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 zero-shot shape preserving compression algorithms. We compressed Llama-2 7B/13B/70B and Llama-3 8/70BB models, using two popular forms of shape-preserving compression, vector quantization NoWag-VQ (NoWag for Vector Quantization), and unstructured/semi-structured pruning NoWag-P (NoWag for Pruning). We found that NoWag-VQ significantly outperforms state-of-the-art zero shot VQ, and that NoWag-P performs competitively against state-of-the-art methods. These results suggest commonalities between these compression paradigms that could inspire future work. Our code is available atthis https URL
View on arXiv@article{liu2025_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={ 2025 }
}