fairseq: A Fast, Extensible Toolkit for Sequence Modeling

fairseq is an open-source sequence modeling toolkit that allows researchers and developers to train custom models for a range of text generation tasks, including translation, summarization, language modeling, and other generation tasks. It is designed to be fast and extensible, providing a flexible foundation for building and experimenting with sequence-to-sequence and language models. The toolkit is based on PyTorch, which gives users access to a widely adopted deep learning framework.

On the systems side, fairseq supports distributed training across multiple GPUs and multiple machines, enabling scaling to large models and datasets. It also supports fast mixed-precision training and inference on modern GPUs, improving computational efficiency. The authors provide a demo video, and the toolkit became a widely used platform for sequence modeling research and applications.