Popular GitHub Repositories for Parameter Efficient Tuning

A curated list of prompt-based paper in computer vision and vision-language learning.

Collection of Tools and Papers related to Adapters / Parameter-Efficient Transfer Learning/ Fine-Tuning

Official implementation for CVPR'23 paper "BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning"

Official implementation of AAAI 2023 paper "Parameter-efficient Model Adaptation for Vision Transformers"

[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 ".

[NeurIPS2023] Parameter-efficient Tuning of Large-scale Multimodal Foundation Model

ZhiJian: A Unifying and Rapidly Deployable Toolbox for Pre-trained Model Reuse

[Preprint] AdaVAE: Exploring Adaptive GPT-2s in VAEs for Language Modeling PyTorch Implementation

[CVPR'25] Lessons Learned from a Unifying Empirical Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual Recognition

Easy wrapper for inserting LoRA layers in CLIP.

Official implementation of CVPR 2024 paper "Prompt Learning via Meta-Regularization".

[ICRA 2024] Official Implementation of the Paper "Parameter-efficient Prompt Learning for 3D Point Cloud Understanding"

This is AlpaGasus2-QLoRA based on LLaMA2 with AlpaGasus mechanism using QLoRA!

Code for fine-tuning Llama2 LLM with custom text dataset to produce film character styled responses

[TIP2024] The code of "GSSF: Generalized Structural Sparse Function for Deep Cross-modal Metric Learning"

A simple code-base for finetuning Large Language Models for classification tasks.

Code for SAFT: Self-Attention Factor-Tuning, a 16x more efficient solution for fine-tuning neural networks

The LLM FineTuning and Evaluation project ? enhances FLAN-T5 models for tasks like summarizing Spanish news articles ???. It features detailed notebooks ? on fine-tuning and evaluating models to optimize performance for specific applications. ?✨

This project is an implementation of the paper: Parameter-Efficient Transfer Learning for NLP, Houlsby [Google], ICML 2019.