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Innovative Model Merging Framework: MuDSC
The author proposes MuDSC as a solution to the inconsistency of unit similarity in weight and activation spaces, enhancing model merging performance across various tasks and architectures.
ZipIt! Merging Models from Different Tasks without Training: A Novel Approach
ZipIt! introduces a novel method for merging models trained on different tasks without retraining, outperforming prior work significantly.
ZipIt! Merging Models from Different Tasks without Training at ICLR 2024
ZipIt! introduces a novel method for merging models trained on different tasks without retraining, outperforming prior work significantly.
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