Kavli Affiliate: Yi Zhou
| First 5 Authors: Mayank Mishra, Matt Stallone, Gaoyuan Zhang, Yikang Shen, Aditya Prasad
| Summary:
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.
| Search Query: ArXiv Query: search_query=au:”Yi Zhou”&id_list=&start=0&max_results=3