fairseq

Fairseq is an open-source toolkit for sequence modeling developed by Facebook AI Research (FAIR). It provides reference implementations for training and evaluating neural sequence-to-sequence models, with an emphasis on translation, language modeling, and speech tasks. Built on PyTorch, fairseq offers modular components for data processing, model architectures, and training loops, enabling researchers to implement and experiment with new ideas efficiently.

Core features include a suite of model architectures such as Transformer-based encoders and decoders, convolutional seq2seq models, and recurrent variants; a flexible training engine with support for distributed data parallelism, mixed-precision training, and multi-GPU scaling; and a command-line interface suite (tools like fairseq-train, fairseq-generate) for training, evaluation, and decoding. The toolkit also ships with pre-trained models and recipes for common tasks, including BART, mBART, and wav2vec 2.0, as well as multilingual translation capabilities through mBART.

Fairseq is released under the MIT license and maintained by FAIR with community contributions. It is designed to be lightweight yet extensible, allowing researchers to add new architectures and training objectives while preserving a common training loop and data pipeline. The project has been widely used in academic research and by industry teams to reproduce results and build custom models for sequence-to-sequence tasks.