目前大语言模型如果要进行微调,主要有两种方式Full parameter fine-tuning和这个Parameter Efficient Fine Tuning。Full parameter fine-tuning显而易见,那就是大语言模型整个语言模型里的各个参数,在微调的过程中都去更新一下,这样的方式显然是非常耗费资源和时间的。于是乎,大家开始走Parameter Ef
The main explored property of delta tuning methods in the work. Other important information of the work. Papers Overview Delta Tuning: A Comprehensive Study of Parameter Efficient Methods for Pre-trained Language Model, Preprint 2022. Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang...
parameter-efficient transfer learning methods:冻结 PLM 的参数,只微调一小部分额外的参数。模型可以避免灾难性遗忘,并且快速迁移到新的 task 上。但是典型的三种方法(adapter,prefix-tuning,low-rank matrices)的可解释性都比较低,并且他们之间看似没有什么联系。所以作者 提出了一个统一的视角,找到他们之间的联系:全...
However, traditional full fine-tuning methods pose significant computational challenges, prompting the emergence of Parameter-Efficient Fine-Tuning (PEFT) methods, especially reparameterization-based PEFT methods. In this survey, we delve into reparameterization-based PEFT methods, which aim to fine-tune ...
OpenDelta is a toolkit for parameter-efficient tuning methods (we dub it asdelta tuning), by which users could flexibly assign (or add) a small amount parameters to update while keeping the most parameters frozen. By using OpenDelta, users could easily implement prefix-tuning, adapters, Lora,...
This “delta tuning” [1] approach can be seen as a refined version of retraining specific layers or appending a classifier to a pre-trained model, aiming for comparable performance as fine-tuning the entire model. Following [1]’s nomenclature, parameter-efficient fine-tuning (PEFT) methods ...
In general, despite the substantial reduction of tunable parameters, different delta-tuning methods are almost comparable to FT in performance in most cases. This demonstrates the potential of driving large-scale PLMs through parameter-efficient adaptation. 2. Despite having different design elements, ...
Parameter-efficient fine-tuning (PEFT) is a method of improving the performance of pretrained large language models (LLMs) and neural networks for specific tasks or data sets. By training a small set of parameters and preserving most of the large pretrained model’s structure, PEFT saves time ...
Parameter-efficient fine-tuning (PEFT) is a set of techniques that adjusts only a portion of parameters within an LLM to save resources. PEFT makes LLM customization more accessible while creating outputs that are comparable to a traditional fine-tuned model. Explore Red Hat AI Traditional ...
在一篇综述文章《Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning》中,将PEFT方法归为四类—— 1、Additive methods:最大且应用最广泛的一类方法。这类方法通过额外参数或者layer,扩大预训练模型的规模,仅仅训练新增的参数。 2、Selective methods:微调一个网络的部分参数。 3、Reparametrizati...