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Since the initial release, community contributions have pushed data efficiency from ~2.4x to 5.5x against modded-nanogpt, more than doubling in a few days. The key changes are: shuffling at the start of each epoch, which had outsized impact on multi-epoch training; learned projections for value embeddings instead of separate embedding tables; swapping squared ReLU for SwiGLU activation; and ensembling multiple models. 10x data efficiency seems reachable in the short term. 100x might be feasible by the end of the year, given how many directions remain unexplored, but it will require serious exploration on the algorithms side.
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但她也深刻意識到捐贈者家人送出的「不可思議的禮物」，讓她能夠親自懷孕並生下自己的孩子。
Алексей Пушков. Фото: Виталий Белоусов / РИА Новости