Instructions to use AIJian/TrustSQL-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AIJian/TrustSQL-4B with Transformers:

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

pipe = pipeline("text-generation", model="AIJian/TrustSQL-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AIJian/TrustSQL-4B")
model = AutoModelForCausalLM.from_pretrained("AIJian/TrustSQL-4B", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AIJian/TrustSQL-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AIJian/TrustSQL-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AIJian/TrustSQL-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AIJian/TrustSQL-4B

SGLang

How to use AIJian/TrustSQL-4B 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 "AIJian/TrustSQL-4B" \
    --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": "AIJian/TrustSQL-4B",
		"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 "AIJian/TrustSQL-4B" \
        --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": "AIJian/TrustSQL-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AIJian/TrustSQL-4B with Docker Model Runner:
```
docker model run hf.co/AIJian/TrustSQL-4B
```

TrustSQL-4B / README.md

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	---
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- text-to-sql
	- sql
	- unknown-schema
	- tool-use
	- reinforcement-learning
	- qwen3
	base_model: Qwen/Qwen3-4B
	---

	# TRUST-SQL-4B

	[![arXiv](https://img.shields.io/badge/arXiv-2603.16448-b31b1b.svg)](https://arxiv.org/abs/2603.16448)
	[![GitHub](https://img.shields.io/badge/GitHub-TrustSQL-black?logo=github)](https://github.com/JaneEyre0530/TrustSQL)
	[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)

	## Overview

	TrustSQL-4B is a fine-tuned Text-to-SQL model based on [Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B), introduced in [TRUST-SQL: Tool-Integrated Multi-Turn Reinforcement Learning for Text-to-SQL over Unknown Schemas](https://arxiv.org/abs/2603.16448). The model is trained with multi-turn reinforcement learning and tool integration for Text-to-SQL over unknown database schemas.

	## Model details

	- Base model: `Qwen/Qwen3-4B`
	- Architecture: `Qwen3ForCausalLM`
	- Parameters: 4.0B
	- Hidden size: 2560
	- Layers: 36
	- Attention heads: 32 Q heads / 8 KV heads
	- Context length: 40,960 tokens
	- Precision: bfloat16

	## Models

	\| Model \| Base \| Link \|
	\|---\|---\|---\|
	\| TrustSQL-4B \| Qwen3-4B \| [AIJian/TrustSQL-4B](https://huggingface.co/AIJian/TrustSQL-4B) \|
	\| TrustSQL-8B \| Qwen3-8B \| [AIJian/TrustSQL-8B](https://huggingface.co/AIJian/TrustSQL-8B) \|

	## Training

	TrustSQL follows a two-stage training pipeline: SFT warm-up followed by Phase-Aware GRPO optimization. The interaction protocol is `Explore → Propose → Generate → Confirm`.

	## Reported results

	All results are reported under the Unknown Schema setting.

	\| Benchmark \| Greedy \| Majority voting \|
	\|---\|---:\|---:\|
	\| BIRD-Dev \| 64.9 \| 67.2 \|
	\| Spider-Test \| 82.8 \| 85.0 \|
	\| Spider-DK \| 71.6 \| 73.8 \|
	\| Spider-Syn \| 74.7 \| 77.3 \|
	\| Spider-Realistic \| 79.9 \| 82.5 \|

	## Recommended inference setup

	This model is designed for an orchestrator that exposes:

	1. A schema exploration tool for tables, columns, keys, and value inspection.
	2. A schema proposal channel that records verified tables and columns.
	3. A SQL execution tool for candidate queries.
	4. A final answer channel for the confirmed SQL.

	Do not provide fabricated schema descriptions as if they were tool observations. The model is intended to ground schema decisions in the environment feedback.

	## Loading

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "AIJian/TrustSQL-4B"

	tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	torch_dtype="auto",
	device_map="auto",
	trust_remote_code=True,
	)
	```

	For prompts, tool schemas, evaluation scripts, and training details, see `https://github.com/JaneEyre0530/TrustSQL`.

	## Limitations

	This checkpoint was trained and evaluated with SQLite-based benchmarks. Its behavior depends on a live, correctly configured tool environment and a finite interaction budget. Validate generated SQL before using it in any sensitive or write-enabled database.

	## Citation

	```bibtex
	@article{jian2026trustsql,
	title = {TRUST-SQL: Tool-Integrated Multi-Turn Reinforcement Learning for Text-to-SQL over Unknown Schemas},
	author = {Jian, Ai and Zhang, Xiaoyun and Du, Wanrou and Ruan, Jingqing and Pei, Jiangbo and Zhang, Weipeng and Zeng, Ke and Cai, Xunliang},
	journal = {arXiv preprint arXiv:2603.16448},
	year = {2026}
	}
	```

	## License

	This project is licensed under the Apache 2.0 License. See the `LICENSE` file for details.