Papers with Code 아카이브

parameter-efficient fine-tuning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 0
평가표: 3
보존된 평가 기록: 10

Parameter-Efficient Fine-Tuning (PEFT) is a technique used to adapt pre-trained models to new tasks with minimal changes to the model's parameters. This approach is particularly useful in scenarios where computational resources are limited or when it is desirable to maintain the original model's performance on the initial task.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/clin0212/hydralora

공식 코드

HydraLoRA: An Asymmetric LoRA Architecture for Efficient Fine-Tuning

https://github.com/clu-uml/mu-bench

공식 코드

MU-Bench: A Multitask Multimodal Benchmark for Machine Unlearning

https://github.com/code4paper-2024/code4paper

공식 코드

Harnessing the Power of Large Language Model for Uncertainty Aware Graph Processing

https://github.com/col-tasas/2025-oco-with-iqcs

공식 코드

CROSSAN: Towards Efficient and Effective Adaptation of Multiple Multimodal Foundation Models for Sequential Recommendation

https://github.com/comeandcode/MRT

공식 코드

Re-Imagining Multimodal Instruction Tuning: A Representation View

https://github.com/computational-cell-analytics/peft-sam

공식 코드

Parameter Efficient Fine-Tuning of Segment Anything Model

https://github.com/csguoh/intlora

공식 코드

IntLoRA: Integral Low-rank Adaptation of Quantized Diffusion Models

https://github.com/csjfwang/forecast-peft

공식 코드

Forecast-PEFT: Parameter-Efficient Fine-Tuning for Pre-trained Motion Forecasting Models

https://github.com/currentf/meft

공식 코드

MEFT: Memory-Efficient Fine-Tuning through Sparse Adapter

https://github.com/cviu-csu/tct-infonce

공식 코드

An efficient framework based on large foundation model for cervical cytopathology whole slide image screening

https://github.com/cyp-jlu-ai/ba-lora

공식 코드

BA-LoRA: Bias-Alleviating Low-Rank Adaptation to Mitigate Catastrophic Inheritance in Large Language Models

https://github.com/d-gurgurov/Injecting-Commonsense-Knowledge-into-LLMs

공식 코드

Adapting Multilingual LLMs to Low-Resource Languages with Knowledge Graphs via Adapters

https://github.com/dadmatech/farexstance

공식 코드

FarExStance: Explainable Stance Detection for Farsi

https://github.com/davevanveen/radadapt

공식 코드

RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models

https://github.com/daviddaiiiii/san-peft

공식 코드

SAN: Hypothesizing Long-Term Synaptic Development and Neural Engram Mechanism in Scalable Model's Parameter-Efficient Fine-Tuning

https://github.com/deepseek-ai/esft

공식 코드

Let the Expert Stick to His Last: Expert-Specialized Fine-Tuning for Sparse Architectural Large Language Models

https://github.com/deepsoftwareanalytics/sotana

공식 코드

SoTaNa: The Open-Source Software Development Assistant

https://github.com/diaoenmao/cola-collaborative-adaptation-with-gradient-learning

공식 코드

ColA: Collaborative Adaptation with Gradient Learning

https://github.com/dong-river/llm_unlearning

공식 코드

UNDIAL: Self-Distillation with Adjusted Logits for Robust Unlearning in Large Language Models

https://github.com/dongping-chen/efft-effective-factor-tuning

공식 코드

Aggregate, Decompose, and Fine-Tune: A Simple Yet Effective Factor-Tuning Method for Vision Transformer

https://github.com/dongzelian/ssf

공식 코드

Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning

https://github.com/dphuongn/FLoRA

공식 코드

FLoRA: Enhancing Vision-Language Models with Parameter-Efficient Federated Learning

https://github.com/ducdauge/sft-llm

공식 코드

Scaling Sparse Fine-Tuning to Large Language Models

https://github.com/dvlab-research/longlora

공식 코드

LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models

https://github.com/ecnu-text-computing/imsm

공식 코드

Interweaving Memories of a Siamese Large Language Model

https://github.com/ecolab-postech/owq

공식 코드

OWQ: Outlier-Aware Weight Quantization for Efficient Fine-Tuning and Inference of Large Language Models

https://github.com/ehsanlatif/ppfl

공식 코드

Privacy-Preserved Automated Scoring using Federated Learning for Educational Research

https://github.com/eric-ai-lab/pectvlm

공식 코드

Parameter-Efficient Cross-lingual Transfer of Vision and Language Models via Translation-based Alignment

https://github.com/even-jk/peft-3d

공식 코드

Point-PEFT: Parameter-Efficient Fine-Tuning for 3D Pre-trained Models

https://github.com/event-ahu/velora

공식 코드

VELoRA: A Low-Rank Adaptation Approach for Efficient RGB-Event based Recognition

https://github.com/extremebird/hydra

공식 코드

Hydra: Multi-head Low-rank Adaptation for Parameter Efficient Fine-tuning

https://github.com/facico/goat-peft

공식 코드

Make LoRA Great Again: Boosting LoRA with Adaptive Singular Values and Mixture-of-Experts Optimization Alignment

https://github.com/farinamatteo/rethinking_fewshot_vlms

공식 코드

Rethinking Few-Shot Adaptation of Vision-Language Models in Two Stages

https://github.com/fc2869/lo-fit

공식 코드

LoFiT: Localized Fine-tuning on LLM Representations

https://github.com/feiyuzhang98/increlora

공식 코드

IncreLoRA: Incremental Parameter Allocation Method for Parameter-Efficient Fine-tuning

https://github.com/fengxueguiren/CoPEFT

공식 코드

CoPEFT: Fast Adaptation Framework for Multi-Agent Collaborative Perception with Parameter-Efficient Fine-Tuning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.