<ul data-eligibleForWebStory="true"><li>Research on large language model compression has focused on methods like quantization, sparsification, and structured pruning to reduce computational costs.</li><li>A new approach called EvoPress introduces dynamic, non-uniform compression methods that adjust compression levels per-block or per-layer to minimize accuracy loss while meeting a global compression threshold.</li><li>EvoPress uses an evolutionary framework to identify optimal compression profiles efficiently, challenging the assumption that compression error is independent across layers in language models.</li><li>The EvoPress framework achieves state-of-the-art results in dynamic compression of various models like Llama, Mistral, and Phi through techniques such as structural pruning, sparsity, and quantization with dynamic bitwidths.</li></ul>

EvoPress: Accurate Dynamic Model Compression via Evolutionary Search

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