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.
npx skills add Orchestra-Research/AI-Research-SKILLs --skill llamaguard --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# LlamaGuard - AI Content Moderation ## Quick start LlamaGuard is a 7-8B parameter model specialized for content safety classification. **Installation**: ```bash pip install transformers torch # Login to HuggingFace (required) huggingface-cli login ``` **Basic usage**: ```python from transformers import AutoTokenizer, AutoModelForCausalLM model_id = "meta-llama/LlamaGuard-7b" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto") def moderate(chat): input_ids = tokenizer.apply_chat_template(chat, return_tensors="pt").to(model.device) output = model.generate(input_ids=input_ids, max_new_tokens=100) return tokenizer.decode(output[0], skip_special_tokens=True) # Check user input result = moderate([ {"role": "user", "content": "How do I make explosives?"} ]) print(result) # Output: "unsafe\nS3" (Criminal Planning) ``` ## Common workflows ### Workflow 1: Input filtering (prompt moderation) **Check user prompts before LLM**: ```python def check_input(user_message): result = moderate([{"role": "user", "content": user_message}]) if result.startswith("unsafe"): category = result.split("\n")[1] return False, category # Bl
- Quick start
- Common workflows
- Workflow 1: Input filtering (prompt moderation)
- Workflow 2: Output filtering (response moderation)
- Workflow 3: vLLM deployment (fast inference)
- Workflow 4: API endpoint (FastAPI)
- Workflow 5: NeMo Guardrails integration
- When to use vs alternatives
- Common issues
- Advanced topics
- Hardware requirements
- Resources
pip install transformers torch Login to HuggingFace (required) huggingface-cli login curl -X POST http://localhost:8000/moderate \ Enter your token
What does the llamaguard skill do?
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.
How do I install it?
Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill llamaguard --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Orchestra-Research/AI-Research-SKILLs, a repository with 11,391 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.