Llama-3.2-11B-Vision-Instruct
| Property | Value |
|---|---|
| Parameter Count | 10.7B |
| Model Type | Vision-Language Model |
| License | Llama 3.2 Community License |
| Tensor Type | BF16 |
What is Llama-3.2-11B-Vision-Instruct?
Llama-3.2-11B-Vision-Instruct is Meta's advanced multimodal vision-language model, part of the Llama 3.2 family. This model represents a significant advancement in AI capabilities, combining powerful language understanding with visual processing abilities. It features optimized performance through Grouped-Query Attention (GQA) and supports multiple languages including English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
Implementation Details
The model utilizes an optimized transformer architecture with auto-regressive capabilities, fine-tuned through supervised learning (SFT) and reinforcement learning with human feedback (RLHF). Notable technical aspects include:
- Memory-efficient implementation with 60% reduced memory usage
- 2x faster processing speeds compared to standard implementations
- BF16 tensor format for optimal performance
- Integrated vision-text processing capabilities
Core Capabilities
- Multimodal processing of both images and text
- Multilingual support across 8 officially supported languages
- Advanced dialogue and instruction-following abilities
- Optimized for retrieval and summarization tasks
- Enhanced safety features through RLHF training
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its combination of vision-language capabilities with significant optimizations in memory usage and processing speed. It's particularly notable for its integration with the Unsloth framework, enabling efficient fine-tuning on limited computational resources.
Q: What are the recommended use cases?
The model excels in multimodal applications including visual question-answering, image-based dialogue, content generation, and multilingual tasks. It's particularly suitable for applications requiring both visual and textual understanding with high efficiency requirements.
