LoRA: Low-Rank Adaptation of Large Language Models 简读finisky.github.io/lora/ 之前我们谈到 Adapters 与 Prompting 都是轻量级的训练方法,所谓 lightweight-finetuning。今天来看一下另一种轻量级训练大语言模型的方法: LoRA: Low-Rank Adaptation of Large Language Models 微调大规模语言模型到特殊领域和...
本文的方法SiRA,将 Sparse MOE 和 lora 结合起来,相比 lora 收敛更快。相比 MoLoRA 节约了计算资源。 方法倒是很直接。也考虑到了 MoE 常规会考虑的Token Capacity 和 Auxiliary Loss. 效果的话: 其实也是半斤八俩,略好。 不过作者也说了 倒是挺合适。
Low-rank adaptation (LoRA) Basically, in LoRA, you create two downstream weight matrices. One transforms the input parameters from the original dimension to the low-rank dimension. And the second matrix transforms the low-rank data to the output dimensions of the original model. During training,...
In the context of LoRA, the concept of rank plays a pivotal role in determining the efficiency and effectiveness of the adaptation process. Remarkably, the paper highlights that the rank of the matrices A and B can be astonishingly low, sometimes as low as one. ...
Q10: What about Layer-wise Optimal Rank Adaptation? (In the previous issue of AI, I mentioned that I wanted to write a more general introduction with a from-scratch code implementation of LoRA sometime if there's interest. According to your feedback, there's a lot of interest, and I pl...
Last week, researchers proposedDoRA: Weight-Decomposed Low-Rank Adaptation, a new alternative to LoRA, which may outperform LoRA by a large margin. DoRA is a promising alternative to standard LoRA (annotated figure from the DoRA paper: https://arxiv.org/abs/2402.09353) ...
We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks. For GPT-3, LoRA can reduce the number of...
LoRA (low rank adaptation) implemented in Rust for use withCandle. This technique interchanges the fully-trainable layers of the model with new, LoRA layers. These LoRA layers act as a wrapper over the original layers, but freeze the original layers. Because they contain fewer trainable paramete...
as shown in 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 (L...
https://github.com/microsoft/LoRAgithub.com/microsoft/LoRA 一、概述 我最开始了解到LoRA是从CV这边的stable diffusion上,很多人使用LoRA去对原始模型进行"微调",从而能生成不同风格的图片。但是这个技术最开始其实是为了大语言模型的微调来的,在NLP这边模型已经是越来越大,现在几百个B的模型都很多了,但是这种...