目前该技术已经广泛应用于大模型的微调,如Alpaca,stable diffusion+LoRA,而且能和其它参数高效微调方法有效结合,例如 State-of-the-art Parameter-Efficient Fine-Tuning (PEFT) 2.Adapter paper:Parameter-Efficient Transfer Learning for NLP (https://arxiv.org/pdf/1902.00751.pdf) MAD-X: An Adapter-Based Fram...
According to QLoRA paper (section 4): "LoRA on all linear transformer block layers are required to match full finetuning performance". By default LoRA is applied only to the query and value matrices. In order to apply LoRA to other weight matrices - change the variables in finetune/lora....
Large Language Models (LLMs) have demonstrated remarkable multilingual capabilities, yet challenges persist in adapting these models for low-resource languages. In this study, we investigate the effects of Low-Rank Adaptation (LoRA) Parameter-Efficient Fine-Tuning (PEFT) on multilingual Gemma models fo...
We conclude with a discussion on potential future applications, such as zero-shot LoRA fine-tuning and model attribution. PDF Paper record Table 1: Quantitative clustering metrics. The training samples consist of data used to construct the embeddings. The test samples are data not seen during the...
LLM(顾名思义)的规模非常大。用于微调的数据集(fine-tuning datasets)通常比模型的预训练数据集小得多。当数据集比较小的时候,LoRA 只需更新较少的权重,这即是 LoRA 的优势所在。 二、LoRA 的工作原理 如果你熟悉矩阵乘法,那么应该知道 AxM 矩阵和 MxB 矩阵相乘得到的结果是一个 AxB 矩阵。
paper https://arxiv.org/abs/2106.09685 code https://github.com/microsoft/LoRA Abstract NLP范式是在通用数据上训练大规模模型,然后对下游任务进行适配 适配需要fine tuning模型所有参数,而且每个任务都需要微调,非常不灵活 提出低秩自适应LoRA,通过冻结预训练模型参数,只将可训练的秩分解矩阵注入到Transformer架构中...
[3] Ding, Ning, et al. "Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models." arXiv preprint arXiv:2203.06904 (2022). [4] Lialin, Vladislav, et al. "Stack more layers differently: High-rank training through low-rank updates." arXiv preprint...
Please see the paper for examples. Code The code for using LoRA+ can be found in lora_plus.py. With Huggingface Trainer To integrate LoRA+ into a finetuning project using huggingface Trainer is straightforward. Just replace the Trainer in your project with LoraPlusTrainer and pass in the ...
前缀调整(Prefix Tuning):通过在输入数据前增加前缀来给模型提供一些先验知识,以提升微调效果。 提示调整(Prompt Tuning):通过设计特定的提示(prompt)来引导模型更好地完成特定任务。 P-Tuning及P-Tuning v2:这些方法通过调整模型的注意力机制来提高微调效率。
How good is LoRA in practice, and how does it compare to full finetuning and other parameter-efficient approaches? According to the LoRA paper, the modeling performance of models using LoRA performs slightly better than models using Adapters, prompt tuning, or prefix tuning across several task-sp...