Llama-4-Scout-17B-16E-Instruct-GGUF
| Property | Value |
|---|---|
| Developer | Meta |
| Parameters | 17B (Activated) / 109B (Total) |
| Context Length | 10M tokens |
| Training Data | ~40T tokens |
| License | Llama 4 Community License |
| Knowledge Cutoff | August 2024 |
What is Llama-4-Scout-17B-16E-Instruct-GGUF?
Llama-4-Scout is Meta's latest multimodal AI model featuring a mixture-of-experts (MoE) architecture. This GGUF version represents a quantized implementation of the model, optimized for efficient deployment while maintaining high performance. With 17 billion activated parameters (109B total), it supports both text and image processing across 12 languages, making it a versatile option for various AI applications.
Implementation Details
The model utilizes a sophisticated MoE architecture with 16 experts, featuring early fusion for native multimodality. It's available in various quantization formats, from 1.78-bit to 4.5-bit, offering different trade-offs between model size and accuracy. The model can process both text and images, with support for up to 5 input images in testing.
- Supports multiple quantization levels (1.78-bit to 4.5-bit)
- 10M token context window
- Native multimodal capabilities
- Optimized for deployment on H100 GPUs
Core Capabilities
- Multilingual support across 12 languages
- Visual recognition and reasoning
- Image captioning and visual QA
- Long-context processing
- Code generation and analysis
Frequently Asked Questions
Q: What makes this model unique?
The model's MoE architecture with 16 experts, combined with its extensive 10M token context window and multimodal capabilities, sets it apart from traditional language models. It offers state-of-the-art performance while maintaining deployment efficiency through various quantization options.
Q: What are the recommended use cases?
The model excels in assistant-like chat, visual reasoning tasks, natural language generation, image captioning, and multilingual applications. It's particularly well-suited for commercial and research applications requiring both text and image understanding.
