{"id":61991,"date":"2025-04-07T07:51:24","date_gmt":"2025-04-07T07:51:24","guid":{"rendered":""},"modified":"2025-04-07T07:51:24","modified_gmt":"2025-04-07T07:51:24","slug":"%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-61991","status":"publish","type":"product","link":"https:\/\/express24.ir\/d\/product\/%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-61991\/","title":{"rendered":"\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 modegpt: \u062a\u062c\u0632\u06cc\u0647 \u0645\u062f\u0648\u0644\u0627\u0631 \u0628\u0631\u0627\u06cc \u0641\u0634\u0631\u062f\u0647 \u0633\u0627\u0632\u06cc \u0645\u062f\u0644 \u0632\u0628\u0627\u0646 \u0628\u0632\u0631\u06af"},"content":{"rendered":"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\nMoDeGPT: Modular Decomposition for Large Language Model Compression<\/td>\n<\/tr>\n
\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 modegpt: \u062a\u062c\u0632\u06cc\u0647 \u0645\u062f\u0648\u0644\u0627\u0631 \u0628\u0631\u0627\u06cc \u0641\u0634\u0631\u062f\u0647 \u0633\u0627\u0632\u06cc \u0645\u062f\u0644 \u0632\u0628\u0627\u0646 \u0628\u0632\u0631\u06af<\/td>\n<\/tr>\n
\u0646\u0648\u06cc\u0633\u0646\u062f\u06af\u0627\u0646 <\/td>\nChi-Heng Lin, Shangqian Gao, James Seale Smith, Abhishek Patel, Shikhar Tuli, Yilin Shen, Hongxia Jin, Yen-Chang Hsu<\/td>\n<\/tr>\n
\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc <\/td>\nPDF<\/td>\n<\/tr>\n
\u0632\u0628\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u062a\u062d\u0648\u06cc\u0644\u06cc <\/td>\n\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc<\/td>\n<\/tr>\n
\u0641\u0631\u0645\u062a \u0645\u0642\u0627\u0644\u0647 \u062a\u0631\u062c\u0645\u0647 \u0634\u062f\u0647 <\/td>\n\u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\n<\/tr>\n
\u0646\u062d\u0648\u0647 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 <\/td>\n\u062f\u0648 \u062a\u0627 \u0633\u0647 \u0631\u0648\u0632 \u067e\u0633 \u0627\u0632 \u062b\u0628\u062a \u0633\u0641\u0627\u0631\u0634 (\u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u062f\u0627\u0646\u0644\u0648\u062f\u06cc)<\/td>\n<\/tr>\n
\u062a\u0639\u062f\u0627\u062f \u0635\u0641\u062d\u0627\u062a<\/td>\n31<\/td>\n<\/tr>\n
\u0644\u06cc\u0646\u06a9 \u062f\u0627\u0646\u0644\u0648\u062f \u0631\u0627\u06cc\u06af\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc<\/td>\n\u062f\u0627\u0646\u0644\u0648\u062f \u0645\u0642\u0627\u0644\u0647<\/a><\/td>\n<\/tr>\n
\u062f\u0633\u062a\u0647 \u0628\u0646\u062f\u06cc \u0645\u0648\u0636\u0648\u0639\u0627\u062a <\/td>\nMachine Learning,Computation and Language,Machine Learning,\u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 , \u0645\u062d\u0627\u0633\u0628\u0647 \u0648 \u0632\u0628\u0627\u0646 , \u06cc\u0627\u062f\u06af\u06cc\u0631\u06cc \u0645\u0627\u0634\u06cc\u0646 ,<\/td>\n<\/tr>\n
\u062a\u0648\u0636\u06cc\u062d\u0627\u062a <\/td>\nSubmitted 13 September, 2024; v1 submitted 18 August, 2024; originally announced August 2024. , Comments: 31 pages, 9 figures , MSC Class: 15A23 (Primary) ACM Class: I.2.7<\/td>\n<\/tr>\n
