This project is an implementation of the paper: Parameter-Efficient Transfer Learning for NLP, Houlsby [Google], ICML 2019.
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Updated
Mar 17, 2024 - Python
This project is an implementation of the paper: Parameter-Efficient Transfer Learning for NLP, Houlsby [Google], ICML 2019.
The code for generating natural distribution shifts on image and text datasets.
Master Thesis on "Comparing Modular Approaches for Parameter-Efficient Fine-Tuning"
Code for SAFT: Self-Attention Factor-Tuning, a 16x more efficient solution for fine-tuning neural networks
Low Tensor Rank adaptation of large language models
Code for fine-tuning Llama2 LLM with custom text dataset to produce film character styled responses
The code for the paper "Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models" (ICCV'23).
KR3: Korean Restaurant Review with Ratings / Experiments on Parameter-efficient Tuning and Task-adaptive Pre-training
This repository contains the source code for the paper "Grouped Pointwise Convolutions Reduce Parameters in Convolutional Neural Networks".
Applied Deep Learning 深度學習之應用 by Vivian Chen 陳縕儂 at NTU CSIE
Code for EACL'23 paper "Udapter: Efficient Domain Adaptation Using Adapters"
Official implementation of CVPR 2024 paper "Prompt Learning via Meta-Regularization".
PANDA: Prompt Transfer Meets Knowledge Distillation for Efficient Model Adaptation
[ICRA 2024] Official Implementation of the Paper "Parameter-efficient Prompt Learning for 3D Point Cloud Understanding"
Evaluate robustness of adaptation methods on large vision-language models
This is AlpaGasus2-QLoRA based on LLaMA2 with AlpaGasus mechanism using QLoRA!
[MICCAI ISIC Workshop 2023] AViT: Adapting Vision Transformers for Small Skin Lesion Segmentation Datasets (an official implementation)
[NeurIPS-2022] Annual Conference on Neural Information Processing Systems
Code for the Findings of NAACL 2022(Long Paper): AdapterBias: Parameter-efficient Token-dependent Representation Shift for Adapters in NLP Tasks
Multi-domain Recommendation with Adapter Tuning
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