Overview

fairseq2 is a sequence modeling framework designed for research and deployment of advanced machine learning models. It builds on the strengths of its predecessor, fairseq, with modernized architecture, enhanced extensibility, and streamlined workflows for training and fine-tuning.

Key Features

Command Line Interface (CLI)

  • Unified commands for training, fine-tuning, and evaluating models.

  • Modular options to customize tasks and configurations.

  • Learn more: CLI

Assets

  • Pre-trained models, datasets, and configurations bundled for easy reuse.

  • Simplified asset management for seamless integration.

  • Learn more: Assets

Model Loader

  • A robust mechanism for loading and saving models.

  • Supports checkpointing, versioning, and compatibility.

Data Pipeline

  • Extensible pipeline for preprocessing, augmentation, and batching.

  • Optimized for large-scale datasets and varied formats.

  • Learn more: Data Pipeline

Gang

  • A novel abstraction for distributed tasks and parallel processing.

  • Simplifies workload distribution and resource management.

Design Principles

Dependency Inversion

  • Decouples components to promote flexibility and testing.

  • Encourages the development of reusable modules.

  • Learn more: Design Philosophy

Simplicity

  • Emphasizes clear APIs and intuitive workflows.

  • Minimizes boilerplate code and unnecessary complexity.

Modularity

  • Designed with modularity in mind.

  • Easy to add new datasets, models and trainers.

  • Learn more: Runtime Extensions

Performance

  • Optimized for scalability and efficiency.

  • Supports state-of-the-art techniques for distributed training.

Community-Centric

  • Active collaboration and contributions from the research community.

  • Comprehensive documentation and resources for onboarding.

  • Learn more: Contributing to fairseq2