README.md

metadata

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

📖Paper | \ud83d\udcbbGitHub Repository | 🤗HuggingFace Collection | 🤖ModelScope |

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

How to use

You can use the model with the transformers library for Text-to-SQL tasks. Make sure you have transformers and torch installed.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "cycloneboy/SLM-SQL-0.5B" # Or any other SLM-SQL model from the collection
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Example for Text-to-SQL
db_schema = """
CREATE TABLE Employee (
    employee_id INTEGER PRIMARY KEY,
    name TEXT,
    department TEXT,
    salary INTEGER
);
CREATE TABLE Department (
    department_id INTEGER PRIMARY KEY,
    name TEXT,
    location TEXT
);
"""
question = "What are the names of employees in the 'Sales' department earning more than 50000?"
prompt = f"Given the database schema:
{db_schema}

Translate the following question to SQL: {question}"

messages = [
    {"role": "system", "content": "You are a helpful assistant that translates natural language questions into SQL queries."},
    {"role": "user", "content": prompt}
]

input_ids = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    input_ids,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)
response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
print(response)
# Expected output similar to: SELECT name FROM Employee WHERE department = 'Sales' AND salary > 50000

Main Results

Performance Comparison of different Text-to-SQL methods on BIRD dev and test dataset.

Model

Model	Base Model	Train Method	Modelscope	HuggingFace
SLM-SQL-Base-0.5B	Qwen2.5-Coder-0.5B-Instruct	SFT	\ud83e\udd16 Modelscope	\ud83e\udd17 HuggingFace
SLM-SQL-0.5B	Qwen2.5-Coder-0.5B-Instruct	SFT + GRPO	\ud83e\udd16 Modelscope	\ud83e\udd17 HuggingFace
CscSQL-Merge-Qwen2.5-Coder-0.5B-Instruct	Qwen2.5-Coder-0.5B-Instruct	SFT + GRPO	\ud83e\udd16 Modelscope	\ud83e\udd17 HuggingFace
SLM-SQL-Base-1.5B	Qwen2.5-Coder-1.5B-Instruct	SFT	\ud83e\udd16 Modelscope	\ud83e\udd17 HuggingFace
SLM-SQL-1.5B	Qwen2.5-Coder-1.5B-Instruct	SFT + GRPO	\ud83e\udd16 Modelscope	\ud83e\udd17 HuggingFace
CscSQL-Merge-Qwen2.5-Coder-1.5B-Instruct	Qwen2.5-Coder-1.5B-Instruct	SFT + GRPO	\ud83e\udd16 Modelscope	\ud83e\udd17 HuggingFace
SLM-SQL-Base-0.6B	Qwen3-0.6B	SFT	\ud83e\udd16 Modelscope	\ud83e\udd17 HuggingFace
SLM-SQL-0.6B	Qwen3-0.6B	SFT + GRPO	\ud83e\udd16 Modelscope	\ud83e\udd17 HuggingFace
SLM-SQL-Base-1.3B	deepseek-coder-1.3b-instruct	SFT	\ud83e\udd16 Modelscope	\ud83e\udd17 HuggingFace
SLM-SQL-1.3B	deepseek-coder-1.3b-instruct	SFT + GRPO	\ud83e\udd16 Modelscope	\ud83e\udd17 HuggingFace
SLM-SQL-Base-1B	Llama-3.2-1B-Instruct	SFT	\ud83e\udd16 Modelscope	\ud83e\udd17 HuggingFace

Dataset

Dataset	Modelscope	HuggingFace
SynsQL-Think-916k	\ud83e\udd16 Modelscope	\ud83e\udd17 HuggingFace
SynsQL-Merge-Think-310k	\ud83e\udd16 Modelscope	\ud83e\udd17 HuggingFace
bird train and dev dataset	\ud83e\udd16 Modelscope	\ud83e\udd17 HuggingFace

TODO

• Release inference code
• Upload Model
• Release training code
• Fix bug
• Update doc

Thanks to the following projects

• csc_sql
• open-r1
• OmniSQL

Citation

@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}, 
}