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[News] Megatron-LM: NVIDIA trains 8.3B GPT-2 using model and data parallelism on 512 GPUs. SOTA in language modelling and SQUAD. Details awaited.

Code: https://github.com/NVIDIA/Megatron-LM

Unlike Open-AI, they have released the complete code for data processing, training, and evaluation.

Detailed writeup: https://nv-adlr.github.io/MegatronLM

From github:

Megatron is a large, powerful transformer. This repo is for ongoing research on training large, powerful transformer language models at scale. Currently, we support model-parallel, multinode training of GPT2 and BERT in mixed precision.Our codebase is capable of efficiently training a 72-layer, 8.3 Billion Parameter GPT2 Language model with 8-way model and 64-way data parallelism across 512 GPUs. We find that bigger language models are able to surpass current GPT2-1.5B wikitext perplexities in as little as 5 epochs of training.For BERT training our repository trains BERT Large on 64 V100 GPUs in 3 days. We achieved a final language modeling perplexity of 3.15 and SQuAD F1-score of 90.7.

Their submission is not in the leaderboard of SQuAD, but this exceeds the previous best single model performance (RoBERTa 89.8).

For language modelling they get zero-shot wikitext perplexity of 17.4 (8.3B model) better than 18.3 of transformer-xl (257M). However they claim it as SOTA when GPT-2 itself has 17.48 ppl, and another model has 16.4 (https://paperswithcode.com/sota/language-modelling-on-wikitext-103)

Sadly they haven’t mentioned anything about release of the model weights.

submitted by /u/Professor_Entropy
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.