GuardReasoner-3B
| Property | Value |
|---|---|
| Base Model | meta-llama/Llama-3.2-3B |
| Training Method | R-SFT and HS-DPO |
| Paper | arXiv:2501.18492 |
| GitHub | Repository |
What is GuardReasoner-3B?
GuardReasoner-3B is a specialized AI model designed to enhance the safety of AI systems through reasoning-based analysis. Built on the LLaMA 3.2-3B architecture, it's specifically trained to evaluate interactions between humans and AI assistants, focusing on harm detection and response analysis.
Implementation Details
The model implements a three-task framework for analyzing AI-human interactions: prompt harmfulness detection, refusal detection, and response harmfulness detection. It utilizes the VLLM framework for efficient inference and includes specialized post-processing for consistent output formatting.
- Fine-tuned using R-SFT (Reasoning-based SFT) methodology
- Enhanced with HS-DPO training approach
- Implements step-by-step reasoning for analysis
- Optimized for GPU utilization with 95% efficiency
Core Capabilities
- Analyzes human requests for potential harm
- Evaluates AI responses for compliance and safety
- Provides structured reasoning for safety decisions
- Supports batch processing of interactions
- Maintains consistent output formatting through post-processing
Frequently Asked Questions
Q: What makes this model unique?
GuardReasoner-3B stands out for its specialized focus on AI safety through structured reasoning. Unlike general-purpose models, it specifically analyzes the safety aspects of AI-human interactions using a three-task framework and step-by-step reasoning approach.
Q: What are the recommended use cases?
The model is ideal for AI safety researchers, developers implementing safety measures in AI systems, and organizations looking to evaluate and improve the safety of their AI interactions. It's particularly useful for analyzing potential harmful content and ensuring appropriate AI responses.
