chunk_content: ne-tune an already fine-tuned checkpoint
  )
  Before creating a PeftModel, you can check the number of trainable parameters in the original model:
  
  
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  print_trainable_parameters(model)
  "trainable params: 85876325 || all params: 85876325 || trainable%: 100.00"
  Next, use get_peft_model to wrap the base model so that “update” matrices are added to the respective places.
  
  
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  from peft import LoraConfig, get_peft_model
  
  config = LoraConfig(