chunk_content: d 'head.fc'. With that in mind,
  here is our LoRA config:
  
  
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  config = LoraConfig(target_modules=r".*\.mlp\.fc\d", modules_to_save=["head.fc"])
  Then we only need to create the PEFT model by passing our base model and the config to get_peft_model:
  
  
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  peft_model = get_peft_model(model, config)
  peft_model.print_trainable_parameters()
  # prints trainable params: 1,064,454 || all params: 56,467,974 || trainable%: 1.88505789139876
  This sho