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Llama4 - AI Language Models Tool

Overview

Llama4 is a large autoregressive Mixture-of-Experts (MoE) multimodal model from Meta, offered in Maverick and Scout variants. It supports native text and image inputs and includes techniques for efficient deployment such as quantization and offloading. Some versions are designed for very long contexts (reported up to 10 million tokens) and target high-capacity multimodal and research workloads.

Key Features

  • Mixture-of-Experts (MoE) architecture for sparse computation
  • Maverick and Scout variants with 17B active parameters each
  • Maverick: ~400B total parameters with 128 experts
  • Scout: ~109B total parameters with 16 experts
  • Native multimodal inputs (text and images)
  • Autoregressive generation for language tasks
  • Support for very long contexts, up to 10 million tokens in some versions
  • Advanced quantization and offloading for efficient deployment

Ideal Use Cases

  • Multimodal chatbots that process text and images
  • Long-document understanding and summarization
  • Research into sparse-expert and MoE model behavior
  • Applications needing extended context windows
  • Retrieval-augmented generation with large context

Getting Started

  • Compare Maverick and Scout to choose the right variant
  • Read the Llama4 documentation on the Hugging Face page
  • Access model artifacts following the documentation instructions
  • Prepare infrastructure for MoE routing, offloading, and quantization
  • Run small multimodal tests before scaling to full context
  • Monitor performance and adjust quantization or offloading settings

Pricing

Pricing and licensing are not disclosed in the provided information. Consult the Hugging Face documentation or Meta announcements for cost and licensing details.

Limitations

  • Specialized deployment required for MoE routing and expert offloading
  • Large total parameter counts increase infrastructure complexity
  • Maximum context length varies by version; not all versions support 10M tokens
  • Efficient inference may require quantization and offloading techniques

Key Information

  • Category: Language Models
  • Type: AI Language Models Tool
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