Paquet, Sebastien
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
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.
The BigCode Project Governance Card
BigCode collaboration, null, Hughes, Sean, de Vries, Harm, Robinson, Jennifer, Ferrandis, Carlos Muñoz, Allal, Loubna Ben, von Werra, Leandro, Ding, Jennifer, Paquet, Sebastien, Jernite, Yacine
This document serves as an overview of the different mechanisms and areas of governance in the BigCode project. It aims to support transparency by providing relevant information about choices that were made during the project to the broader public, and to serve as an example of intentional governance of an open research project that future endeavors can leverage to shape their own approach. The first section, Project Structure, covers the project organization, its stated goals and values, its internal decision processes, and its funding and resources. The second section, Data and Model Governance, covers decisions relating to the questions of data subject consent, privacy, and model release.