\u062a\u0648\u0636\u06cc\u062d\u0627\u062a \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc <\/td>\n\u0627\u0631\u0633\u0627\u0644 \u0634\u062f\u0647 13 \u0633\u067e\u062a\u0627\u0645\u0628\u0631 2024 \u061bV1 \u0627\u0631\u0633\u0627\u0644 \u0634\u062f\u0647 18 \u0627\u0648\u062a 2024 \u061b\u062f\u0631 \u0627\u0628\u062a\u062f\u0627 \u0627\u0648\u062a 2024 \u0627\u0639\u0644\u0627\u0645 \u0634\u062f \u060c \u0646\u0638\u0631\u0627\u062a: 31 \u0635\u0641\u062d\u0647 \u060c 9 \u0634\u06a9\u0644 \u060c \u06a9\u0644\u0627\u0633 MSC: 15A23 (\u0627\u0648\u0644\u06cc\u0647) \u06a9\u0644\u0627\u0633 ACM: I.2.7<\/td>\n<\/tr>\n
\u0627\u0637\u0644\u0627\u0639\u0627\u062a \u0628\u06cc\u0634\u062a\u0631 \u0627\u0632 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \u062f\u0631 \u067e\u0627\u06cc\u06af\u0627\u0647 \u0647\u0627\u06cc \u0639\u0644\u0645\u06cc <\/td>\n\n INSPIRE HEP<\/a>
\n
\n NASA ADS<\/a>
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\n Google Scholar<\/a>
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\n Semantic Scholar<\/a>
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\n arXiv<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\r\n\r\n \r\n \r\n \r\n \r\n
\u0641\u0631\u0645\u062a \u0627\u0631\u0627\u0626\u0647 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u062a\u062d\u0648\u06cc\u0644 \u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\r\n <\/tr>\r\n
\u0632\u0645\u0627\u0646 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u0628\u06cc\u0646 2 \u062a\u0627 3 \u0631\u0648\u0632 \u067e\u0633 \u0627\u0632 \u062b\u0628\u062a \u0633\u0641\u0627\u0631\u0634<\/td>\r\n <\/tr>\r\n\t
\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647 <\/td>\r\n \u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.<\/td>\r\n <\/tr>\r\n\t\t
\u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 <\/td>\r\n \u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.<\/td>\r\n <\/tr>\r\n<\/table>\r\n\r\n\n

\u0686\u06a9\u06cc\u062f\u0647<\/h2>\n

Large Language Models (LLMs) have reshaped the landscape of artificial intelligence by demonstrating exceptional performance across various tasks. However, substantial computational requirements make their deployment challenging on devices with limited resources. Recently, compression methods using low-rank matrix techniques have shown promise, yet these often lead to degraded accuracy or introduce significant overhead in parameters and inference latency. This paper introduces \\textbf{Mo}dular \\textbf{De}composition (MoDeGPT), a novel structured compression framework that does not need recovery fine-tuning while resolving the above drawbacks. MoDeGPT partitions the Transformer block into modules comprised of matrix pairs and reduces the hidden dimensions via reconstructing the module-level outputs. MoDeGPT is developed based on a