Agent skill · writing content · davila7
llamaguard
Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.
Why this skill is useful
Adds a specialized moderation model for filtering unsafe content that enhances AI safety capabilities.
What it needs
Requires torch, transformers, vllm installed locally. Requires huggingface account access. About 4k tokens when loaded. Last updated 2026-08-06. 30,138 stars on the source repository.
What this skill does
LlamaGuard - AI Content Moderation Quick start LlamaGuard is a 7-8B parameter model specialized for content safety classification. Installation: Basic usage: Common workflows Workflow 1: Input filtering (prompt moderation) Check user prompts before LLM: Safety categories: S1: Violence & Hate S2: Sexual Content S3: Guns & Illegal Weapons S4: Regulated Substances S5: Suicide & Self-Harm S6: Criminal Planning Workflow 2: Output filtering (response moderation) Check LLM responses before showing to user: Workflow 3: vLLM deployment (fast inference) Production-ready serving: Throughput: ~50-100 requests/sec on single A100 Workflow 4: API endpoint (FastAPI) Serve as moderation API: Usage: Workflow 5: NeMo Guardrails integration Use with NVIDIA Guardrails: When to use vs alternatives Use LlamaGuard when: Need pre-trained moderation model Want high accuracy (94-95%) Have GPU resources (7-8B model) Need detailed safety categories Building production LLM apps Model versions: LlamaGuard 1 (7B): Original, 6 categories LlamaGuard 2 (8B): Improved, 6 categories LlamaGuard 3 (8B): Latest (2024), enhanced Use alternatives instead: OpenAI Moderation API: Simpler, API-based, free Perspective API: Google's toxicity detection NeMo Guardrails: More comprehensive safety framework Constitutional AI: Training-time safety Common issues Issue: Model access denied Login to HuggingFace: Accept license on model page: https://huggingface.co/meta-llama/LlamaGuard-7b Issue: High latency (>500ms) Use vLLM for 10× speedup: Enable tensor parallelism: Issue: False positives Use threshold-based filtering: Issue: OOM on GPU Use 8-bit quantization: Advanced topics Custom categories: See references/custom-categories.md for fine-tuning LlamaGuard with domain-specific safety categories. Performance benchmarks: See references/benchmarks.md for accuracy comparison with other moderation APIs and latency optimization. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills davila7/safety-alignment-llamaguard