Harm de Vries

Loubna Ben allal

Federico Cassano

Joel Lamy-Poirier

Nouamane Tazi

Ao Tang

Dmytro Pykhtar

Jiawei Liu

Yuxiang Wei

Tianyang Liu

Max Tian

Denis Kocetkov

Arthur Zucker

Younes Belkada

Zijian Wang

Qian Liu

Dmitry Abulkhanov

Indraneil Paul

Zhuang Li … (voir 46 de plus)

Wen-Ding Li

Megan L. Risdal

Jia LI

Jian Zhu

Terry Yue Zhuo

Evgenii Zheltonozhskii

Nii Osae Osae Dade

Wenhao Yu

Lucas Krauss

Naman Jain

Yixuan Su

Xuanli He

Edoardo Abati

Yekun Chai

Niklas Muennighoff

Xiangru Tang

Muhtasham Oblokulov

Christopher Akiki

Marc Marone

Chenghao Mou

Mayank Mishra

Alex Gu

Binyuan Hui

Tri Dao

Armel Zebaze

Olivier Dehaene

Nicolas Patry

Canwen Xu

Julian McAuley

Han Hu

Torsten Scholak

Sebastien Paquet

Jennifer Robinson

Carolyn Jane Anderson

Md. Mostofa Ali Patwary

Nima Tajbakhsh

Yacine Jernite

Carlos Muñoz Ferrandis

Lingming Zhang

Sean Hughes

Thomas Wolf

Arjun Guha

Leandro Von Werra

The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), … (voir plus)introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we carefully select other high-quality data sources, such as GitHub pull requests, Kaggle notebooks, and code documentation. This results in a training set that is 4x larger than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size. In addition, it matches or outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder- 33B is the best-performing model at code completion for high-resource languages, we find that StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several low-resource languages. We make the model weights available under an OpenRAIL license and ensure full transparency regarding the training data by releasing the SoftWare Heritage persistent IDentifiers (SWHIDs) of the source code data.

2024-02-29

ArXiv (prépublication)

StarCoder 2 and The Stack v2: The Next Generation

Anton Lozhkov

Loubna Ben allal

Federico Cassano

Joel Lamy-Poirier

Nouamane Tazi

Ao Tang

Dmytro Pykhtar

Jiawei Liu

Yuxiang Wei

Tianyang Liu

Max Tian

Denis Kocetkov

Arthur Zucker

Younes Belkada

Zijian Wang

Qian Liu

Dmitry Abulkhanov

Indraneil Paul

Zhuang Li … (voir 46 de plus)

Wen-Ding Li

Megan L. Risdal

Jia LI

Jian Zhu

Terry Yue Zhuo

Evgenii Zheltonozhskii

Nii Osae Osae Dade

Wenhao Yu

Lucas Krauss

Naman Jain

Yixuan Su

Xuanli He

Edoardo Abati

Yekun Chai

Niklas Muennighoff

Xiangru Tang

Muhtasham Oblokulov

Christopher Akiki

Marc Marone

Chenghao Mou

Mayank Mishra

Alex Gu

Binyuan Hui

Tri Dao

Armel Zebaze

Olivier Dehaene

Nicolas Patry

Canwen Xu

Julian McAuley

Han Hu

Torsten Scholak

Sebastien Paquet

Jennifer Robinson

Carolyn Jane Anderson

Md. Mostofa Ali Patwary

Nima Tajbakhsh

Yacine Jernite

Carlos Muñoz Ferrandis

Lingming Zhang

Sean Hughes

Thomas Wolf

Arjun Guha

Leandro Von Werra

The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), … (voir plus)introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we carefully select other high-quality data sources, such as GitHub pull requests, Kaggle notebooks, and code documentation. This results in a training set that is 4x larger than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size. In addition, it matches or outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder- 33B is the best-performing model at code completion for high-resource languages, we find that StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several low-resource languages. We make the model weights available under an OpenRAIL license and ensure full transparency regarding the training data by releasing the SoftWare Heritage persistent IDentifiers (SWHIDs) of the source code data.

