GitHub - NVIDIA/Megatron-LM: Ongoing research training transformer models at scale

🚀 Dive into the world of large-scale transformer language models with NVIDIA's Megatron-LM! 🤖📚 Train models with billions of parameters, explore memory-efficient techniques, and tackle various NLP tasks. Check out this powerhouse tool for cutting-edge AI research! #AI #NLP #MegatronLM

NVIDIA's Megatron-LM repository focuses on developing large-scale transformer language models like GPT, BERT, and T5.
Megatron is capable of model-parallel and data-parallel pre-training of language models with hundreds of billions of parameters.
The repository demonstrates scaling studies up to 1 trillion parameter models on 3072 GPUs using NVIDIA's Selene supercomputer.
Various model sizes are tested with differing attention heads, hidden sizes, layers, and batch sizes to show scalability.
Memory-efficient techniques like activation checkpointing, distributed optimizer, and FlashAttention are supported for large model training.
Megatron is used for different tasks like training generative language models, downstream task evaluation, and text generation.
Retro and InstructRetro are specialized models for autoregressive language model pretraining with retrieval augmentation.
The repository provides scripts for data preprocessing, BERT and GPT pretraining, and evaluation on various tasks like RACE and MNLI.
Detailed instructions are given for setting up the environment, downloading checkpoints, and running different evaluation tasks on trained models.
Reproducibility in training is emphasized to ensure bitwise reproducibility for model checkpoints, losses, and accuracy metrics.