Skill · em Dados, IA e pesquisa

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.

Procedência

Antes de instalar

1 arquivo · 9 KB · só texto, nenhum script

Instalar na sua CLI

O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.

Claude Code

Neste projeto: instala em .claude/skills/llamaguard/.

d=".claude/skills/llamaguard"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/safety-alignment-llamaguard"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md"

Global: instala em ~/.claude/skills/llamaguard/.

d="$HOME/.claude/skills/llamaguard"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/safety-alignment-llamaguard"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md"

Codex

Neste projeto: instala em .agents/skills/llamaguard/.

d=".agents/skills/llamaguard"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/safety-alignment-llamaguard"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md"

Global: instala em ~/.agents/skills/llamaguard/.

d="$HOME/.agents/skills/llamaguard"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/safety-alignment-llamaguard"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md"

Antigravity

Neste projeto: instala em .agents/skills/llamaguard/.

d=".agents/skills/llamaguard"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/safety-alignment-llamaguard"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md"

Global: instala em ~/.gemini/antigravity-cli/skills/llamaguard/.

d="$HOME/.gemini/antigravity-cli/skills/llamaguard"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/safety-alignment-llamaguard"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md"

Peça ao Rook

Já usa o Rook Labs? Cole no chat do Rook: instale a skill https://rooklabs.sh/marketplace/cct.llamaguard

Prévia do SKILL.md

---
name: llamaguard
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.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Safety Alignment, LlamaGuard, Content Moderation, Meta, Guardrails, Safety Classification, Input Filtering, Output Filtering, AI Safety]
dependencies: [transformers, torch, vllm]
---

# 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?"}
])
…

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