chunk_content: scaling factor, and the dropout probability of the LoRA layers. If you want to use a different 🤗 PEFT method, replace LoraConfig with the appropriate class.
  Next, the script wraps the base model and peft_config with the get_peft_model() function to create a PeftModel. 
  
  
  	Copied
   def main():
  +    accelerator = Accelerator()
       model_name_or_path = "t5-base"
       base_path = "temp/data/FinancialPhraseBank-v1.0"
  +    peft_config = LoraConfig(
   