phi4-r1-guard
| Property | Value |
|---|---|
| Base Model | unsloth/phi-4-bnb-4bit |
| License | Apache-2.0 |
| Author | grounded-ai |
| Model URL | Hugging Face |
What is phi4-r1-guard?
phi4-r1-guard is a specialized reasoning model developed by Jlonge4, built upon the phi-4 architecture and optimized using Unsloth and Hugging Face's TRL library. This model serves as an intelligent content evaluation system, focusing on three critical aspects of AI safety and quality control: toxicity detection, hallucination identification, and RAG relevance assessment.
Implementation Details
The model has been engineered to provide structured reasoning and binary classifications through a consistent output format. It utilizes a sophisticated prompt template system and can be easily integrated using the vLLM framework for efficient inference.
- Built with optimized training using Unsloth (2x faster training)
- Implements standardized formatting with reasoning and answer sections
- Supports batch processing and GPU optimization
- Includes built-in tokenization and chat template functionality
Core Capabilities
- Toxicity Detection: Evaluates content for hate speech, harassment, and inappropriate language
- Hallucination Detection: Compares model outputs against reference information to identify factual inconsistencies
- RAG Relevance Assessment: Determines if retrieved context matches query requirements
- Structured Output: Provides detailed reasoning followed by binary classification
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its specialized focus on content evaluation tasks and its structured approach to providing reasoning alongside classifications. The combination of toxicity, hallucination, and RAG relevance assessment in a single model makes it particularly valuable for content moderation and AI system validation.
Q: What are the recommended use cases?
The model is ideal for content moderation systems, AI response validation pipelines, and RAG system optimization. It can be integrated into larger systems to provide automated quality control and safety checks for AI-generated content.
