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arXiv · 2409.20094

Aggressive Post-Training Compression on Extremely Large Language Models

Zining Zhang·Yao Chen·Bingsheng He·Zhenjie Zhang
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Abstract

The increasing size and complexity of Large Language Models (LLMs) pose challenges for their deployment on personal computers and mobile devices. Aggressive post-training model compression is necessary to reduce the models' size, but it often results in significant accuracy loss. To address this challenge, we propose a novel network pruning technology that utilizes over 0.7 sparsity and less than 8 bits of quantization. Our approach enables the compression of prevailing LLMs within a couple of hours while maintaining a relatively small accuracy loss. In experimental evaluations, our method demonstrates effectiveness and potential for practical deployment. By making LLMs available on domestic devices, our work can facilitate a new era of natural language processing applications with wide-ranging impacts.

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Zining Zhang, Yao Chen, Bingsheng He, Zhenjie Zhang. 2024-09-30. Aggressive Post-Training Compression on Extremely Large Language Models. https://arxiv.org/abs/2409.20094

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