VaultGemma: A Differentially Private Gemma Model

Fuente: arXiv
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Hauptverfasser: Sinha, Amer, Mesnard, Thomas, McKenna, Ryan, Liu, Daogao, Choquette-Choo, Christopher A., Huang, Yangsibo, Yu, Da, Kaissis, George, Charles, Zachary, Liu, Ruibo, Chua, Lynn, Kamath, Pritish, Manurangsi, Pasin, He, Steve, Zhang, Chiyuan, Ghazi, Badih, Pigem, Borja De Balle, Eruvbetine, Prem, Warkentin, Tris, Joulin, Armand, Kumar, Ravi
Format: Preprint
Veröffentlicht: 2025
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author Sinha, Amer
Mesnard, Thomas
McKenna, Ryan
Liu, Daogao
Choquette-Choo, Christopher A.
Huang, Yangsibo
Yu, Da
Kaissis, George
Charles, Zachary
Liu, Ruibo
Chua, Lynn
Kamath, Pritish
Manurangsi, Pasin
He, Steve
Zhang, Chiyuan
Ghazi, Badih
Pigem, Borja De Balle
Eruvbetine, Prem
Warkentin, Tris
Joulin, Armand
Kumar, Ravi
author_facet Sinha, Amer
Mesnard, Thomas
McKenna, Ryan
Liu, Daogao
Choquette-Choo, Christopher A.
Huang, Yangsibo
Yu, Da
Kaissis, George
Charles, Zachary
Liu, Ruibo
Chua, Lynn
Kamath, Pritish
Manurangsi, Pasin
He, Steve
Zhang, Chiyuan
Ghazi, Badih
Pigem, Borja De Balle
Eruvbetine, Prem
Warkentin, Tris
Joulin, Armand
Kumar, Ravi
contents We introduce VaultGemma 1B, a 1 billion parameter model within the Gemma family, fully trained with differential privacy. Pretrained on the identical data mixture used for the Gemma 2 series, VaultGemma 1B represents a significant step forward in privacy-preserving large language models. We openly release this model to the community
format Preprint
id arxiv_https___arxiv_org_abs_2510_15001
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle VaultGemma: A Differentially Private Gemma Model
Sinha, Amer
Mesnard, Thomas
McKenna, Ryan
Liu, Daogao
Choquette-Choo, Christopher A.
Huang, Yangsibo
Yu, Da
Kaissis, George
Charles, Zachary
Liu, Ruibo
Chua, Lynn
Kamath, Pritish
Manurangsi, Pasin
He, Steve
Zhang, Chiyuan
Ghazi, Badih
Pigem, Borja De Balle
Eruvbetine, Prem
Warkentin, Tris
Joulin, Armand
Kumar, Ravi
Cryptography and Security
Artificial Intelligence
We introduce VaultGemma 1B, a 1 billion parameter model within the Gemma family, fully trained with differential privacy. Pretrained on the identical data mixture used for the Gemma 2 series, VaultGemma 1B represents a significant step forward in privacy-preserving large language models. We openly release this model to the community
title VaultGemma: A Differentially Private Gemma Model
topic Cryptography and Security
Artificial Intelligence
url https://arxiv.org/abs/2510.15001