Reducing Fine-Tuning Memory Overhead by Approximate and Memory-Sharing
Backpropagation

Reducing Fine-Tuning Memory Overhead by Approximate and Memory-Sharing Backpropagation

24 June 2024

Yuchen Yang

Jun Xu

Papers citing "Reducing Fine-Tuning Memory Overhead by Approximate and Memory-Sharing Backpropagation"

5 / 5 papers shown

Title
CompAct: Compressed Activations for Memory-Efficient LLM Training Yara Shamshoum Nitzan Hodos Yuval Sieradzki Assaf Schuster MQ VLM 42 0 0 20 Oct 2024
P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks Xiao Liu Kaixuan Ji Yicheng Fu Weng Lam Tam Zhengxiao Du Zhilin Yang Jie Tang VLM 238 805 0 14 Oct 2021
MLP-Mixer: An all-MLP Architecture for Vision Ilya O. Tolstikhin N. Houlsby Alexander Kolesnikov Lucas Beyer Xiaohua Zhai ... Andreas Steiner Daniel Keysers Jakob Uszkoreit Mario Lucic Alexey Dosovitskiy 271 2,603 0 04 May 2021
The Power of Scale for Parameter-Efficient Prompt Tuning Brian Lester Rami Al-Rfou Noah Constant VPVLM 280 3,844 0 18 Apr 2021
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Jinpeng Wang Amanpreet Singh Julian Michael Felix Hill Omer Levy Samuel R. Bowman ELM 297 6,956 0 20 Apr 2018