Instructions to use simone-papicchio/Think2SQL-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use simone-papicchio/Think2SQL-7B with Transformers:

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

pipe = pipeline("text-generation", model="simone-papicchio/Think2SQL-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("simone-papicchio/Think2SQL-7B")
model = AutoModelForCausalLM.from_pretrained("simone-papicchio/Think2SQL-7B")
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

vLLM

How to use simone-papicchio/Think2SQL-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "simone-papicchio/Think2SQL-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "simone-papicchio/Think2SQL-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/simone-papicchio/Think2SQL-7B

SGLang

How to use simone-papicchio/Think2SQL-7B 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 "simone-papicchio/Think2SQL-7B" \
    --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": "simone-papicchio/Think2SQL-7B",
		"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 "simone-papicchio/Think2SQL-7B" \
        --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": "simone-papicchio/Think2SQL-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use simone-papicchio/Think2SQL-7B with Docker Model Runner:
```
docker model run hf.co/simone-papicchio/Think2SQL-7B
```

simone-papicchio commited on Apr 25, 2025

Commit

1aa7668

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+---
+license: apache-2.0
+base_model:
+- Qwen/Qwen2.5-Coder-7B-Instruct
+---
+## Model Information
+This model is the reasoning model for Text2SQL task introduced in [Think2SQL: Reinforce LLM Reasoning Capabilities for Text2SQL](https://arxiv.org/abs/2504.15077)
+## Intended use
+The best model performance are given with its System and User prompt.
+The model is intended to use with three input: question, evidence and the database schema.
+Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
+Make sure to update your transformers installation via `pip install --upgrade transformers`.
+```python
+import transformers
+import torch
+model_id = "simone-papicchio/Think2SQL-7B"
+pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.bfloat16},
+    device_map="auto",
+)
+system_message = (
+    "You are a helpful AI Assistant that provides well-reasoned and detailed responses. "
+    "You first think about the reasoning process as an internal monologue and then provide the user with the answer. "
+    "Respond in the following format: <think>\n...\n</think>\n<answer>\n...\n</answer>"
+).strip()
+user_message = (
+    "Answer the following question with the SQL code. Use the piece of evidence and base your answer on the database schema. "
+    "Given the question, the evidence and the database schema, return in the <answer> tags only the SQL script that addresses the question.\n"
+    "Question:\n{question}\n\n"
+    "Evidence:\n{evidence}\n\n"
+    "Database Schema:\n{schema}\n\n"
+    "Return only the SQL script enclosed in <answer> tags."
+).strip()
+messages = [
+    {"role": "system", "content": system_message},
+    {"role": "user", "content": user_message},
+]
+outputs = pipeline(
+    messages,
+    max_new_tokens=256,
+)
+print(outputs[0]["generated_text"][-1])
+```
+## Citation
+```bitex
+@misc{papicchio2025think2sqlreinforcellmreasoning,
+      title={Think2SQL: Reinforce LLM Reasoning Capabilities for Text2SQL},
+      author={Simone Papicchio and Simone Rossi and Luca Cagliero and Paolo Papotti},
+      year={2025},
+      eprint={2504.15077},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2504.15077},
+}
+```