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Decheng Wu

2 accepted papers

2026

Tequila: Deadzone-free Ternary Quantization for Large Language Models

ICLR 2026poster

Quantization techniques are essential for the deployment of Large Language Models (LLMs) on edge devices. However, prevailing methods often rely on mixed-precision multiplication that lacks efficient hardware support, making it not feasible. Ternary weight quantization addresses this by constraining…

Cited by 0SourcecodeScholar
2023

EasyQuant: An Efficient Data-free Quantization Algorithm for LLMs

EMNLP 2023long main

Large language models (LLMs) have proven to be very superior to conventional methods in various tasks. However, their expensive computations and high memory requirements are prohibitive for deployment. Model quantization is an effective method for reducing this overhead. The problem is that in most…

Cited by 0SourceScholar