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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/zsc000722/ppt

공식 코드

Positional Prompt Tuning for Efficient 3D Representation Learning

https://github.com/zstarn70/rlrr

공식 코드

Low-Rank Rescaled Vision Transformer Fine-Tuning: A Residual Design Approach

https://github.com/zuwenqiang/ept

공식 코드

Embedded Prompt Tuning: Towards Enhanced Calibration of Pretrained Models for Medical Images

https://github.com/zwtnju/peft

공식 코드

A Comprehensive Evaluation of Parameter-Efficient Fine-Tuning on Software Engineering Tasks

https://github.com/zyy-2001/cola

공식 코드

CoLA: Collaborative Low-Rank Adaptation

https://github.com/zzhustc2016/lamo

공식 코드

Addressing Overprescribing Challenges: Fine-Tuning Large Language Models for Medication Recommendation Tasks

https://github.com/12kimih/hicupid

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/BatsResearch/LexC-Gen

QLoRA: Efficient Finetuning of Quantized LLMs

https://github.com/Davidzhangyuanhan/NOAH

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/HectorHHZ/Sparse_Matrix_Tuning

Sparse Matrix in Large Language Model Fine-tuning

https://github.com/Leeroo-AI/mergoo

When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications

https://github.com/Lightning-AI/lit-llama

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/Luohh5/Chain-of-Exemplar

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/MS-P3/code6/tree/main/peft

PEFT for Speech: Unveiling Optimal Placement, Merging Strategies, and Ensemble Techniques

https://github.com/MindSpore-scientific-2/code-4/tree/main/dora

DoRA: Enhancing Parameter-Efficient Fine-Tuning with Dynamic Rank Distribution

https://github.com/MindSpore-scientific/code-4/tree/main/dora

DoRA: Enhancing Parameter-Efficient Fine-Tuning with Dynamic Rank Distribution

https://github.com/OPPO-Mente-Lab/Subject-Diffusion

GlyphDraw: Seamlessly Rendering Text with Intricate Spatial Structures in Text-to-Image Generation

https://github.com/Owen-Oertell/rlcm

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/PKD667/political-alignement

PoliTune: Analyzing the Impact of Data Selection and Fine-Tuning on Economic and Political Biases in Large Language Models

https://github.com/Pointcept/GPT4Point

Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following

https://github.com/QwenLM/Qwen-7B

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/TUDB-Labs/mLoRA

mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs

https://github.com/ZhangYuanhan-AI/NOAH

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/agendd/rwkv-asr

An Embarrassingly Simple Approach for LLM with Strong ASR Capacity

https://github.com/aigtext/glyphcontrol-release

GlyphDraw: Seamlessly Rendering Text with Intricate Spatial Structures in Text-to-Image Generation

https://github.com/aiiu-lab/dfd-fcg

Towards More General Video-based Deepfake Detection through Facial Feature Guided Adaptation for Foundation Model

https://github.com/aleemsidra/convlora

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/arielnlee/Platypus

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/arkel23/clca

Visual Query Tuning: Towards Effective Usage of Intermediate Representations for Parameter and Memory Efficient Transfer Learning

https://github.com/asad-aali/splitwiser

RadAdapt: Radiology Report Summarization via Lightweight Domain Adaptation of Large Language Models

https://github.com/ayyucedemirbas/DoRA

DoRA: Weight-Decomposed Low-Rank Adaptation

https://github.com/bigdata-ustc/Zero-1-to-3

FedJudge: Federated Legal Large Language Model

https://github.com/bminixhofer/nnsplit

Segment Any Text: A Universal Approach for Robust, Efficient and Adaptable Sentence Segmentation

https://github.com/brandon3964/multimodal-task-vector

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/catid/dora

DoRA: Weight-Decomposed Low-Rank Adaptation

https://github.com/chenlicodebank/lora_on_analog_hardware

LoRA: Low-Rank Adaptation of Large Language Models

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.