fairseq2

We have hosted the application fairseq2 in order to run this application in our online workstations with Wine or directly.

Quick description about fairseq2:

fairseq2 is a modern, modular sequence modeling framework developed by Meta AI Research as a complete redesign of the original fairseq library. Built from the ground up for scalability, composability, and research flexibility, fairseq2 supports a broad range of language, speech, and multimodal content generation tasks, including instruction fine-tuning, reinforcement learning from human feedback (RLHF), and large-scale multilingual modeling. Unlike the original fairseq—which evolved into a large, monolithic codebase—fairseq2 introduces a clean, plugin-oriented architecture designed for long-term maintainability and rapid experimentation. It supports multi-GPU and multi-node distributed training using DDP, FSDP, and tensor parallelism, capable of scaling up to 70B+ parameter models. The framework integrates seamlessly with PyTorch 2.x features such as torch.compile, Fully Sharded Data Parallel (FSDP), and modern configuration management.

Features:

• Composable and deterministic configuration system
• High-throughput C++ streaming data pipeline for text and speech
• Recipes for instruction fine-tuning, preference optimization, and RLHF
• Native vLLM integration for optimized generation and inference
• Supports 70B+ parameter models with DDP, FSDP, and tensor parallelism
• Modular, next-generation fairseq with a clean, extensible architecture

Programming Language: C, C++, Python, Unix Shell.
Categories: