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parameter-efficient fine-tuning

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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/xue-xia/mapsam

공식 코드

MapSAM: Adapting Segment Anything Model for Automated Feature Detection in Historical Maps

https://github.com/xuyige/softcot

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SoftCoT: Soft Chain-of-Thought for Efficient Reasoning with LLMs

https://github.com/xvyaward/owq

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

https://github.com/xvyaward/qeft

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QEFT: Quantization for Efficient Fine-Tuning of LLMs

https://github.com/xypb/mama

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Multi-View and Multi-Scale Alignment for Contrastive Language-Image Pre-training in Mammography

https://github.com/xypu98/cwsam

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ClassWise-SAM-Adapter: Parameter Efficient Fine-tuning Adapts Segment Anything to SAR Domain for Semantic Segmentation

https://github.com/xzy-xzy/CiteCheck

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CiteCheck: Towards Accurate Citation Faithfulness Detection

https://github.com/yeachan-kr/c2a

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C2A: Client-Customized Adaptation for Parameter-Efficient Federated Learning

https://github.com/yermandy/deepfake-detection

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Unlocking the Hidden Potential of CLIP in Generalizable Deepfake Detection

https://github.com/yibozhong/lily

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Low-Rank Interconnected Adaptation across Layers

https://github.com/yifanycc/loretta

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LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language Models

https://github.com/ykwon0407/datainf

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DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models

https://github.com/ymy-k/hi-sam

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Hi-SAM: Marrying Segment Anything Model for Hierarchical Text Segmentation

https://github.com/ysj9909/DG-SoRA

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SoRA: Singular Value Decomposed Low-Rank Adaptation for Domain Generalizable Representation Learning

https://github.com/yuelinan/fedjudge

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FedJudge: Federated Legal Large Language Model

https://github.com/yuhkalhic/ssmlora

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SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model

https://github.com/yulongmao1/dora

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DoRA: Enhancing Parameter-Efficient Fine-Tuning with Dynamic Rank Distribution

https://github.com/yutinghe-list/tamp

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Imaging foundation model for universal enhancement of non-ideal measurement CT

https://github.com/zarharan/farexstance

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FarExStance: Explainable Stance Detection for Farsi

https://github.com/zefang-liu/TPP-LLM

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TPP-LLM: Modeling Temporal Point Processes by Efficiently Fine-Tuning Large Language Models

https://github.com/zhan7721/crosslingual_ser

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Cross-Lingual Speech Emotion Recognition: Humans vs. Self-Supervised Models

https://github.com/zhangleye/Bridge-LLM-QA

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Question answering system of bridge design specification based on large language model

https://github.com/zhaohengyuan1/sct

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SCT: A Simple Baseline for Parameter-Efficient Fine-Tuning via Salient Channels

https://github.com/zhengxiangshi/dept

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DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning

https://github.com/zhengzhi-1997/llm-trsr

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Harnessing Large Language Models for Text-Rich Sequential Recommendation

https://github.com/zhliuworks/clipmole

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Mixture of Low-rank Experts for Transferable AI-Generated Image Detection

https://github.com/zhoujiahuan1991/icml2025-gaprompt

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GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model

https://github.com/zhoujiahuan1991/icml2025-vgp

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Vision Graph Prompting via Semantic Low-Rank Decomposition

https://github.com/zhuyunqi96/LoraLPrun

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Parameter-Efficient Fine-Tuning with Layer Pruning on Free-Text Sequence-to-Sequence Modeling

https://github.com/ziplab/spt

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Sensitivity-Aware Visual Parameter-Efficient Fine-Tuning

https://github.com/ziplab/stitched_llama

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Efficient Stitchable Task Adaptation

https://github.com/ziyuguo99/point-bind_point-llm

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Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following

https://github.com/zju-llms/awesome-loras

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A Survey on LoRA of Large Language Models

https://github.com/znhy1024/protoco

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Prompt to be Consistent is Better than Self-Consistent? Few-Shot and Zero-Shot Fact Verification with Pre-trained Language Models

https://github.com/zongqianli/promptcompression-survey

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Prompt Compression for Large Language Models: A Survey

https://github.com/zongqianli/pt-moe

공식 코드

PT-MoE: An Efficient Finetuning Framework for Integrating Mixture-of-Experts into Prompt Tuning

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