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Soofi S outperforms larger European models while maintaining a fraction of the active parameters, redefining the potential of open-source foundation models.
English LLMs dominate math reasoning with a richer set of parameters, while lower-resource languages struggle with significant gaps.
Translated datasets can significantly elevate the quality of German language models, with KletterMix showing measurable performance gains over existing resources.
LLMs can get a free performance boost: decoupling compute and capacity within each layer lets you beat standard transformers at the same FLOPs.
Looping helps transformers think harder on math problems, while memory lets them remember more commonsense facts, and combining both beats simply scaling up layers.
