IDEFICS-9b-instruct
| Property | Value |
|---|---|
| Parameter Count | 8.93B parameters |
| Model Type | Multimodal (Image-Text) |
| License | MIT + Meta's License |
| Training Data | 4 datasets (OBELICS, Wikipedia, LAION, PMD) |
What is idefics-9b-instruct?
IDEFICS-9b-instruct is an instruction-tuned version of the base IDEFICS model, designed for advanced image-text understanding tasks. Built by HuggingFaceM4, it represents a significant step forward in open-source multimodal AI, capable of processing both images and text to generate coherent, contextually relevant responses.
Implementation Details
The model builds upon two primary components: CLIP ViT-H-14 for vision processing and LLaMA-7B for text generation. It implements a sophisticated architecture including Perceiver Resampler blocks with 6 layers and 64 latents, utilizing 16 attention heads with a 96-dimensional head space.
- Trained on a diverse dataset mixture including OBELICS, Wikipedia, LAION, and PMD
- Uses mixed-precision bfloat16 training with DeepSpeed ZeRO-3 optimization
- Implements gradient checkpointing for efficient training
Core Capabilities
- Visual question answering with high accuracy
- Image captioning and description generation
- Multi-image storytelling
- Zero-shot and few-shot learning for various visual tasks
- Conversational interactions about visual content
Frequently Asked Questions
Q: What makes this model unique?
IDEFICS-9b-instruct stands out for its instruction-tuned capabilities and open-source nature, making it accessible for research and development while maintaining competitive performance with closed-source alternatives.
Q: What are the recommended use cases?
The model excels in image-text tasks like visual question answering, image captioning, and interactive discussions about visual content. However, it should not be used for critical decision-making or high-stakes applications.
