Papers with Code 아카이브
parameter-efficient fine-tuning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/Nexround/LoKI
공식 코드LoKI: Low-damage Knowledge Implanting of Large Language Models
https://github.com/OPPO-Mente-Lab/GlyphDraw
공식 코드GlyphDraw: Seamlessly Rendering Text with Intricate Spatial Structures in Text-to-Image Generation
https://github.com/OSU-MLB/SSL-Foundation-Models
공식 코드Revisiting semi-supervised learning in the era of foundation models
https://github.com/On-Point-RND/GIFT_SW
공식 코드GIFT-SW: Gaussian noise Injected Fine-Tuning of Salient Weights for LLMs
https://github.com/PLUM-Lab/AR-RAG
공식 코드AR-RAG: Autoregressive Retrieval Augmentation for Image Generation
https://github.com/PongNJ/V-RoAst
공식 코드V-RoAst: Visual Road Assessment. Can VLM be a Road Safety Assessor Using the iRAP Standard?
https://github.com/Qrange-group/SUR-adapter
공식 코드SUR-adapter: Enhancing Text-to-Image Pre-trained Diffusion Models with Large Language Models
https://github.com/Rain9876/ShareLoRA
공식 코드https://github.com/Raman1121/FairTune
공식 코드FairTune: Optimizing Parameter Efficient Fine Tuning for Fairness in Medical Image Analysis
https://github.com/Social-AI-Studio/InstructAV
공식 코드InstructAV: Instruction Fine-tuning Large Language Models for Authorship Verification
https://github.com/TUDB-Labs/multi-lora-fine-tune
공식 코드mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs
https://github.com/TamSiuhin/Per-Pcs
공식 코드Personalized Pieces: Efficient Personalized Large Language Models through Collaborative Efforts
https://github.com/TracyGuo2001/NLoRA
공식 코드NLoRA: Nyström-Initiated Low-Rank Adaptation for Large Language Models
https://github.com/Tree-Shu-Zhao/KALAHash.pytorch
공식 코드KALAHash: Knowledge-Anchored Low-Resource Adaptation for Deep Hashing
https://github.com/WillHawkins3/deepfakesondemand
공식 코드Deepfakes on Demand: the rise of accessible non-consensual deepfake image generators
https://github.com/WoodScene/TaSL
공식 코드KIF: Knowledge Identification and Fusion for Language Model Continual Learning
https://github.com/X-LANCE/SLAM-LLM
공식 코드An Embarrassingly Simple Approach for LLM with Strong ASR Capacity
https://github.com/Zarharan/NLE-for-fact-checking
공식 코드Tell Me Why: Explainable Public Health Fact-Checking with Large Language Models
https://github.com/ZichenMiao/Coeff_Tuning
공식 코드Coeff-Tuning: A Graph Filter Subspace View for Tuning Attention-Based Large Models
https://github.com/ZongqianLi/Prompt-Compression-Survey
공식 코드https://github.com/abhishekpanigrahi1996/Skill-Localization-by-grafting
공식 코드Task-Specific Skill Localization in Fine-tuned Language Models
https://github.com/abman23/on-device-llm-wifi-roaming
공식 코드https://github.com/agi-edgerunners/llm-adapters
공식 코드Astraios: Parameter-Efficient Instruction Tuning Code Large Language Models
https://github.com/agwabom/pema
공식 코드PEMA: An Offsite-Tunable Plug-in External Memory Adaptation for Language Models
https://github.com/ahmetustun/fairseq
공식 코드When does Parameter-Efficient Transfer Learning Work for Machine Translation?
https://github.com/aim-uofa/loraprune
공식 코드LoRAPrune: Structured Pruning Meets Low-Rank Parameter-Efficient Fine-Tuning
https://github.com/aisaka0v0/ts-whisper
공식 코드https://github.com/akalikong/ilora
공식 코드Customizing Language Models with Instance-wise LoRA for Sequential Recommendation
https://github.com/akhtarvision/weather-regional
공식 코드Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA Region
https://github.com/aksh555/lora-soups
공식 코드LoRA Soups: Merging LoRAs for Practical Skill Composition Tasks
https://github.com/alanansell/peft
공식 코드https://github.com/alenai97/peft-mllm
공식 코드An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models
https://github.com/alibaba/federatedscope
공식 코드https://github.com/allen4747/Ferret
공식 코드Ferret: Federated Full-Parameter Tuning at Scale for Large Language Models
https://github.com/allenai/hyperdecoders
공식 코드Hyperdecoders: Instance-specific decoders for multi-task NLP
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.