Instructions to use cycloneboy/SLM-SQL-Base-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cycloneboy/SLM-SQL-Base-0.6B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="cycloneboy/SLM-SQL-Base-0.6B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cycloneboy/SLM-SQL-Base-0.6B")
model = AutoModelForCausalLM.from_pretrained("cycloneboy/SLM-SQL-Base-0.6B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use cycloneboy/SLM-SQL-Base-0.6B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cycloneboy/SLM-SQL-Base-0.6B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cycloneboy/SLM-SQL-Base-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cycloneboy/SLM-SQL-Base-0.6B

SGLang

How to use cycloneboy/SLM-SQL-Base-0.6B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "cycloneboy/SLM-SQL-Base-0.6B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cycloneboy/SLM-SQL-Base-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "cycloneboy/SLM-SQL-Base-0.6B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cycloneboy/SLM-SQL-Base-0.6B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cycloneboy/SLM-SQL-Base-0.6B with Docker Model Runner:
```
docker model run hf.co/cycloneboy/SLM-SQL-Base-0.6B
```

SLM-SQL-Base-0.6B / README.md

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	---
	pipeline_tag: text-generation
	library_name: transformers
	license: cc-by-nc-4.0
	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) \|
	🤗[HuggingFace](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">

	## 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},
	}
	```