Showing 2025 · cs.LGShow all
2 papers · 2 filters
cs.LG2025
OPTIMA: Optimal One-shot Pruning for LLMs via Quadratic Programming Reconstruction
Mohammad Mozaffari, Samuel Kushnir, Maryam Mehri Dehnavi +1
Post-training pruning cuts the inference cost of large language models, yet it faces a trade-off: simple heuristics that zero weights are fast but degrade accuracy, while principle…
cs.LG2025
CafeQ: Calibration-free Quantization via Learned Transformations and Adaptive Rounding
Ziteng Sun, Adrian Benton, Samuel Kushnir +4
Post-training quantization is an effective method for reducing the serving cost of large language models, where the standard approach is to use a round-to-nearest quantization leve…