theoretical framework that utilizes three well-established matrix decomposition algorithms — Nystr\u00f6m approximation, CR decomposition, and SVD — and applies them to our redefined transformer modules. Our comprehensive experiments show MoDeGPT, without backward propagation, matches or surpasses previous structured compression methods that rely on gradient information, and saves 98% of compute costs on compressing a 13B model. On \\textsc{Llama}-2\/3 and OPT models, MoDeGPT maintains 90-95% zero-shot performance with 25-30% compression rates. Moreover, the compression can be done on a single GPU within a few hours and increases the inference throughput by up to 46%.<\/p>\n

\u0686\u06a9\u06cc\u062f\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc (\u062a\u0631\u062c\u0645\u0647 \u0645\u0627\u0634\u06cc\u0646\u06cc)<\/h2>\n

\u0645\u062f\u0644 \u0647\u0627\u06cc \u0628\u0632\u0631\u06af \u0632\u0628\u0627\u0646 (LLM) \u0628\u0627 \u0646\u0634\u0627\u0646 \u062f\u0627\u062f\u0646 \u0639\u0645\u0644\u06a9\u0631\u062f \u0627\u0633\u062a\u062b\u0646\u0627\u06cc\u06cc \u062f\u0631 \u06a9\u0627\u0631\u0647\u0627\u06cc \u0645\u062e\u062a\u0644\u0641 \u060c \u0645\u0646\u0638\u0631\u0647 \u0647\u0648\u0634 \u0645\u0635\u0646\u0648\u0639\u06cc \u0631\u0627 \u062a\u063a\u06cc\u06cc\u0631 \u0634\u06a9\u0644 \u062f\u0627\u062f\u0647 \u0627\u0646\u062f.\u0628\u0627 \u0627\u06cc\u0646 \u062d\u0627\u0644 \u060c \u0627\u0644\u0632\u0627\u0645\u0627\u062a \u0645\u062d\u0627\u0633\u0628\u0627\u062a\u06cc \u0642\u0627\u0628\u0644 \u062a\u0648\u062c\u0647\u06cc \u0628\u0627\u0639\u062b \u0645\u06cc \u0634\u0648\u062f \u0627\u0633\u062a\u0642\u0631\u0627\u0631 \u0622\u0646\u0647\u0627 \u0628\u0631\u0627\u06cc \u062f\u0633\u062a\u06af\u0627\u0647 \u0647\u0627\u06cc\u06cc \u0628\u0627 \u0645\u0646\u0627\u0628\u0639 \u0645\u062d\u062f\u0648\u062f \u0628\u0647 \u0686\u0627\u0644\u0634 \u0628\u06a9\u0634\u062f.\u0628\u0647 \u062a\u0627\u0632\u06af\u06cc \u060c \u0631\u0648\u0634 \u0647\u0627\u06cc \u0641\u0634\u0631\u062f\u0647 \u0633\u0627\u0632\u06cc \u0628\u0627 \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0627\u0632 \u062a\u06a9\u0646\u06cc\u06a9 \u0647\u0627\u06cc \u0645\u0627\u062a\u0631\u06cc\u0633 \u062f\u0631\u062c\u0647 \u067e\u0627\u06cc\u06cc\u0646 \u0646\u0648\u06cc\u062f \u0631\u0627 \u0646\u0634\u0627\u0646 \u062f\u0627\u062f\u0647 \u0627\u0646\u062f \u060c \u0627\u0645\u0627 \u0627\u06cc\u0646 \u0645\u0648\u0627\u0631\u062f \u0627\u063a\u0644\u0628 \u0645\u0646\u062c\u0631 \u0628\u0647 \u062f\u0642\u062a \u062a\u062e\u0631\u06cc\u0628 \u0634\u062f\u0647 \u06cc\u0627 \u0645\u0639\u0631\u0641\u06cc \u0633\u0631\u0628\u0627\u0631 \u0642\u0627\u0628\u0644 \u062a\u0648\u062c\u0647\u06cc \u062f\u0631 \u067e\u0627\u0631\u0627\u0645\u062a\u0631\u0647\u0627 \u0648 \u062a\u0623\u062e\u06cc\u0631 \u0627\u0633\u062a\u0646\u0628\u0627\u0637 \u0645\u06cc \u0634\u0648\u0646\u062f.