LoRA(Low-Rank Adaptation)是一种用于大型语言模型(LLMs)的微调技术。它的核心思想是在不显著增加参数数量的情况下,通过在模型中引入低秩(low-rank)矩阵来适应特定的任务或数据集。这种方法允许模型在保持原有预训练知识的同时,快速适应新的任务或领域。 LoRA的主要用途包括: 模型微调:LoRA可以用于对预训练的大型语言...
the low-rank adaptation matrix potentiallyamplifies the important features for specific downstream tasks that were learned but not emphasized in the general pre-training model,其实也就是说这个矩阵不会放大原来预训练模型的热症,而是放大了特殊的下游任务的特征,这也是我们微调的目的 Conclusions LoRA: a effici...
总的来说,LoRA通过在原有的复杂模型上添加一个相对简单的修改来实现微调,既节省了计算资源,又保持了模型的高性能。 论文训练环境 显卡,英伟达 Tesla V100 数据集如下图 《LoRa: Low-Rank Adaptation of Large Language Models》 论文摘要 Abstract An important paradigm of natural language processing consists of ...
理论上lora可以支持任何线性层,包括transformer中的4个attention矩阵和2个feed forward中的矩阵,论文旨在attention上做了实验,它限制总参数量不变的情况下观察是在attention其中一个矩阵上,放一个更高秩的lora,还是在多个attention的矩阵上,分别放置低秩一点的lora效果好? 结论是把秩分散到多个矩阵上,效果会优于集中在...
论文《 LoRA: Low-Rank Adaptation of Large Language Models》提出将权重变化 ΔW 分解为秩较低的表示。(LoRA 不会直接分解矩阵,而是通过反向传播来学习分解的矩阵)。 在仔细研究 LoRA 之前,我们先简要解释一下规则微调期间的训练程序。首先是权重变化 ΔW。假设 W 表示给定神经网络层中的权重矩阵。然后,使用常规...
Well, what's the alternative? In the domain of LLM, researchers have developed Efficient fine-tuning methods. LoRA, especially, tackles the very problem the community currently has: end users with Open-sourced stable-diffusion model want to try various other fine-tuned model that is created by...
As one of the most popular parameter-efficient fine-tuning (PEFT) methods, low-rank adaptation (LoRA) is commonly applied to fine-tune large language models (LLMs). However, updating the weights of LoRA blocks effectively and expeditiously is challenging due to the long calculation path in the...
基于清华开源的chatglm6b分别用lora和ptuning两种方式微调,没有使用量化的的情况下,lora需要29G显存,ptuning需要24G显存,最后用微调后的模型做推理需要13G显存(和原chatglm6b一样),供参考～ 参考这位大佬的帖子(感谢分享～):https://github.com/HarderThenHarder/transformers_tasks/tree/main/LLM/finetune 展开更多...
LoRA in Large Language Models (LLMs) Resources Contributing License Introduction Low-Rank Adaptation (LoRA) is a powerful technique used to enhance the performance of various machine learning models by reducing the rank of weight matrices in neural networks. This repository aims to gather all the ...
title: LoRA: Low-Rank Adaptation of Large Language Models LoRA 在大模型任务上, LoRA 是一个堪比瑞士军刀的小而美的工具,能够极大的降低大模型在下游任务的微调训练成本,在LLM、diffussion等方面得到了广泛的应用。 What? LoRA(“Low-Rank Adaptation” ) 是一种高效的自适应下游任务策略,既不引入推理延迟,也...