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 41–50 of 935 papers

Title	Date	Tasks	Status	Hype
A Survey on Federated Fine-tuning of Large Language Models	Mar 15, 2025	Federated Learningparameter-efficient fine-tuning	CodeCode Available	2
CoLLiE: Collaborative Training of Large Language Models in an Efficient Way	Dec 1, 2023	GPUparameter-efficient fine-tuning	CodeCode Available	2
FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models	Oct 16, 2023	Federated Learningparameter-efficient fine-tuning	CodeCode Available	2
Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Models	Sep 14, 2024	parameter-efficient fine-tuningPrivacy Preserving	CodeCode Available	2
mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs	Dec 5, 2023	GPULarge Language Model	CodeCode Available	2
FairMedFM: Fairness Benchmarking for Medical Imaging Foundation Models	Jul 1, 2024	BenchmarkingFairness	CodeCode Available	2
Full Parameter Fine-tuning for Large Language Models with Limited Resources	Jun 16, 2023	GPUparameter-efficient fine-tuning	CodeCode Available	2
Any2Point: Empowering Any-modality Large Models for Efficient 3D Understanding	Apr 11, 2024	3D geometryparameter-efficient fine-tuning	CodeCode Available	2
Efficient Remote Sensing with Harmonized Transfer Learning and Modality Alignment	Apr 28, 2024	Cross-Modal RetrievalImage Retrieval	CodeCode Available	2
Dynamic Tuning Towards Parameter and Inference Efficiency for ViT Adaptation	Mar 18, 2024	Mixture-of-Expertsparameter-efficient fine-tuning	CodeCode Available	2

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All datasets BoolQ HellaSwag WinoGrande

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	LLaMA2-7b	Accuracy (% )	82.63	—	Unverified
2	LLaMA2-7b	Accuracy (% )	82.63	—	Unverified
3	LLaMA2-7b	Accuracy (% )	81.93	—	Unverified
4	LLaMA2-7b	Accuracy (% )	80.28	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LLaMA2-7b	Accuracy (% )	76.68	—	Unverified
2	LLaMA2-7b	Accuracy (% )	76.67	—	Unverified
3	LLaMA2-7b	Accuracy (% )	76.27	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LLaMA2-7b	Accuracy (% )	70.8	—	Unverified
2	LLaMA2-7b	Accuracy (% )	70.09	—	Unverified
3	LLaMA2-7b	Accuracy (% )	69.85	—	Unverified