3 papers
cs.LG2025
ARMOR: High-Performance Semi-Structured Pruning via Adaptive Matrix Factorization
Lawrence Liu, Alexander Liu, Mengdi Wang +2
Large language models (LLMs) present significant deployment challenges due to their immense computational and memory requirements. While semi-structured pruning, particularly 2:4 s…
cs.LG2025
NoWag: A Unified Framework for Shape Preserving Compression of Large Language Models
Lawrence Liu, Inesh Chakrabarti, Yixiao Li +3
Large language models (LLMs) exhibit remarkable performance across various natural language processing tasks but suffer from immense computational and memory demands, limiting thei…
cs.CL2025
IDEA Prune: An Integrated Enlarge-and-Prune Pipeline in Generative Language Model Pretraining
Yixiao Li, Xianzhi Du, Ajay Jaiswal +4
Recent advancements in large language models have intensified the need for efficient and deployable models within limited inference budgets. Structured pruning pipelines have shown…