Apple Intelligence Foundation Language Models
We present foundation language models developed to power Apple Intelligence features, including a ~3 billion parameter model designed to run efficiently on devices and a large server-based language model designed for Private Cloud Compute. These models are designed to perform a wide range of tasks e...
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| creator | Gunter, Tom Wang, Zirui Pang, Ruoming Bowen, Zhang Chung-Cheng, Chiu Qiu, David Yin, Dong Yin, Guoli Huang, Haoshuo Wang, Jianyu Ye, Ke Chen, Qibin Keunebroek, Quentin Wiseman, Sam Evans, Syd Tao, Lei Rathod, Vivek Kong, Xiang Du, Xianzhi Li, Yanghao Wang, Yongqiang Rashid, Al Albin, Madappally Jose Bencomo, Alfredo Vanderby, Allison Hansen, Andrew Jain, Ankur Wu, Bugu Maalouf, Charlie Erdenebileg, Chinguun Dulhanty, Chris Moritz, Dominik Kang, Doug Ladd, Evan Shi, Fangping Bai, Felix Chu, Frank Kotek, Hadas Hannah Gillis Coleman Li, Jane Bigham, Jeffrey Lai, Jeff Cheung, Jessica Jiulong Shan Zhou, Joe Li, John Qin, Jun Singh, Karanjeet Cordell, Maria Hay, Nicole Shahdadpuri, Nilesh Godwin, Otto Dighe, Pranay Shah, Sanskruti Guha, Saptarshi Sirovica, Sasha Ma, Shuang Kim, Sulgi Jayaram, Suma Paidi, Varsha Kumar, Vivek Wang, Xin Zheng, Xin Walker, Cheng Shrager, Yael Yang, Ye Tanaka, Yasu Guo, Yihao Meng, Yunsong Zhao Tang Luo Aygar, Alp Walkingshaw, Andrew Lin, Antonie Ramerth, Brent Bartels, Chris Chaney, Chris Hochstrasser, Gabriel Seguin, Guillaume Belousova, Irina Pelemans, Joris Yang, Karen Najibi, Mahyar Horton, Max Cho, Minsik Bhendawade, Nikhil Dong, Patrick Maj, Piotr Agrawal, Pulkit Fu, Qichen Poston, Regan Xu, Sam Liu, Shuangning Heeramun, Tashweena Merth, Thomas Rayala, Uday Cui, Victor Vivek Rangarajan Sridhar Zhang, Wencong Zhao, Yang Yin Xia |
| description | We present foundation language models developed to power Apple Intelligence features, including a ~3 billion parameter model designed to run efficiently on devices and a large server-based language model designed for Private Cloud Compute. These models are designed to perform a wide range of tasks efficiently, accurately, and responsibly. This report describes the model architecture, the data used to train the model, the training process, how the models are optimized for inference, and the evaluation results. We highlight our focus on Responsible AI and how the principles are applied throughout the model development. |
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These models are designed to perform a wide range of tasks efficiently, accurately, and responsibly. This report describes the model architecture, the data used to train the model, the training process, how the models are optimized for inference, and the evaluation results. We highlight our focus on Responsible AI and how the principles are applied throughout the model development.</description><identifier>EISSN: 2331-8422</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Intelligence</subject><ispartof>arXiv.org, 2024-07</ispartof><rights>2024. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). 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Sridhar</creatorcontrib><creatorcontrib>Zhang, Wencong</creatorcontrib><creatorcontrib>Zhao, Yang</creatorcontrib><creatorcontrib>Yin Xia</creatorcontrib><title>Apple Intelligence Foundation Language Models</title><title>arXiv.org</title><description>We present foundation language models developed to power Apple Intelligence features, including a ~3 billion parameter model designed to run efficiently on devices and a large server-based language model designed for Private Cloud Compute. These models are designed to perform a wide range of tasks efficiently, accurately, and responsibly. This report describes the model architecture, the data used to train the model, the training process, how the models are optimized for inference, and the evaluation results. 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arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope></search><sort><creationdate>20240729</creationdate><title>Apple Intelligence Foundation Language Models</title><author>Gunter, Tom ; Wang, Zirui ; Pang, Ruoming ; Bowen, Zhang ; Chung-Cheng, Chiu ; Qiu, David ; Yin, Dong ; Yin, Guoli ; Huang, Haoshuo ; Wang, Jianyu ; Ye, Ke ; Chen, Qibin ; Keunebroek, Quentin ; Wiseman, Sam ; Evans, Syd ; Tao, Lei ; Rathod, Vivek ; Kong, Xiang ; Du, Xianzhi ; Li, Yanghao ; Wang, Yongqiang ; Rashid, Al ; Albin, Madappally Jose ; Bencomo, Alfredo ; Vanderby, Allison ; Hansen, Andrew ; Jain, Ankur ; Wu, Bugu ; Maalouf, Charlie ; Erdenebileg, Chinguun ; Dulhanty, Chris ; Moritz, Dominik ; Kang, Doug ; Ladd, Evan ; Shi, Fangping ; Bai, Felix ; Chu, Frank ; Kotek, Hadas ; Hannah Gillis Coleman ; Li, Jane ; Bigham, Jeffrey ; Lai, Jeff ; Cheung, Jessica ; Jiulong Shan ; Zhou, Joe ; Li, John ; Qin, Jun ; Singh, Karanjeet ; Cordell, Maria ; Hay, Nicole ; Shahdadpuri, Nilesh ; Godwin, Otto ; Dighe, Pranay ; Shah, Sanskruti ; Guha, Saptarshi ; Sirovica, Sasha ; Ma, Shuang ; Kim, Sulgi ; Jayaram, Suma ; Paidi, Varsha ; Kumar, Vivek ; Wang, Xin ; Zheng, Xin ; Walker, Cheng ; Shrager, Yael ; Yang, Ye ; Tanaka, Yasu ; Guo, Yihao ; Meng, Yunsong ; Zhao Tang Luo ; Aygar, Alp ; Walkingshaw, Andrew ; Lin, Antonie ; Ramerth, Brent ; Bartels, Chris ; Chaney, Chris ; Hochstrasser, Gabriel ; Seguin, Guillaume ; Belousova, Irina ; Pelemans, Joris ; Yang, Karen ; Najibi, Mahyar ; Horton, Max ; Cho, Minsik ; Bhendawade, Nikhil ; Dong, Patrick ; Maj, Piotr ; Agrawal, Pulkit ; Fu, Qichen ; Poston, Regan ; Xu, Sam ; Liu, Shuangning ; Heeramun, Tashweena ; Merth, Thomas ; 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Yang</creatorcontrib><creatorcontrib>Yin Xia</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gunter, Tom</au><au>Wang, Zirui</au><au>Pang, Ruoming</au><au>Bowen, Zhang</au><au>Chung-Cheng, Chiu</au><au>Qiu, David</au><au>Yin, Dong</au><au>Yin, Guoli</au><au>Huang, Haoshuo</au><au>Wang, Jianyu</au><au>Ye, Ke</au><au>Chen, Qibin</au><au>Keunebroek, Quentin</au><au>Wiseman, Sam</au><au>Evans, Syd</au><au>Tao, Lei</au><au>Rathod, Vivek</au><au>Kong, Xiang</au><au>Du, Xianzhi</au><au>Li, Yanghao</au><au>Wang, Yongqiang</au><au>Rashid, Al</au><au>Albin, Madappally Jose</au><au>Bencomo, Alfredo</au><au>Vanderby, Allison</au><au>Hansen, Andrew</au><au>Jain, Ankur</au><au>Wu, Bugu</au><au>Maalouf, Charlie</au><au>Erdenebileg, Chinguun</au><au>Dulhanty, Chris</au><au>Moritz, Dominik</au><au>Kang, Doug</au><au>Ladd, Evan</au><au>Shi, Fangping</au><au>Bai, Felix</au><au>Chu, Frank</au><au>Kotek, Hadas</au><au>Hannah Gillis Coleman</au><au>Li, Jane</au><au>Bigham, Jeffrey</au><au>Lai, Jeff</au><au>Cheung, Jessica</au><au>Jiulong Shan</au><au>Zhou, Joe</au><au>Li, John</au><au>Qin, Jun</au><au>Singh, Karanjeet</au><au>Cordell, Maria</au><au>Hay, Nicole</au><au>Shahdadpuri, Nilesh</au><au>Godwin, Otto</au><au>Dighe, Pranay</au><au>Shah, Sanskruti</au><au>Guha, Saptarshi</au><au>Sirovica, Sasha</au><au>Ma, Shuang</au><au>Kim, Sulgi</au><au>Jayaram, Suma</au><au>Paidi, Varsha</au><au>Kumar, Vivek</au><au>Wang, Xin</au><au>Zheng, Xin</au><au>Walker, Cheng</au><au>Shrager, Yael</au><au>Yang, Ye</au><au>Tanaka, Yasu</au><au>Guo, Yihao</au><au>Meng, Yunsong</au><au>Zhao Tang Luo</au><au>Aygar, Alp</au><au>Walkingshaw, Andrew</au><au>Lin, Antonie</au><au>Ramerth, Brent</au><au>Bartels, Chris</au><au>Chaney, Chris</au><au>Hochstrasser, Gabriel</au><au>Seguin, Guillaume</au><au>Belousova, Irina</au><au>Pelemans, Joris</au><au>Yang, Karen</au><au>Najibi, Mahyar</au><au>Horton, Max</au><au>Cho, Minsik</au><au>Bhendawade, Nikhil</au><au>Dong, Patrick</au><au>Maj, Piotr</au><au>Agrawal, Pulkit</au><au>Fu, Qichen</au><au>Poston, Regan</au><au>Xu, Sam</au><au>Liu, Shuangning</au><au>Heeramun, Tashweena</au><au>Merth, Thomas</au><au>Rayala, Uday</au><au>Cui, Victor</au><au>Vivek Rangarajan Sridhar</au><au>Zhang, Wencong</au><au>Zhao, Yang</au><au>Yin Xia</au><format>book</format><genre>document</genre><ristype>GEN</ristype><atitle>Apple Intelligence Foundation Language Models</atitle><jtitle>arXiv.org</jtitle><date>2024-07-29</date><risdate>2024</risdate><eissn>2331-8422</eissn><abstract>We present foundation language models developed to power Apple Intelligence features, including a ~3 billion parameter model designed to run efficiently on devices and a large server-based language model designed for Private Cloud Compute. These models are designed to perform a wide range of tasks efficiently, accurately, and responsibly. This report describes the model architecture, the data used to train the model, the training process, how the models are optimized for inference, and the evaluation results. We highlight our focus on Responsible AI and how the principles are applied throughout the model development.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | EISSN: 2331-8422 |
| ispartof | arXiv.org, 2024-07 |
| issn | 2331-8422 |
| language | eng |
| recordid | cdi_proquest_journals_3087037090 |
| source | Free E- Journals |
| subjects | Intelligence |
| title | Apple Intelligence Foundation Language Models |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T04%3A44%3A36IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=document&rft.atitle=Apple%20Intelligence%20Foundation%20Language%20Models&rft.jtitle=arXiv.org&rft.au=Gunter,%20Tom&rft.date=2024-07-29&rft.eissn=2331-8422&rft_id=info:doi/&rft_dat=%3Cproquest%3E3087037090%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3087037090&rft_id=info:pmid/&rfr_iscdi=true |