What specific categories of content does LlamaGuard filter?

LlamaGuard filters content across six main categories: Violence & Hate, Sexual Content, Guns & Illegal Weapons, Regulated Substances, Suicide & Self-Harm, and Criminal Planning.

How does LlamaGuard compare to the OpenAI Moderation API?

LlamaGuard offers similar high accuracy (94-95%) but allows for self-hosted deployment, providing better data privacy, lower long-term costs for high volumes, and more granular control over safety categories.

Can LlamaGuard be used in high-traffic production environments?

Yes, it is designed for production use. When deployed with vLLM on hardware like an NVIDIA A100, it can handle 50-100 requests per second with very low latency.

What are the hardware requirements for running LlamaGuard?

LlamaGuard requires an NVIDIA GPU. It needs approximately 14GB of VRAM for FP16 precision, but this can be reduced to 7GB using 8-bit quantization or 4GB using 4-bit quantization.

LlamaGuard Content Moderation

Name: LlamaGuard Content Moderation
Author: zechenzhangAGI

byzechenzhangAGI

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384

Security & Testing

Moderates and filters AI inputs and outputs using Meta's specialized safety alignment models to prevent harmful content generation and ensure compliance.

About

LlamaGuard is a professional-grade safety alignment tool that leverages Meta's 7B and 8B parameter models to provide high-accuracy content moderation for Large Language Models. It classifies interactions across six critical safety categories—including violence, sexual content, and criminal planning—ensuring that both user prompts and model responses remain within defined safety boundaries. This skill is essential for developers building production-ready AI applications that require robust guardrails, high-performance inference via vLLM, and seamless integration with safety frameworks like NeMo Guardrails to maintain user trust and system integrity.

Key Features

Supports high-throughput production deployment using vLLM, HuggingFace, and Sagemaker
Provides deterministic moderation results with sub-100ms latency on optimized hardware
Delivers 94-95% accuracy for both input prompt and output response filtering
Integrates seamlessly with NVIDIA NeMo Guardrails for comprehensive safety logic
Classifies content across six specialized safety categories (violence, hate, weapons, etc.)
384 GitHub stars

Use Cases

Filtering AI-generated responses before they reach the end user to ensure safety compliance
Building high-performance, self-hosted moderation APIs for enterprise AI applications
Implementing real-time prompt moderation to block malicious or unsafe user inputs

About

Key Features

Supports high-throughput production deployment using vLLM, HuggingFace, and Sagemaker
Provides deterministic moderation results with sub-100ms latency on optimized hardware
Delivers 94-95% accuracy for both input prompt and output response filtering
Integrates seamlessly with NVIDIA NeMo Guardrails for comprehensive safety logic
Classifies content across six specialized safety categories (violence, hate, weapons, etc.)
384 GitHub stars

Use Cases

Filtering AI-generated responses before they reach the end user to ensure safety compliance
Building high-performance, self-hosted moderation APIs for enterprise AI applications
Implementing real-time prompt moderation to block malicious or unsafe user inputs