NoWag: A Unified Framework for Shape Preserving Compression of Large Language Models

• URL: http://arxiv.org/abs/2504.14569v1
• Date: Sun, 20 Apr 2025 11:00:29 GMT
• Title: NoWag: A Unified Framework for Shape Preserving Compression of Large Language Models
• Authors: Lawrence Liu, Inesh Chakrabarti, Yixiao Li, Mengdi Wang, Tuo Zhao, Lin F. Yang,
• Abstract summary: Large language models (LLMs) exhibit remarkable performance across various natural language processing tasks.<n>LLMs suffer from immense computational and memory demands, limiting their deployment in resource-constrained environments.<n>We propose NoWag: (Normalized Weight and Activation Guided Compression) as a unified framework for zero-shot shape preserving compression algorithms.
• Score: 63.271278137295006
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 at https://github.com/LawrenceRLiu/NoWag

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