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Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product Operators., , , , , and . ACL/IJCNLP (1), page 5388-5398. Association for Computational Linguistics, (2021)Small Pre-trained Language Models Can be Fine-tuned as Large Models via Over-Parameterization., , , , and . ACL (1), page 3819-3834. Association for Computational Linguistics, (2023)YuLan: An Open-source Large Language Model., , , , , , , , , and 28 other author(s). CoRR, (2024)Compressing deep neural networks by matrix product operators., , , , , , and . CoRR, (2019)Unlocking Data-free Low-bit Quantization with Matrix Decomposition for KV Cache Compression., , , , , and . ACL (1), page 2430-2440. Association for Computational Linguistics, (2024)Parameter-Efficient Mixture-of-Experts Architecture for Pre-trained Language Models., , , , and . COLING, page 3263-3273. International Committee on Computational Linguistics, (2022)Enhancing Scalability of Pre-trained Language Models via Efficient Parameter Sharing., , , , and . EMNLP (Findings), page 13771-13785. Association for Computational Linguistics, (2023)Enhancing Parameter-efficient Fine-tuning with Simple Calibration Based on Stable Rank., , , , and . LREC/COLING, page 6024-6035. ELRA and ICCL, (2024)Unlocking Data-free Low-bit Quantization with Matrix Decomposition for KV Cache Compression., , , , , and . CoRR, (2024)Discovering symbolic expressions with parallelized tree search., , , , , and . CoRR, (2024)