Granite Code Models: A Family of Open Foundation Models for Code Intelligence
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| Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| author | Mishra, Mayank Stallone, Matt Zhang, Gaoyuan Shen, Yikang Prasad, Aditya Soria, Adriana Meza Merler, Michele Selvam, Parameswaran Surendran, Saptha Singh, Shivdeep Sethi, Manish Dang, Xuan-Hong Li, Pengyuan Wu, Kun-Lung Zawad, Syed Coleman, Andrew White, Matthew Lewis, Mark Pavuluri, Raju Koyfman, Yan Lublinsky, Boris de Bayser, Maximilien Abdelaziz, Ibrahim Basu, Kinjal Agarwal, Mayank Zhou, Yi Johnson, Chris Goyal, Aanchal Patel, Hima Shah, Yousaf Zerfos, Petros Ludwig, Heiko Munawar, Asim Crouse, Maxwell Kapanipathi, Pavan Salaria, Shweta Calio, Bob Wen, Sophia Seelam, Seetharami Belgodere, Brian Fonseca, Carlos Singhee, Amith Desai, Nirmit Cox, David D. Puri, Ruchir Panda, Rameswar |
| author_facet | Mishra, Mayank Stallone, Matt Zhang, Gaoyuan Shen, Yikang Prasad, Aditya Soria, Adriana Meza Merler, Michele Selvam, Parameswaran Surendran, Saptha Singh, Shivdeep Sethi, Manish Dang, Xuan-Hong Li, Pengyuan Wu, Kun-Lung Zawad, Syed Coleman, Andrew White, Matthew Lewis, Mark Pavuluri, Raju Koyfman, Yan Lublinsky, Boris de Bayser, Maximilien Abdelaziz, Ibrahim Basu, Kinjal Agarwal, Mayank Zhou, Yi Johnson, Chris Goyal, Aanchal Patel, Hima Shah, Yousaf Zerfos, Petros Ludwig, Heiko Munawar, Asim Crouse, Maxwell Kapanipathi, Pavan Salaria, Shweta Calio, Bob Wen, Sophia Seelam, Seetharami Belgodere, Brian Fonseca, Carlos Singhee, Amith Desai, Nirmit Cox, David D. Puri, Ruchir Panda, Rameswar |
| contents | Large Language Models (LLMs) trained on code are revolutionizing the software development process. Increasingly, code LLMs are being integrated into software development environments to improve the productivity of human programmers, and LLM-based agents are beginning to show promise for handling complex tasks autonomously. Realizing the full potential of code LLMs requires a wide range of capabilities, including code generation, fixing bugs, explaining and documenting code, maintaining repositories, and more. In this work, we introduce the Granite series of decoder-only code models for code generative tasks, trained with code written in 116 programming languages. The Granite Code models family consists of models ranging in size from 3 to 34 billion parameters, suitable for applications ranging from complex application modernization tasks to on-device memory-constrained use cases. Evaluation on a comprehensive set of tasks demonstrates that Granite Code models consistently reaches state-of-the-art performance among available open-source code LLMs. The Granite Code model family was optimized for enterprise software development workflows and performs well across a range of coding tasks (e.g. code generation, fixing and explanation), making it a versatile all around code model. We release all our Granite Code models under an Apache 2.0 license for both research and commercial use. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_04324 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Granite Code Models: A Family of Open Foundation Models for Code Intelligence Mishra, Mayank Stallone, Matt Zhang, Gaoyuan Shen, Yikang Prasad, Aditya Soria, Adriana Meza Merler, Michele Selvam, Parameswaran Surendran, Saptha Singh, Shivdeep Sethi, Manish Dang, Xuan-Hong Li, Pengyuan Wu, Kun-Lung Zawad, Syed Coleman, Andrew White, Matthew Lewis, Mark Pavuluri, Raju Koyfman, Yan Lublinsky, Boris de Bayser, Maximilien Abdelaziz, Ibrahim Basu, Kinjal Agarwal, Mayank Zhou, Yi Johnson, Chris Goyal, Aanchal Patel, Hima Shah, Yousaf Zerfos, Petros Ludwig, Heiko Munawar, Asim Crouse, Maxwell Kapanipathi, Pavan Salaria, Shweta Calio, Bob Wen, Sophia Seelam, Seetharami Belgodere, Brian Fonseca, Carlos Singhee, Amith Desai, Nirmit Cox, David D. Puri, Ruchir Panda, Rameswar Artificial Intelligence Computation and Language Software Engineering Large Language Models (LLMs) trained on code are revolutionizing the software development process. Increasingly, code LLMs are being integrated into software development environments to improve the productivity of human programmers, and LLM-based agents are beginning to show promise for handling complex tasks autonomously. Realizing the full potential of code LLMs requires a wide range of capabilities, including code generation, fixing bugs, explaining and documenting code, maintaining repositories, and more. In this work, we introduce the Granite series of decoder-only code models for code generative tasks, trained with code written in 116 programming languages. The Granite Code models family consists of models ranging in size from 3 to 34 billion parameters, suitable for applications ranging from complex application modernization tasks to on-device memory-constrained use cases. Evaluation on a comprehensive set of tasks demonstrates that Granite Code models consistently reaches state-of-the-art performance among available open-source code LLMs. The Granite Code model family was optimized for enterprise software development workflows and performs well across a range of coding tasks (e.g. code generation, fixing and explanation), making it a versatile all around code model. We release all our Granite Code models under an Apache 2.0 license for both research and commercial use. |
| title | Granite Code Models: A Family of Open Foundation Models for Code Intelligence |
| topic | Artificial Intelligence Computation and Language Software Engineering |
| url | https://arxiv.org/abs/2405.04324 |