4M 21 L

🚀 4M: Massively Multimodal Masked Modeling

A framework for training any-to-any multimodal foundation models. Scalable. Open-sourced. Across tens of modalities and tasks.

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✨ Features

4M is a framework for training "any-to-any" foundation models, using tokenization and masking to scale to many diverse modalities. Models trained using 4M can perform a wide range of vision tasks, transfer well to unseen tasks and modalities, and are flexible and steerable multimodal generative models. We are releasing code and models for "4M: Massively Multimodal Masked Modeling" (here denoted 4M-7), as well as "4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities" (here denoted 4M-21).

📦 Installation

For install instructions, please see https://github.com/apple/ml-4m.

💻 Usage Examples

Basic Usage

from fourm.models.fm import FM
fm = FM.from_pretrained('EPFL-VILAB/4M-21_L')

Please see README_GENERATION.md for more detailed instructions and https://github.com/apple/ml-4m for other 4M model and tokenizer checkpoints.

📚 Documentation

Citation

If you find this repository helpful, please consider citing our work:

@inproceedings{4m,
    title={{4M}: Massively Multimodal Masked Modeling},
    author={David Mizrahi and Roman Bachmann and O{\u{g}}uzhan Fatih Kar and Teresa Yeo and Mingfei Gao and Afshin Dehghan and Amir Zamir},
    booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
    year={2023},
}

@article{4m21,
    title={{4M-21}: An Any-to-Any Vision Model for Tens of Tasks and Modalities},
    author={Roman Bachmann and O{\u{g}}uzhan Fatih Kar and David Mizrahi and Ali Garjani and Mingfei Gao and David Griffiths and Jiaming Hu and Afshin Dehghan and Amir Zamir},
    journal={arXiv 2024},
    year={2024},
}

📄 License

The model weights in this repository are released under the Sample Code license as found in the LICENSE file.