Proceedings of the 40th International Conference on Machine Learning,PMLR 202:2397-2430,2023.
Abstract
How do large language models (LLMs) develop and evolve over the course of training? How do these patterns change as models scale? To answer these questions, we introduce Pythia, a suite of 16 LLMs all trained on public data seen in the exact same order and ranging in size from 70M to 12B parameters. We provide public access to 154 checkpoints for each one of the 16 models, alongside tools to download and reconstruct their exact training dataloaders for further study. We intend Pythia to facilitate research in many areas, and we present several case studies including novel results in memorization, term frequency effects on few-shot performance, and reducing gender bias. We demonstrate that this highly controlled setup can be used to yield novel insights toward LLMs and their training dynamics. Trained models, analysis code, training code, and training data can be found at https://github.com/EleutherAI/pythia.
Cite this Paper
BibTeX
@InProceedings{pmlr-v202-biderman23a, title = {Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling}, author = {Biderman, Stella and Schoelkopf, Hailey and Anthony, Quentin Gregory and Bradley, Herbie and O'Brien, Kyle and Hallahan, Eric and Khan, Mohammad Aflah and Purohit, Shivanshu and Prashanth, Usvsn Sai and Raff, Edward and Skowron, Aviya and Sutawika, Lintang and Van Der Wal, Oskar}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, pages = {2397--2430}, year = {2023}, editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan}, volume = {202}, series = {Proceedings of Machine Learning Research}, month = {23--29 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v202/biderman23a/biderman23a.pdf}, url = {https://proceedings.mlr.press/v202/biderman23a.html}, abstract = {How do large language models (LLMs) develop and evolve over the course of training? How do these patterns change as models scale? To answer these questions, we introduce Pythia, a suite of 16 LLMs all trained on public data seen in the exact same order and ranging in size from 70M to 12B parameters. We provide public access to 154 checkpoints for each one of the 16 models, alongside tools to download and reconstruct their exact training dataloaders for further study. We intend Pythia to facilitate research in many areas, and we present several case studies including novel results in memorization, term frequency effects on few-shot performance, and reducing gender bias. We demonstrate that this highly controlled setup can be used to yield novel insights toward LLMs and their training dynamics. Trained models, analysis code, training code, and training data can be found at https://github.com/EleutherAI/pythia.}}
Endnote
%0 Conference Paper%T Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling%A Stella Biderman%A Hailey Schoelkopf%A Quentin Gregory Anthony%A Herbie Bradley%A Kyle O’Brien%A Eric Hallahan%A Mohammad Aflah Khan%A Shivanshu Purohit%A Usvsn Sai Prashanth%A Edward Raff%A Aviya Skowron%A Lintang Sutawika%A Oskar Van Der Wal%B Proceedings of the 40th International Conference on Machine Learning%C Proceedings of Machine Learning Research%D 2023%E Andreas Krause%E Emma Brunskill%E Kyunghyun Cho%E Barbara Engelhardt%E Sivan Sabato%E Jonathan Scarlett%F pmlr-v202-biderman23a%I PMLR%P 2397--2430%U https://proceedings.mlr.press/v202/biderman23a.html%V 202%X How do large language models (LLMs) develop and evolve over the course of training? How do these patterns change as models scale? To answer these questions, we introduce Pythia, a suite of 16 LLMs all trained on public data seen in the exact same order and ranging in size from 70M to 12B parameters. We provide public access to 154 checkpoints for each one of the 16 models, alongside tools to download and reconstruct their exact training dataloaders for further study. We intend Pythia to facilitate research in many areas, and we present several case studies including novel results in memorization, term frequency effects on few-shot performance, and reducing gender bias. We demonstrate that this highly controlled setup can be used to yield novel insights toward LLMs and their training dynamics. Trained models, analysis code, training code, and training data can be found at https://github.com/EleutherAI/pythia.
APA
Biderman, S., Schoelkopf, H., Anthony, Q.G., Bradley, H., O’Brien, K., Hallahan, E., Khan, M.A., Purohit, S., Prashanth, U.S., Raff, E., Skowron, A., Sutawika, L. & Van Der Wal, O.. (2023). Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling. Proceedings of the 40th International Conference on Machine Learning, in Proceedings of Machine Learning Research 202:2397-2430 Available from https://proceedings.mlr.press/v202/biderman23a.html.
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