2024-02-29

ArXiv (prépublication)

StarCoder 2 and The Stack v2: The Next Generation

Anton Lozhkov

Loubna Ben allal

Federico Cassano

Joel Lamy-Poirier

Nouamane Tazi

Ao Tang

Dmytro Pykhtar

Jiawei Liu

Yuxiang Wei

Tianyang Liu

Max Tian

Denis Kocetkov

Arthur Zucker

Younes Belkada

Zijian Wang

Qian Liu

Dmitry Abulkhanov

Indraneil Paul

Zhuang Li … (voir 46 de plus)

Wen-Ding Li

Megan L. Risdal

Jia LI

Jian Zhu

Terry Yue Zhuo

Evgenii Zheltonozhskii

Nii Osae Osae Dade

Wenhao Yu

Lucas Krauss

Naman Jain

Yixuan Su

Xuanli He

Edoardo Abati

Yekun Chai

Niklas Muennighoff

Xiangru Tang

Muhtasham Oblokulov

Christopher Akiki

Marc Marone

Chenghao Mou

Mayank Mishra

Alex Gu

Binyuan Hui

Tri Dao

Armel Zebaze

Olivier Dehaene

Nicolas Patry

Canwen Xu

Julian McAuley

Han Hu

Torsten Scholak

Sebastien Paquet

Jennifer Robinson

Carolyn Jane Anderson

Md. Mostofa Ali Patwary

Nima Tajbakhsh

Yacine Jernite

Carlos Muñoz Ferrandis

Lingming Zhang

Sean Hughes

Thomas Wolf

Arjun Guha

Leandro Von Werra

The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), … (voir plus)introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we carefully select other high-quality data sources, such as GitHub pull requests, Kaggle notebooks, and code documentation. This results in a training set that is 4x larger than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size. In addition, it matches or outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder- 33B is the best-performing model at code completion for high-resource languages, we find that StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several low-resource languages. We make the model weights available under an OpenRAIL license and ensure full transparency regarding the training data by releasing the SoftWare Heritage persistent IDentifiers (SWHIDs) of the source code data.

2024-02-29

ArXiv (prépublication)

StarCoder 2 and The Stack v2: The Next Generation

Anton Lozhkov

Loubna Ben allal

Federico Cassano

Joel Lamy-Poirier

Nouamane Tazi

Ao Tang

Dmytro Pykhtar

Jiawei Liu

Yuxiang Wei

Tianyang Liu

Max Tian

Denis Kocetkov

Arthur Zucker

Younes Belkada

Zijian Wang

Qian Liu

Dmitry Abulkhanov

Indraneil Paul

Zhuang Li … (voir 46 de plus)

Wen-Ding Li

Megan L. Risdal

Jia LI

Jian Zhu

Terry Yue Zhuo

Evgenii Zheltonozhskii

Nii Osae Osae Dade

Wenhao Yu

Lucas Krauss

Naman Jain

Yixuan Su

Xuanli He

Edoardo Abati

Yekun Chai

Niklas Muennighoff

Xiangru Tang

Muhtasham Oblokulov

Christopher Akiki

Marc Marone

Chenghao Mou

Mayank Mishra

Alex Gu

Binyuan Hui

Tri Dao

Armel Zebaze

Olivier Dehaene

Nicolas Patry

Canwen Xu

Julian McAuley

Han Hu

Torsten Scholak

Sebastien Paquet

Jennifer Robinson

Carolyn Jane Anderson

Md. Mostofa Ali Patwary

Nima Tajbakhsh

Yacine Jernite

Carlos Muñoz Ferrandis

Lingming Zhang

Sean Hughes

Thomas Wolf

Arjun Guha

Leandro Von Werra

The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), … (voir plus)introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we carefully select other high-quality data sources, such as GitHub pull requests, Kaggle notebooks, and code documentation. This results in a training set that is 4x larger than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size. In addition, it matches or outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder- 33B is the best-performing model at code completion for high-resource languages, we find that StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several low-resource languages. We make the model weights available under an OpenRAIL license and ensure full transparency regarding the training data by releasing the SoftWare Heritage persistent IDentifiers (SWHIDs) of the source code data.

