Large language models (LLMs) are yielding remarkable results for many NLP tasks, but training them is challenging due to the demand for a lot of GPU memory and extended training time. To address these challenges, various parallelism paradigms have been developed, along with memory-saving techniques to enable the effective training of LLMs. In this article, we will describe these methods.
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