Hello everyone, I am Wei Xue AI. Today I will introduce to you the practical application of large models 16 - a fine-tuning technique for large pre-trained models: the practical application of the Adapter-Tuning method, with a detailed introduction to the principle. Adapter-Tuning is a technique for fine-tuning large pre-trained models, which can increase model performance while keeping the number of model parameters small. This technology inserts an adapter into the middle layer of a pre-trained model to allow only a small number of parameters to be modified when fine-tuning a specific task, thereby improving the efficiency and speed of fine-tuning.
Unlike traditional fine-tuning methods, Adapter-Tuning only requires adding adapters without retraining the entire model. This makes Adapter-Tuning more scalable and flexible, as the same adapter can be used for multiple different tasks without the need to retrain the entire model for each task.
1. Introduction
In the field of NLP, fine-tuning pre-trained large models is an important part of the NLP research process. Technical research on fine-tuning large models has developed rapidly in recent years. Fine-tuning often involves updating all parameters of these large models, which can cost us a lot of computing resources and time. The Adapter-Tuning method was used in the 2019 paper "Parameter-Efficient Transfer Learning for NLP". This method makes the fine-tuning of large models more efficient by introducing a small number of parameters. The Adapter-Tuning method is in the pre-trained model The Adapter module is introduced while keeping the original parameters unchanged for training. For example, by adding an adapter module layer to some layers of the transformer model, the principle parameters remain unchanged during training, and only the parameters of the adapter module layer are trained. This can greatly save computing resources and training time.
2. Mathematical principles of Adapter-Tuning
The core idea of Adapter-Tuning is to insert small trainable layers or "adapters" into the pre-trained model. Mathematically, these adapters can be understood as functions that transform representations at different levels of the model.
let us consider a