Mixture of Experts (MoE) framework has become a popular architecture for large language models due to its superior performance compared to dense models. However, training MoEs from scratch in a large-scale regime is prohibitively expensive.

This motivates us to consider a challenging classification problem with intrinsic cluster structures.

Models were trained with 32 experts, with experts placed every 2 layers - except where explicitly stated. The learned contrastive temperature parameter is initialised at 10. We train models at batch size 16,384 for 781,250 steps at resolution 224. These are B/16 models trained for 100,000 steps at batch size 8192. The default training data is mixed with data from JFT -4B with a ratio of 3:1.