SOTAVerified

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 651–675 of 935 papers

TitleStatusHype
AFLoRA: Adaptive Freezing of Low Rank Adaptation in Parameter Efficient Fine-Tuning of Large Models—0
AdaFish: Fast low-rank parameter-efficient fine-tuning by using second-order information—0
FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications—0
Improving LoRA in Privacy-preserving Federated Learning—0
Dynamic Tuning Towards Parameter and Inference Efficiency for ViT AdaptationCode2
Let's Focus on Neuron: Neuron-Level Supervised Fine-tuning for Large Language Model—0
Empirical Studies of Parameter Efficient Methods for Large Language Models of Code and Knowledge Transfer to RCode0
Introducing Routing Functions to Vision-Language Parameter-Efficient Fine-Tuning with Low-Rank BottlenecksCode0
PYRA: Parallel Yielding Re-Activation for Training-Inference Efficient Task AdaptationCode1
Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated ExpertsCode1
An Empirical Study of Parameter Efficient Fine-tuning on Vision-Language Pre-train Model—0
Targeted Efficient Fine-tuning: Optimizing Parameter Updates with Data-Driven Sample Selection—0
Block-wise LoRA: Revisiting Fine-grained LoRA for Effective Personalization and Stylization in Text-to-Image Generation—0
Matrix-Transformation Based Low-Rank Adaptation (MTLoRA): A Brain-Inspired Method for Parameter-Efficient Fine-Tuning—0
Tracking Meets LoRA: Faster Training, Larger Model, Stronger PerformanceCode2
RIFF: Learning to Rephrase Inputs for Few-shot Fine-tuning of Language ModelsCode0
STAR: Constraint LoRA with Dynamic Active Learning for Data-Efficient Fine-Tuning of Large Language ModelsCode0
FineDiffusion: Scaling up Diffusion Models for Fine-grained Image Generation with 10,000 ClassesCode1
ResLoRA: Identity Residual Mapping in Low-Rank Adaption—0
MELoRA: Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-TuningCode1
Inducing Generalization across Languages and Tasks using Featurized Low-Rank Mixtures—0
DiffuseKronA: A Parameter Efficient Fine-tuning Method for Personalized Diffusion Models—0
DropBP: Accelerating Fine-Tuning of Large Language Models by Dropping Backward PropagationCode1
MIP: CLIP-based Image Reconstruction from PEFT Gradients—0
A Fine-tuning Enhanced RAG System with Quantized Influence Measure as AI Judge—0
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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