DoRA: Weight-Decomposed Low-Rank Adaptation
DoRA enhances fine-tuning by decomposing weights into magnitude and direction components, outperforming LoRA.
ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models
ALoRA method enhances parameter-efficient fine-tuning by dynamically allocating low-rank adaptation ranks.
ALoRA: Allocating Low-Rank Adaptation for Fine-tuning Large Language Models
ALoRA introduces a novel approach to dynamically adjust the intrinsic rank during adaptation, outperforming recent baselines in various tasks.
DoRA: Weight-Decomposed Low-Rank Adaptation
DoRA enhances fine-tuning efficiency by decomposing weights into magnitude and direction components, outperforming LoRA.
DoRA: Weight-Decomposed Low-Rank Adaptation Unveiled
DoRA introduces a novel weight decomposition analysis to enhance fine-tuning capabilities, outperforming LoRA across various tasks and architectures.
Efficient Fine-Tuning of Large Convolutional Models for Task-Specific Representation
The author introduces a method for efficient fine-tuning of large convolutional models by focusing on adjusting filter atoms, achieving task-specific representation with minimal parameters.
Efficient Multitask Multilingual Model Adaptation with Featurized Low-Rank Mixtures
Featurized Low-rank Mixtures (FLix) offer a novel approach to efficient multitask multilingual tuning, outperforming standard methods in diverse data mixtures.
© 2024 by Linnk AI