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LoFT: Low-Rank Adaptation That Behaves Like Full Fine-Tuning

LoFT: Low-Rank Adaptation That Behaves Like Full Fine-Tuning

Nurbek Tastan
Stefanos Laskaridis
Martin Takáč
Karthik Nandakumar
Samuel Horváth
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Papers citing "LoFT: Low-Rank Adaptation That Behaves Like Full Fine-Tuning"

4 / 4 papers shown
Title
DeLoRA: Decoupling Angles and Strength in Low-rank Adaptation
DeLoRA: Decoupling Angles and Strength in Low-rank Adaptation
Massimo Bini
Leander Girrbach
Zeynep Akata
203
1
0
23 Mar 2025
Initialization using Update Approximation is a Silver Bullet for Extremely Efficient Low-Rank Fine-Tuning
Initialization using Update Approximation is a Silver Bullet for Extremely Efficient Low-Rank Fine-Tuning
Kaustubh Ponkshe
Raghav Singhal
Eduard A. Gorbunov
Alexey Tumanov
Samuel Horváth
Praneeth Vepakomma
271
7
0
29 Nov 2024
LoRA-Pro: Are Low-Rank Adapters Properly Optimized?
LoRA-Pro: Are Low-Rank Adapters Properly Optimized?
Zhengbo Wang
Jian Liang
Ran He
Zilei Wang
Tieniu Tan
170
29
0
25 Jul 2024
PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models
PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models
Fanxu Meng
Zhaohui Wang
Muhan Zhang
VLM
153
104
0
03 Apr 2024