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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/jl-er/rwkv-peft

Balancing LoRA Performance and Efficiency with Simple Shard Sharing

https://github.com/jzhang38/longmamba

LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models

https://github.com/kohakublueleaf/lycoris

Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

https://github.com/labmlai/annotated_deep_learning_paper_implementations

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/lif31up/attention-is-all-you-need

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/lif31up/extended-BERT-for-low-rank-adaption

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/lighterswang/biomedclip-lora

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/liucongg/chatglm-finetuning

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/llamafamily/llama-chinese

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/longyuewangdcu/chinese-llama-2

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/lxe/simple-llama-finetuner

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/maxzanella/clip-lora

Low-Rank Few-Shot Adaptation of Vision-Language Models

https://github.com/mchl-labs/stambecco

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/mistralai/mistral-finetune

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/mistralai/mistral-inference

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/mistralai/mistral-src

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/mkshing/DiffFit-pytorch

BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models

https://github.com/nifangbaage/explict-visual-prompt

Explicit Visual Prompting for Universal Foreground Segmentations

https://github.com/open-finance-lab/finlora

S-LoRA: Serving Thousands of Concurrent LoRA Adapters

https://github.com/openaccess-ai-collective/axolotl

LongLoRA: Efficient Fine-tuning of Long-Context Large Language Models

https://github.com/openmedlab/pulse

QLoRA: Efficient Finetuning of Quantized LLMs

https://github.com/openrobotlab/pointllm

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

https://github.com/oseyosey/ccds

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/phoebussi/alpaca-cot

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/phymhan/soda-diffusion

Spectrum-Aware Parameter Efficient Fine-Tuning for Diffusion Models

https://github.com/piero2c/BitInfer

BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models

https://github.com/pilancilab/caldera

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/pkd667/political-alignment

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

https://github.com/punica-ai/punica

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/pytorch/opacus

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/qizekun/ShapeLLM

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

https://github.com/qwenlm/qwen

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/qwenlm/qwen-vl

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/seanzhang-zhichen/llama3-chinese

DoRA: Weight-Decomposed Low-Rank Adaptation

https://github.com/simula/pointdetectcount

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/slp-rl/salmon

TASTE: Text-Aligned Speech Tokenization and Embedding for Spoken Language Modeling

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