6 papers
ADMM-Q: An Improved Hessian-based Weight Quantizer for Post-Training Quantization of Large Language Models
Ryan Lucas, Mehdi Makni, Xiang Meng +2
Quantization is an effective strategy to reduce the storage and computation footprint of large language models (LLMs). Post-training quantization (PTQ) is a leading approach for co…
Reasoning Models Can be Accurately Pruned Via Chain-of-Thought Reconstruction
Ryan Lucas, Kayhan Behdin, Zhipeng Wang +3
Reasoning language models such as DeepSeek-R1 produce long chain-of-thought traces during inference time which make them costly to deploy at scale. We show that using compression t…
MOONSHOT : A Framework for Multi-Objective Pruning of Vision and Large Language Models
Gabriel Afriat, Xiang Meng, Shibal Ibrahim +2
Weight pruning is a common technique for compressing large neural networks. We focus on the challenging post-training one-shot setting, where a pre-trained model is compressed with…
3BASiL: An Algorithmic Framework for Sparse plus Low-Rank Compression of LLMs
Mehdi Makni, Xiang Meng, Rahul Mazumder
Sparse plus Low-Rank decomposition of Large Language Models (LLMs) has emerged as a promising direction in model compression, aiming to decompose pre-t…
A GPU-accelerated Nonlinear Branch-and-Bound Framework for Sparse Linear Models
Xiang Meng, Ryan Lucas, Rahul Mazumder
We study exact sparse linear regression with an penalty and develop a branch-and-bound (BnB) algorithm explicitly designed for GPU execution. Starting from a perspe…
Preserving Deep Representations In One-Shot Pruning: A Hessian-Free Second-Order Optimization Framework
Ryan Lucas, Rahul Mazumder
We present SNOWS, a one-shot post-training pruning framework aimed at reducing the cost of vision network inference without retraining. Current leading one-shot pruning methods min…