Optimizing Large Language Models with Granularity: Unveiling New Scaling Laws for Mixture of Experts

The rapid advancement of large language models (LLMs) has significantly impacted various domains, offering unprecedented capabilities in processing and generating human language. Despite their remarkable achievements, the substantial computational costs of training these gargantuan models have raised financial and environmental sustainability concerns. In this context, exploring Mixture of Experts (MoE) models emerges as a pivotal […]

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