Parameter-Efficient Finetuning (PEFT) and Adapter Modules in Transformers

Author(s): Saif Ali Kheraj

Originally published on Towards AI.

Fine-tuning large pre-trained models is an essential step in adapting them to specific tasks. However, traditional full fine-tuning requires updating all parameters, leading to high computational costs, increased memory usage, and a risk of overfitting. To address these challenges, researchers have developed Parameter-Efficient Fine-Tuning (PEFT) methods, which allow models to adapt to new tasks while modifying only a small subset of parameters.

Among PEFT techniques, Adapter Modules have emerged as a popular solution, enabling efficient fine-tuning while maintaining the general knowledge stored in the pre-trained model. This article explores PEFT, the role of adapters in transformers, their advantages over full fine-tuning, and an end-to-end PyTorch implementation.

Figure 1: https://www.researchgate.net/figure/The-overall-architecture-of-Adapter-Tuning-Note-that-the-original-parameters-of-the_fig1_372917644

Before we dive into fine-tuning, it’s crucial to understand what pretraining actually means. Pretraining is the process where a large Transformer-based model (like BERT, GPT, or T5) learns from massive text datasets before fine-tuning on a specific task. Pretraining is done using self-supervised learning and typically follows one of these strategies:

Goal: Predict missing words in a sentence.Example:Input: The cat sat on the [MASK].Model Prediction: The cat sat on the mat.Goal: Predict the next word in a sequence.Example:Input: The cat sat on theModel Prediction: mat.Goal: Convert one sequence into another, such as translation or summarization.Example… Read the full blog for free on Medium.

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Published via Towards AI