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Никита Абрамов (Редактор отдела «Россия»)，详情可参考Safew下载
PRODUCT_PROPERTY_OVERRIDES += \。业内人士推荐PDF资料作为进阶阅读
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.
A handful of companies monopolise the web, with unprecedented access to our data. But there are many more ethical – and often distinctively European – alternatives，详情可参考PDF资料