home / papers / 2023 / Gemini: A Family of Highly Capable Multimodal Models
📄 Gemini: A Family of Highly Capable Multimodal Models
Summary
This technical report from Google introduces Gemini 1.0, a family of models trained jointly on text, images, audio and video from the start, instead of adding vision or speech to a text-only LLM later. The aim is one system that handles all modalities well and still competes with specialist models in each. The architecture is a Transformer decoder with a 32k-token context and efficient attention. It was trained on TPUv4 and TPUv5e clusters spread across several datacenters. There are three sizes: Ultra, Pro and Nano, which runs on-device. After pre-training, the models were post-trained into chat variants (Gemini Apps) and developer variants (Gemini API). Gemini Ultra sets a new best result on 30 of 32 reported benchmarks, including every image, video and speech benchmark tested. It is described as the first model to pass human-expert level on MMLU. The report also covers safety work: impact assessments, red-teaming and external testing. The models are proprietary and available only through Google services.
Key points
Gemini Ultra scores 90.0% on MMLU, above the human-expert level. On MMMU it scores 62.4%, more than 5 points ahead of the previous best model.
Ultra's state-of-the-art wins break down as 10/12 text and reasoning tasks, 9/9 image, 6/6 video and 5/5 speech recognition and speech translation benchmarks.
Nano comes in two versions, Nano-1 (1.8B parameters) and Nano-2 (3.25B). Both are distilled from larger Gemini models and quantized to 4 bits for phones and other devices.
Audio goes in directly as 16kHz Universal Speech Model (USM) features, and video goes in as a sequence of frames. The models can also generate images using discrete image tokens.
Training kept redundant copies of the model state in memory instead of saving periodic checkpoints, which raised goodput (useful training throughput) from 85% to 97%. AlphaCode 2, an agent built on Gemini, ranks in the top 15% of Codeforces entrants.
Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.
@misc{team2023gemini,
title = {{Gemini: A Family of Highly Capable Multimodal Models}},
author = {Gemini Team, Rohan Anil, Sebastian Borgeaud, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M. Dai, Anja Hauth, Katie Millican, David Silver, Melvin Johnson, Ioannis Antonoglou, Julian Schrittwieser, Amelia Glaese, Jilin Chen, Emily Pitler, Timothy Lillicrap, Angeliki Lazaridou, Orhan Firat, James Molloy, Michael Isard, Paul R. Barham, Tom Hennigan, Benjamin Lee, Fabio Viola, Malcolm Reynolds, Yuanzhong Xu, Ryan Doherty, Eli Collins, Clemens Meyer, Eliza Rutherford, Erica Moreira, Kareem Ayoub, Megha Goel, Jack Krawczyk, Cosmo Du, Ed Chi, Heng-Tze Cheng, Eric Ni, Purvi Shah, Patrick Kane, Betty Chan, Manaal Faruqui, Aliaksei Severyn, Hanzhao Lin, YaGuang Li, Yong Cheng, Abe Ittycheriah, Mahdis Mahdieh, Mia Chen, Pei Sun, Dustin Tran, Sumit Bagri, Balaji Lakshminarayanan, Jeremiah Liu, Andras Orban, Fabian Güra, Hao Zhou, Xinying Song, Aurelien Boffy, Harish Ganapathy, Steven Zheng, HyunJeong Choe, Ágoston Weisz, Tao Zhu, Yifeng Lu, Siddharth Gopal, Jarrod Kahn, Maciej Kula, Jeff Pitman, Rushin Shah, Emanuel Taropa, Majd {Al Merey}, Martin Baeuml, Zhifeng Chen, Laurent {El Shafey}, Yujing Zhang, Olcan Sercinoglu, George Tucker, Enrique Piqueras, Maxim Krikun, Iain Barr, Nikolay Savinov, Ivo Danihelka, Becca Roelofs, Anaïs White, Anders Andreassen, Tamara {von Glehn}, Lakshman Yagati, Mehran Kazemi, Lucas Gonzalez, Misha Khalman, Jakub Sygnowski, Alexandre Frechette, Charlotte Smith, Laura Culp, Lev Proleev, Yi Luan, Xi Chen, James Lottes, Nathan Schucher, Federico Lebron, Alban Rrustemi, Natalie Clay, Phil Crone, Tomas Kocisky, Jeffrey Zhao, Bartek Perz, Dian Yu, Heidi Howard, Adam Bloniarz, Jack W. Rae, Han Lu, Laurent Sifre, Marcello Maggioni, Fred Alcober, Dan Garrette, Megan Barnes, Shantanu Thakoor, Jacob Austin, Gabriel Barth-Maron, William Wong, Rishabh Joshi, Rahma Chaabouni, Deeni Fatiha, Arun Ahuja, Gaurav Singh Tomar, Evan Senter, Martin Chadwick, Ilya Kornakov, Nithya Attaluri, Iñaki Iturrate, Ruibo Liu, Yunxuan Li, Sarah Cogan, Jeremy Chen, Chao Jia, Chenjie Gu, Qiao Zhang, Jordan Grimstad, Ale Jakse Hartman, Xavier Garcia, Thanumalayan Sankaranarayana Pillai, Jacob Devlin, Michael Laskin, Diego {de Las Casas}, Dasha Valter, Connie Tao, Lorenzo Blanco, Adrià Puigdomènech Badia, David Reitter, Mianna Chen, Jenny Brennan, Clara Rivera, Sergey Brin, Shariq Iqbal, Gabriela Surita, Jane Labanowski, Abhi Rao, Stephanie Winkler, Emilio Parisotto, Yiming Gu, Kate Olszewska, Ravi Addanki, Antoine Miech, Annie Louis, Denis Teplyashin, Geoff Brown, Elliot Catt, Jan Balaguer, Jackie Xiang, Pidong Wang, Zoe Ashwood, Anton Briukhov, Albert Webson, Sanjay Ganapathy, Smit Sanghavi, Ajay Kannan, Ming-Wei Chang, Axel Stjerngren, Josip Djolonga, Yuting Sun, Ankur Bapna, Matthew Aitchison, Pedram Pejman, Henryk Michalewski, Tianhe Yu, Cindy Wang, Juliette Love, Junwhan Ahn, Dawn Bloxwich, Kehang Han, Peter Humphreys, Thibault Sellam, James Bradbury, Varun Godbole, Sina Samangooei, Bogdan Damoc, Alex Kaskasoli, Sébastien M. R. 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year = {2023},
doi = {10.48550/arxiv.2312.11805},
eprint = {2312.11805},
archivePrefix = {arXiv},
url = {https://doi.org/10.48550/arxiv.2312.11805}
}
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