Llama Guard

Llama Guard is a natively multimodal input-output safeguard model geared towards Human-AI conversation use cases. If the input is determined to be safe, the response will be Safe. Else, the response will be Unsafe, followed by one or more of the violating categories from the MLCommons Taxonomy of Hazards:

• S1: Violent Crimes.
• S2: Non-Violent Crimes.
• S3: Sex Crimes.
• S4: Child Sexual Exploitation.
• S5: Defamation.
• S6: Specialized Advice.
• S7: Privacy.
• S8: Intellectual Property.
• S9: Indiscriminate Weapons.
• S10: Hate.
• S11: Suicide & Self-Harm.
• S12: Sexual Content.
• S13: Elections.
• S14: Code Interpreter Abuse.

This repository contains a Streamlit app for exploring content moderation with Llama Guard 4 on Groq. Sign up for an account at GroqCloud and get an API token, which you'll need for this project. See this blog for more details, and deploy this app on Railway or similar platforms to explore it further.

Here's a sample response by Llama Guard upon detecting a prompt that violated a specific category.