4M: Massively Multimodal Masked Modeling
A framework for training any-to-any multimodal foundation models. Scalable. Open-sourced. Across tens of modalities and tasks.
Official implementation and pre-trained models for :
4M: Massively Multimodal Masked Modeling, NeurIPS 2023 (Spotlight) David Mizrahi*, Roman Bachmann*, Oğuzhan Fatih Kar, Teresa Yeo, Mingfei Gao, Afshin Dehghan, Amir Zamir
4M-21: An Any-to-Any Vision Model for Tens of Tasks and Modalities, arXiv 2024 Roman Bachmann*, Oğuzhan Fatih Kar*, David Mizrahi*, Ali Garjani, Mingfei Gao, David Griffiths, Jiaming Hu, Afshin Dehghan, Amir Zamir
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
This model can be loaded from Hugging Face Hub as follows:
from fourm.models.fm import FM
fm = FM.from_pretrained('EPFL-VILAB/4M-21_B')
Please see README_GENERATION.md for more detailed instructions and https://github.com/apple/ml-4m for other 4M model and tokenizer checkpoints.
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.