1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.CL2025
Command A: An Enterprise-Ready Large Language Model
Team Cohere, :, Aakanksha +227
In this report we describe the development of Command A, a powerful large language model purpose-built to excel at real-world enterprise use cases. Command A is an agent-optimised…
cs.CL2024
Nexus: Specialization meets Adaptability for Efficiently Training Mixture of Experts
Nikolas Gritsch, Qizhen Zhang, Acyr Locatelli +2
Efficiency, specialization, and adaptability to new data distributions are qualities that are hard to combine in current Large Language Models. The Mixture of Experts (MoE) archite…
cs.LG2024★ 1 cited
BAM! Just Like That: Simple and Efficient Parameter Upcycling for Mixture of Experts
Qizhen Zhang, Nikolas Gritsch, Dwaraknath Gnaneshwar +8
The Mixture of Experts (MoE) framework has become a popular architecture for large language models due to its superior performance over dense models. However, training MoEs from sc…