All models use a vocabulary size of 51,200 and a sequence length of 2048. To demonstrate how the code scales with multiple GPUs and model sizes, we consider GPT models from 1 billion all the way to 1 trillion parameters. Our codebase is capable of efficiently training very large (hundreds of billions of parameters) language models with both model and data parallelism. Megatron is also used in NeMo Megatron, a framework to help enterprises overcome the challenges of building and training sophisticated natural language processing models with billions and trillions of parameters.

Multi-Stage Prompting for Knowledgeable Dialogue Generation.

Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model.

Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models.

Few-shot Instruction Prompts for Pretrained Language Models to Detect Social Biases.

Training Question Answering Models From Synthetic Data.

RACE Reading Comprehension Dataset Leaderboard.

MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models.

Local Knowledge Powered Conversational Agents.

Large Scale Multi-Actor Generative Dialog Modeling.

End-to-End Training of Neural Retrievers for Open-Domain Question Answering.BioMegatron: Larger Biomedical Domain Language Model.

We developed efficient, model-parallel ( tensor, sequence, and pipeline), and multi-node pre-training of transformer based models such as GPT, BERT, and T5 using mixed precision.īelow are some of the projects where we have directly used Megatron: This repository is for ongoing research on training large transformer language models at scale. Megatron ( 1, 2, and 3) is a large, powerful transformer developed by the Applied Deep Learning Research team at NVIDIA.

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