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Learned scaling matrices can dramatically enhance LLM compression efficiency, achieving lower effective rank and better performance than traditional methods.
Replacing softmax with a calibrated clipped-linear approximation can dramatically speed up Transformer inference on edge devices without sacrificing accuracy.
