EvoPress: Accurate Dynamic Model Compression via Evolutionary Search

• URL: http://arxiv.org/abs/2410.14649v2
• Date: Tue, 01 Jul 2025 14:41:08 GMT
• Title: EvoPress: Accurate Dynamic Model Compression via Evolutionary Search
• Authors: Oliver Sieberling, Denis Kuznedelev, Eldar Kurtic, Dan Alistarh,
• Abstract summary: EvoPress is an evolutionary framework for dynamic compression of large language models.<n>It identifies optimal compression profiles in a highly efficient manner.<n>We set new benchmarks for structural pruning (block/layer dropping), unstructured sparsity, and quantization with dynamic bitwidths.
• Score: 33.86918407429272
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The high computational costs of large language models (LLMs) have led to a flurry of research on LLM compression, via methods such as quantization, sparsification, or structured pruning. A new frontier in this area is given by dynamic, non-uniform compression methods, which adjust the compression levels (e.g., sparsity) per-block or even per-layer in order to minimize accuracy loss, while guaranteeing a global compression threshold. Yet, current methods rely on estimating the importance of a given layer, implicitly assuming that layers contribute independently to the overall compression error. We begin from the motivating observation that this independence assumption does not generally hold for LLM compression: pruning a model further may even significantly recover performance. To address this, we propose EvoPress, a novel evolutionary framework for dynamic LLM compression. By formulating dynamic compression as a general optimization problem, EvoPress identifies optimal compression profiles in a highly efficient manner, and generalizes across diverse models and compression techniques. Via EvoPress, we achieve state-of-the-art performance for dynamic compression of Llama, Mistral, and Phi models, setting new benchmarks for structural pruning (block/layer dropping), unstructured sparsity, and quantization with dynamic bitwidths. Our code is available at https://github.com/IST-DASLab/EvoPress}.

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