Mini toolsDefinition
Empirical principles describing how AI model performance improves with increases in model size, data, and compute.
Detailed Explanation
Empirical principles describing how model performance scales with increases in model size, dataset size, and computational budget, often used to predict the performance of larger models.
Use Cases
Predicting performance of larger models before training, guiding resource allocation for model development, informing decisions on model architecture vs data size trade-offs.
