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Last week we released NanoGPT Slowrun , an open repo for data-efficient learning algorithms. The rules are simple: train on 100M tokens from FineWeb, use as much compute as you want, lowest validation loss wins. Improvements are submitted as PRs to the repo and merged if they lower val loss. The constraint is the inverse of speedruns like modded-nanogpt , which optimize wall-clock time. Those benchmarks have been hugely productive, but optimizing for speed filters out expensive ideas: heavy regularization, second-order optimizers, gradient descent alternatives. Slowrun is built for exactly those ideas.
。PDF资料是该领域的重要参考
Москалькова рассказала о реакции родственников на освобождение пленных бойцов СВО20:47
«Политически и иначе». МИД Ирана рассказал о помощи России и Китая на фоне ударов Запада02:24
。PDF资料对此有专业解读
are better able to keep up with dynamic Python code.
Акция протеста прошла у посольства Украины в стране ЕС20:39。clash下载对此有专业解读