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.
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MaskPro learns categorical distributions over groups of M weights to generate exact (N:M) sparsity via N-way sampling without replacement and stabilizes training with a moving average tracker of loss residuals.
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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.
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MaskPro: Linear-Space Probabilistic Learning for Strict (N:M)-Sparsity on LLMs
MaskPro learns categorical distributions over groups of M weights to generate exact (N:M) sparsity via N-way sampling without replacement and stabilizes training with a moving average tracker of loss residuals.