Instructions to use UCSC-VLAA/STAR1-R1-Distill-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use UCSC-VLAA/STAR1-R1-Distill-7B with Transformers:

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

pipe = pipeline("text-generation", model="UCSC-VLAA/STAR1-R1-Distill-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("UCSC-VLAA/STAR1-R1-Distill-7B")
model = AutoModelForCausalLM.from_pretrained("UCSC-VLAA/STAR1-R1-Distill-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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use UCSC-VLAA/STAR1-R1-Distill-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "UCSC-VLAA/STAR1-R1-Distill-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": "UCSC-VLAA/STAR1-R1-Distill-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/UCSC-VLAA/STAR1-R1-Distill-7B

SGLang

How to use UCSC-VLAA/STAR1-R1-Distill-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 "UCSC-VLAA/STAR1-R1-Distill-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": "UCSC-VLAA/STAR1-R1-Distill-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 "UCSC-VLAA/STAR1-R1-Distill-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": "UCSC-VLAA/STAR1-R1-Distill-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use UCSC-VLAA/STAR1-R1-Distill-7B with Docker Model Runner:
```
docker model run hf.co/UCSC-VLAA/STAR1-R1-Distill-7B
```

STAR1-R1-Distill-7B

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---
library_name: transformers
license: apache-2.0
datasets:
- UCSC-VLAA/STAR-1
base_model:
- deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
---

# 🌟 STAR-1: Safer Alignment of Reasoning LLMs with 1K Data

<p align="center">
📃 <a href="https://arxiv.org/abs/2504.01903" target="_blank">Paper</a> ｜🤗 <a href="https://huggingface.co/datasets/UCSC-VLAA/STAR-1" target="_blank">STAR-1 Data</a> | 🤗 <a href="https://huggingface.co/collections/UCSC-VLAA/star-1-67edda2a042e8ba3e955e522" target="_blank">STAR-1 Model</a> |  📚 <a href="https://ucsc-vlaa.github.io/STAR-1/" target="_blank">Project Page</a>
</p>

## Introduction
[**STAR-1**](https://huggingface.co/datasets/UCSC-VLAA/STAR-1) is a high-quality safety dataset designed to enhance safety alignment in large reasoning models (LRMs) like DeepSeek-R1.

- Built on the principles of diversity, deliberative reasoning, and rigorous filtering, STAR-1 integrates and refines data from multiple sources to provide policy-grounded reasoning samples.
- The dataset contains **1,000** carefully selected examples, each aligned with best safety practices through GPT-4o-based evaluation.
- Fine-tuning with STAR-1 leads to significant safety improvements across multiple benchmarks, with minimal impact on reasoning capabilities.

We open-sourced our [STAR1-R1-Distill-7B](https://huggingface.co/UCSC-VLAA/STAR1-R1-Distill-7B) model here, which is fine-tuned on [STAR-1](https://huggingface.co/datasets/UCSC-VLAA/STAR-1) dataset.

## Artifacts
### Data

| Dataset    | Num. of Sample | URL                                                                 |
|------------|----------------|----------------------------------------------------------------------|
| STAR-1     | 1K             | 🤗 [UCSC-VLAA/STAR-1](https://huggingface.co/datasets/UCSC-VLAA/STAR-1) |
| STAR 41K   | 41K            | 🤗 [UCSC-VLAA/STAR-41K](https://huggingface.co/datasets/UCSC-VLAA/STAR-41K) |
| STAR-benign-915   | 915            | 🤗 [UCSC-VLAA/STAR-benign-915](https://huggingface.co/datasets/UCSC-VLAA/STAR-benign-915) |



### Model
| Model                          | Type                                      | URL                                                                                   |
|--------------------------------|-------------------------------------------|----------------------------------------------------------------------------------------|
| `STAR1`-R1-Distill-1.5B        | R1-Distill-Qwen-1.5B trained on STAR-1    | 🤗 [UCSC-VLAA/STAR1-R1-Distill-1.5B](https://huggingface.co/UCSC-VLAA/STAR1-R1-Distill-1.5B) |
| `STAR1`-R1-Distill-7B          | R1-Distill-Qwen-7B trained on STAR-1      | 🤗 [UCSC-VLAA/STAR1-R1-Distill-7B](https://huggingface.co/UCSC-VLAA/STAR1-R1-Distill-7B)     |
| `STAR1`-R1-Distill-8B          | R1-Distill-Llama-8B trained on STAR-1     | 🤗 [UCSC-VLAA/STAR1-R1-Distill-8B](https://huggingface.co/UCSC-VLAA/STAR1-R1-Distill-8B)     |
| `STAR1`-R1-Distill-14B         | R1-Distill-Qwen-14B trained on STAR-1     | 🤗 [UCSC-VLAA/STAR1-R1-Distill-14B](https://huggingface.co/UCSC-VLAA/STAR1-R1-Distill-14B)   |
| `STAR1`-R1-Distill-32B         | R1-Distill-Qwen-32B trained on STAR-1     | 🤗 [UCSC-VLAA/STAR1-R1-Distill-32B](https://huggingface.co/UCSC-VLAA/STAR1-R1-Distill-32B)   |

## Evaluation
See our github [repo](https://github.com/UCSC-VLAA/STAR-1?tab=readme-ov-file#evaluation-sec-31).

## Acknowledgement
This work is partially supported by a gift from Open Philanthropy. We thank the NAIRR Pilot Program and the Microsoft Accelerate Foundation Models Research Program for supporting our computing needs.


## Citation
```
@article{wang2025star1saferalignmentreasoning,
    title={STAR-1: Safer Alignment of Reasoning LLMs with 1K Data}, 
    author={Zijun Wang and Haoqin Tu and Yuhan Wang and Juncheng Wu and Jieru Mei and Brian R. Bartoldson and Bhavya Kailkhura and Cihang Xie},
    year={2025},
    journal = {arXiv preprint arXiv:2504.01903}
}