license: other
license_name: sample-code-license
license_link: LICENSE
library_name: ml-4m
4M: Massively Multimodal Masked Modeling
David Mizrahi*, Roman Bachmann*, Oğuzhan Fatih Kar, Teresa Yeo, Mingfei Gao, Afshin Dehghan, Amir Zamir
Official implementation and pre-trained models for "4M: Massively Multimodal Masked Modeling" (NeurIPS 2023).
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.
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-7_B_COYO700M')
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.
Safetensors checkpoints are hosted under https://huggingface.co/EPFL-VILAB/4M.
Citation
If you find this repository helpful, please consider citing our work:
@inproceedings{mizrahi20234m,
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},
}
License
The model weights in this repository are released under the Sample Code license as found in the LICENSE file.