Instructions to use cycloneboy/CscSQL-Merge-Qwen2.5-Coder-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cycloneboy/CscSQL-Merge-Qwen2.5-Coder-7B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="cycloneboy/CscSQL-Merge-Qwen2.5-Coder-7B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("cycloneboy/CscSQL-Merge-Qwen2.5-Coder-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("cycloneboy/CscSQL-Merge-Qwen2.5-Coder-7B-Instruct", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use cycloneboy/CscSQL-Merge-Qwen2.5-Coder-7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cycloneboy/CscSQL-Merge-Qwen2.5-Coder-7B-Instruct"
# 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/CscSQL-Merge-Qwen2.5-Coder-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cycloneboy/CscSQL-Merge-Qwen2.5-Coder-7B-Instruct

SGLang

How to use cycloneboy/CscSQL-Merge-Qwen2.5-Coder-7B-Instruct 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/CscSQL-Merge-Qwen2.5-Coder-7B-Instruct" \
    --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/CscSQL-Merge-Qwen2.5-Coder-7B-Instruct",
		"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/CscSQL-Merge-Qwen2.5-Coder-7B-Instruct" \
        --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/CscSQL-Merge-Qwen2.5-Coder-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cycloneboy/CscSQL-Merge-Qwen2.5-Coder-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/cycloneboy/CscSQL-Merge-Qwen2.5-Coder-7B-Instruct
```

CscSQL-Merge-Qwen2.5-Coder-7B-Instruct / README.md

cycloneboy

Add comprehensive model card for CSC-SQL model (#1)

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	---
	pipeline_tag: text-generation
	library_name: transformers
	license: cc-by-nc-4.0
	tags:
	- text-to-sql
	- reinforcement-learning
	- qwen
	---

	# CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning

	This repository contains the `CscSQL-Grpo-Qwen2.5-Coder-7B-Instruct` model, a key component of the CSC-SQL framework, as presented in the paper [CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning](https://huggingface.co/papers/2505.13271).

	## Abstract
	Large language models (LLMs) have demonstrated strong capabilities in translating natural language questions about relational databases into SQL queries. In particular, test-time scaling techniques such as Self-Consistency and Self-Correction can enhance SQL generation accuracy by increasing computational effort during inference. However, these methods have notable limitations: Self-Consistency may select suboptimal outputs despite majority votes, while Self-Correction typically addresses only syntactic errors. To leverage the strengths of both approaches, we propose CSC-SQL, a novel method that integrates Self-Consistency and Self-Correction. CSC-SQL selects the two most frequently occurring outputs from parallel sampling and feeds them into a merge revision model for correction. Additionally, we employ the Group Relative Policy Optimization (GRPO) algorithm to fine-tune both the SQL generation and revision models via reinforcement learning, significantly enhancing output quality. Experimental results confirm the effectiveness and generalizability of CSC-SQL. On the BIRD private test set, our 7B model achieves 71.72% execution accuracy, while the 32B model achieves 73.67%.

	For more details, refer to the [paper](https://huggingface.co/papers/2505.13271) and the [official GitHub repository](https://github.com/CycloneBoy/csc_sql).

	## Framework Overview
	![CSC-SQL Framework](https://raw.githubusercontent.com/CycloneBoy/csc_sql/main/data/image/csc_sql_framework.png)

	## Code
	The official code repository for CSC-SQL is available on GitHub: [https://github.com/CycloneBoy/csc_sql](https://github.com/CycloneBoy/csc_sql)

	## Main Results
	Performance comparison of different Text-to-SQL methods on BIRD dev and test dataset:
	![CSC-SQL Results](https://raw.githubusercontent.com/CycloneBoy/csc_sql/main/data/image/csc_sql_result_main.png)
	<img src="https://raw.githubusercontent.com/CycloneBoy/csc_sql/main/data/image/csc_sql_result_main.png" height="500" alt="csc_sql_result main">

	## Model Checkpoints
	This model is part of a collection of checkpoints related to CSC-SQL, also available on Hugging Face:

	\| Model \| HuggingFace \|
	\|-------------------------------\|--------------------------------------------------------------------------------------------\|
	\| CscSQL-Merge-Qwen2.5-Coder-3B-Instruct \| [🤗 HuggingFace](https://huggingface.co/cycloneboy/CscSQL-Merge-Qwen2.5-Coder-3B-Instruct) \|
	\| CscSQL-Merge-Qwen2.5-Coder-7B-Instruct \| [🤗 HuggingFace](https://huggingface.co/cycloneboy/CscSQL-Merge-Qwen2.5-Coder-7B-Instruct) \|
	\| CscSQL-Grpo-Qwen2.5-Coder-3B-Instruct \| [🤗 HuggingFace](https://huggingface.co/cycloneboy/CscSQL-Grpo-Qwen2.5-Coder-3B-Instruct) \|
	\| CscSQL-Grpo-XiYanSQL-QwenCoder-3B-2502 \| [🤗 HuggingFace](https://huggingface.co/cycloneboy/CscSQL-Grpo-XiYanSQL-QwenCoder-3B-2502) \|
	\| CscSQL-Grpo-Qwen2.5-Coder-7B-Instruct \| [🤗 HuggingFace](https://huggingface.co/cycloneboy/CscSQL-Grpo-Qwen2.5-Coder-7B-Instruct) \|
	\| CscSQL-Grpo-XiYanSQL-QwenCoder-7B-2502 \| [🤗 HuggingFace](https://huggingface.co/cycloneboy/CscSQL-Grpo-XiYanSQL-QwenCoder-7B-2502) \|

	## Usage
	You can load this model using the `transformers` library. Here's a basic example for inference:

	```python
	import torch
	from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig

	model_name = "cycloneboy/CscSQL-Grpo-Qwen2.5-Coder-7B-Instruct"

	# Load model and tokenizer
	tokenizer = AutoTokenizer.from_pretrained(model_name)
	model = AutoModelForCausalLM.from_pretrained(
	model_name,
	torch_dtype=torch.bfloat16, # Or torch.float16 depending on your hardware
	device_map="auto"
	)
	model.eval()

	# Example prompt for text-to-SQL generation
	# Note: The prompt format might need to align with the model's specific training
	# and database schema format for optimal text-to-SQL performance.
	prompt = "Translate the following question to SQL: 'What are the names of all employees?'"

	# Encode the prompt
	input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.device)

	# Set generation configuration based on the model's generation_config.json
	generation_config = GenerationConfig(
	bos_token_id=tokenizer.bos_token_id,
	eos_token_id=[tokenizer.eos_token_id, 151643], # Include <\|endoftext\|> as eos_token_id
	pad_token_id=tokenizer.bos_token_id, # Or use tokenizer.pad_token_id if different
	temperature=0.7,
	max_new_tokens=512,
	do_sample=True,
	top_p=0.8,
	repetition_penalty=1.1,
	top_k=20,
	)

	# Generate SQL query
	output_ids = model.generate(
	input_ids,
	generation_config=generation_config
	)

	# Decode the generated SQL
	generated_sql = tokenizer.decode(output_ids[0], skip_special_tokens=True)
	print(generated_sql)

	# For detailed usage, including how to integrate with the full CSC-SQL framework
	# for improved accuracy via reinforcement learning, please refer to the
	# official GitHub repository: https://github.com/CycloneBoy/csc_sql
	```

	## Citation
	If you find this work helpful or inspiring, please feel free to cite it:

	```bibtex
	@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},
	}
	```