Instructions to use craterlabs/Struct-SQL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use craterlabs/Struct-SQL with Transformers:

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

pipe = pipeline("text-generation", model="craterlabs/Struct-SQL")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("craterlabs/Struct-SQL")
model = AutoModelForMultimodalLM.from_pretrained("craterlabs/Struct-SQL")
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 Settings

vLLM

How to use craterlabs/Struct-SQL with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/craterlabs/Struct-SQL

SGLang

How to use craterlabs/Struct-SQL 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 "craterlabs/Struct-SQL" \
    --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": "craterlabs/Struct-SQL",
		"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 "craterlabs/Struct-SQL" \
        --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": "craterlabs/Struct-SQL",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use craterlabs/Struct-SQL with Docker Model Runner:
```
docker model run hf.co/craterlabs/Struct-SQL
```

KhushbooThaker commited on Jan 15

Commit

dec4134

verified ·

1 Parent(s): 3267dff

Update README.md

Browse files

Files changed (1) hide show

README.md +24 -3

README.md CHANGED Viewed

@@ -64,15 +64,36 @@ model = AutoModelForCausalLM.from_pretrained(
 inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
 outputs = model.generate(**inputs, max_new_tokens=1200)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-# Citation
-### If you use this model or method in your research, please cite our paper:
 @article{thaker2025knowledge,
   title={Knowledge Distillation with Structured Chain-of-Thought for Text-to-SQL},
   author={Thaker, Khushboo and Bresler, Yony},
   journal={arXiv preprint arXiv:2512.17053},
   year={2025}
-}

 inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
 outputs = model.generate(**inputs, max_new_tokens=1200)
 print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+## Intended Use
+Struct-SQL-4B is intended for **research and academic use** in tasks involving **Text-to-SQL generation** and **semantic parsing over relational databases**. The model is particularly suited for studying:
+- Knowledge distillation techniques that leverage **structured intermediate representations**
+- Explicit **query planning** as an alternative to unstructured chain-of-thought reasoning
+- Error reduction in SQL generation, including syntactic validity and schema grounding
+- Compact language models for complex reasoning under limited parameter budgets
+The model is not optimized for direct deployment in production database systems without additional validation and safety constraints.
+---
+## Limitations
+- Evaluation is confined to the SQLite-based BIRD benchmark
+- The model may generate logically plausible but incorrect SQL for highly complex multi-hop queries
+---
+## Citation
+```bibtex
 @article{thaker2025knowledge,
   title={Knowledge Distillation with Structured Chain-of-Thought for Text-to-SQL},
   author={Thaker, Khushboo and Bresler, Yony},
   journal={arXiv preprint arXiv:2512.17053},
   year={2025}
+}