2024-02-29

ArXiv (prépublication)

StarCoder 2 and The Stack v2: The Next Generation

Anton Lozhkov

Loubna Ben allal

Federico Cassano

Joel Lamy-Poirier

Nouamane Tazi

Ao Tang

Dmytro Pykhtar

Jiawei Liu

Yuxiang Wei

Tianyang Liu

Max Tian

Denis Kocetkov

Arthur Zucker

Younes Belkada

Zijian Wang

Qian Liu

Dmitry Abulkhanov

Indraneil Paul

Zhuang Li … (voir 46 de plus)

Wen-Ding Li

Megan L. Risdal

Jia LI

Jian Zhu

Terry Yue Zhuo

Evgenii Zheltonozhskii

Nii Osae Osae Dade

Wenhao Yu

Lucas Krauss

Naman Jain

Yixuan Su

Xuanli He

Edoardo Abati

Yekun Chai

Niklas Muennighoff

Xiangru Tang

Muhtasham Oblokulov

Christopher Akiki

Marc Marone

Chenghao Mou

Mayank Mishra

Alex Gu

Binyuan Hui

Tri Dao

Armel Zebaze

Olivier Dehaene

Nicolas Patry

Canwen Xu

Julian McAuley

Han Hu

Torsten Scholak

Sebastien Paquet

Jennifer Robinson

Carolyn Jane Anderson

Md. Mostofa Ali Patwary

Nima Tajbakhsh

Yacine Jernite

Carlos Muñoz Ferrandis

Lingming Zhang

Sean Hughes

Thomas Wolf

Arjun Guha

Leandro Von Werra

The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), … (voir plus)introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we carefully select other high-quality data sources, such as GitHub pull requests, Kaggle notebooks, and code documentation. This results in a training set that is 4x larger than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size. In addition, it matches or outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder- 33B is the best-performing model at code completion for high-resource languages, we find that StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several low-resource languages. We make the model weights available under an OpenRAIL license and ensure full transparency regarding the training data by releasing the SoftWare Heritage persistent IDentifiers (SWHIDs) of the source code data.

2024-02-29

ArXiv (prépublication)

StarCoder 2 and The Stack v2: The Next Generation

Anton Lozhkov

Loubna Ben allal

Federico Cassano

Joel Lamy-Poirier

Nouamane Tazi

Ao Tang

Dmytro Pykhtar

Jiawei Liu

Yuxiang Wei

Tianyang Liu

Max Tian

Denis Kocetkov

Arthur Zucker

Younes Belkada

Zijian Wang

Qian Liu

Dmitry Abulkhanov

Indraneil Paul

Zhuang Li … (voir 46 de plus)

Wen-Ding Li

Megan L. Risdal

Jia LI

Jian Zhu

Terry Yue Zhuo

Evgenii Zheltonozhskii

Nii Osae Osae Dade

Wenhao Yu

Lucas Krauss

Naman Jain

Yixuan Su

Xuanli He

Edoardo Abati

Yekun Chai

Niklas Muennighoff

Xiangru Tang

Muhtasham Oblokulov

Christopher Akiki

Marc Marone

Chenghao Mou

Mayank Mishra

Alex Gu

Binyuan Hui

Tri Dao

Armel Zebaze

Olivier Dehaene

Nicolas Patry

Canwen Xu

Julian McAuley

Han Hu

Torsten Scholak

Sebastien Paquet

Jennifer Robinson

Carolyn Jane Anderson

Mostofa Ali Patwary

Nima Tajbakhsh

Yacine Jernite

Carlos Muñoz Ferrandis

Lingming Zhang

Sean Hughes

Thomas Wolf

Arjun Guha

Leandro Von Werra

The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), … (voir plus)introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we carefully select other high-quality data sources, such as GitHub pull requests, Kaggle notebooks, and code documentation. This results in a training set that is 4x larger than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size. In addition, it matches or outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder- 33B is the best-performing model at code completion for high-resource languages, we find that StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several low-resource languages. We make the model weights available under an OpenRAIL license and ensure full transparency regarding the training data by releasing the SoftWare Heritage persistent IDentifiers (SWHIDs) of the source code data.

