[ICCV 2023 oral] This is the official repository for our paper: ''Sensitivity-Aware Visual Parameter-Efficient Fine-Tuning''.

Fine tuning Mistral-7b with PEFT(Parameter Efficient Fine-Tuning) and LoRA(Low-Rank Adaptation) on Puffin Dataset(multi-turn conversations between GPT-4 and real humans)

Parameter Efficient Fine-Tuning for CLIP

This repository contains the lab work for Coursera course on "Generative AI with Large Language Models".

Exploring the potential of fine-tuning Large Language Models (LLMs) like Llama2 and StableLM for medical entity extraction. This project focuses on adapting these models using PEFT, Adapter V2, and LoRA techniques to efficiently and accurately extract drug names and adverse side-effects from pharmaceutical texts

A Production-Ready, Scalable RAG-powered LLM-based Context-Aware QA App

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

A framework to optimize Parameter-Efficient Fine-Tuning for Fairness in Medical Image Analysis

[ICLR 2024] This is the repository for the paper titled "DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning"

Code for the EACL 2024 paper: "Small Language Models Improve Giants by Rewriting Their Outputs"

[CVPR2024] The code of "UniPT: Universal Parallel Tuning for Transfer Learning with Efficient Parameter and Memory"

[WACV 2024] MACP: Efficient Model Adaptation for Cooperative Perception.

[SIGIR'24] The official implementation code of MOELoRA.

This is the official repository of the papers "Parameter-Efficient Transfer Learning of Audio Spectrogram Transformers" and "Efficient Fine-tuning of Audio Spectrogram Transformers via Soft Mixture of Adapters".

CorDA: Context-Oriented Decomposition Adaptation of Large Language Models

Fine-Tuned LLM-Based FAQ Generation for University Admissions: A project involving the fine-tuning of state-of-the-art language models, including LLaMA-3 8b, LLaMA-2 7b, Mistral 7b, T5, and BART, leveraging QLoRA PEFT.

[ICML2024 (Oral)] Official PyTorch implementation of DoRA: Weight-Decomposed Low-Rank Adaptation

This repository contain a project which goal is to find new parameter efficient fine tuning framework in order to improve performance of Deep Artificial Neural Network onto "out of distribution" data (OOD). In this specific case you can find Multi-task Learning problem.

A generalized framework for subspace tuning methods in parameter efficient fine-tuning.

[ECCV 2024] - Improving Zero-shot Generalization of Learned Prompts via Unsupervised Knowledge Distillation