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--- |
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license: other |
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license_name: sample-code-license |
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license_link: LICENSE |
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library_name: ml-4m |
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--- |
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# 4M: Massively Multimodal Masked Modeling |
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*David Mizrahi\*, Roman Bachmann\*, Oğuzhan Fatih Kar, Teresa Yeo, Mingfei Gao, Afshin Dehghan, Amir Zamir* |
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Official implementation and pre-trained models for "4M: Massively Multimodal Masked Modeling" (NeurIPS 2023). |
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[`Website`](https://4m.epfl.ch) | [`Paper`](https://arxiv.org/abs/2312.06647) | [`GitHub`](https://github.com/apple/ml-4m) |
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4M is a framework for training "any-to-any" foundation models, using tokenization and masking to scale to many diverse modalities. |
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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. |
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## Installation |
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For install instructions, please see https://github.com/apple/ml-4m. |
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## Usage |
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This model can be loaded from Hugging Face Hub as follows: |
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```python |
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from fourm.models.fm import FM |
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fm = FM.from_pretrained('EPFL-VILAB/4M-7_B_COYO700M') |
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``` |
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Please see https://github.com/apple/ml-4m/blob/main/README_GENERATION.md for more detailed instructions and https://github.com/apple/ml-4m for other 4M model and tokenizer checkpoints. |
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Safetensors checkpoints are hosted under https://huggingface.co/EPFL-VILAB/4M. |
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## Citation |
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If you find this repository helpful, please consider citing our work: |
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``` |
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@inproceedings{mizrahi20234m, |
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title={{4M}: Massively Multimodal Masked Modeling}, |
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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}, |
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booktitle={Thirty-seventh Conference on Neural Information Processing Systems}, |
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year={2023}, |
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} |
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``` |
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## License |
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The model weights in this repository are released under the Sample Code license as found in the [LICENSE](LICENSE) file. |
