Gemstones: A Model Suite for Multi-Faceted Scaling Laws

Tom Goldstein (University of Maryland) · Micah Goldblum (Columbia University) · Sean McLeish (University of Maryland) · John Kirchenbauer (University of Maryland, College Park) · Siddharth Singh (University of Maryland, College Park) · Abhinav Bhatele (University of Maryland, College Park) · Ashwinee Panda (University of Maryland) · David Miller (Department of Computer Science, University of Maryland, College Park)
ablationsarchitectural shapescheckpointscomplex studiescooldownexperimental designgemstones datasethyperparameter choiceslearning ratemodel suiteparameter sensitivityscaling lawsscaling prescriptionstransformerswidth-depth relationship

Scaling laws are typically fit using a family of models with a narrow range of frozen hyperparameter choices. In this work we study scaling laws using multiple architectural shapes and hyperparameter choices, highlighting their impact on resulting prescriptions. As a primary artifact of our research, we release the Gemstones: an open-source scaling law dataset, consisting of over 4000 checkpoints from transformers with up to 2 billion parameters and diverse architectural shapes; including ablations over learning rate and cooldown. Our checkpoints enable more complex studies of scaling, such as analyzing the relationship between width and depth. By examining our model suite, we find that the prescriptions of scaling laws can be highly sensitive to the experimental design process and the specific model checkpoints used during fitting.