🍎Apple Machine Learning•Stalecollected in 30h
Hyperparameter Transfer Across All Scaling Axes
#research#apple-ml#mu-p#model-scalingcompleted-parameterisationapple-ml
⚡ 30-Second TL;DR
What Changed
Unified width-depth scaling
Why It Matters
Simplifies tuning for large-scale models, boosting performance via small-scale searches. Accelerates development of stable, high-performing neural networks.
What To Do Next
Evaluate benchmark claims against your own use cases before adoption.
Who should care:Researchers & Academics
Key Points
- •Unified width-depth scaling
- •μP extension for full axes
- •Improves training stability/performance
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Original source: Apple Machine Learning ↗
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