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Sayak Paul
2023-02-07 11:11:36 +05:30
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[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/sayakpaul/peft/blob/example/img-cls/examples/image_classification/image_classification_peft_lora.ipynb) <!-- Update the Colab Link -->
We provide a notebook (`image_classification_peft_lora.ipynb`) where we learn how to use [LoRA](https://arxiv.org/abs/2106.09685) from 🤗 PEFT to fine-tune an image classification modelby ONLY using **0.6%** of the original trainable parameters of the model.
We provide a notebook (`image_classification_peft_lora.ipynb`) where we learn how to use [LoRA](https://arxiv.org/abs/2106.09685) from 🤗 PEFT to fine-tune an image classification modelby ONLY using **0.7%** of the original trainable parameters of the model.
LoRA adds low-rank "update matrices" to certain blocks in the underlying model (in this case the attention blocks) and ONLY trains those matrices during fine-tuning. During inference, these update matrices are _merged_ with the original model parameters. For more details, check out the [original LoRA paper](https://arxiv.org/abs/2106.09685).
LoRA adds low-rank "update matrices" to certain blocks in the underlying model (in this case the attention blocks) and ONLY trains those matrices during fine-tuning. During inference, these update matrices are _merged_ with the original model parameters. For more details, check out the [original LoRA paper](https://arxiv.org/abs/2106.09685).