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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/andytu28/VQT
공식 코드https://github.com/applied-machine-learning-lab/moelora-peft
공식 코드When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications
https://github.com/arijitthegame/structured-matrices-peft
공식 코드Structured Unrestricted-Rank Matrices for Parameter Efficient Fine-tuning
https://github.com/arnav0400/vit-slim
공식 코드One-for-All: Generalized LoRA for Parameter-Efficient Fine-tuning
https://github.com/artidoro/qlora
공식 코드https://github.com/aryopg/clinical_peft
공식 코드Parameter-Efficient Fine-Tuning of LLaMA for the Clinical Domain
https://github.com/asad14053/AdCare-VLM
공식 코드https://github.com/assert-kth/repairllama
공식 코드RepairLLaMA: Efficient Representations and Fine-Tuned Adapters for Program Repair
https://github.com/aswahd/sam2radiology
공식 코드Sam2Rad: A Segmentation Model for Medical Images with Learnable Prompts
https://github.com/aswahd/samradiology
공식 코드Sam2Rad: A Segmentation Model for Medical Images with Learnable Prompts
https://github.com/atharva20038/music4all
공식 코드Music for All: Representational Bias and Cross-Cultural Adaptability of Music Generation Models
https://github.com/attentionx/instructblip_peft
공식 코드Towards Efficient Visual-Language Alignment of the Q-Former for Visual Reasoning Tasks
https://github.com/autogluon/autogluon
공식 코드Convolution Meets LoRA: Parameter Efficient Finetuning for Segment Anything Model
https://github.com/baohaoliao/mefts
공식 코드Make Pre-trained Model Reversible: From Parameter to Memory Efficient Fine-Tuning
https://github.com/benzakenelad/BitFit
공식 코드BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models
https://github.com/bibisbar/LLaVA-Steering
공식 코드https://github.com/bigcode-project/astraios
공식 코드Astraios: Parameter-Efficient Instruction Tuning Code Large Language Models
https://github.com/bilican/sparse_peft
공식 코드Exploring Sparsity for Parameter Efficient Fine Tuning Using Wavelets
https://github.com/biomedia-mbzuai/salt
공식 코드SALT: Singular Value Adaptation with Low-Rank Transformation
https://github.com/brain-mmo-lab/zo_llm
공식 코드Leveraging Coordinate Momentum in SignSGD and Muon: Memory-Optimized Zero-Order
https://github.com/buaa-clab/reasoningv
공식 코드ReasoningV: Efficient Verilog Code Generation with Adaptive Hybrid Reasoning Model
https://github.com/bumble666/pha
공식 코드Prototype-based HyperAdapter for Sample-Efficient Multi-task Tuning
https://github.com/callanwu/star
공식 코드https://github.com/cambridgeltl/autopeft
공식 코드AutoPEFT: Automatic Configuration Search for Parameter-Efficient Fine-Tuning
https://github.com/camera-mri/spark2024
공식 코드https://github.com/cchen-cc/ma-sam
공식 코드MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation
https://github.com/cert-lab/abba
공식 코드ABBA: Highly Expressive Hadamard Product Adaptation for Large Language Models
https://github.com/chantalmp/radialog
공식 코드https://github.com/chaos96/fourierft
공식 코드Parameter-Efficient Fine-Tuning with Discrete Fourier Transform
https://github.com/chasonshi/melora
공식 코드MELoRA: Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-Tuning
https://github.com/christianyang37/chiwun
공식 코드https://github.com/chunmeifeng/fedins
공식 코드https://github.com/cindyxinyiwang/emea
공식 코드Efficient Test Time Adapter Ensembling for Low-resource Language Varieties
https://github.com/cityuhkai/sbora
공식 코드https://github.com/clin0212/awesome-federated-llm-learning
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.