LoGo preserves the reported scaling behavior of full-attention Transformers from 200M through 3.3B parameters, matching or improving the baseline on the listed language-modeling and commonsense metrics.

LoGo preserves the reported scaling behavior of full-attention Transformers from 200M through 3.3B parameters, matching or improving the baseline on the listed language-modeling and commonsense metrics. · CiteArk

测量结果

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指标与条件	论文报告	最近可用实测	证据判断
comparison: full-attention Transformer · model_variant: LoGo · reported_scope: single run · parameter_count: 3.3B · training_corpus: in-house corpus similar to Seed-OSS	1.996 loss	—	尚未评估
dataset: WikiText · model_variant: LoGo · reported_scope: single run · parameter_count: 3.3B	14.785 perplexity	—	尚未评估
model_variant: LoGo · reported_scope: single reported evaluation · parameter_count: 3.3B	58.26 percentage_points	—	尚未评估

LoGo preserves the reported scaling behavior of full-attention Transformers from 200M through 3.3B parameters, matching or improving the baseline on the listed language-modeling and commonsense metrics.

报告指标与观测值

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测量结果