Scaling laws for neural language models
We study empirical scaling laws for language model performance on the cross-entropy loss. The loss scales as a power-law with model size, dataset size, and the amount of compute used for training, with some trends spanning more than seven orders of...
Topics: ModelsInfrastructure
Entities: ModelsInfrastructure