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

State-of-the-art language models like T5 have revolutionized the NLP landscape, but their computational demands hinder a large portion of the research community. To address this challenge, we present nano<PRE_TAG>T5</POST_TAG>, a specially-optimized PyTorch framework for efficient pre-training and fine-tuning of T5 models. Drawing on insights from optimizer differences and prioritizing efficiency, nano<PRE_TAG>T5</POST_TAG> allows a <PRE_TAG>T5-Base</POST_TAG> model to be pre-trained on a single GPU in just 16 hours, without any loss in performance. With the introduction of this open-source framework, we hope to widen the accessibility to language modelling research and cater to the community's demand for more user-friendly T5 (Encoder-Decoder) implementations. We make our contributions, including configurations, codebase, pre-training insights, and pre-trained models, available to the public.