2024-02-29

ArXiv (prépublication)

StarCoder 2 and The Stack v2: The Next Generation

Anton Lozhkov

Loubna Ben allal

Federico Cassano

Joel Lamy-Poirier

Nouamane Tazi

Ao Tang

Dmytro Pykhtar

Jiawei Liu

Yuxiang Wei

Tianyang Liu

Max Tian

Denis Kocetkov

Arthur Zucker

Younes Belkada

Zijian Wang

Qian Liu

Dmitry Abulkhanov

Indraneil Paul

Zhuang Li … (voir 46 de plus)

Wen-Ding Li

Megan L. Risdal

Jia LI

Jian Zhu

Terry Yue Zhuo

Evgenii Zheltonozhskii

Nii Osae Osae Dade

Wenhao Yu

Lucas Krauss

Naman Jain

Yixuan Su

Xuanli He

Edoardo Abati

Yekun Chai

Niklas Muennighoff

Xiangru Tang

Muhtasham Oblokulov

Christopher Akiki

Marc Marone

Chenghao Mou

Mayank Mishra

Alex Gu

Binyuan Hui

Tri Dao

Armel Zebaze

Olivier Dehaene

Nicolas Patry

Canwen Xu

Julian McAuley

Han Hu

Torsten Scholak

Sebastien Paquet

Jennifer Robinson

Carolyn Jane Anderson

Md. Mostofa Ali Patwary

Nima Tajbakhsh

Yacine Jernite

Carlos Muñoz Ferrandis

Lingming Zhang

Sean Hughes

Thomas Wolf

Arjun Guha

Leandro Von Werra

The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), … (voir plus)introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we carefully select other high-quality data sources, such as GitHub pull requests, Kaggle notebooks, and code documentation. This results in a training set that is 4x larger than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size. In addition, it matches or outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder- 33B is the best-performing model at code completion for high-resource languages, we find that StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several low-resource languages. We make the model weights available under an OpenRAIL license and ensure full transparency regarding the training data by releasing the SoftWare Heritage persistent IDentifiers (SWHIDs) of the source code data.

2024-02-29

ArXiv (prépublication)

StarCoder 2 and The Stack v2: The Next Generation

Anton Lozhkov

Loubna Ben allal

Federico Cassano

Joel Lamy-Poirier

Nouamane Tazi

Ao Tang

Dmytro Pykhtar

Jiawei Liu

Yuxiang Wei

Tianyang Liu

Max Tian

Denis Kocetkov

Arthur Zucker

Younes Belkada

Zijian Wang

Qian Liu

Dmitry Abulkhanov

Indraneil Paul

Zhuang Li … (voir 46 de plus)

Wen-Ding Li

Megan L. Risdal

Jia LI

Jian Zhu

Terry Yue Zhuo

Evgenii Zheltonozhskii

Nii Osae Osae Dade

Wenhao Yu

Lucas Krauss

Naman Jain

Yixuan Su

Xuanli He

Edoardo Abati

Yekun Chai

Niklas Muennighoff

Xiangru Tang

Muhtasham Oblokulov

Christopher Akiki

Marc Marone

Chenghao Mou

Mayank Mishra

Alex Gu

Binyuan Hui

Tri Dao

Armel Zebaze

Olivier Dehaene

Nicolas Patry

Canwen Xu

Julian McAuley

Han Hu

Torsten Scholak

Sebastien Paquet

Jennifer Robinson

Carolyn Jane Anderson

Md. Mostofa Ali Patwary

Nima Tajbakhsh

Yacine Jernite

Carlos Muñoz Ferrandis

Lingming Zhang

Sean Hughes

Thomas Wolf

Arjun Guha

Leandro Von Werra

The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), … (voir plus)introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we carefully select other high-quality data sources, such as GitHub pull requests, Kaggle notebooks, and code documentation. This results in a training set that is 4x larger than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size. In addition, it matches or outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder- 33B is the best-performing model at code completion for high-resource languages, we find that StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several low-resource languages. We make the model weights available under an OpenRAIL license and ensure full transparency regarding the training data by releasing the SoftWare Heritage persistent IDentifiers (SWHIDs) of the source code data.

