Llama4 - AI Language Models Tool

Overview

Llama4 is a large autoregressive Mixture-of-Experts (MoE) multimodal model from Meta, available as Maverick and Scout variants. Both variants use 17B active parameters while differing in total parameter and expert counts. The models support native multimodal inputs (text and images), very long context lengths in some versions, and rely on advanced quantization and offloading techniques for efficient deployment.

Key Features

• Mixture-of-Experts architecture with 17B active parameters
• Two variants: Maverick (~400B total, 128 experts) and Scout (~109B total, 16 experts)
• Native multimodal inputs: text and images
• Support for long context lengths, up to 10 million tokens in some versions
• Advanced quantization and offloading for memory-efficient deployment

Ideal Use Cases

• Multimodal applications combining image and text understanding
• Tasks requiring extremely long context windows
• Research into MoE scaling, sparsity, and efficiency
• Deployments benefiting from quantization and offloading techniques

Getting Started

• Visit the Llama4 documentation on the Hugging Face Transformers page
• Choose a variant: Maverick (128 experts) or Scout (16 experts)
• Review model architecture and hardware requirements for MoE deployment
• Download model files or access them via the Hugging Face Hub
• Apply quantization and offloading techniques for memory-efficient inference
• Validate multimodal inputs and long-context behavior with test workloads

Pricing

Pricing not disclosed; no pricing information provided in the source.

Limitations

• MoE architecture increases deployment complexity compared with dense models
• Requires advanced quantization and offloading for efficient inference
• Large total parameter counts may complicate storage despite 17B active parameters
• Long context support may require specialized infrastructure and memory

Key Information

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