project-free-llama/Llama-Guard-3-1B
Llama Guard 3-1B by Meta is a 1 billion parameter Llama-3.2-1B fine-tuned model designed for content safety classification in LLM inputs and responses, supporting a 32768 token context length. It classifies content against the MLCommons standardized 13-category hazards taxonomy, offering a cost-effective moderation system. This model is optimized for deployment, with a pruned and quantized version available for mobile devices, and supports content safety in multiple languages including English, French, German, and Spanish.
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Llama Guard 3-1B: Content Safety Classifier
Llama Guard 3-1B, developed by Meta, is a 1 billion parameter model fine-tuned from Llama-3.2-1B specifically for content safety classification. It functions as an LLM-based safeguard, capable of classifying both user prompts and LLM responses to identify unsafe content. The model outputs text indicating whether content is safe or unsafe, and if unsafe, lists the violated content categories.
Key Capabilities
- Content Moderation: Classifies content based on the MLCommons standardized 13-category hazards taxonomy, covering areas like violent crimes, hate speech, sexual content, and specialized advice.
- Dual-purpose Classification: Can be used for both input (prompt) and output (response) moderation in LLM applications.
- Multilingual Support: Supports content safety classification in English, French, German, Hindi, Italian, Portuguese, Spanish, and Thai.
- Deployment Optimized: Features a pruned and quantized version (Llama Guard 3-1B -INT4) designed for efficient deployment, including on mobile devices.
- Customizable Taxonomy: Allows users to provide custom safety categories or exclude default ones during inference.
What Makes It Different?
Llama Guard 3-1B is distinguished by its focus on cost-effective and efficient content moderation aligned with an industry-standard taxonomy. Unlike general-purpose LLMs, it is specifically trained to identify and categorize safety hazards, making it a specialized tool for building safer AI applications. Its smaller size (1B parameters) and optimized versions offer a lower deployment cost compared to larger safety models like Llama Guard 3-8B, while still providing robust classification capabilities. The model's training incorporates distillation from the 8B version and advanced pruning techniques to maintain performance despite its compact size.
Good For
- Developers needing an efficient and specialized content moderation layer for their LLM applications.
- Applications requiring real-time safety classification of user inputs and AI-generated responses.
- Use cases where resource constraints (e.g., mobile deployment) necessitate a smaller, optimized safety model.
- Projects aiming to align content safety with the MLCommons hazard taxonomy for standardized moderation.