Summary of Granite Code Models: a Family Of Open Foundation Models For Code Intelligence, by Mayank Mishra et al.

Granite Code Models: A Family of Open Foundation Models for Code Intelligence

by 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

First submitted to arxiv on: 7 May 2024

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Summary difficulty	Written by	Summary
High	Paper authors	High Difficulty Summary Read the original abstract here
Medium	GrooveSquid.com (original content)	Medium Difficulty Summary Large Language Models (LLMs) trained on code are transforming the software development process. These LLMs are being integrated into development environments to enhance human programmers’ productivity, while agent-based models show promise in handling complex tasks autonomously. To unlock the full potential of code LLMs, capabilities like code generation, bug fixing, and documentation are crucial. This work introduces the Granite series of decoder-only code models for generative tasks, trained on 116 programming languages. The Granite Code model family consists of models ranging from 3 to 34 billion parameters, suitable for applications such as complex application modernization or memory-constrained use cases. Evaluation on various tasks demonstrates that Granite Code models consistently achieves state-of-the-art performance among available open-source code LLMs.
Low	GrooveSquid.com (original content)	Low Difficulty Summary This paper is about a new way to make computers write code faster and better. Trained computer models called Large Language Models (LLMs) are being used to help humans write code more efficiently. These models can even do tasks on their own, like fixing bugs or explaining how code works. To get the most out of these models, they need to be able to generate code, fix mistakes, and explain things clearly. The researchers introduced a new family of computer models called Granite Code that can do all this and more. They trained these models on 116 different programming languages and tested them on various coding tasks. The results show that Granite Code models are the best among similar open-source models.