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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/clementromac/lamorel

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/cloneofsimo/lora

Compacter: Efficient Low-Rank Hypercomplex Adapter Layers

https://github.com/cloudygoose/fewshot_lama

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

https://github.com/cornell-zhang/llm-datatypes

QLoRA: Efficient Finetuning of Quantized LLMs

https://github.com/daniel-furman/sft-demos

QLoRA: Efficient Finetuning of Quantized LLMs

https://github.com/davisyoshida/lorax

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/debjitpaul/refiner

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/dexter-gt-86/sinklora

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

https://github.com/dongping-chen/mixset

Aggregate, Decompose, and Fine-Tune: A Simple Yet Effective Factor-Tuning Method for Vision Transformer

https://github.com/dreamboat26/fuzzy-logic

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/durham/rwkv-finetune-script

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/erikaawang/probing-multilingual-dynamics

QLoRA: Efficient Finetuning of Quantized LLMs

https://github.com/explainableml/delora

Advancing Parameter Efficiency in Fine-tuning via Representation Editing

https://github.com/facebookresearch/large_concept_model

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

https://github.com/facebookresearch/pytorch-dp

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/facico/chinese-vicuna

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/flagai-open/aquila2

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/flagalpha/llama2-chinese

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/flowersteam/lamorel

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/google-deepmind/thunnini

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/gt-ripl/selective-projection-decay

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/h2oai/alpaca-lora

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/h2oai/h2o-llmstudio

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/haoychen3/DMIH

Hiding Images in Diffusion Models by Editing Learned Score Functions

https://github.com/heekhero/DTL

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/hellokevin07/elastictrainer

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/hitz-zentroa/this-is-not-a-dataset

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/hiyouga/llama-efficient-tuning

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/huggingface/text-generation-inference

QLoRA: Efficient Finetuning of Quantized LLMs

https://github.com/if001/rwkv-lm-lora-ja

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/ikergarcia1996/sequence-labeling-llms

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/internlm/xtuner

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/ist-daslab/panzamail

RoSA: Accurate Parameter-Efficient Fine-Tuning via Robust Adaptation

https://github.com/ivan-tang-3d/viewrefer3d

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

https://github.com/jackaduma/Alpaca-LoRA-RLHF-PyTorch

LoRA: Low-Rank Adaptation of Large Language Models

https://github.com/jerrywu-code/susgen

QLoRA: Efficient Finetuning of Quantized LLMs

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