Llama Guard3 8B - Details

Llama Guard3 8B is a large language model developed by Meta Llama Enterprise. It features 8b parameters and operates under the Llama 31 Community License Agreement (LLAMA-31-CCLA). The model is designed to focus on real-time content safety classification, ensuring secure and appropriate interactions through advanced monitoring and filtering capabilities.

Llama Guard3 8B is a Llama-3.1-8B pretrained model fine-tuned for content safety classification. It detects unsafe content across 14 hazard categories including violent crimes, hate speech, privacy violations, and code interpreter abuse. The model classifies both inputs and responses to ensure real-time safety monitoring. It delivers industry-leading system-level safety performance and is specifically designed to work with Llama 3.1 for enhanced moderation capabilities.

Llama Guard3 8B is a mid-scale model with 8b parameters, offering a balance between performance and resource efficiency for handling moderate complexity tasks. Its context length of 4k tokens is suitable for short to moderately long inputs but may limit its ability to process extended texts. The parameter size enables efficient deployment while maintaining robust content safety classification, while the context length ensures effective real-time monitoring without excessive computational demands.

• Parameter Size: 8b
• Context Length: 4k

Llama Guard3 8B is a multilingual model designed for potential applications in content moderation for LLM inputs and responses, offering possible tools to identify and manage unsafe content across multiple languages including english, italian, french, portuguese, thai, hindi, german, and spanish. It could serve as a possible component for safety enforcement in search and code interpreter tool calls, ensuring potential alignment with system-level safety standards. Its deployment alongside Llama 3.1 might offer possible enhancements to overall moderation frameworks, though further investigation is needed to confirm effectiveness in specific scenarios. The model’s multilingual support suggests possible adaptability to diverse linguistic environments, but its use in any context requires careful evaluation to ensure alignment with intended goals.

• content moderation for llm inputs and responses
• safety enforcement for search and code interpreter tool calls
• deployment alongside llama 3.1 to improve system-level safety

Llama Guard3 8B is a multilingual model with possible applications in content moderation for LLM inputs and responses, offering possible tools to identify and manage unsafe content across multiple languages including english, italian, french, portuguese, thai, hindi, german, and spanish. It could serve as a possible component for safety enforcement in search and code interpreter tool calls, ensuring possible alignment with system-level safety standards. Its deployment alongside Llama 3.1 might offer possible enhancements to overall moderation frameworks, though further investigation is needed to confirm effectiveness in specific scenarios. The model’s multilingual support suggests possible adaptability to diverse linguistic environments, but its use in any context requires careful evaluation to ensure alignment with intended goals. Each application must be thoroughly evaluated and tested before use.

• content moderation for llm inputs and responses
• safety enforcement for search and code interpreter tool calls
• deployment alongside llama 3.1 to improve system-level safety

Llama Guard3 8B’s medium q4 version requires a GPU with at least 16GB VRAM and a system with 32GB RAM for optimal performance, balancing precision and efficiency. This configuration ensures smooth operation while maintaining reasonable computational demands. The model’s quantized versions include fp16, q2, q3, q4, q5, q6, q8.

Llama Guard3 8B is a content safety classification model with 8b parameters, designed to detect unsafe content across 14 hazard categories in real-time inputs and responses, operating under the Llama 31 Community License Agreement and optimized for integration with Llama 3.1 to enhance system-level safety. Its multilingual support and fine-tuned architecture make it suitable for diverse moderation tasks while balancing performance and resource efficiency.

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