\u062f\u0631 \u0627\u06cc\u0646 \u0645\u0642\u0627\u0644\u0647 \\ textbf {mo} dular \\ textbf {de} \u062a\u0631\u06a9\u06cc\u0628 (modegpt) \u060c \u06cc\u06a9 \u0686\u0627\u0631\u0686\u0648\u0628 \u0641\u0634\u0631\u062f\u0647 \u0633\u0627\u0632\u06cc \u0633\u0627\u062e\u062a\u0627\u0631 \u06cc\u0627\u0641\u062a\u0647 \u062c\u062f\u06cc\u062f \u06a9\u0647 \u0646\u06cc\u0627\u0632\u06cc \u0628\u0647 \u062a\u0646\u0638\u06cc\u0645 \u062e\u0648\u0628 \u062f\u0631 \u0636\u0645\u0646 \u062d\u0644 \u0627\u0634\u06a9\u0627\u06cc\u0631 \u0641\u0648\u0642 \u0646\u06cc\u0633\u062a \u060c \u0645\u0639\u0631\u0641\u06cc \u0634\u062f\u0647 \u0627\u0633\u062a.Partitions Modegpt \u0628\u0644\u0648\u06a9 \u062a\u0631\u0627\u0646\u0633\u0641\u0648\u0631\u0645\u0627\u062a\u0648\u0631 \u0631\u0627 \u0628\u0647 \u0645\u0627\u0698\u0648\u0644\u0647\u0627\u06cc \u0645\u062a\u0634\u06a9\u0644 \u0627\u0632 \u062c\u0641\u062a \u0647\u0627\u06cc \u0645\u0627\u062a\u0631\u06cc\u0633 \u0648 \u0627\u0628\u0639\u0627\u062f \u067e\u0646\u0647\u0627\u0646 \u0627\u0632 \u0637\u0631\u06cc\u0642 \u0628\u0627\u0632\u0633\u0627\u0632\u06cc \u062e\u0631\u0648\u062c\u06cc \u0647\u0627\u06cc \u0633\u0637\u062d \u0645\u0627\u0698\u0648\u0644 \u06a9\u0627\u0647\u0634 \u0645\u06cc \u062f\u0647\u062f.Modegpt \u0628\u0631 \u0627\u0633\u0627\u0633 \u06cc\u06a9 \u0686\u0627\u0631\u0686\u0648\u0628 \u0646\u0638\u0631\u06cc \u0633\u0627\u062e\u062a\u0647 \u0634\u062f\u0647 \u0627\u0633\u062a \u06a9\u0647 \u0627\u0632 \u0633\u0647 \u0627\u0644\u06af\u0648\u0631\u06cc\u062a\u0645 \u0647\u0627\u06cc \u062a\u062c\u0632\u06cc\u0647 \u0645\u0627\u062a\u0631\u06cc\u0633 \u0628\u0647 \u062e\u0648\u0628\u06cc \u062a\u062b\u0628\u06cc\u062a \u0634\u062f\u0647-\u062a\u0642\u0631\u06cc\u0628 Nystr\u00f6m \u060c \u062a\u062c\u0632\u06cc\u0647 CR \u0648 SVD \u0627\u0633\u062a\u0641\u0627\u062f\u0647 \u0645\u06cc \u06a9\u0646\u062f-\u0648 \u0622\u0646\u0647\u0627 \u0631\u0627 \u062f\u0631 \u0645\u0627\u0698\u0648\u0644 \u0647\u0627\u06cc \u062a\u0631\u0627\u0646\u0633\u0641\u0648\u0631\u0645\u0627\u062a\u0648\u0631 \u062a\u0639\u0631\u06cc\u0641 \u0634\u062f\u0647 \u0645\u0627 \u0627\u0639\u0645\u0627\u0644 \u0645\u06cc \u06a9\u0646\u062f.\u0622\u0632\u0645\u0627\u06cc\u0634 \u0647\u0627\u06cc \u062c\u0627\u0645\u0639 \u0645\u0627 \u0646\u0634\u0627\u0646 \u0645\u06cc \u062f\u0647\u062f Modegpt \u060c \u0628\u062f\u0648\u0646 \u0627\u0646\u062a\u0634\u0627\u0631 \u0639\u0642\u0628 \u060c \u0627\u0632 \u0631\u0648\u0634\u0647\u0627\u06cc \u0641\u0634\u0631\u062f\u0647 \u0633\u0627\u0632\u06cc \u0633\u0627\u062e\u062a\u0627\u0631\u06cc \u0642\u0628\u0644\u06cc \u06a9\u0647 \u0628\u0647 \u0627\u0637\u0644\u0627\u0639\u0627\u062a \u0634\u06cc\u0628 \u0645\u062a\u06a9\u06cc \u0647\u0633\u062a\u0646\u062f \u060c \u0645\u0637\u0627\u0628\u0642\u062a \u06cc\u0627 \u067e\u06cc\u0634\u06cc \u0645\u06cc \u06af\u06cc\u0631\u062f \u0648 98 \u066a \u0627\u0632 \u0647\u0632\u06cc\u0646\u0647 \u0647\u0627\u06cc \u0645\u062d\u0627\u0633\u0628\u0647 \u0631\u0627 \u062f\u0631 \u0641\u0634\u0631\u062f\u0647 \u0633\u0627\u0632\u06cc \u06cc\u06a9 \u0645\u062f\u0644 13B \u0630\u062e\u06cc\u0631\u0647 \u0645\u06cc \u06a9\u0646\u062f.