Instructions to use AIJian/TrustSQL-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AIJian/TrustSQL-8B with Transformers:

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

pipe = pipeline("text-generation", model="AIJian/TrustSQL-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AIJian/TrustSQL-8B")
model = AutoModelForCausalLM.from_pretrained("AIJian/TrustSQL-8B", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AIJian/TrustSQL-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AIJian/TrustSQL-8B"
# 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-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AIJian/TrustSQL-8B

SGLang

How to use AIJian/TrustSQL-8B 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-8B" \
    --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-8B",
		"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-8B" \
        --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-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AIJian/TrustSQL-8B with Docker Model Runner:
```
docker model run hf.co/AIJian/TrustSQL-8B
```

TRUST-SQL-8B

Overview

TrustSQL-8B is a fine-tuned Text-to-SQL model based on Qwen3-8B, introduced in TRUST-SQL: Tool-Integrated Multi-Turn Reinforcement Learning for Text-to-SQL over Unknown Schemas. 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/Qwen3-8B
Architecture: Qwen3ForCausalLM
Parameters: 8.2B
Hidden size: 4096
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
TrustSQL-8B	Qwen3-8B	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	65.8	67.7
Spider-Test	83.9	86.5
Spider-DK	72.1	75.7
Spider-Syn	75.4	77.4
Spider-Realistic	82.1	84.1

The model reaches 65.8 EX on BIRD-Dev with greedy decoding and 67.7 EX with majority voting.

Recommended inference setup

This model is intended to run inside a tool-using agent loop:

Explore database metadata and inspect relevant values.
Propose only tables and columns verified by tool output.
Generate and execute a candidate SQL query.
Confirm the final answer, or return to exploration when evidence is insufficient.

The explicit proposal checkpoint is important: it helps prevent schema hallucination and provides a boundary for the phase-aware training objective.

Loading

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "AIJian/TrustSQL-8B"

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. Inference requires a live and safe metadata/execution environment. Always validate generated SQL and use read-only permissions unless write access is explicitly required and controlled.

Citation

@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}
}