AutoDistill: an End-to-End Framework to Explore and Distill
Hardware-Efficient Language Models

AutoDistill: an End-to-End Framework to Explore and Distill Hardware-Efficient Language Models

21 January 2022

Zongwei Zhou

Papers citing "AutoDistill: an End-to-End Framework to Explore and Distill Hardware-Efficient Language Models"

4 / 4 papers shown

Title
Domain Adaptable Fine-Tune Distillation Framework For Advancing Farm Surveillance Raza Imam Muhammad Huzaifa Nabil Mansour Shaher Bano Mirza Fouad Lamghari 25 0 0 10 Feb 2024
Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey Dalin Zhang Kaixuan Chen Yan Zhao B. Yang Li-Ping Yao Christian S. Jensen 48 3 0 22 Aug 2022
Q-BERT: Hessian Based Ultra Low Precision Quantization of BERT Sheng Shen Zhen Dong Jiayu Ye Linjian Ma Z. Yao A. Gholami Michael W. Mahoney Kurt Keutzer MQ 236 578 0 12 Sep 2019
GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding Alex Jinpeng Wang Amanpreet Singh Julian Michael Felix Hill Omer Levy Samuel R. Bowman ELM 304 6,996 0 20 Apr 2018