistencytexttosql, |
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title={CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning}, |
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author={Lei Sheng and Shuai-Shuai Xu}, |
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year={2025}, |
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eprint={2505.13271}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL}, |
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url={https://arxiv.org/abs/2505.13271}, |
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} |
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``` |
