Instructions to use XGenerationLab/XiYanSQL-QwenCoder-32B-2412 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use XGenerationLab/XiYanSQL-QwenCoder-32B-2412 with Transformers:

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

pipe = pipeline("text-generation", model="XGenerationLab/XiYanSQL-QwenCoder-32B-2412")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("XGenerationLab/XiYanSQL-QwenCoder-32B-2412")
model = AutoModelForCausalLM.from_pretrained("XGenerationLab/XiYanSQL-QwenCoder-32B-2412")
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 XGenerationLab/XiYanSQL-QwenCoder-32B-2412 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XGenerationLab/XiYanSQL-QwenCoder-32B-2412"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XGenerationLab/XiYanSQL-QwenCoder-32B-2412",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/XGenerationLab/XiYanSQL-QwenCoder-32B-2412

SGLang

How to use XGenerationLab/XiYanSQL-QwenCoder-32B-2412 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 "XGenerationLab/XiYanSQL-QwenCoder-32B-2412" \
    --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": "XGenerationLab/XiYanSQL-QwenCoder-32B-2412",
		"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 "XGenerationLab/XiYanSQL-QwenCoder-32B-2412" \
        --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": "XGenerationLab/XiYanSQL-QwenCoder-32B-2412",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use XGenerationLab/XiYanSQL-QwenCoder-32B-2412 with Docker Model Runner:
```
docker model run hf.co/XGenerationLab/XiYanSQL-QwenCoder-32B-2412
```

XGenerationLab commited on Dec 4, 2025

Commit

5dbd355

verified ·

1 Parent(s): 9368557

Update README.md

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@@ -109,4 +109,15 @@ generated_ids = [
 response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 ```
 ## Acknowledgments
-If you find our work useful, please give us a citation or a like, so we can make a greater contribution to the open-source community!

 response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 ```
 ## Acknowledgments
+If you find our work useful, please give us a citation or a like, so we can make a greater contribution to the open-source community!
+```bibtex
+@article{XiYanSQL,
+      title={XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL},
+      author={Yifu Liu and Yin Zhu and Yingqi Gao and Zhiling Luo and Xiaoxia Li and Xiaorong Shi and Yuntao Hong and Jinyang Gao and Yu Li and Bolin Ding and Jingren Zhou},
+      year={2025},
+      eprint={2507.04701},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2507.04701},
+}
+```