Summary:
– Language models are costly to train and deploy, prompting researchers to explore model distillation.
– Model distillation involves training a smaller student model to mimic the performance of a larger teacher model.
– The goal is to achieve efficient deployment while maintaining performance levels.
Author’s Take:
The introduction of a distillation scaling law by Apple’s AI paper highlights a move towards training efficient language models using a compute-optimal approach. By focusing on distillation techniques, the industry can work towards cost-effective solutions without compromising on model performance.
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