10 citations · 12 across the 7 of their papers we have counts for
4 papers · 1 filter
Data-Prep-Kit: getting your data ready for LLM application development
David Wood, Boris Lublinsky, Alexy Roytman +21
Data preparation is the first and a very important step towards any Large Language Model (LLM) development. This paper introduces an easy-to-use, extensible, and scale-flexible ope…
Scaling Granite Code Models to 128K Context
Matt Stallone, Vaibhav Saxena, Leonid Karlinsky +19
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 mo…
EdgeSphere: A Three-Tier Architecture for Cognitive Edge Computing
Christian Makaya, Keith Grueneberg, Bongjun Ko +3
Computing at the edge is increasingly important as Internet of Things (IoT) devices at the edge generate massive amounts of data and pose challenges in transporting all that data t…
Granite Code Models: A Family of Open Foundation Models for Code Intelligence
Mayank Mishra, Matt Stallone, Gaoyuan Zhang +43
Large Language Models (LLMs) trained on code are revolutionizing the software development process. Increasingly, code LLMs are being integrated into software development environmen…