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A framework for efficient scaling law construction that formulates data collection as a Bayesian optimization problem is proposed, and metrics for comparing scaling law fitting methods under constrained compute budgets are introduced.
This study establishes a first baseline and scaling procedure for the development of future OpenEuroLLM models, and characterize the dependence of loss on model capacity and dataset size, evaluating recently proposed scaling forms that explicitly model their interaction.
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