eDKM: An Efficient and Accurate Train-time Weight Clustering for Large Language Models

no code implementations • 2 Sep 2023 • Minsik Cho, Keivan A. Vahid, Qichen Fu, Saurabh Adya, Carlo C Del Mundo, Mohammad Rastegari, Devang Naik, Peter Zatloukal

Since Large Language Models or LLMs have demonstrated high-quality performance on many complex language tasks, there is a great interest in bringing these LLMs to mobile devices for faster responses and better privacy protection.

Clustering Quantization

DKM: Differentiable K-Means Clustering Layer for Neural Network Compression

no code implementations • ICLR 2022 • Minsik Cho, Keivan A. Vahid, Saurabh Adya, Mohammad Rastegari

For MobileNet-v1, which is a challenging DNN to compress, DKM delivers 63. 9% top-1 ImageNet1k accuracy with 0. 72 MB model size (22. 4x model compression factor).

Clustering Neural Network Compression