Llama Guard3 1B
Llama Guard3 1B is a large language model developed by Meta Llama Enterprise with 1b parameters, designed for real-time content safety classification. It operates under the Llama 32 Acceptable Use Policy (Llama-32-AUP) and Llama 32 Community License Agreement (LLAMA-32-COMMUNITY), ensuring compliance and responsible usage in content moderation tasks.
Description of Llama Guard3 1B
Llama Guard3 1B is a fine-tuned Llama-3.2-1B model designed for content safety classification, capable of analyzing both prompts and responses to determine if they are safe or unsafe. It identifies specific violated content categories when unsafe content is detected and aligns with the MLCommons standardized hazards taxonomy to streamline moderation system deployment. The model is available in two versions, including a pruned and quantized variant optimized for mobile devices, and operates under the Llama 32 Acceptable Use Policy and Llama 32 Community License Agreement.
Parameters & Context Length of Llama Guard3 1B
Llama Guard3 1B has 1b parameters, placing it in the small model category, which ensures fast and resource-efficient performance for tasks like content safety classification. Its 4k context length is suitable for short to moderate tasks but limits its ability to process very long texts. The compact size and moderate context length make it ideal for real-time applications where speed and efficiency are critical, though it may lack the depth of larger models for complex or extended reasoning.
- Parameter Size: 1b
- Context Length: 4k
Possible Intended Uses of Llama Guard3 1B
Llama Guard3 1B is designed for content safety classification, with possible applications in moderating user-generated content, filtering unsafe responses in chatbot interactions, and enforcing content safety policies on AI-driven platforms. Its multilingual capabilities, supporting languages like English, Italian, French, Portuguese, Thai, Hindi, German, and Spanish, suggest potential uses in diverse linguistic environments. However, these possible uses require thorough investigation to ensure alignment with specific requirements and contexts. The model’s focus on real-time classification and standardized hazard taxonomy highlights its potential for scenarios where rapid, consistent safety checks are needed, though further testing is essential to validate effectiveness.
- moderating user-generated content for safety compliance
- filtering unsafe responses in chatbot interactions
- enforcing content safety policies in ai-driven platforms
Possible Applications of Llama Guard3 1B
Llama Guard3 1B is a multilingual model with possible applications in scenarios requiring real-time content safety checks, such as moderating user-generated content for compliance, filtering unsafe responses in chatbots, or enforcing safety policies on AI-driven platforms. Its ability to classify both prompts and responses could make it a possible tool for managing community guidelines in online spaces, though these possible uses require careful evaluation to ensure alignment with specific needs. The model’s support for multiple languages, including English, Italian, French, and others, suggests possible deployment in diverse environments, but further testing is essential to confirm effectiveness. Each possible application must be thoroughly assessed before implementation to ensure reliability and suitability.
- moderating user-generated content for safety compliance
- filtering unsafe responses in chatbot interactions
- enforcing content safety policies in AI-driven platforms
Quantized Versions & Hardware Requirements of Llama Guard3 1B
Llama Guard3 1B with the q4 quantization offers a balanced trade-off between precision and performance, requiring a GPU with at least 8GB VRAM for efficient operation, alongside a system with 32GB RAM for stability. This configuration ensures compatibility with mid-range hardware while maintaining reasonable inference speeds. Other quantized versions include fp16, q2, q3, q5, q6, q8.
- fp16, q2, q3, q4, q5, q6, q8
Conclusion
Llama Guard3 1B is a fine-tuned Llama-3.2-1B model optimized for real-time content safety classification, capable of analyzing both prompts and responses to detect unsafe content while aligning with standardized hazard taxonomies. It supports multiple languages and offers two versions, including a pruned variant for mobile deployment, making it suitable for applications like moderating user-generated content and enforcing safety policies.