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parameter-efficient fine-tuning

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.

Papers

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Showing 101–150 of 935 papers

TitleStatusHype
Unified Parameter-Efficient Unlearning for LLMsCode1
FonTS: Text Rendering with Typography and Style ControlsCode1
Exploring Foundation Models Fine-Tuning for Cytology ClassificationCode1
MapSAM: Adapting Segment Anything Model for Automated Feature Detection in Historical MapsCode1
MambaPEFT: Exploring Parameter-Efficient Fine-Tuning for MambaCode1
Expanding Sparse Tuning for Low Memory UsageCode1
C2A: Client-Customized Adaptation for Parameter-Efficient Federated LearningCode1
IntLoRA: Integral Low-rank Adaptation of Quantized Diffusion ModelsCode1
KD-LoRA: A Hybrid Approach to Efficient Fine-Tuning with LoRA and Knowledge DistillationCode1
Natural GaLore: Accelerating GaLore for memory-efficient LLM Training and Fine-tuningCode1
Unlocking the Capabilities of Masked Generative Models for Image Synthesis via Self-GuidanceCode1
LoRA Soups: Merging LoRAs for Practical Skill Composition TasksCode1
Prompt Compression for Large Language Models: A SurveyCode1
AlphaLoRA: Assigning LoRA Experts Based on Layer Training QualityCode1
Towards Efficient Visual-Language Alignment of the Q-Former for Visual Reasoning TasksCode1
MTL-LoRA: Low-Rank Adaptation for Multi-Task LearningCode1
SLiM: One-shot Quantization and Sparsity with Low-rank Approximation for LLM Weight CompressionCode1
Parameter-Efficient Fine-Tuning of State Space ModelsCode1
Enhancing Zeroth-order Fine-tuning for Language Models with Low-rank StructuresCode1
Parameter Efficient Fine-tuning via Explained Variance AdaptationCode1
Imaging foundation model for universal enhancement of non-ideal measurement CTCode1
TPP-LLM: Modeling Temporal Point Processes by Efficiently Fine-Tuning Large Language ModelsCode1
Vision-Language Models are Strong Noisy Label DetectorsCode1
PACE: Marrying generalization in PArameter-efficient fine-tuning with Consistency rEgularizationCode1
Lessons and Insights from a Unifying Study of Parameter-Efficient Fine-Tuning (PEFT) in Visual RecognitionCode1
Propulsion: Steering LLM with Tiny Fine-TuningCode1
Do Vision Foundation Models Enhance Domain Generalization in Medical Image Segmentation?Code1
Efficient Localized Adaptation of Neural Weather Forecasting: A Case Study in the MENA RegionCode1
Sam2Rad: A Segmentation Model for Medical Images with Learnable PromptsCode1
Ferret: Federated Full-Parameter Tuning at Scale for Large Language ModelsCode1
MoRe Fine-Tuning with 10x Fewer ParametersCode1
CVPT: Cross-Attention help Visual Prompt Tuning adapt visual taskCode1
Positional Prompt Tuning for Efficient 3D Representation LearningCode1
SORSA: Singular Values and Orthonormal Regularized Singular Vectors Adaptation of Large Language ModelsCode1
V-RoAst: Visual Road Assessment. Can VLM be a Road Safety Assessor Using the iRAP Standard?Code1
TDS-CLIP: Temporal Difference Side Network for Image-to-Video Transfer LearningCode1
Customizing Language Models with Instance-wise LoRA for Sequential RecommendationCode1
KIF: Knowledge Identification and Fusion for Language Model Continual LearningCode1
TS-SAM: Fine-Tuning Segment-Anything Model for Downstream TasksCode1
Forecast-PEFT: Parameter-Efficient Fine-Tuning for Pre-trained Motion Forecasting ModelsCode1
RoLoRA: Fine-tuning Rotated Outlier-free LLMs for Effective Weight-Activation QuantizationCode1
Embedded Prompt Tuning: Towards Enhanced Calibration of Pretrained Models for Medical ImagesCode1
Increasing Model Capacity for Free: A Simple Strategy for Parameter Efficient Fine-tuningCode1
Personalized Pieces: Efficient Personalized Large Language Models through Collaborative EffortsCode1
An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language ModelsCode1
MEFT: Memory-Efficient Fine-Tuning through Sparse AdapterCode1
Light-PEFT: Lightening Parameter-Efficient Fine-Tuning via Early PruningCode1
LoFiT: Localized Fine-tuning on LLM RepresentationsCode1
SVFT: Parameter-Efficient Fine-Tuning with Singular VectorsCode1
MLAE: Masked LoRA Experts for Visual Parameter-Efficient Fine-TuningCode1
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All datasetsBoolQHellaSwagWinoGrande

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1LLaMA2-7bAccuracy (% )82.63—Unverified
2LLaMA2-7bAccuracy (% )82.63—Unverified
3LLaMA2-7bAccuracy (% )81.93—Unverified
4LLaMA2-7bAccuracy (% )80.28—Unverified
#ModelMetricClaimedVerifiedStatus
1LLaMA2-7bAccuracy (% )76.68—Unverified
2LLaMA2-7bAccuracy (% )76.67—Unverified
3LLaMA2-7bAccuracy (% )76.27—Unverified
#ModelMetricClaimedVerifiedStatus
1LLaMA2-7bAccuracy (% )70.8—Unverified
2LLaMA2-7bAccuracy (% )70.09—Unverified
3LLaMA2-7bAccuracy (% )69.85—Unverified