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Delphi Project: Extrapolated 300x Prediction Large Model Training, Error Only 0.2%
AIMPACT News, May 12 (UTC+8), William Barr Held posted a tweet introducing the Delphi project as the first step of Marin. The project pre-trains multiple small models with a single recipe, then extrapolates 300 times, successfully predicting a training run with 25 billion parameters and 600 billion tokens, with an error of only 0.2%. Delphi aims to achieve predictable scaling to train better open-source models. (Source: InFoQ)