2024-02-29

ArXiv (prépublication)

StarCoder 2 and The Stack v2: The Next Generation

Anton Lozhkov

Loubna Ben allal

Federico Cassano

Joel Lamy-Poirier

Nouamane Tazi

Ao Tang

Dmytro Pykhtar

Jiawei Liu

Yuxiang Wei

Tianyang Liu

Max Tian

Denis Kocetkov

Arthur Zucker

Younes Belkada

Zijian Wang

Qian Liu

Dmitry Abulkhanov

Indraneil Paul

Zhuang Li … (voir 46 de plus)

Wen-Ding Li

Megan L. Risdal

Jia LI

Jian Zhu

Terry Yue Zhuo

Evgenii Zheltonozhskii

Nii Osae Osae Dade

Wenhao Yu

Lucas Krauss

Naman Jain

Yixuan Su

Xuanli He

Edoardo Abati

Yekun Chai

Niklas Muennighoff

Xiangru Tang

Muhtasham Oblokulov

Christopher Akiki

Marc Marone

Chenghao Mou

Mayank Mishra

Alex Gu

Binyuan Hui

Tri Dao

Armel Zebaze

Olivier Dehaene

Nicolas Patry

Canwen Xu

Julian McAuley

Han Hu

Torsten Scholak

Sebastien Paquet

Jennifer Robinson

Carolyn Jane Anderson

Md. Mostofa Ali Patwary

Nima Tajbakhsh

Yacine Jernite

Carlos Muñoz Ferrandis

Lingming Zhang

Sean Hughes

Thomas Wolf

Arjun Guha

Leandro Von Werra

The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), … (voir plus)introduces StarCoder2. In partnership with Software Heritage (SWH), we build The Stack v2 on top of the digital commons of their source code archive. Alongside the SWH repositories spanning 619 programming languages, we carefully select other high-quality data sources, such as GitHub pull requests, Kaggle notebooks, and code documentation. This results in a training set that is 4x larger than the first StarCoder dataset. We train StarCoder2 models with 3B, 7B, and 15B parameters on 3.3 to 4.3 trillion tokens and thoroughly evaluate them on a comprehensive set of Code LLM benchmarks. We find that our small model, StarCoder2-3B, outperforms other Code LLMs of similar size on most benchmarks, and also outperforms StarCoderBase-15B. Our large model, StarCoder2- 15B, significantly outperforms other models of comparable size. In addition, it matches or outperforms CodeLlama-34B, a model more than twice its size. Although DeepSeekCoder- 33B is the best-performing model at code completion for high-resource languages, we find that StarCoder2-15B outperforms it on math and code reasoning benchmarks, as well as several low-resource languages. We make the model weights available under an OpenRAIL license and ensure full transparency regarding the training data by releasing the SoftWare Heritage persistent IDentifiers (SWHIDs) of the source code data.

2024-02-29

ArXiv (prépublication)

StarCoder: may the source be with you!

Loubna Ben allal

Yangtian Zi

Niklas Muennighoff

Denis Kocetkov

Chenghao Mou

Marc Marone

Christopher Akiki

Jia LI

Jenny Chim

Qian Liu

Evgenii Zheltonozhskii

Terry Yue Zhuo

Thomas Wang

Olivier Dehaene

Mishig Davaadorj

Joel Lamy-Poirier

Joao Monteiro

Oleh Shliazhko

Nicolas Gontier … (voir 49 de plus)

