Parameter-efficient Fine-tuning Methods

로그인

통찰 - Parameter-efficient Fine-tuning Methods

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.

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.

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.

DoRA: Weight-Decomposed Low-Rank Adaptation

DoRA enhances fine-tuning efficiency by decomposing weights into magnitude and direction components, outperforming LoRA.

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.

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.

DoRA: Weight-Decomposed Low-Rank Adaptation

DoRA enhances fine-tuning by decomposing weights into magnitude and direction components, outperforming LoRA.

소개

제품 | 리소스

통찰