Search Results for author: Weilin Zhao

Found 13 papers, 9 papers with code

BMInf: An Efficient Toolkit for Big Model Inference and Tuning

1 code implementation ACL 2022 Xu Han, Guoyang Zeng, Weilin Zhao, Zhiyuan Liu, Zhengyan Zhang, Jie zhou, Jun Zhang, Jia Chao, Maosong Sun

In recent years, large-scale pre-trained language models (PLMs) containing billions of parameters have achieved promising results on various NLP tasks.

Quantization Scheduling

BurstAttention: An Efficient Distributed Attention Framework for Extremely Long Sequences

1 code implementation14 Mar 2024 Sun Ao, Weilin Zhao, Xu Han, Cheng Yang, Zhiyuan Liu, Chuan Shi, Maosong Sun

Effective attention modules have played a crucial role in the success of Transformer-based large language models (LLMs), but the quadratic time and memory complexities of these attention modules also pose a challenge when processing long sequences.

Mastering Text, Code and Math Simultaneously via Fusing Highly Specialized Language Models

no code implementations13 Mar 2024 Ning Ding, Yulin Chen, Ganqu Cui, Xingtai Lv, Weilin Zhao, Ruobing Xie, BoWen Zhou, Zhiyuan Liu, Maosong Sun

Underlying data distributions of natural language, programming code, and mathematical symbols vary vastly, presenting a complex challenge for large language models (LLMs) that strive to achieve high performance across all three domains simultaneously.


Ouroboros: Speculative Decoding with Large Model Enhanced Drafting

1 code implementation21 Feb 2024 Weilin Zhao, Yuxiang Huang, Xu Han, Chaojun Xiao, Zhiyuan Liu, Maosong Sun

In this paper, we introduce Ouroboros, which constructs a phrase candidate pool from the verification process of LLMs to provide candidates for draft generation of the small model.

Text Generation

CPET: Effective Parameter-Efficient Tuning for Compressed Large Language Models

no code implementations15 Jul 2023 Weilin Zhao, Yuxiang Huang, Xu Han, Zhiyuan Liu, Zhengyan Zhang, Maosong Sun

Parameter-efficient tuning (PET) has been widely explored in recent years because it tunes much fewer parameters (PET modules) than full-parameter fine-tuning (FT) while still stimulating sufficient knowledge from large language models (LLMs) for downstream tasks.

OpenDelta: A Plug-and-play Library for Parameter-efficient Adaptation of Pre-trained Models

1 code implementation5 Jul 2023 Shengding Hu, Ning Ding, Weilin Zhao, Xingtai Lv, Zhen Zhang, Zhiyuan Liu, Maosong Sun

The scale of large pre-trained models (PTMs) poses significant challenges in adapting to downstream tasks due to the high optimization overhead and storage costs associated with full-parameter fine-tuning.

OpenPrompt: An Open-source Framework for Prompt-learning

2 code implementations ACL 2022 Ning Ding, Shengding Hu, Weilin Zhao, Yulin Chen, Zhiyuan Liu, Hai-Tao Zheng, Maosong Sun

Prompt-learning has become a new paradigm in modern natural language processing, which directly adapts pre-trained language models (PLMs) to $cloze$-style prediction, autoregressive modeling, or sequence to sequence generation, resulting in promising performances on various tasks.

PTR: Prompt Tuning with Rules for Text Classification

1 code implementation24 May 2021 Xu Han, Weilin Zhao, Ning Ding, Zhiyuan Liu, Maosong Sun

This indicates that PTR is a promising approach to take advantage of both human prior knowledge and PLMs for those complicated classification tasks.

Natural Language Inference Relation Classification +4

Cannot find the paper you are looking for? You can Submit a new open access paper.