\u062f\u0631 \\ textsc {llama} -2\/3 \u0648 \u0645\u062f\u0644 \u0647\u0627\u06cc OPT \u060c Modegpt \u0639\u0645\u0644\u06a9\u0631\u062f \u0635\u0641\u0631 90-95 \u066a \u0628\u0627 \u0646\u0631\u062e \u0641\u0634\u0631\u062f\u0647 \u0633\u0627\u0632\u06cc 25-30 \u066a \u0631\u0627 \u062d\u0641\u0638 \u0645\u06cc \u06a9\u0646\u062f.\u0639\u0644\u0627\u0648\u0647 \u0628\u0631 \u0627\u06cc\u0646 \u060c \u0641\u0634\u0631\u062f\u0647 \u0633\u0627\u0632\u06cc \u0631\u0627 \u0645\u06cc \u062a\u0648\u0627\u0646 \u0637\u06cc \u0686\u0646\u062f \u0633\u0627\u0639\u062a \u0628\u0631 \u0631\u0648\u06cc \u06cc\u06a9 GPU \u0648\u0627\u062d\u062f \u0627\u0646\u062c\u0627\u0645 \u062f\u0627\u062f \u0648 \u062a\u0648\u0627\u0646 \u0627\u0633\u062a\u0646\u062a\u0627\u062c \u0631\u0627 \u062a\u0627 46 \u066a \u0627\u0641\u0632\u0627\u06cc\u0634 \u0645\u06cc \u062f\u0647\u062f.<\/p>\n\r\n\r\n \r\n \r\n \r\n \r\n
\u0641\u0631\u0645\u062a \u0627\u0631\u0627\u0626\u0647 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u062a\u062d\u0648\u06cc\u0644 \u0628\u0647 \u0635\u0648\u0631\u062a \u0641\u0627\u06cc\u0644 \u0648\u0631\u062f<\/td>\r\n <\/tr>\r\n
\u0632\u0645\u0627\u0646 \u062a\u062d\u0648\u06cc\u0644 \u062a\u0631\u062c\u0645\u0647 \u0645\u0642\u0627\u0644\u0647 <\/td>\r\n \u0628\u06cc\u0646 2 \u062a\u0627 3 \u0631\u0648\u0632 \u067e\u0633 \u0627\u0632 \u062b\u0628\u062a \u0633\u0641\u0627\u0631\u0634<\/td>\r\n <\/tr>\r\n\t
\u06a9\u06cc\u0641\u06cc\u062a \u062a\u0631\u062c\u0645\u0647 <\/td>\r\n \u0628\u0633\u06cc\u0627\u0631 \u0628\u0627\u0644\u0627. \u0645\u0642\u0627\u0644\u0647 \u0641\u0642\u0637 \u062a\u0648\u0633\u0637 \u0645\u062a\u0631\u062c\u0645\u06cc\u0646 \u0628\u0627 \u0645\u062f\u0631\u06a9 \u062f\u0627\u0646\u0634\u06af\u0627\u0647\u06cc \u0645\u062a\u0631\u062c\u0645\u06cc \u062a\u0631\u062c\u0645\u0647 \u0645\u06cc\u200c\u0634\u0648\u062f.<\/td>\r\n <\/tr>\r\n\t\t
\u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 <\/td>\r\n \u06a9\u0644\u06cc\u0647 \u062c\u062f\u0627\u0648\u0644 \u0648 \u0641\u0631\u0645\u0648\u0644 \u0647\u0627 \u0646\u06cc\u0632 \u062f\u0631 \u0641\u0627\u06cc\u0644 \u062a\u062d\u0648\u06cc\u0644\u06cc \u0648\u0631\u062f \u062f\u0631\u062c \u0645\u06cc\u200c\u0634\u0648\u0646\u062f.<\/td>\r\n <\/tr>\r\n<\/table>\r\n\r\n\n","protected":false},"excerpt":{"rendered":"

