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easyScaling Laws

A team training a 7B parameter LLM has a budget of 1.4×10²¹ FLOPs. Their initial plan was to train on 200B tokens. A colleague who read the Chinchilla paper says: "You're significantly over-parametrized for your compute budget." What does the Chinchilla scaling law predict is the compute-optimal allocation for this compute budget, and why does it matter?

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  • 1.Concepts over memorization.
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