StarCoder 2 and The Stack v2: The Next Generation
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arXiv
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| Natura: | Preprint |
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2024
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| author | Lozhkov, Anton Li, Raymond Allal, Loubna Ben Cassano, Federico Lamy-Poirier, Joel Tazi, Nouamane Tang, Ao Pykhtar, Dmytro Liu, Jiawei Wei, Yuxiang Liu, Tianyang Tian, Max Kocetkov, Denis Zucker, Arthur Belkada, Younes Wang, Zijian Liu, Qian Abulkhanov, Dmitry Paul, Indraneil Li, Zhuang Li, Wen-Ding Risdal, Megan Li, Jia Zhu, Jian Zhuo, Terry Yue Zheltonozhskii, Evgenii Dade, Nii Osae Osae Yu, Wenhao Krauß, Lucas Jain, Naman Su, Yixuan He, Xuanli Dey, Manan Abati, Edoardo Chai, Yekun Muennighoff, Niklas Tang, Xiangru Oblokulov, Muhtasham Akiki, Christopher Marone, Marc Mou, Chenghao Mishra, Mayank Gu, Alex Hui, Binyuan Dao, Tri Zebaze, Armel Dehaene, Olivier Patry, Nicolas Xu, Canwen McAuley, Julian Hu, Han Scholak, Torsten Paquet, Sebastien Robinson, Jennifer Anderson, Carolyn Jane Chapados, Nicolas Patwary, Mostofa Tajbakhsh, Nima Jernite, Yacine Ferrandis, Carlos Muñoz Zhang, Lingming Hughes, Sean Wolf, Thomas Guha, Arjun von Werra, Leandro de Vries, Harm |
| author_facet | Lozhkov, Anton Li, Raymond Allal, Loubna Ben Cassano, Federico Lamy-Poirier, Joel Tazi, Nouamane Tang, Ao Pykhtar, Dmytro Liu, Jiawei Wei, Yuxiang Liu, Tianyang Tian, Max Kocetkov, Denis Zucker, Arthur Belkada, Younes Wang, Zijian Liu, Qian Abulkhanov, Dmitry Paul, Indraneil Li, Zhuang Li, Wen-Ding Risdal, Megan Li, Jia Zhu, Jian Zhuo, Terry Yue Zheltonozhskii, Evgenii Dade, Nii Osae Osae Yu, Wenhao Krauß, Lucas Jain, Naman Su, Yixuan He, Xuanli Dey, Manan Abati, Edoardo Chai, Yekun Muennighoff, Niklas Tang, Xiangru Oblokulov, Muhtasham Akiki, Christopher Marone, Marc Mou, Chenghao Mishra, Mayank Gu, Alex Hui, Binyuan Dao, Tri Zebaze, Armel Dehaene, Olivier Patry, Nicolas Xu, Canwen McAuley, Julian Hu, Han Scholak, Torsten Paquet, Sebastien Robinson, Jennifer Anderson, Carolyn Jane Chapados, Nicolas Patwary, Mostofa Tajbakhsh, Nima Jernite, Yacine Ferrandis, Carlos Muñoz Zhang, Lingming Hughes, Sean Wolf, Thomas Guha, Arjun von Werra, Leandro de Vries, Harm |
| contents | The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_19173 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | StarCoder 2 and The Stack v2: The Next Generation Lozhkov, Anton Li, Raymond Allal, Loubna Ben Cassano, Federico Lamy-Poirier, Joel Tazi, Nouamane Tang, Ao Pykhtar, Dmytro Liu, Jiawei Wei, Yuxiang Liu, Tianyang Tian, Max Kocetkov, Denis Zucker, Arthur Belkada, Younes Wang, Zijian Liu, Qian Abulkhanov, Dmitry Paul, Indraneil Li, Zhuang Li, Wen-Ding Risdal, Megan Li, Jia Zhu, Jian Zhuo, Terry Yue Zheltonozhskii, Evgenii Dade, Nii Osae Osae Yu, Wenhao Krauß, Lucas Jain, Naman Su, Yixuan He, Xuanli Dey, Manan Abati, Edoardo Chai, Yekun Muennighoff, Niklas Tang, Xiangru Oblokulov, Muhtasham Akiki, Christopher Marone, Marc Mou, Chenghao Mishra, Mayank Gu, Alex Hui, Binyuan Dao, Tri Zebaze, Armel Dehaene, Olivier Patry, Nicolas Xu, Canwen McAuley, Julian Hu, Han Scholak, Torsten Paquet, Sebastien Robinson, Jennifer Anderson, Carolyn Jane Chapados, Nicolas Patwary, Mostofa Tajbakhsh, Nima Jernite, Yacine Ferrandis, Carlos Muñoz Zhang, Lingming Hughes, Sean Wolf, Thomas Guha, Arjun von Werra, Leandro de Vries, Harm Software Engineering Artificial Intelligence The BigCode project, an open-scientific collaboration focused on the responsible development of Large Language Models for Code (Code LLMs), 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. |
| title | StarCoder 2 and The Stack v2: The Next Generation |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2402.19173 |