code: def main():
      args = parse_args()
  
      device = torch.device(get_accelerator().device_name(0))
  
      args.end_of_conversation_token = "<|endoftext|>"
      additional_special_tokens = (
          args.end_of_conversation_token if args.add_eot_token else None
      )
      tokenizer = load_hf_tokenizer(
          args.model_name_or_path_baseline,
          fast_tokenizer=True,
          add_special_tokens=additional_special_tokens,
      )
  
      model_baseline = create_hf_model(
          AutoModelForCausalLM,
          args.model_name_or_path_baseline,
          tokenizer,
          None,
      )
      model_fintuned = create_hf_model(
          AutoModelForCausalLM,
          args.model_name_or_path_finetune,
          tokenizer,
          None,
      )
  
      model_baseline.to(device)
      model_fintuned.to(device)
  
      # One observation: if the prompt ends with a space " ", there is a high chance that
      # the original model (without finetuning) will stuck and produce no response.
      # Finetuned models have less such issue. Thus following prompts all end with ":"
      # to make it a more meaningful comparison.
      if args.language == "English":
          prompts = [
              "Human: Please tell me about Microsoft in a few sentence? Assistant:",
              "Human: Explain the moon landing to a 6 year old in a few sentences. Assistant:",
              "Human: Write a short poem about a wise frog. Assistant:",
              "Human: Who was president of the United States in 1955? Assistant:",
              "Human: How does a telescope work? Assistant:",
              "Human: Why do birds migrate south for the winter? Assistant:",
          ]
      elif args.language == "Chinese":
          prompts = [
              "Human: 请用几句话介绍一下微软? Assistant:",
              "Human: 用几句话向6岁的孩子解释登月。 Assistant:",
              "Human: 写一首关于一只聪明的青蛙的短诗。 Assistant:",
              "Human: 谁是1955年的美国总统? Assistant:",
              "Human: 望远镜是如何工作的? Assistant:",
              "Human: 鸟类为什么要南迁过冬? Assistant:",
          ]
      elif args.language == "Japanese":
          prompts = [
              "Human: マイクロソフトについて簡単に教えてください。 Assistant:",
              "Human: 6歳児に月面着陸を短い文で説明する。 Assistant:",
              "Human: 賢いカエルについて短い詩を書いてください。 Assistant:",
              "Human: 1955年のアメリカ合衆国大統領は誰? Assistant:",
              "Human: 望遠鏡はどのように機能しますか? Assistant:",
              "Human: 鳥が冬に南に移動するのはなぜですか? Assistant:",
          ]
  
      prompt_eval(
          args,
          model_baseline,
          model_fintuned,
          tokenizer,
          device,
          prompts,
      )
  
file_path: deepspeedexamples/applications/DeepSpeed-Chat/training/step1_supervised_finetuning/prompt_eval.py
imports: import argparse
  import logging
  import torch
  from transformers import AutoModelForCausalLM
  from dschat.utils.model.model_utils import create_hf_model
  from dschat.utils.utils import load_hf_tokenizer
  from deepspeed import get_accelerator
label_desc: Use image node_id 6 for calling a global function with example usage: main() without return types