NASH: A Simple Unified Framework of Structured Pruning for Accelerating Encoder-Decoder Language Models

16 October 2023

Papers citing "NASH: A Simple Unified Framework of Structured Pruning for Accelerating Encoder-Decoder Language Models"

6 / 6 papers shown

Title
HESSO: Towards Automatic Efficient and User Friendly Any Neural Network Training and Pruning Tianyi Chen Xiaoyi Qu David Aponte Colby R. Banbury Jongwoo Ko Tianyu Ding Yong Ma Vladimir Lyapunov Ilya Zharkov Luming Liang 70 1 0 11 Sep 2024
Training language models to follow instructions with human feedback Long Ouyang Jeff Wu Xu Jiang Diogo Almeida Carroll L. Wainwright ... Amanda Askell Peter Welinder Paul Christiano Jan Leike Ryan J. Lowe OSLM ALM 303 11,730 0 04 Mar 2022
Scale Efficiently: Insights from Pre-training and Fine-tuning Transformers Yi Tay Mostafa Dehghani J. Rao W. Fedus Samira Abnar Hyung Won Chung Sharan Narang Dani Yogatama Ashish Vaswani Donald Metzler 185 110 0 22 Sep 2021
The Lottery Ticket Hypothesis for Pre-trained BERT Networks Tianlong Chen Jonathan Frankle Shiyu Chang Sijia Liu Yang Zhang Zhangyang Wang Michael Carbin 148 345 0 23 Jul 2020
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 225 571 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 294 6,927 0 20 Apr 2018