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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/sultan-hassan/CosmoGemma

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/sunildkumar/lora_from_scratch

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/tatsu-lab/stanford_alpaca

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/teelinsan/camoscio

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/theodb/asr-whisper-finetuning

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/thuml/AutoTimes

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/tloen/alpaca-lora

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/uds-lsv/llmft

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/varungumma/fairseq

A Comprehensive Analysis of Adapter Efficiency

https://github.com/vermouthdky/SimTeG

SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning

https://github.com/videocrafter/videocrafter

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/vivo-ai-lab/bluelm

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/wenet-e2e/west

An Embarrassingly Simple Approach for LLM with Strong ASR Capacity

https://github.com/wwxu21/cut

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/yangletliu/finlora

S-LoRA: Serving Thousands of Concurrent LoRA Adapters

https://github.com/ying1123/vtc-artifact

S-LoRA: Serving Thousands of Concurrent LoRA Adapters

https://github.com/yjiangcm/lion

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/yjiangcm/lte

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/zhanglab-aim/lingo

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/zhaolingao/langptune

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/zhaoolee/garss

CleaR: Towards Robust and Generalized Parameter-Efficient Fine-Tuning for Noisy Label Learning

https://github.com/zhengxiangshi/powerfulpromptft

DePT: Decomposed Prompt Tuning for Parameter-Efficient Fine-tuning

https://github.com/zhenqincn/fedkseed

Federated Full-Parameter Tuning of Billion-Sized Language Models with Communication Cost under 18 Kilobytes

https://github.com/zhuyuhua1/contrib/tree/gaussian_adaptive_attention/application/gaussian_adaptive_attention

Density Adaptive Attention is All You Need: Robust Parameter-Efficient Fine-Tuning Across Multiple Modalities

https://github.com/ziyuguo99/viewrefer3d

Point-PEFT: Parameter-Efficient Fine-Tuning for 3D Pre-trained Models

https://github.com/zjunlp/trice

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/zrrskywalker/point-bind

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

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