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CoCurve method prunes LLMs by analyzing cross-module dependencies

Researchers have developed CoCurve, a novel training-free method for structured pruning of large language models (LLMs). Unlike previous methods that assess computational units independently, CoCurve considers the interdependencies between attention heads and feed-forward networks within Transformers. By analyzing the co-pruning curvature, which quantifies the additional loss from removing multiple units simultaneously, CoCurve can more effectively identify and remove redundant components without requiring fine-tuning or labels. AI

IMPACT This method could lead to more efficient LLM deployment by enabling effective pruning without the need for retraining.

RANK_REASON The cluster describes a novel method presented in a research paper on arXiv.

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AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

CoCurve method prunes LLMs by analyzing cross-module dependencies

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Zhiren Gong, Zihao Zeng, Zijie Wang, Tiantong Wang, Chau Yuen, Wei Yang Bryan Lim ·

    CoCurve: Cross-Module Co-Pruning Curvature for Training-Free Structured LLM Pruning

    arXiv:2607.17568v1 Announce Type: cross Abstract: Structured pruning compresses large language models (LLMs) by removing whole computational units, such as attention heads and feed-forward (FFN) channel groups. Most training-free methods, however, rank these units independently, …

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    CoCurve: Cross-Module Co-Pruning Curvature for Training-Free Structured LLM Pruning

    Structured pruning compresses large language models (LLMs) by removing whole computational units, such as attention heads and feed-forward (FFN) channel groups. Most training-free methods, however, rank these units independently, implicitly treating the loss from pruning a set as…