Llama-3.2-11B-Vision-Instruct-unsloth-bnb-4bit
| Property | Value |
|---|---|
| Model Size | 11B parameters |
| Release Date | September 25, 2024 |
| License | Llama 3.2 Community License |
| Developer | Meta (Original model) / Unsloth (Optimization) |
| Supported Languages | English, German, French, Italian, Portuguese, Hindi, Spanish, Thai |
What is Llama-3.2-11B-Vision-Instruct-unsloth-bnb-4bit?
This is an optimized version of Meta's Llama 3.2 Vision model, featuring Unsloth's innovative Dynamic 4-bit quantization technology. The model maintains the powerful capabilities of the original 11B parameter vision-language model while significantly reducing memory requirements and improving inference speed.
Implementation Details
The model utilizes Unsloth's Dynamic 4-bit Quants technology, which selectively preserves certain parameters from quantization to maintain model accuracy. This implementation achieves a 2x speed improvement and 60% memory reduction compared to the original model.
- Optimized transformer architecture with Grouped-Query Attention (GQA)
- Supports both vision and text processing capabilities
- Compatible with GGUF, vLLM export options
- Includes supervised fine-tuning (SFT) and RLHF optimization
Core Capabilities
- Multilingual dialogue processing across 8 officially supported languages
- Vision-language understanding and generation
- Agentic retrieval and summarization tasks
- Efficient processing with reduced memory footprint
- Maintains high accuracy despite quantization
Frequently Asked Questions
Q: What makes this model unique?
The model combines Meta's powerful Llama 3.2 architecture with Unsloth's Dynamic 4-bit quantization, offering significant performance improvements while maintaining model quality. It's particularly notable for achieving 2x faster processing and 60% reduced memory usage.
Q: What are the recommended use cases?
This model is ideal for applications requiring vision-language processing, multilingual dialogue systems, and tasks involving image understanding and text generation. It's particularly suitable for deployment scenarios where computational efficiency is crucial.
