10 citations · 15 across the 5 of their papers we have counts for
5 papers
Power Scheduler: A Batch Size and Token Number Agnostic Learning Rate Scheduler
Yikang Shen, Matthew Stallone, Mayank Mishra +6
Finding the optimal learning rate for language model pretraining is a challenging task. This is not only because there is a complicated correlation between learning rate, batch siz…
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…
The infrastructure powering IBM's Gen AI model development
Talia Gershon, Seetharami Seelam, Brian Belgodere +143
AI Infrastructure plays a key role in the speed and cost-competitiveness of developing and deploying advanced AI models. The current demand for powerful AI infrastructure for model…
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…
Dense Training, Sparse Inference: Rethinking Training of Mixture-of-Experts Language Models
Bowen Pan, Yikang Shen, Haokun Liu +5
Mixture-of-Experts (MoE) language models can reduce computational costs by 2-4 compared to dense models without sacrificing performance, making them more efficient in compu…