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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/ist-daslab/halo
공식 코드HALO: Hadamard-Assisted Lower-Precision Optimization for LLMs
https://github.com/ist-daslab/rosa
공식 코드RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation
https://github.com/ivan-tang-3d/any2point
공식 코드Any2Point: Empowering Any-modality Large Models for Efficient 3D Understanding
https://github.com/ivan-tang-3d/peft-3d
공식 코드Point-PEFT: Parameter-Efficient Fine-Tuning for 3D Pre-trained Models
https://github.com/ivan-tang-3d/point-peft
공식 코드Point-PEFT: Parameter-Efficient Fine-Tuning for 3D Pre-trained Models
https://github.com/ivanvykopal/adapter-prompt-evaluation
공식 코드https://github.com/jabhinav/prompt-tuning-strikes-back-with-lopa
공식 코드Prompt Tuning Strikes Back: Customizing Foundation Models with Low-Rank Prompt Adaptation
https://github.com/jekunz/peft-la
공식 코드Train More Parameters But Mind Their Placement: Insights into Language Adaptation with PEFT
https://github.com/jerryfeng2003/pointgst
공식 코드Parameter-Efficient Fine-Tuning in Spectral Domain for Point Cloud Learning
https://github.com/jhhuangkay/gradient-weight-normalized-low-rank-projection-for-efficient-llm-training
공식 코드Gradient Weight-normalized Low-rank Projection for Efficient LLM Training
https://github.com/jiangpenghe/cl-lora
공식 코드CL-LoRA: Continual Low-Rank Adaptation for Rehearsal-Free Class-Incremental Learning
https://github.com/jie040109/mlae
공식 코드MLAE: Masked LoRA Experts for Visual Parameter-Efficient Fine-Tuning
https://github.com/jieshibo/memvp
공식 코드Memory-Space Visual Prompting for Efficient Vision-Language Fine-Tuning
https://github.com/jinda-liu/r-lora
공식 코드R-LoRA: Random Initialization of Multi-Head LoRA for Multi-Task Learning
https://github.com/jinlhe/pefomed
공식 코드PeFoMed: Parameter Efficient Fine-tuning of Multimodal Large Language Models for Medical Imaging
https://github.com/jiwanhur/unlockmgm
공식 코드Unlocking the Capabilities of Masked Generative Models for Image Synthesis via Self-Guidance
https://github.com/jjgenailab/iisan
공식 코드https://github.com/jl-er/bone
공식 코드Balancing LoRA Performance and Efficiency with Simple Shard Sharing
https://github.com/jl-er/disha
공식 코드Balancing LoRA Performance and Efficiency with Simple Shard Sharing
https://github.com/josipjukic/adapter-al
공식 코드Parameter-Efficient Language Model Tuning with Active Learning in Low-Resource Settings
https://github.com/jusiro/fewshot-finetuning
공식 코드https://github.com/juyongjiang/kasa
공식 코드KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language Models
https://github.com/juzhengz/lori
공식 코드LoRI: Reducing Cross-Task Interference in Multi-Task Low-Rank Adaptation
https://github.com/jxhe/unify-parameter-efficient-tuning
공식 코드Towards a Unified View of Parameter-Efficient Transfer Learning
https://github.com/jzheng20/Course_projects
공식 코드https://github.com/kaiseem/ist
공식 코드https://github.com/kerem-cekmeceli/foundation-models-for-medical-imagery
공식 코드Do Vision Foundation Models Enhance Domain Generalization in Medical Image Segmentation?
https://github.com/kikyo-16/coco-mulla-repo
공식 코드https://github.com/kimsekeun/medivista-sam
공식 코드MediViSTA: Medical Video Segmentation via Temporal Fusion SAM Adaptation for Echocardiography
https://github.com/kinit-sk/adapter-prompt-evaluation
공식 코드https://github.com/kinit-sk/deni
공식 코드https://github.com/kinit-sk/semeval-2024-task-8-machine-text-detection
공식 코드KInIT at SemEval-2024 Task 8: Fine-tuned LLMs for Multilingual Machine-Generated Text Detection
https://github.com/kongds/mora
공식 코드MoRA: High-Rank Updating for Parameter-Efficient Fine-Tuning
https://github.com/kyutai-labs/moshivis
공식 코드Vision-Speech Models: Teaching Speech Models to Converse about Images
https://github.com/lamp-benchmark/lamp
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.