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 nanoT5, a specially-optimized PyTorch framework for efficient pre-training and fine-tuning of T5 models. Drawing on insights from optimizer differences and prioritizing efficiency, nanoT5 allows a T5-Base 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.

翻译：以T5为代表的先进语言模型彻底革新了自然语言处理领域，但其计算需求限制了大部分研究社区的参与。为应对这一挑战，我们提出nanoT5，这是一个专为高效预训练和微调T5模型而优化的PyTorch框架。通过利用优化器差异的见解并优先考虑效率，nanoT5使得T5-Base模型能够在单个GPU上仅用16小时完成预训练，且性能毫无损失。随着这一开源框架的推出，我们希望拓宽语言建模研究的可及性，满足社区对更易于使用的T5（编码器-解码器）实现的需求。我们将包括配置、代码库、预训练见解及预训练模型在内的所有贡献公之于众。