Apple Discovers Model Distillation Scaling Law! Stronger Teacher Models Aren't Always Better
Apple researchers uncover a new distillation scaling law, optimizing computational resources for better model performance. Learn how to improve AI efficiency and reduce costs.
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The performance of distillation models can now be quantified.
As we know, knowledge distillation technology is widely used in the field of large models. It allows for significant model size compression while maintaining certain performance, reducing model latency, and improving model accuracy. At the same time, it enables the integration and transfer of knowledge domains.
Recently, Apple researchers proposed a Distillation Scaling Law, based on computational budgets and their distribution between students and teachers, which now allows us to estimate the performance of distillation models.