Llama-4-Scout-17B-16E-Instruct-unsloth-bnb-4bit
| Property | Value |
|---|---|
| Base Model | Llama 4 Scout |
| Parameters | 17B activated (109B total) |
| Quantization | 4-bit with Dynamic Quants |
| License | Llama 4 Community License |
| Knowledge Cutoff | August 2024 |
What is Llama-4-Scout-17B-16E-Instruct-unsloth-bnb-4bit?
This is a highly optimized 4-bit quantized version of Meta's Llama 4 Scout model, specifically designed to provide efficient deployment while maintaining high accuracy through Unsloth's Dynamic Quants technology. The base model is a mixture-of-experts architecture featuring 17B activated parameters across 16 experts, capable of handling both text and multimodal inputs.
Implementation Details
The model implements a sophisticated mixture-of-experts (MoE) architecture with early fusion for native multimodality. It supports a context length of up to 10M tokens and has been trained on approximately 40T tokens of diverse data.
- Selective 4-bit quantization using Unsloth's Dynamic Quants technology
- Optimized for deployment on modern GPU hardware
- Maintains high accuracy despite aggressive compression
- Compatible with Unsloth's deployment framework
Core Capabilities
- Multilingual support for 12 languages including Arabic, English, French, German, and others
- Native multimodal processing for text and images
- High-performance visual reasoning and image understanding
- Advanced coding and mathematical reasoning capabilities
- Long-context understanding with 10M token support
Frequently Asked Questions
Q: What makes this model unique?
This model combines the powerful capabilities of Llama 4 Scout with Unsloth's innovative 4-bit quantization technology, making it possible to run a 17B parameter model efficiently while maintaining high performance across various tasks.
Q: What are the recommended use cases?
The model excels in commercial and research applications requiring multilingual capabilities, visual reasoning, coding, and general language understanding. It's particularly well-suited for assistant-like chat applications, visual reasoning tasks, and applications requiring efficient deployment on limited hardware resources.
