Large language models (LLMs) exhibit remarkable performance across various natural language processing tasks but suffer from immense computational and memory demands, limiting their deployment in resource-constrained environments. To address this challenge, we propose NoWag (Normalized Weight and Activation Guided Compression), a unified framework for one-shot shape preserving compression algorithms. We apply NoWag to compress Llama-2 (7B, 13B, 70B) and Llama-3 (8B, 70B) models using two popular shape-preserving techniques: vector quantization (NoWag-VQ) and unstructured/semi-structured pruning (NoWag-P). Our results show that NoWag-VQ significantly outperforms state-of-the-art one-shot vector quantization methods, while NoWag-P performs competitively against leading pruning techniques. These findings highlight underlying commonalities between these compression paradigms and suggest promising directions for future research. Our code is available at https://github.com/LawrenceRLiu/NoWag
@article{arxiv.2504.14569,
title = {NoWag: A Unified Framework for Shape Preserving Compression of Large Language Models},
author = {Lawrence Liu and Inesh Chakrabarti and Yixiao Li and Mengdi Wang and Tuo Zhao and Lin F. Yang},
journal= {arXiv preprint arXiv:2504.14569},
year = {2026}
}