Instructions to use UCSC-VLAA/STAR1-R1-Distill-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use UCSC-VLAA/STAR1-R1-Distill-14B with Transformers:

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

pipe = pipeline("text-generation", model="UCSC-VLAA/STAR1-R1-Distill-14B")
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-14B")
model = AutoModelForCausalLM.from_pretrained("UCSC-VLAA/STAR1-R1-Distill-14B")
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 UCSC-VLAA/STAR1-R1-Distill-14B 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-14B"
# 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-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/UCSC-VLAA/STAR1-R1-Distill-14B

SGLang

How to use UCSC-VLAA/STAR1-R1-Distill-14B 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-14B" \
    --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-14B",
		"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-14B" \
        --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-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use UCSC-VLAA/STAR1-R1-Distill-14B with Docker Model Runner:
```
docker model run hf.co/UCSC-VLAA/STAR1-R1-Distill-14B
```

STAR1-R1-Distill-14B / README.md

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	---
	library_name: transformers
	license: apache-2.0
	datasets:
	- UCSC-VLAA/STAR-1
	base_model:
	- deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
	---

	# 🌟 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-14B](https://huggingface.co/UCSC-VLAA/STAR1-R1-Distill-14B) 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}
	}