\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc MoDeGPT: Modular Decomposition for Large Language Model Compression \u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc \u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 modegpt: […]<\/p>\n","protected":false},"featured_media":27,"comment_status":"open","ping_status":"open","template":"","meta":{"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}}},"product_cat":[21],"product_tag":[],"class_list":{"0":"post-61991","1":"product","2":"type-product","3":"status-publish","4":"has-post-thumbnail","6":"product_cat-21","7":"pmpro-has-access","8":"desktop-align-left","9":"tablet-align-left","10":"mobile-align-left","12":"first","13":"instock","14":"downloadable","15":"shipping-taxable","16":"purchasable","17":"product-type-simple"},"yoast_head":"\n\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 modegpt: \u062a\u062c\u0632\u06cc\u0647 \u0645\u062f\u0648\u0644\u0627\u0631 \u0628\u0631\u0627\u06cc \u0641\u0634\u0631\u062f\u0647 \u0633\u0627\u0632\u06cc \u0645\u062f\u0644 \u0632\u0628\u0627\u0646 \u0628\u0632\u0631\u06af - \u0641\u0631\u0648\u0634\u06af\u0627\u0647 \u0627\u06a9\u0633\u067e\u0631\u0633<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/express24.ir\/d\/product\/\u062a\u0631\u062c\u0645\u0647-\u0641\u0627\u0631\u0633\u06cc-\u0645\u0642\u0627\u0644\u0647-61991\/\" \/>\n<meta property=\"og:locale\" content=\"fa_IR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 modegpt: \u062a\u062c\u0632\u06cc\u0647 \u0645\u062f\u0648\u0644\u0627\u0631 \u0628\u0631\u0627\u06cc \u0641\u0634\u0631\u062f\u0647 \u0633\u0627\u0632\u06cc \u0645\u062f\u0644 \u0632\u0628\u0627\u0646 \u0628\u0632\u0631\u06af - \u0641\u0631\u0648\u0634\u06af\u0627\u0647 \u0627\u06a9\u0633\u067e\u0631\u0633\" \/>\n<meta property=\"og:description\" content=\"\u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0627\u0646\u06af\u0644\u06cc\u0633\u06cc MoDeGPT: Modular Decomposition for Large Language Model Compression \u0639\u0646\u0648\u0627\u0646 \u0645\u0642\u0627\u0644\u0647 \u0628\u0647 \u0641\u0627\u0631\u0633\u06cc \u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 modegpt: […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/express24.ir\/d\/product\/\u062a\u0631\u062c\u0645\u0647-\u0641\u0627\u0631\u0633\u06cc-\u0645\u0642\u0627\u0644\u0647-61991\/\" \/>\n<meta property=\"og:site_name\" content=\"\u0641\u0631\u0648\u0634\u06af\u0627\u0647 \u0627\u06a9\u0633\u067e\u0631\u0633\" \/>\n<meta property=\"og:image\" content=\"https:\/\/express24.ir\/d\/wp-content\/uploads\/2024\/02\/Elsevier_logo_2019.svg_.png\" \/>\n\t<meta property=\"og:image:width\" content=\"440\" \/>\n\t<meta property=\"og:image:height\" content=\"486\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/express24.ir\/d\/product\/%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-61991\/\",\"url\":\"https:\/\/express24.ir\/d\/product\/%d8%aa%d8%b1%d8%ac%d9%85%d9%87-%d9%81%d8%a7%d8%b1%d8%b3%db%8c-%d9%85%d9%82%d8%a7%d9%84%d9%87-61991\/\",\"name\":\"\u062a\u0631\u062c\u0645\u0647 \u0641\u0627\u0631\u0633\u06cc \u0645\u0642\u0627\u0644\u0647 modegpt: \u062a\u062c\u0632\u06cc\u0647 \u0645\u062f\u0648\u0644\u0627\u0631 \u0628\u0631\u0627\u06cc \u0641\u0634\u0631\u062f\u0647 \u0633\u0627\u0632\u06cc \u0645\u062f\u0644 \u0632\u0628\u0627\u0646 \u0628\u0632\u0631\u06af - 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