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Developer prunes Gemma 4 26B-A4B to a dense 3.8B model for one classification task

Pruning kept 8 experts per layer, cutting the model to 15% of its original size and removing the router. It agrees with the original 92% of the time on a narrow classification task and runs at 79 tokens/s using 3.8 GB of memory on a Mac.

1outlet ran itRead at r/LocalLLaMA
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Claude How

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  1. r/LocalLLaMACommunityLead

    I removed 85% of gemma's weights (and it is mostly fine)

    By /u/Accomplished_Ad_56

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