GLoRA:One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning O、Abstract 本文在 LoRA 的基础上,提出一种广义 LoRA (GLoRA,Generalized LoRA)。与 LoRA 相比,GLoRA 的理论更加通用,它采用一个通用的模块,来优化预训练的模型权重。 一、Motivation nameformulatheoryweakness VPT [x1,Z1,E1]=L1(...
本文介绍使用PEFT( 参数高效微调, Parameter Efficient Fine-Tuning)的LoRA方法,来通过调整模型的一小部分参数来实现模型的fine-tuning。 使用的微调方法为 LoRA(低秩适应, Low Rank Adaptation)在微调过程中通过…
Matrix-Transformation Based Low-Rank Adaptation (MTLoRA): A Brain-Inspired Method for Parameter-Efficient Fine-Tuning 标题:MTLoRA:一种受大脑启发的参数高效微调方法 地址:https://arxiv.org/pdf/2403.…
其先在Pre-training阶段通过一个模型在大规模无监督语料上预先训练一个预训练语言模型(Pre-trained Language Model,PLM),然后在Fine-tuning阶段基于训练好的语言模型在具体的下游任务上再次进行微调(Fine-tuning),以获得适应下游任务的模型。
DyLoRA: Parameter-Efficient Tuning of Pretrained Models using Dynamic Search-Free Low Rank Adaptation https://arxiv.org/pdf/2210.07558v2.pdf https://github.com/huawei-noah/KD-NLP/tree/main/DyLoRA Part1前言 LoRA存在的问题: rank的值是固定的,训练完成后不能修改。
从来没有想过这样finetuning能够几乎性能不掉，所以笔者想了解下PEFT(parameter-efficient finetuning)这...
figure 1, which is not possible on a single A100-40 GB card. Hence, to overcome this memory capacity limitation on a single A100 GPU, we can use a parameter-efficient fine-tuning (PEFT) technique. We will be using one such technique known as Low-Rank Adaptation (LoRA) for t...
为了解决微调参数量太多的问题,同时也要保证微调效果,急需研发出参数高效的微调方法(Parameter Efficient Fine Tuning, PEFT)。目前,已经涌现出不少参数高效的微调方法,其中主流的方法包括: LoRA P-tuning v2 Freeze 2. LoRA 微调方法 2.1 LoRA 微调方法的基本概念...
[3] QLoRA: Efficient Finetuning of Quantized LLMs [4] AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning [5] Delta-LoRA: Fine-Tuning High-Rank Parameters with the Delta of Low-Rank Matrices [6] Finding structure with randomness: Probabilistic algorithms for constructing appro...
[2] Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning [3] QLoRA: Efficient Finetuning of Quantized LLMs [4] AdaLoRA: Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning [5] Delta-LoRA: Fine-Tuning High-Rank Parameters with the Delta of Low-Rank Matrices...