UCSC-VLAA/STAR1-R1-Distill-14B
TEXT GENERATIONConcurrency Cost:1Model Size:14.8BQuant:FP8Ctx Length:32kPublished:Apr 3, 2025License:apache-2.0Architecture:Transformer Open Weights Cold
UCSC-VLAA/STAR1-R1-Distill-14B is a 14.8 billion parameter language model developed by UCSC-VLAA, fine-tuned on the STAR-1 safety dataset. This model is based on the Qwen architecture and is specifically designed to enhance safety alignment in large reasoning models. It achieves significant safety improvements across benchmarks while maintaining reasoning capabilities, making it suitable for applications requiring robust safety in AI outputs.
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Model Overview
UCSC-VLAA/STAR1-R1-Distill-14B is a 14.8 billion parameter model developed by UCSC-VLAA, fine-tuned using the STAR-1 dataset. This model is built upon the Qwen architecture and is specifically engineered to improve the safety alignment of large reasoning models (LRMs).
Key Capabilities
- Enhanced Safety Alignment: Fine-tuned with the STAR-1 dataset, which consists of 1,000 carefully selected, policy-grounded reasoning samples evaluated by GPT-4o.
- Reasoning Preservation: Designed to achieve significant safety improvements with minimal impact on the model's core reasoning capabilities.
- Diverse Data Integration: The STAR-1 dataset integrates and refines data from multiple sources, emphasizing diversity and deliberative reasoning.
Good For
- Applications requiring language models with improved safety characteristics.
- Scenarios where maintaining strong reasoning performance alongside safety alignment is crucial.
- Developers looking for a model fine-tuned on a high-quality, rigorously filtered safety dataset.