chunk_content: raModel, LoraConfig, get_peft_model
  
  config = LoraConfig(r=32, lora_alpha=64, target_modules=["q_proj", "v_proj"], lora_dropout=0.05, bias="none")
  After you set up the LoraConfig, wrap it and the base model with the get_peft_model() function to create a PeftModel. Print out the number of trainable parameters to see how much more efficient LoRA is compared to fully training the model!
  
  
  	Copied
  model = get_peft_model(model, config)
  model.print_t