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---
library_name: transformers
license: cc-by-nc-4.0
pipeline_tag: text-generation
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) | 💾[GitHub](https://github.com/CycloneBoy/slm_sql) |
🤗[HuggingFace Collection](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">
## Sample Usage
You can use the model with the `transformers` library. Here's an example:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Load the tokenizer and model (e.g., SLM-SQL-1.5B)
model_name = "cycloneboy/SLM-SQL-1.5B" # Adjust this to the specific model you want to use
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
# Define the input prompt (natural language question for SQL)
prompt = "what are the names of all employees?"
# Prepare the input for the model
input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.device)
# Generate the SQL query
output_ids = model.generate(input_ids, max_new_tokens=100, num_beams=1, do_sample=False)
generated_sql = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print("Generated SQL Query:")
print(generated_sql)
```
## 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},
}
``` |