idefics2-8b
| Property | Value |
|---|---|
| Parameter Count | 8.4B |
| License | Apache 2.0 |
| Architecture | Vision-Language Model |
| Authors | HuggingFaceM4 |
| Paper | https://huggingface.co/papers/2405.02246 |
What is idefics2-8b?
idefics2-8b is a state-of-the-art multimodal AI model that can process both images and text in an interleaved sequence. Built on SigLIP and Mistral-7B architectures, it represents a significant advancement in vision-language modeling, offering enhanced OCR capabilities and native resolution image handling up to 980x980 pixels.
Implementation Details
The model utilizes a simplified architecture for integrating visual features into the language backbone, employing a learned Perceiver pooling and MLP modality projection. It's trained in two stages: first with fixed-resolution images (384x384) and then with native resolution support.
- Native resolution processing up to 980x980 pixels
- Improved OCR capabilities through specialized training data
- Simplified visual feature integration architecture
- Support for Flash-attention 2 and 4-bit quantization
Core Capabilities
- Visual Question Answering with strong performance on benchmark datasets
- Document and chart understanding
- Multi-image reasoning and story generation
- Text transcription and OCR tasks
- Conversational AI with visual context
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its ability to handle images in their native resolution and aspect ratios, significantly improved OCR capabilities, and competitive performance despite being 10x smaller than its predecessor.
Q: What are the recommended use cases?
The model excels in visual question answering, document understanding, image captioning, and multi-image reasoning tasks. It's particularly suitable for applications requiring OCR capabilities and visual reasoning.
