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A plethora of new adaptive optimizers are designed to efficiently estimate and use minibatch gradient statistics to shape parameter updates, but they are typically benchmarked at a single batch size. Hyperparameter scaling rules promise to preserve performance as batch size and gradient noise change, suggesting that th...
This report presents an open pretraining recipe that trains a collection of Puro-2B models from scratch on up to 1.4 trillion tokens with FP8 precision on consumer-grade RTX 5090 GPUs, and derives a Puro Cost Scaling Law that relates training cost to average model performance.
This report presents an open pretraining recipe that trains a collection of Puro-2B models from scratch on up to 1.4 trillion tokens with FP8 precision on consumer-grade RTX 5090 GPUs, and derives a Puro Cost Scaling Law that relates training cost to average model performance.
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