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Llama-4-Scout-17B-16E-Instruct

Llama 4 Scout is Meta's first MoE entry in the Llama series: 17B parameters per expert across 16 experts, with a small number active per token. The instruct variant follows instructions and handles image-text inputs natively, supporting 12 languages. Scout targets deployments where multimodal capability is needed at a lower active-parameter cost than dense Llama 3 models.

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Fields below are copied from the tags and counters on the HuggingFace repository meta-llama/Llama-4-Scout-17B-16E-Instruct at our last fetch. They are set by the uploader, not verified by us; rows with no tag are omitted. How this page is made.

Publisher (HF namespace)
meta-llama
Pipeline tag
image-text-to-text
Library
Transformers
Framework tags
PyTorch
Weight formats
safetensors
License tag
other — read the license file in the repo before relying on it
Lineage
Language tags
Arabic (ar), German (de), English (en), Spanish (es), French (fr), Hindi (hi), Indonesian (id), Italian (it), Portuguese (pt), Thai (th), Filipino (tl), Vietnamese (vi)
Papers cited
arXiv:2204.05149
Downloads (HF counter at last fetch)
411,931
Likes (HF counter at last fetch)
1,333
Model card
https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct

Use cases

  • Multilingual instruction following with image input support in 12 languages
  • Multimodal document understanding and image captioning at production scale
  • Building open-weight multimodal assistants with Meta's supported architecture
  • Code generation and reasoning tasks in a MoE architecture
  • Comparing MoE vs dense Llama trade-offs in production deployments

Pros

  • First Llama model with native MoE and multimodal capability
  • 12 language coverage including Asian and Semitic scripts
  • 1287 likes; widely adopted across the Llama ecosystem
  • TGI and Azure deployment; Apache-equivalent Llama 4 Community License

Cons

  • Llama 4 Community License restricts use beyond 700M monthly active users
  • MoE routing adds inference complexity vs simpler dense models
  • Vision capabilities are newer and less battle-tested than the text stack
  • 16E MoE architecture requires specific serving infrastructure for efficient routing

Tags

transformerssafetensorsllama4image-text-to-textfacebookmetapytorchllamaconversationalardeenesfrhiiditptthtl