--- 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) | 🤗[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) | ## News + `July 31, 2025`: Upload model to modelscope and huggingface. + `July 30, 2025`: Publish the paper to arxiv ## Abstract 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. ### Framework slmsql_framework ### Main Results slm_sql_result slmsql_bird_main slmsql_spider_main Performance Comparison of different Text-to-SQL methods on BIRD dev and test dataset. slmsql_ablation_study ## How to Use You can easily use this model with the Hugging Face `transformers` library. Below is a general example for inference: ```python from transformers import AutoModelForCausalLM, AutoTokenizer import torch # Load the model and tokenizer model_name = "cycloneboy/SLM-SQL-1.5B" # Example: You can choose other models from the table below tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=torch.bfloat16, # or torch.float16, adjust based on your GPU device_map="auto" # Automatically map model to available devices ) model.eval() # Example prompt for Text-to-SQL # Replace this with your natural language query for a specific database schema prompt = """ [Instruction]: Given the following database schema, generate a SQL query that answers the question. [Schema]: CREATE TABLE Student (StuID INT, Name TEXT, Age INT, Sex TEXT, Major TEXT, Advisor INT, Graduated BOOL); CREATE TABLE Course (CrsID INT, Title TEXT, Dept TEXT, Credits INT); CREATE TABLE Enrollment (StuID INT, CrsID INT, Grade REAL); CREATE TABLE Advisor (AdvID INT, Name TEXT, Dept TEXT); [Question]: What is the average age of students who are taking 'Database' course? """ inputs = tokenizer(prompt, return_tensors="pt").to(model.device) # Generate SQL query outputs = model.generate( **inputs, max_new_tokens=256, num_beams=1, # Adjust for different decoding strategies do_sample=False, temperature=0.0, top_p=1.0, eos_token_id=tokenizer.eos_token_id ) generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) print(generated_text) # The output will contain the prompt and the generated SQL. # You might need to parse the generated_text to extract only the SQL query. ``` ## 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}, } ```