AI Development
Chinchilla Scaling
The finding from DeepMind's 2022 Chinchilla paper that, for a fixed compute budget, model size and training tokens should grow in equal proportion: double the parameters, double the data.
In depth
The finding from DeepMind's 2022 Chinchilla paper that, for a fixed compute budget, model size and training tokens should grow in equal proportion: double the parameters, double the data. By training over 400 models, the authors concluded that large models of the time were significantly undertrained. Their 70B Chinchilla model, trained on about 4x more data than the 280B Gopher with the same compute, outperformed it and was cheaper to run. The rule of thumb that falls out is roughly 20 training tokens per parameter.
Primary source: arxiv.org
Example
In practice, developers reach for Chinchilla Scaling when they need the capability described above as part of an AI feature or workflow.
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FAQ
Common questions
What is Chinchilla Scaling?
The finding from DeepMind's 2022 Chinchilla paper that, for a fixed compute budget, model size and training tokens should grow in equal proportion: double the parameters, double the data.
Why does Chinchilla Scaling matter for AI developers?
Chinchilla Scaling sits in the AI Development part of the AI stack. Understanding it helps you make better decisions when building, debugging, and shipping AI features.
Where can I learn more about Chinchilla Scaling?
Developers Digest publishes tutorials and videos that cover AI Development topics including Chinchilla Scaling. Check the blog and YouTube channel for hands-on walkthroughs.
Related
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Empirical relationships showing that a language model's loss falls as a predictable power law as you increase parameters, training data, and training compute.
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FSDP (Fully Sharded Data Parallel)
A PyTorch training method that splits model parameters, gradients, and optimizer state across GPUs instead of copying them all onto every device as plain data parallelism does.
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