facebookresearch/metaseq

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6548 stars · 714 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Metaseq is a PyTorch codebase from Meta AI for training and working with large-scale Open Pre-trained Transformers (OPT), forked from fairseq. It provides training workflows and APIs for models up to 175B parameters, with community integrations for inference via Hugging Face, DeepSpeed, FasterTransformer, and others.

Use cases

• train large language models on hundreds of GPUs
• reproduce or extend the OPT-175B training pipeline
• fine-tune large transformer models with data and model parallelism
• convert OPT checkpoints for use with inference engines like FasterTransformer or CTranslate2
• study the training log of a 175B parameter model
• run large-scale pretraining experiments in PyTorch

When to choose

• you need to pretrain or fine-tune very large transformer models with distributed training
• you want to work directly with the OPT model family in its original codebase
• you need a fairseq-compatible codebase for large-scale LLM research

When to avoid

• you just want to run inference with OPT models - use Hugging Face Transformers instead
• you need actively maintained tooling - the repo is largely in maintenance mode
• you are training small models where simpler frameworks like Hugging Face Trainer suffice

Facets

library · maturity maintenance

llm-training llm-inference deep-learning machine-learning large-language-models deep-learning machine-learning artificial-intelligence python opt transformers large-scale-training fairseq-fork facebook-research distributed-training gpu linux docker

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