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State-of-the-art language models like T5 have revolutionized the NLPlandscape, but their computational demands hinder a large portion of theresearch community. To address this challenge, we present nanoT5, aspecially-optimized PyTorch framework for efficient pre-training andfine-tuning of T5 models. Drawing on insights from optimizer differences andprioritizing efficiency, nanoT5 allows a T5-Base model to be pre-trained on asingle GPU in just 16 hours, without any loss in performance. With theintroduction of this open-source framework, we hope to widen the accessibilityto language modelling research and cater to the community's demand for moreuser-friendly T5 (Encoder-Decoder) implementations. We make our contributions,including configurations, codebase, pre-training insights, and pre-trainedmodels, available to the public.

nanoT5: A PyTorch Framework for Pre-training and Fine-tuning T5-style Models with Limited Resources