Llama-4-Scout-17B-4E-Instruct Open-Source Multimodal Model - Supports 12 Languages and Image Understanding

Llama 4 Scout 17B 4E Instruct

Developed by shadowlilac

Llama 4 Scout is a 17-billion-parameter multimodal model with a Mixture of Experts (MoE) architecture, introduced by Meta. It supports 12 languages and image understanding, featuring a topk=4 expert dynamic fusion mechanism.

Large Language Model

Transformers

Supports Multiple Languages#Multimodal MoE Architecture #16-Expert Dynamic Fusion #Multilingual Image Understanding

Downloads 53

Release Time : 4/7/2025

Model Overview

A native multimodal AI model supporting text and multimodal interactions, delivering industry-leading performance in text and image understanding. Requires SFT/RLHF fine-tuning to restore optimal performance.

Model Features

Mixture of Experts Architecture

Utilizes a topk=4 expert dynamic fusion mechanism, combining 17B active parameters with 16 expert modules for efficient inference.

Native Multimodal Support

Integrates text and image understanding capabilities, supporting early multimodal feature fusion.

Ultra-Long Context Processing

Supports context lengths of up to 1 million tokens, ideal for long-document understanding and generation tasks.

Multilingual Optimization

Specifically optimized for generation and understanding in 12 languages, including support for Southeast Asian languages.

Model Capabilities

Multilingual text generation

Image content understanding

Code generation and completion

Cross-modal reasoning

Long-document processing

Use Cases

Content Generation

Multilingual Content Creation

Generates marketing copy and social media content in 12 languages for global enterprises.

Achieves localized expression while maintaining brand voice consistency.

Intelligent Assistant

Multimodal Customer Service System

Understands user queries through mixed text and image inputs and provides solutions.

Increases issue resolution rate by 30% on e-commerce platforms.

EdTech

Language Learning Applications

Provides multilingual translation and grammar correction for Southeast Asian learners.

Supports learning scenarios for languages like Tagalog.

🚀 Llama 4 Scout with Dynamic Expert Fusion

A specialized version of Llama 4 Scout with topk=4 experts and dynamic expert fusion, aiming for high - performance text and multimodal understanding.

🚀 Quick Start

Llama 4 Scout with topk=4 experts and dynamic expert fusion is presented here. It requires healing via SFT/RLHF to restore performance and achieves a 64.28% MMLU score. When quantized to 4bit, it barely fits on an RTX 4090.

✨ Features

Multimodal Capabilities: The Llama 4 collection of models are natively multimodal AI models, enabling text and multimodal experiences.
Mixture - of - Experts Architecture: Leverage a mixture - of - experts architecture to offer industry - leading performance in text and image understanding.
Efficient Models: Two efficient models in the Llama 4 series are launched, Llama 4 Scout and Llama 4 Maverick.

📚 Documentation

Model Information

The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. These models leverage a mixture - of - experts architecture to offer industry - leading performance in text and image understanding.

These Llama 4 models mark the beginning of a new era for the Llama ecosystem. We are launching two efficient models in the Llama 4 series, Llama 4 Scout, a 17 billion parameter model with 16 experts, and Llama 4 Maverick, a 17 billion parameter model with 128 experts.

Model developer: Meta

Model Architecture: The Llama 4 models are auto - regressive language models that use a mixture - of - experts (MoE) architecture and incorporate early fusion for native multimodality.

Property	Details
Model Name	Llama 4 Scout (17Bx16E), Llama 4 Maverick (17Bx128E)
Training Data	A mix of publicly available, licensed data and information from Meta's products and services. This includes publicly shared posts from Instagram and Facebook and people's interactions with Meta AI. Learn more in our Privacy Center.
Params	Llama 4 Scout: 17B (Activated), 109B (Total); Llama 4 Maverick: 17B (Activated), 400B (Total)
Input modalities	Multilingual text and image
Output modalities	Multilingual text and code
Context length	Llama 4 Scout: 10M; Llama 4 Maverick: 1M
Token count	Llama 4 Scout: ~40T; Llama 4 Maverick: ~22T
Knowledge cutoff	August 2024

Supported languages: Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese.

Model Release Date: April 5, 2025

Status: This is a static model trained on an offline dataset. Future versions of the tuned models may be released as we improve model behavior with community feedback.

License: A custom commercial license, the Llama 4 Community License Agreement, is available at: [https://github.com/meta - llama/llama - models/blob/main/models/llama4/LICENSE](https://github.com/meta - llama/llama - models/blob/main/models/llama4/LICENSE)

Where to send questions or comments about the model: Instructions on how to provide feedback or comments on the model can be found in the Llama [README](https://github.com/meta - llama/llama - models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 4 in applications, please go [here](https://github.com/meta - llama/llama - cookbook).

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