LLMilestonesA milestone timeline of AI and large language models

Act II · Scaling up

PaperDeepMind

Chinchilla: data matters too

For the same compute, a smaller model trained on more data beats a bigger one.

70Bparameters, beating the 280B Gopher

Why it matters

Corrected the scaling laws. "About 20 tokens per parameter" became an industry rule of thumb and pointed the way for efficient open models.

Built on

Scaling Laws

Led to

LLaMA

Source: arxiv.org ↗

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