Nicholas Meade

Armel Zebaze

Ming-Ho Yee

Logesh Kumar Umapathi

Jian Zhu

Ben Lipkin

Muhtasham Oblokulov

Zhiruo Wang

Rudra Murthy

Jason T Stillerman

Siva Sankalp Patel

Dmitry Abulkhanov

Marco Zocca

Zhihan Zhang

N. Fahmy

Urvashi Bhattacharyya

Wenhao Yu

Swayam Singh

Sasha Luccioni

Paulo Villegas

M. Kunakov

Jan Ebert

Fedor Zhdanov

Manuel Romero

Tony Lee

Nadav Timor

Jennifer Ding

Claire S Schlesinger

Hailey Schoelkopf

Jana Ebert

Tri Dao

Mayank Mishra

Alex Gu

Jennifer Robinson

Sean Hughes

Carolyn Jane Anderson

Brendan Dolan-Gavitt

Danish Contractor

Siva Reddy

Daniel Fried

Dzmitry Bahdanau

Yacine Jernite

Carlos Muñoz Ferrandis

Sean M. Hughes

Thomas Wolf

Arjun Guha

Leandro Von Werra

The BigCode community, an open-scientific collaboration working on the responsible development of Large Language Models for Code (Code LLMs)… (voir plus), introduces StarCoder and StarCoderBase: 15.5B parameter models with 8K context length, infilling capabilities and fast large-batch inference enabled by multi-query attention. StarCoderBase is trained on 1 trillion tokens sourced from The Stack, a large collection of permissively licensed GitHub repositories with inspection tools and an opt-out process. We fine-tuned StarCoderBase on 35B Python tokens, resulting in the creation of StarCoder. We perform the most comprehensive evaluation of Code LLMs to date and show that StarCoderBase outperforms every open Code LLM that supports multiple programming languages and matches or outperforms the OpenAI code-cushman-001 model. Furthermore, StarCoder outperforms every model that is fine-tuned on Python and still retains its performance on other programming languages. We take several important steps towards a safe open-access model release, including an improved PII redaction pipeline and a novel attribution tracing tool, and make the StarCoder models publicly available under a more commercially viable version of the Open Responsible AI Model license.

2023-12-17

TMLR (accepté)

openreview.net

RepoFusion: Training Code Models to Understand Your Repository

Disha Shrivastava

Denis Kocetkov

Dzmitry Bahdanau

Torsten Scholak

Despite the huge success of Large Language Models (LLMs) in coding assistants like GitHub Copilot, these models struggle to understand the c… (voir plus)ontext present in the repository (e.g., imports, parent classes, files with similar names, etc.), thereby producing inaccurate code completions. This effect is more pronounced when using these assistants for repositories that the model has not seen during training, such as proprietary software or work-in-progress code projects. Recent work has shown the promise of using context from the repository during inference. In this work, we extend this idea and propose RepoFusion, a framework to train models to incorporate relevant repository context. Experiments on single-line code completion show that our models trained with repository context significantly outperform much larger code models as CodeGen-16B-multi (

2023-06-19

ArXiv (prépublication)

openreview.net

The Stack: 3 TB of permissively licensed source code

Denis Kocetkov

Loubna Ben allal

Jia LI

Chenghao Mou

Carlos Muñoz Ferrandis

Yacine Jernite

Margaret Mitchell

Sean Hughes

Thomas Wolf

Dzmitry Bahdanau

Leandro Von Werra

Large Language Models (LLMs) play an ever-increasing role in the field of Artificial Intelligence (AI)--not only for natural language proces… (voir plus)sing but also for code understanding and generation. To stimulate open and responsible research on LLMs for code, we introduce The Stack, a 3.1 TB dataset consisting of permissively licensed source code in 30 programming languages. We describe how we collect the full dataset, construct a permissively licensed subset, present a data governance plan, discuss limitations, and show promising results on text2code benchmarks by training 350M-parameter decoders on different Python subsets. We find that (1) near-deduplicating the data significantly boosts performance across all experiments, and (2) it is possible to match previously reported HumanEval and MBPP performance using only permissively licensed data. We make the dataset available at https://hf.co/BigCode, provide a tool called"Am I in The Stack"(https://hf.co/spaces/bigcode/in-the-stack) for developers to search The Stack for copies of their code, and provide a process for code to be removed from the dataset by following the instructions at https://www.bigcode-project.org/docs/about/the-stack/.

2023-06-06

TMLR (accepté)