MMF is a modular framework for vision-and-language multimodal research from Meta AI (formerly Facebook AI Research), built on PyTorch and open-sourced on GitHub. It has earned more than 5,600 stars and remains actively developed, with its latest updates in 2026.
MMF was created to make multimodal research reproducible and comparable: it bundles state-of-the-art model implementations, standardized datasets and benchmark evaluation for tasks such as visual question answering (VQA), image captioning, TextVQA (reading text in images), dialog and the Hateful Memes challenge. Everything is configured through YAML files, so experiments can be reproduced exactly and new models can be benchmarked against the same baselines. Its modular design separates datasets, models, processors and training loops, making it easy to swap components, run multi-task training, or plug in your own model. A model zoo of pretrained checkpoints lets researchers fine-tune strong baselines instead of starting from scratch, and MMF has underpinned entries in multiple research challenges. That design has made it a common starting point for challenge submissions and course projects alike: a new idea can be evaluated against published numbers within days rather than months, and the community has extended it with additional datasets and models beyond the original Meta AI set.
MMF is completely free and open source. You only pay for your own training compute.
MMF is for multimodal AI researchers and engineers who want strong, well-benchmarked vision-language baselines โ and a sane framework for prototyping new models against standard datasets.