Granite Code Models: A Family of Open Foundation Models for Code Intelligence
<|reference_start|>Granite Code Models: A Family of Open Foundation Models for Code Intelligence: 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.<|reference_end|>
@article{mishra2024granite,
    title={Granite Code Models: A Family of Open Foundation Models for Code
  Intelligence},
    author={Mayank Mishra, Matt Stallone, Gaoyuan Zhang, Yikang Shen, Aditya
  Prasad, Adriana Meza Soria, Michele Merler, Parameswaran Selvam, Saptha
  Surendran, Shivdeep Singh, Manish Sethi, Xuan-Hong Dang, Pengyuan Li,
  Kun-Lung Wu, Syed Zawad, Andrew Coleman, Matthew White, Mark Lewis, Raju
  Pavuluri, Yan Koyfman, Boris Lublinsky, Maximilien de Bayser, Ibrahim
  Abdelaziz, Kinjal Basu, Mayank Agarwal, Yi Zhou, Chris Johnson, Aanchal
  Goyal, Hima Patel, Yousaf Shah, Petros Zerfos, Heiko Ludwig, Asim Munawar,
  Maxwell Crouse, Pavan Kapanipathi, Shweta Salaria, Bob Calio, Sophia Wen,
  Seetharami Seelam, Brian Belgodere, Carlos Fonseca, Amith Singhee, Nirmit
  Desai, David D. Cox, Ruchir Puri, Rameswar Panda},
    journal={arXiv preprint arXiv:2405.04324},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.04324},
    primaryClass={cs.AI cs.CL cs.SE}
}