Mixture of Experts (2)

Mixture of Experts Explained

Key Takeaway: Mixture-of-experts (MoE) models enable more compute-efficient pretraining and faster inference compared to dense models, but face challenges with generalization during fine-tuning. Recent work on instruction tuning shows promise for improving MoE fine-tuning. MoEs replace the feedforward layers in transformers with sparse MoE layers composed of experts and a…

GPT-4: 8 Models in One; The Secret is Out

Key Takeaway The key takeaway is that GPT-4 is actually an ensemble of 8 separate 220-billion parameter models rather than one single giant model. This mixture of experts approach allows each sub-model to specialize, combining to create one powerful model. Read More