A Unified Library for Parameter-Efficient and Modular Transfer Learning
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Updated
May 31, 2024 - Jupyter Notebook
A Unified Library for Parameter-Efficient and Modular Transfer Learning
[ICML2024] Official PyTorch implementation of DoRA: Weight-Decomposed Low-Rank Adaptation
Collection of Tools and Papers related to Adapters / Parameter-Efficient Transfer Learning/ Fine-Tuning
On Transferability of Prompt Tuning for Natural Language Processing
Code for SAFT: Self-Attention Factor-Tuning, a 16x more efficient solution for fine-tuning neural networks
[CVPR2024] The code of "UniPT: Universal Parallel Tuning for Transfer Learning with Efficient Parameter and Memory"
[ICLR 2024] This is the repository for the paper titled "DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning"
[ICML 2024] Official code for the paper "Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark ".
Official implementation of CVPR 2024 paper "Prompt Learning via Meta-Regularization".
Multi-domain Recommendation with Adapter Tuning
This project is an implementation of the paper: Parameter-Efficient Transfer Learning for NLP, Houlsby [Google], ICML 2019.
A collection of parameter-efficient transfer learning papers focusing on computer vision and multimodal domains.
This repository contains the source code for the paper "Grouped Pointwise Convolutions Reduce Parameters in Convolutional Neural Networks".
Low Tensor Rank adaptation of large language models
[ICRA 2024] Official Implementation of the Paper "Parameter-efficient Prompt Learning for 3D Point Cloud Understanding"
[MICCAI ISIC Workshop 2023] AViT: Adapting Vision Transformers for Small Skin Lesion Segmentation Datasets (an official implementation)
Master Thesis on "Comparing Modular Approaches for Parameter-Efficient Fine-Tuning"
Code for fine-tuning Llama2 LLM with custom text dataset to produce film character styled responses
[NeurIPS-2022] Annual Conference on Neural Information Processing Systems
A curated list of prompt-based paper in computer vision and vision-language learning.
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