llava-llama-3-8b-v1_1-gguf
| Property | Value |
|---|---|
| Parameter Count | 312M |
| Model Type | Image-to-Text |
| Format | GGUF |
| Visual Encoder | CLIP-L |
| Resolution | 336x336 |
What is llava-llama-3-8b-v1_1-gguf?
This is an advanced multimodal model that combines Meta's Llama-3-8B-Instruct architecture with CLIP-ViT-Large visual processing capabilities. It's specifically designed for image-to-text tasks and has been fine-tuned using ShareGPT4V-PT and InternVL-SFT datasets, making it particularly effective at visual understanding and description tasks.
Implementation Details
The model employs a sophisticated architecture combining a frozen LLM and LoRA ViT approach, utilizing an MLP projector for visual-language alignment. It achieves impressive benchmark scores, notably 72.3% on MMBench Test (EN) and 66.4% on MMBench Test (CN).
- Integrates CLIP-ViT-Large-patch14-336 for visual processing
- Implements MLP projector for multimodal fusion
- Supports 336x336 resolution image inputs
- Available in both FP16 and INT4 quantized versions
Core Capabilities
- High-performance image description and analysis
- Strong multilingual capabilities (English and Chinese)
- Efficient memory usage through GGUF format
- Seamless integration with llama.cpp and Ollama
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its efficient architecture combining Llama-3 with CLIP, achieving state-of-the-art performance on various visual benchmarks while maintaining a relatively compact size. Its GGUF format makes it highly deployable across different platforms.
Q: What are the recommended use cases?
The model excels in image description, visual question answering, and multimodal understanding tasks. It's particularly suitable for applications requiring detailed image analysis and natural language generation based on visual inputs.
