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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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📖[Hugging Face Paper](https://huggingface.co/papers/2507.22478) | |
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📚[arXiv Paper](https://arxiv.org/abs/2507.22478) | |
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💻[GitHub Repository](https://github.com/CycloneBoy/slm_sql) | |
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🤗[Hugging Face Models Collection](https://huggingface.co/collections/cycloneboy/slm-sql-688b02f99f958d7a417658dc) | |
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🤖[ModelScope Models 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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## Introduction |
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> Large language models (LLMs) have demonstrated strong performance in translating natural language questions into SQL |
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> queries (Text-to-SQL). In contrast, small language models (SLMs) ranging from 0.5B to 1.5B parameters currently |
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> underperform on Text-to-SQL tasks due to their limited logical reasoning capabilities. However, SLMs offer inherent |
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> advantages in inference speed and suitability for edge deployment. To explore their potential in Text-to-SQL |
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> applications, we leverage recent advancements in post-training techniques. Specifically, we used the open-source |
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> SynSQL-2.5M dataset to construct two derived datasets: SynSQL-Think-916K for SQL generation and |
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> SynSQL-Merge-Think-310K |
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> for SQL merge revision. We then applied supervised fine-tuning and reinforcement learning-based post-training to the |
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> SLM, followed by inference using a corrective self-consistency approach. Experimental results validate the |
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> effectiveness |
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> and generalizability of our method, SLM-SQL. On the BIRD development set, the five evaluated models achieved an |
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> average |
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> improvement of 31.4 points. Notably, the 0.5B model reached 56.87\% execution accuracy (EX), while the 1.5B model |
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> 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="slm_sql_ablation_study"> |
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## Usage |
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This model can be used with the Hugging Face `transformers` library for text-to-SQL generation. |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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# Load model and tokenizer |
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# Replace "cycloneboy/SLM-SQL-0.5B" with the specific model checkpoint you want to use. |
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model_id = "cycloneboy/SLM-SQL-0.5B" |
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True) |
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# Set the model to evaluation mode |
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model.eval() |
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# Define the natural language question and database schema (replace with your data) |
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user_query = "What are the names of all employees who earn more than 50000?" |
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database_schema = """ |
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CREATE TABLE employees ( |
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employee_id INT PRIMARY KEY, |
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name VARCHAR(255), |
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salary DECIMAL(10, 2) |
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); |
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""" |
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# Construct the conversation using the model's chat template |
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# The model expects schema and question to generate the SQL query. |
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# The prompt format below is a common way to combine schema and question for Text-to-SQL. |
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full_prompt = f""" |
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You are a Text-to-SQL model. |
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Given the following database schema: |
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{database_schema} |
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Generate the SQL query for the question: |
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{user_query} |
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""" |
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messages = [ |
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{"role": "user", "content": full_prompt.strip()} |
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] |
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# Apply the chat template and tokenize inputs |
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input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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inputs = tokenizer(input_text, return_tensors="pt").to(model.device) |
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# Generate the SQL query |
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with torch.no_grad(): |
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outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.6, top_p=0.9, do_sample=True, |
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eos_token_id=[tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|im_end|>")]) |
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# Decode the generated text and extract the assistant's response |
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generated_text = tokenizer.decode(outputs[0], skip_special_tokens=False) |
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# The Qwen-style chat template wraps assistant's response between <|im_start|>assistant |
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and <|im_end|> |
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assistant_prefix = "<|im_start|>assistant\ |
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" |
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if assistant_prefix in generated_text: |
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sql_query = generated_text.split(assistant_prefix, 1)[1].strip() |
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# Remove any trailing special tokens like <|im_end|> |
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sql_query = sql_query.split("<|im_end|>", 1)[0].strip() |
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else: |
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sql_query = generated_text # Fallback in case prompt format differs unexpectedly |
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print(f"User Query: {user_query} |
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Generated SQL: {sql_query}") |
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# Example of a potential output for the given query and schema: |
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# Generated SQL: SELECT name FROM employees WHERE salary > 50000; |
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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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} |
