A type of ML model that is trained on a broad dataset. These models are generally used for general or broad use cases, and may be adaptable through fine-tuning.

Foundation models like the ones powering OpenAI’s ChatGPT, can cost hundreds of millions of dollars to train.

The term was coined in Aug. 2021 at Stanford.

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Foundation models are near universally based on Transformers.