LlamaGuard
技能 活跃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.
To provide a robust, pre-trained AI model for filtering harmful or inappropriate content in LLM inputs and outputs, ensuring safer AI interactions.
功能
- Specialized moderation model (Meta's LlamaGuard 7-8B)
- 6 detailed safety categories (violence, sexual, weapons, substances, self-harm, criminal planning)
- High accuracy (94-95%)
- Multiple deployment options (vLLM, HuggingFace, Sagemaker)
- Integration with NeMo Guardrails
使用场景
- Moderating user prompts before sending to an LLM
- Filtering LLM responses before displaying them to users
- Implementing content safety guardrails in production AI applications
- Detecting and classifying various types of harmful content
非目标
- Performing general text generation or summarization
- Acting as a general-purpose chatbot
- Replacing the need for LLM alignment training itself
工作流
- Install necessary Python libraries (transformers, torch).
- Log in to HuggingFace CLI.
- Load the LlamaGuard model and tokenizer.
- Prepare chat input using the tokenizer's template.
- Generate moderation output from the model.
- Parse the output to determine safety status and category.
- Block or allow content based on the moderation result.
先决条件
- Python 3.7+
- transformers library
- torch library
- HuggingFace CLI login with token
- GPU resources (recommended for performance)
Trust
- warning:Issues Attention17 issues opened, 4 closed in the last 90 days, indicating a low closure rate and potentially slow maintainer response.
Compliance
- info:GDPRThe skill moderates content but does not inherently process personal data. However, the LLM itself might process PII if present in the input, and this is not explicitly sanitized.
Execution
- warning:Pinned dependenciesDependencies are listed but not explicitly pinned with versions, and there's no lockfile mentioned for the Python environment, posing a risk for reproducibility and stability.
安装
npx skills add davila7/claude-code-templates通过 npx 运行 Vercel skills CLI(skills.sh)— 需要本地安装 Node.js,以及至少一个兼容 skills 的智能体(Claude Code、Cursor、Codex 等)。前提是仓库遵循 agentskills.io 格式。
质量评分
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