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  LoRA makes fine-tuning more efficient by drastically reducing the number of trainable parameters.
  The original pre-trained weights are kept frozen, which means you can have multiple lightweight and portable LoRA models for various downstream tasks built on top of them.
  LoRA is orthogonal to many other parameter-efficient methods and can be combined with many of them.
  Performance of models fine-tuned using LoRA is comparable to the perfor