Scaling Granite Code Models to 128K Context
Authors: name: Matt Stallone, name: Vaibhav Saxena, name: Leonid Karlinsky, name: Bridget McGinn, name: Tim Bula, name: Mayank Mishra, name: Adriana Meza Soria, name: Gaoyuan Zhang, name: Aditya Prasad, name: Yikang Shen, name: Saptha Surendran, name: Shanmukha Guttula, name: Hima Patel, name: Parameswaran Selvam, name: Xuan-Hong Dang, name: Yan Koyfman, name: Atin Sood, name: Rogerio Feris, name: Nirmit Desai, name: David D. Cox, name: Ruchir Puri, name: Rameswar Panda

This paper introduces long-context Granite code models that support effective
context windows of up to 128K tokens. Our solution for scaling context length
of Granite 3B/8B code models from 2K/4K to 128K consists of a light-weight
continual pretraining by gradually increasing its RoPE base frequency with
repository-level file packing and length-upsampled long-context data.
Additionally, we also release instruction-tuned models with long-context
support which are derived by further finetuning the long context base models on
a mix of permissively licensed short and long-context instruction-response
pairs. While comparing to the original short-context Granite code models, our
long-context models achieve significant improvements on long-context tasks
without any noticeable performance degradation on regular code completion
benchmarks (e.g., HumanEval). We release all our long-context Granite code
models under an Apache 2.0 license for both research and commercial use.