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NVlabs/DoRA ★ 998 70 APPLICATION

The repository provides the official PyTorch implementation of DoRA (Weight-Decomposed Low-Rank Adaptation), a parameter-efficient fine-tuning method. It integrates with Hugging Face PEFT and includes training/evaluation scripts for vision-language and language tasks (e.g., VQA, ScienceQA, commonsense reasoning). The architecture is modular, with core components like DoRA tuners, adapter controllers, and low-rank layers. Entry points include training scripts (finetune.py, train_dora.py), evaluation scripts (evaluate.py, run_llava.py), and serving tools (gradio_web_server.py). External dependencies include PyTorch, Transformers, PEFT, bitsandbytes, and dataset-specific libraries.

RATING: 7.8/10 MAINT: active DRIFT: low
#parameter-efficient-finetuning #dora #lora #adapter-pattern #peft #vision-language #multimodal #commonsense-reasoning #deep-learning #deep-neural-networks #instruction-tuning #large-language-models #large-vision-language-models #parameter-efficient-fine-tuning #parameter-efficient-tuning #vision-and-language
▸ 4 reusable components found
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