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To train better open models, we need predictable scaling. Delphi is Marin’s first step: we pretrained many small models with one recipe, then extrapolated 300× to predict a 25B-param / 600B-token run with just 0.2% error. Getting there took some work 🧵 https://t.co/HmlVFl11ag
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"text": "To train better open models, we need predictable scaling.\n\nDelphi is Marin’s first step: we pretrained many small models with one recipe, then extrapolated 300× to predict a 25B-param / 600B-token run with just 0.2% error.\n\nGetting there took some work 🧵 https://t.co/HmlVFl11ag",
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