Welcome Llama 4 Maverick & Scout on Hugging Face

We are incredibly excited to welcome the next generation of large language models from Meta to the Hugging Face Hub: Llama 4 Maverick (~400B) and Llama 4 Scout (~109B)! 🤗 Both are Mixture of Experts (MoE) models with 17B active parameters.

Released today, these powerful, natively multimodal models represent a significant leap forward. We've worked closely with Meta to ensure seamless integration into the Hugging Face ecosystem, including both transformers and TGI from day one.

This is just the start of our journey with Llama 4. Over the coming days we’ll continue to collaborate with the community to build amazing models, datasets, and applications with Maverick and Scout! 🔥

What is Llama 4?

Llama 4, developed by Meta, introduces a new auto-regressive Mixture-of-Experts (MoE) architecture. This generation includes two models:

• The highly capable Llama 4 Maverick with 17B active parameters out of ~400B total, with 128 experts.
• The efficient Llama 4 Scout also has 17B active parameters out of ~109B total, using just 16 experts.

Both models leverage early fusion for native multimodality, enabling them to process text and image inputs. Maverick and Scout are both trained on up to 40 trillion tokens on data encompassing 200 languages (with specific fine-tuning support for 12 languages including Arabic, Spanish, German, and Hindi).

For deployment, Llama 4 Scout is designed for accessibility, fitting on a single server-grade GPU via on-the-fly 4-bit or 8-bit quantization, while Maverick is available in BF16 and FP8 formats. These models are released under the custom Llama 4 Community License Agreement, available on the model repositories.

Features and Integrations on Hugging Face

To help the community leverage these state-of-the-art models immediately, we're thrilled to announce the following integrations:

Context Length and Architecture Choices

Model	Instruct	Context Length
Scout (16E)	✅	10M
Maverick (128E)	✅	1M
Scout (16E)		256K
Maverick (128E)		256K

• QK Normalization

Click to expand Evaluation Results