Instructions to use UCSC-VLAA/STAR1-R1-Distill-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use UCSC-VLAA/STAR1-R1-Distill-1.5B with Transformers:

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

pipe = pipeline("text-generation", model="UCSC-VLAA/STAR1-R1-Distill-1.5B")
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-1.5B")
model = AutoModelForCausalLM.from_pretrained("UCSC-VLAA/STAR1-R1-Distill-1.5B")
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-1.5B 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-1.5B"
# 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-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/UCSC-VLAA/STAR1-R1-Distill-1.5B

SGLang

How to use UCSC-VLAA/STAR1-R1-Distill-1.5B 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-1.5B" \
    --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-1.5B",
		"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-1.5B" \
        --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-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use UCSC-VLAA/STAR1-R1-Distill-1.5B with Docker Model Runner:
```
docker model run hf.co/UCSC-VLAA/STAR1-R1-Distill-1.5B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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

📃 Paper ｜🤗 STAR-1 Data | 🤗 STAR-1 Model | 📚 Project Page

Introduction

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-1.5B model here, which is fine-tuned on STAR-1 dataset.

Artifacts

Data

Dataset	Num. of Sample	URL
STAR-1	1K	🤗 UCSC-VLAA/STAR-1
STAR 41K	41K	🤗 UCSC-VLAA/STAR-41K
STAR-benign-915	915	🤗 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
`STAR1`-R1-Distill-7B	R1-Distill-Qwen-7B trained on STAR-1	🤗 UCSC-VLAA/STAR1-R1-Distill-7B
`STAR1`-R1-Distill-8B	R1-Distill-Llama-8B trained on STAR-1	🤗 UCSC-VLAA/STAR1-R1-Distill-8B
`STAR1`-R1-Distill-14B	R1-Distill-Qwen-14B trained on STAR-1	🤗 UCSC-VLAA/STAR1-R1-Distill-14B
`STAR1`-R1-Distill-32B	R1-Distill-Qwen-32B trained on STAR-1	🤗 UCSC-VLAA/STAR1-R1-Distill-32B

Evaluation

See our github repo.

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}
}