Use when training or fine-tuning a deep model and tuning it for best accuracy. Covers transfer learning, two-phase fine-tuning, discriminative/layer-wise LR, LR schedules + warmup, regularization tuned to over/under-fit (dropout, weight decay, label smoothing, MixUp), mixed precision, gradient checkpointing, EMA, checkpoint/resume, and fitting a GPU/time budget. Triggers on 'fine-tune', 'train the model', 'best hyperparameters', 'overfitting', 'learning rate', 'out of memory', 'training is too slow'.
How to install training-optimization?
mxslr/mlcraft/training-optimization
$npx -y skills add mxslr/mlcraft --skill training-optimizationInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/mxslr/mlcraft" --skill "mxslr/mlcraft/training-optimization"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/mxslr/mlcraft" that are relevant to the current task. Run `npx skills add "https://github.com/mxslr/mlcraft"` and select the relevant skills, then follow their instructions.
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