chunk_content: netune.
  
  
  	Copied
  from transformers import AutoModelForSeq2SeqLM
  
  model_name_or_path = "bigscience/mt0-large"
  tokenizer_name_or_path = "bigscience/mt0-large"
  model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)
  Wrap your base model and peft_config with the get_peft_model function to create a PeftModel. To get a sense of the number of trainable parameters in your model, use the print_trainable_parameters method. In this case, you’re