chunk_content: Image classification using LoRA
  	
  This guide demonstrates how to use LoRA, a low-rank approximation technique, to fine-tune an image classification model.
  By using LoRA from 🤗 PEFT, we can reduce the number of trainable parameters in the model to only 0.77% of the original.
  LoRA achieves this reduction by adding low-rank “update matrices” to specific blocks of the model, such as the attention
  blocks. During fine-tuning, only these matrices are 