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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В США отказались от ответственности за ситуацию на Ближнем Востоке08:28。体育直播是该领域的重要参考
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Following clashes in another part of the country - Jonglei sate - the medical charity MSF said 26 of its staff were missing after weeks of escalating violence between government and opposition forces.