Training Compute-Optimal Large Language Models
Hoffmann et al., 2022, Training Compute-Optimal Large Language Models (Advances in Neural Information Processing Systems 35 (NeurIPS 2022))
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4 Chinchilla
- Why was Chinchilla built with 70B parameters?The analysis put the optimal size for Gopher’s budget between 40B and 70B parameters; 70B on 1.4T tokens was chosen at the larger end of that range for data and computational efficiency reasons.
- Hoffmann et al., 2022, Training Compute-Optimal Large Language Models (Advances in Neural Information Processing Systems 35 (NeurIPS 2022)), §4
Source
- Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, Elena Buchatskaya, Trevor Cai, Eliza Rutherford, Diego de Las Casas, Lisa Anne Hendricks, Johannes Welbl, Aidan Clark, Tom Hennigan, Eric Noland, Katie Millican, George van den Driessche, Bogdan Damoc, Aurelia Guy, Simon Osindero, Karen Simonyan, Erich Elsen, Oriol Vinyals, Jack W. Rae and Laurent Sifre. 2022. Training Compute-Optimal Large Language Models. In Advances in Neural Information Processing Systems 35 (NeurIPS 2022). https://papers.nips.cc/paper_files/paper/2022/hash/c1e2faff6f588870935f114ebe04a3e5-Abstract-Conference.html