# Llamaguard

Meta's 7-8B specialized moderation model for LLM input/output filtering.

- Repository: https://github.com/Orchestra-Research/AI-Research-SKILLs (14176 stars)
- Category: AI and agents
- License: MIT
- Last updated: 2026-10-11
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill llamaguard -g -a claude-code -y`
Codex: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill llamaguard -g -a codex -y`
Cursor: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill llamaguard -g -a cursor -y`
Gemini CLI: `npx skills add Orchestra-Research/AI-Research-SKILLs --skill llamaguard -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add Orchestra-Research/AI-Research-SKILLs`

## Description

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.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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  # Blocked
    else:
        return True, None  # Safe

# Example
safe, category = check_input("How do I hack a website?")
if not safe:
    print(f"Request blocked: {category}")
    # Return error to user
else:
    # Send to LLM
    response = llm.generate(user_message)
```

**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)

The rest: https://github.com/Orchestra-Research/AI-Research-SKILLs/blob/HEAD/07-safety-alignment/llamaguard/SKILL.md