This post aims to provide a high-level understanding of foundation models, which are trained on broad and massive datasets, can be adapted for various downstream applications, and utilize unsupervised and semi-supervised learning methods. Foundation models have been used to develop popular apps such as Lensa and ChatGPT, and several open-source alternatives like ChatLLA MA. Training a foundation model requires significant amounts of data, which is often provided by organizations like OpenAI and Google. The process of adapting these models for downstream tasks involves fine-tuning or in-context learning methods, which can be computationally expensive but allow the models to perform tasks beyond their original training scope. Foundation models are available for various applications such as text generation, speech recognition, image generation, and more, and can be downloaded and customized using Truss, an open-source model serving framework.