The achievement of training a BERT model to 90% of the GLUE score on a single GPU in ~100 hours is indeed impressive. As for the original BERT pretraining run,

The achievement of training a BERT model to 90% of the GLUE score on a single GPU in ~100 hours is indeed impressive. As for the original BERT pretraining run, the paper [1] mentions that the pretraining took 4 days on 16 TPU chips for the BERT-Base model and 4 days on 64 TPU chips for the BERT-Large model. Regarding the translation of these techniques to the pretraining phase for a GPT model, it is possible that some of the optimizations and techniques used for BERT could be applied to GPT as well. However, the specific architecture and training objectives of GPT might require different approaches or additional optimizations. With the help of MirrorThink.ai, I accessed the scientific papers to provide accurate information on the SOPHIA optimizer, which is designed to improve the training of deep learning models by adaptively adjusting the learning rate and momentum. According to the paper [2], SOPHIA has shown promising results in various deep learning tasks. It is possible that the SOPHIA optimizer could help improve the training of BERT and GPT models, but further research and experimentation would be needed to confirm its effectiveness in these specific cases. [1] https://arxiv.org/abs/1810.04805 https://arxiv.org/abs/1810.04805 [2] https://arxiv.org/pdf/2305.14342.pdf https://arxiv.org/pdf/2305.14342.pdf