Llama-3.2-11B-Vision-Instruct-FP8-dynamic
| Property | Value |
|---|---|
| Parameter Count | 10.7B |
| Model Type | Vision-Language Model |
| License | llama3.2 |
| Supported Languages | English, German, French, Italian, Portuguese, Hindi, Spanish, Thai |
| Optimization | FP8 Quantization |
What is Llama-3.2-11B-Vision-Instruct-FP8-dynamic?
This model is an optimized version of Meta's Llama-3.2-11B-Vision-Instruct, specifically designed for efficient deployment while maintaining performance. It features FP8 quantization for both weights and activations, reducing memory requirements by approximately 50% compared to the original model.
Implementation Details
The model employs sophisticated quantization techniques, including symmetric per-channel quantization for linear operators within transformer blocks. It utilizes dynamic per-token quantization for activations, achieving optimal balance between efficiency and performance.
- Weight quantization: FP8 format with per-channel scaling
- Activation quantization: Dynamic FP8 with per-token optimization
- Integration with vLLM for efficient deployment
- 50% reduction in disk size and GPU memory requirements
Core Capabilities
- Multimodal processing (text and image inputs)
- Assistant-like chat functionality
- Support for 8 different languages
- Optimized for commercial and research applications
- Efficient deployment through vLLM backend
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its efficient implementation of FP8 quantization while maintaining the capabilities of the original Llama-3.2 vision model. The dynamic quantization approach for activations makes it particularly suitable for deployment scenarios where resource optimization is crucial.
Q: What are the recommended use cases?
The model is ideal for commercial and research applications requiring multimodal understanding in multiple languages. It's particularly well-suited for assistant-like chat applications that need to process both text and images while maintaining efficient resource usage.
