Welcoming Llama Guard 4 on Hugging Face Hub

TL;DR: Today, Meta releases Llama Guard 4, a 12B dense (not a MoE!) multimodal safety model, and two new Llama Prompt Guard 2 models. This release comes with multiple open model checkpoints, along with an interactive notebook for you to get started easily 🤗. Model checkpoints can be found in Llama 4 Collection.

Table-of-Contents

• What is Llama Guard 4?
• Model Details
  • Llama Guard 4
  • Llama Prompt Guard 2
• Getting Started using 🤗transformers
  • Llama Guard 4
  • Llama Prompt Guard 2
• Useful Resources

What is Llama Guard 4?

Vision and large language models deployed to production can be exploited to generate unsafe output through jail breaking image and text prompts. Unsafe content in production varies from being harmful or inappropriate to violating privacy or intellectual property.

New safeguard models address this issue by evaluating image and text, and the content generated by the model. User messages classified as unsafe are not passed to vision and large language models, and unsafe assistant responses can be filtered out by production services.

Llama Guard 4 is a new multimodal model designed to detect inappropriate content in images and text, whether used as input or generated as output by the model. It’s a dense 12B model pruned from Llama 4 Scout model, and it can run on a single GPU (24 GB of VRAM). It can evaluate both text-only and image+text inputs, making it suitable for filtering both inputs and outputs of large language models. This enables flexible moderation pipelines where prompts are analyzed before reaching the model, and generated responses are reviewed afterwards for safety. It can also understand multiple languages.

The model can classify 14 types of hazard defined in the MLCommons hazard taxonomy, along with code interpreter abuse.

The list of categories detected by the model can be configured by the user on inference, as we’ll see later.