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, AutoModelForCausalLM

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

84ceb25

verified ·

1 Parent(s): fb1e887

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -19,7 +19,7 @@ arxiv: 2512.17053
 # Struct-SQL-8B: Knowledge Distillation with Structured Chain-of-Thought
-**Struct-SQL** is a specialized Text-to-SQL model based on **Qwen3-4B-Instruct**. It was trained using a novel Knowledge Distillation (KD) framework that transfers **structured reasoning** (Query Execution Plans) from a state-of-the-art teacher LLM (GPT-4o) to a smaller student model.
 Unlike standard distillation methods that rely on unstructured Chain-of-Thought (CoT), Struct-SQL learns to generate a formal, logical blueprint (a query plan) before generating the final SQL. This approach significantly reduces syntactic errors and schema hallucinations.

 # Struct-SQL-8B: Knowledge Distillation with Structured Chain-of-Thought
+**Struct-SQL** is a specialized Text-to-SQL model based on [**Qwen3-4B-Instruct-2507**](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507). It was trained using a novel Knowledge Distillation (KD) framework that transfers **structured reasoning** (Query Execution Plans) from a state-of-the-art teacher LLM (GPT-4o) to a smaller student model.
 Unlike standard distillation methods that rely on unstructured Chain-of-Thought (CoT), Struct-SQL learns to generate a formal, logical blueprint (a query plan) before generating the final SQL. This approach significantly reduces syntactic errors and schema hallucinations.