2 citations · 2 across the 4 of their papers we have counts for
4 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…
OAC: Output-adaptive Calibration for Accurate Post-training Quantization
Ali Edalati, Alireza Ghaffari, Mahsa Ghazvini Nejad +4
Deployment of Large Language Models (LLMs) has major computational costs, due to their rapidly expanding size. Compression of LLMs reduces the memory footprint, latency, and energy…
AdCorDA: Classifier Refinement via Adversarial Correction and Domain Adaptation
Lulan Shen, Ali Edalati, Brett Meyer +2
This paper describes a simple yet effective technique for refining a pretrained classifier network. The proposed AdCorDA method is based on modification of the training set and mak…
Robustness to distribution shifts of compressed networks for edge devices
Lulan Shen, Ali Edalati, Brett Meyer +2
It is necessary to develop efficient DNNs deployed on edge devices with limited computation resources. However, the compressed networks often execute new tasks in the target domain…