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To address this, we propose a hybrid structured pruning method based on layer and channel pruning to compress LLMs, enabling their efficient deployment in power grid systems. Our method first removes noncritical layers based on inter-layer cosine similarity, thereby achieving a significant reduction in model parameters. Then channel-pruning based on PCA is employed, aiming to improve model inference speed while preserving performance. Experimental results on Wikitext2 and PTB demonstrate that at a 30% pruning rate, our method exhibits a superior performance retention compared to single-layer pruning and channel-wise pruning methods. For generation tasks, the model inference speed is increased by 5.91%. Our work provides a novel insight for future research on structured pruning, and inspires more research on composite pruning from multi-dimensional perspectives.<\/jats:p>","DOI":"10.1142\/s0218001425580042","type":"journal-article","created":{"date-parts":[[2025,10,24]],"date-time":"2025-10-24T03:26:33Z","timestamp":1761276393000},"source":"Crossref","is-referenced-by-count":0,"title":["Efficient Compression of Large Language Model based on Hybrid Layer and Channel Pruning"],"prefix":"10.1142","volume":"39","author":[{"given":"Ruixuan","family":"Lu","sequence":"first","affiliation":[{"name":"State Grid Anhui Electric Power Co., Ltd. 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