SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models

25 May 2024

Papers citing "SPP: Sparsity-Preserved Parameter-Efficient Fine-Tuning for Large Language Models"

6 / 6 papers shown

Title
Dynamic Low-Rank Sparse Adaptation for Large Language Models Weizhong Huang Yuxin Zhang Xiawu Zheng Ye Liu Jing Lin Yiwu Yao Rongrong Ji 97 1 0 21 Feb 2025
ThinK: Thinner Key Cache by Query-Driven Pruning Yuhui Xu Zhanming Jie Hanze Dong Lei Wang Xudong Lu Aojun Zhou Amrita Saha Caiming Xiong Doyen Sahoo 72 14 0 30 Jul 2024
Effective Interplay between Sparsity and Quantization: From Theory to Practice Simla Burcu Harma Ayan Chakraborty Elizaveta Kostenok Danila Mishin Dongho Ha ... Martin Jaggi Ming Liu Yunho Oh Suvinay Subramanian Amir Yazdanbakhsh MQ 44 5 0 31 May 2024
PERP: Rethinking the Prune-Retrain Paradigm in the Era of LLMs Max Zimmer Megi Andoni Christoph Spiegel Sebastian Pokutta VLM 52 10 0 23 Dec 2023
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks Torsten Hoefler Dan Alistarh Tal Ben-Nun Nikoli Dryden Alexandra Peste MQ 141 684 0 31 Jan 2021
Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism M. Shoeybi M. Patwary Raul Puri P. LeGresley Jared Casper Bryan Catanzaro MoE 245 1,821 0 17 Sep 2019