chunk_content: configured, and the base model is wrapped, the print_trainable_parameters helper function lets us explore
  the number of trainable parameters. Since we’re interested in performing parameter-efficient fine-tuning,
  we should expect to see a lower number of trainable parameters from the lora_model in comparison to the original model
  which is indeed the case here.
  You can also manually verify what modules are trainable in the lora_model.
  
  
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