10 citations · 10 across the 1 of their papers we have counts for
3 papers
Generating Verifiable Chain of Thoughts from Exection-Traces
Shailja Thakur, Vaibhav Saxena, Rohan Kulkarni +4
Getting language models to reason correctly about code requires training on data where each reasoning step can be checked. Current synthetic Chain-of-Thought (CoT) training data of…
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…
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…