1 citations · 1 across the 5 of their papers we have counts for
5 papers
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…
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…
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…
Divergent Token Metrics: Measuring degradation to prune away LLM components -- and optimize quantization
Björn Deiseroth, Max Meuer, Nikolas Gritsch +4
Large Language Models (LLMs) have reshaped natural language processing with their impressive capabilities. However, their ever-increasing size has raised concerns about their effec…
Simulating and visualizing COVID-19 contact tracing with Corona-Warn-App for increased understanding of its privacy-preserving design
Nikolas Gritsch, Benjamin Tegeler, Faheem Hassan Zunjani
The world is under an ongoing pandemic, COVID-19, of a scale last seen a century ago. Contact tracing is one of the most critical and highly effective tools for containing and brea…