Mixture of Experts (MoEs) are a type of neural network architecture that can improve the efficiency and performance of large language models. Instead of activating all parameters for every input, MoEs selectively activate specialized sub-networks, or "experts," which can lead to faster inference and reduced computational cost. This approach allows models to scale to much larger sizes while remaining computationally feasible. Hugging Face has published a blog post detailing the architecture and implementation of MoEs within the Transformer framework. AI
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RANK_REASON Blog post detailing a specific model architecture (Mixture of Experts) and its implementation within Transformers.
