Description
IDEFICS (Image-aware Decoder Enhanced à la Flamingo with Interleaved Cross-attention) is an open-access visual language model developed to advance transparency and democratization in AI. It is an open reproduction of DeepMind's Flamingo, a state-of-the-art visual language model that has not been publicly released. Similar to models like GPT-4, IDEFICS can process arbitrary sequences of images and text, generating coherent text outputs.
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Built exclusively on publicly available data and models, including LLaMA v1 and OpenCLIP, IDEFICS is available in two variants: a base version and an instructed version. Each variant comes in two parameter sizes: 9 billion and 80 billion. The project emphasizes transparency by using only public data, providing tools to explore training datasets, sharing technical lessons learned, and conducting internal ethical evaluations through adversarial prompting (red teaming) before release.
IDEFICS excels at tasks such as answering questions about images, describing visual content, and creating stories grounded in multiple images. Its performance is comparable to the original closed-source Flamingo model across various image-text understanding benchmarks. The model was trained on a mix of openly available datasets like Wikipedia, Public Multimodal Dataset, and LAION, along with a newly created 115B token dataset called OBELICS, which comprises 141 million interleaved image-text web documents.
The development team is committed to fostering open research in multimodal AI systems. IDEFICS aims to serve as a robust foundation for the AI community, complementing other open reproductions like OpenFlamingo. The project's ethical charter guided decisions, prioritizing self-criticism, transparency, and fairness. Users are encouraged to explore the demo, model cards, and dataset card, and provide feedback to aid in the continuous improvement of these models and the accessibility of large multimodal AI.
- CategoryAI Models & LLMs
- Pricing ModelOpen source
- DeploymentCloud
- PlatformsWeb
IDEFICS Visual Language Model Highlights
Open-access visual language model
Reproduces state-of-the-art capabilities
Accepts interleaved image and text inputs
Generates text outputs
Comparable performance to proprietary models
Available in 9B and 80B parameter sizes
Base and instructed versions offered
Trained on publicly available data and models
Supports multimodal AI research
Includes ethical evaluation through red teaming
Interactive visualization of training dataset available
Getting Started with IDEFICS Visual Language Model
Access model: Find IDEFICS models on the Hugging Face Hub.
Set up environment: Ensure you have the latest transformers version installed.
Load model and processor: Import necessary classes and load the desired IDEFICS checkpoint.
Prepare inputs: Create prompts with interleaved text strings and image URLs or PIL Images.
Integrate via API: Use the `generate` method with prepared inputs and generation arguments.
Decode output: Process the generated IDs to obtain human-readable text.
Run inference: Execute the code to generate text based on multimodal inputs.
IDEFICS Visual Language Model's Use Cases
- Image Question Answering
- Visual Content Description
- Multimodal Storytelling
- Open Research Foundation
- AI Transparency
- Democratizing AI
