Adapter Tuning for Large Foundation Models

This white paper serves as a technical reference outlining Google's approach to adapter tuning for large foundation models. It details the process of tuning a pre-trained model to perform specific tasks using user-provided training datasets. The document discusses the significance of foundation models in artificial intelligence and machine learning, emphasizing their capability to learn from large datasets and adapt to various applications. It introduces Parameter Efficient Fine Tuning (PEFT) as a method for efficiently modifying foundation models by adding minimal parameters. The paper also addresses security and privacy principles related to customer data, including encryption and customer-managed encryption keys. Furthermore, it describes the design and implementation of adapter tuning on Google Cloud's Vertex AI, highlighting the training process, data security measures, and the deployment of adapter weights for inference. The document concludes with security considerations to ensure data protection throughout the tuning and serving processes.