ARMOR is a one-shot post-training algorithm that factorizes weight matrices into a 2:4 sparse core wrapped by adaptive block-diagonal matrices, outperforming existing semi-structured pruning on Llama and Qwen models.
As Algorithm 2 states, the learning rateηis determined via localβ-smoothness, which are calculated in D
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ARMOR: High-Performance Semi-Structured Pruning via Adaptive Matrix Factorization
ARMOR is a one-shot post-training algorithm that factorizes weight matrices into a 2:4 sparse core wrapped by adaptive block-diagonal matrices, outperforming existing semi-structured pruning on Llama and Qwen models.