CHERRY combines selective ground-truth token training, recurrent depth compression from 48 to 6 layers, and mixture-of-efficient-experts to achieve competitive loss with fewer parameters on a 1.8B Korean model.
A.X-K1: A sovereign Korean foundation model.Preprint at https://arxiv
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CHERRY: Compressed Hierarchical Experts with Recurrent Representational Yield
CHERRY combines selective ground-truth token training, recurrent depth compression from 48 to 6 layers, and mixture-of-efficient-experts to achieve competitive loss with fewer parameters on a 1.8B Korean model.