大规模语言模型的跨云联合训练关键技术

摘要：大规模语言模型在人工智能领域发挥着越来越重要的作用。然而，随着模型参数规模的不断增加，模型训练所需的算力资源也随之变得更加庞大，导致很多情况下单个算力集群难以满足大规模语言模型的训练需求。因此，大规模语言模型的跨云联合训练成为了解决这一问题的有效方式。本文以自然语言处理大模型的跨云预训练和微调为例，介绍了大规模语言模型跨云训练的主要挑战和关键技术，并探讨了这些技术在跨云训练过程中的具体应用、实际效果和未来场景。

关键词：大规模语言模型；算力资源；跨云训练；自然语言处理

Abstract: Large-scale language models are playing an increasingly important role in the field of artificial intelligence. However, as the scale of model parameters continues to grow, the computational resources required for model training also become significantly larger. This often leads to situations where a single computing cluster is insufficient to meet the training needs of large-scale language models. Therefore, cross-cloud joint training of large-scale language models has emerged as an effective solution to address this challenge. In this study, taking cross-cloud pre-training and fine-tuning of natural language processing models as examples, we introduce the main challenges and key technologies involved in cross-cloud training of large-scale language models. The specific applications, practical effects, and future scenarios of these technologies in the cross-cloud training process are explored.

Keywords: large-scale language model; computational resource; cross-cloud training; natural language processing