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The Mathematics of Training LLMs — with Quentin Anthony of Eleuther AI

Latent Space: The AI Engineer Podcast

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Optimizing Training Processes for Language Models

This chapter examines the complexities of optimizing training processes for language models, with a focus on the Atom optimizer and its memory implications. It discusses the balance between model and optimizer state parallelism, as well as strategies for effective memory management and distribution across GPUs. The conversation also delves into advanced concepts in distributed training, emphasizing emerging research areas like multimodal models and communication bottlenecks.

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