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In this work, we develop and release Yuan 2.0, a series of large languagemodels with parameters ranging from 2.1 billion to 102.6 billion. The LocalizedFiltering-based Attention (LFA) is introduced to incorporate prior knowledge oflocal dependencies of natural language into Attention. A data filtering andgenerating system is presented to build pre-training and fine-tuning dataset inhigh quality. A distributed training method with non-uniform pipeline parallel,data parallel, and optimizer parallel is proposed, which greatly reduces thebandwidth requirements of intra-node communication, and achieves goodperformance in large-scale distributed training. Yuan 2.0 models displayimpressive ability in code generation, math problem-solving, and chattingcompared with existing models. The latest version of YUAN 2.0, including modelweights and source code, is accessible at Github.

YUAN 2.0: A Large Language Model with Localized Filtering-based Attention