Llama-3.2-11B-Vision-Instruct-bnb-4bit
| Property | Value |
|---|---|
| Parameter Count | 6.05B |
| Model Type | Vision-Language Model |
| License | Llama 3.2 Community License |
| Precision | 4-bit quantized |
What is Llama-3.2-11B-Vision-Instruct-bnb-4bit?
This is a 4-bit quantized version of Meta's Llama 3.2 vision-language model, optimized by Unsloth for efficient inference. It combines powerful language understanding with visual capabilities, making it suitable for multimodal applications while requiring significantly less memory than the original model.
Implementation Details
The model utilizes Grouped-Query Attention (GQA) for improved inference scalability and has been optimized to run with 60% less memory compared to the original implementation. It supports multiple tensor types including F32, BF16, and U8, offering flexibility in deployment scenarios.
- 4-bit quantization for efficient memory usage
- Optimized transformer architecture with GQA
- Multimodal capabilities supporting both text and vision inputs
- Compatible with various deployment options including GGUF and vLLM
Core Capabilities
- Visual and text understanding
- Multilingual support (English primary, with additional language capabilities)
- Efficient inference with reduced memory footprint
- Suitable for conversational AI applications
- Optimized for instruction-tuning tasks
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient 4-bit quantization while maintaining the powerful capabilities of the Llama 3.2 architecture. It offers a 60% reduction in memory usage while delivering comparable performance to the original model.
Q: What are the recommended use cases?
The model is ideal for applications requiring both visual and textual understanding, including image-based conversations, visual question answering, and multimodal applications where memory efficiency is crucial.
