2 citations · 3 across the 3 of their papers we have counts for
3 papers
cs.AI2024★ 1 cited
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…
cs.LG2024★ 2 cited
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…
cs.SE2023
Rapid Development of Compositional AI
Lee Martie, Jessie Rosenberg, Veronique Demers +12
Compositional AI systems, which combine multiple artificial intelligence components together with other application components to solve a larger problem, have no known pattern of d…