skip to main content
Back to Project Search

Efficient Quantization of Large Language Models

Project

Project Details

Program
Computer Science
Field of Study
computer science, artificial intelligence, mathematics, machine learning
Division
Computer, Electrical and Mathematical Sciences and Engineering
Faculty Lab Link
https://richtarik.org

Project Description

Quantizing large language models has become a standard way to reduce their memory and computational costs. Typically, existing methods focus on breaking down the problem into individual layer-wise sub-problems, and minimizing per-layer error, measured via various metrics.

About the Researcher

Peter Richtarik
Professor, Computer Science
Computer, Electrical and Mathematical Science and Engineering Division

Affiliations

Education Profile

  • PhD, Operations Research, Cornell University, 2007
  • MS, Operations Research, Cornell University, 2006
  • Mgr, Mathematics, Comenius University, 2001
  • Bc, Management Comenius University, 2001
  • Bc, Mathematics, Comenius University, 2000

Research Interests

Prof. Richtarik's research interests lie at the intersection of mathematics, computer science, machine learning, optimization, numerical linear algebra, high performance computing and applied probability. He is interested in developing zero, first, and second-order algorithms for convex and nonconvex optimization problems described by big data, with a particular focus on randomized, parallel and distributed methods. He is the co-inventor of federated learning, a Google platform for machine learning on mobile devices preserving privacy of users' data.

Selected Publications

  • R. M. Gower, D. Goldfarb and P. Richtarik. Stochastic block BFGS: squeezing more curvature out of data, Proceedings of The 33rd International Conference on Machine Learning, pp. 1869-1878, 2016
  • J. Konecny, J. Liu, P. Richtarik and M. Takac. Mini-batch semi-stochastic gradient descent in the proximal setting, IEEE Journal of Selected Topics in Signal Processing 10(2), 242a-255, 2016
  • P. Richtarik and M. Takac. Parallel coordinate descent methods for big data optimization Mathematical Programming 156(1):433a-484, 2016
  • R. M. Gower and P. Richtarik. Randomized iterative methods for linear systems, SIAM Journal on Matrix Analysis and Applications 36(4):1660-1690, 2015
  • O. Fercoq and P. Richtarik. Accelerated, parallel and proximal coordinate descent. SIAM Journal on Optimization 25(4):1997a-2023, 2015

Desired Project Deliverables

The goals of this project: - propose new quantization methods, outperforming existing approaches - write a research paper describing the results and submit it to a leading AI/ML conference such as ICML, ICLR, NeurIPS

Recommended Student Background

Vladimir Malinovskii, Andrei Panferov, Ivan Ilin, Han Guo, Peter Richtárik, and Dan Alistarh. Pushing the Limit
Vladimir Malinovskii, Denis Mazur, Ivan Ilin, Denis Kuznedelev, Konstantin Burlachenko, Kai Yi, Dan Alistarh, P
On this page

We are shaping the
World of Research

Be part of the journey with VSRP

Find a Project
3-6 months
Internship period
100+
Research Projects
3.5/4
Cumulative GPA
310
Interns a Year