A Unified Library for Parameter-Efficient and Modular Transfer Learning
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Updated
Jun 15, 2024 - Jupyter Notebook
A Unified Library for Parameter-Efficient and Modular Transfer Learning
[ICML 2024] Official code for the paper "Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark ".
A collection of parameter-efficient transfer learning papers focusing on computer vision and multimodal domains.
Low Tensor Rank adaptation of large language models
[ICML2024 (Oral)] 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"
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.
This repository contains the source code for the paper "Grouped Pointwise Convolutions Reduce Parameters in Convolutional Neural Networks".
[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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