code: def generate_alpaca2_few_shot_prompt(
      self, subject, dev_df, cot=False
  ):
      prompt = f"以下是中国关于{subject}考试的单项选择题，请选出其中的正确答案。\n\n"
      prompt_template = (
          "[INST] <<SYS>>\n"
          "{system_prompt}\n"
          "<</SYS>>\n\n"
          "{instruction} [/INST]好的，我会结合{subject}相关知识回答"
      )
  
      prompt = prompt_template.format_map(
          {
              "instruction": prompt,
              "system_prompt": DEFAULT_SYSTEM_PROMPT,
              "subject": subject,
          }
      )
      k = self.k
      if self.k == -1:
          k = dev_df.shape[0]
      for i in range(k):
          line = dev_df.iloc[i, :]
          q = line["question"]
          for choice in self.choices:
              q += f'\n{choice}. {line[f"{choice}"]}'
  
          a = line["answer"]
          prompt += "[INST] " + q + "\n答案：[/INST]" + a + "\n"
      return prompt
  
file_path: BigDL/python/llm/dev/benchmark/ceval/evaluators/llama.py
imports: import re
  import random
  from tqdm import tqdm
  import numpy
  import torch
  from transformers import LlamaTokenizer, GenerationConfig
  from bigdl.llm.transformers import AutoModelForCausalLM
  from evaluators.evaluator import Evaluator
label_desc: Use image node_id 5 for calling the LlamaEvaluator obj's underlying member method code with example usage: obj.generate_alpaca2_few_shot_prompt(subject, dev_df, cot) and returns: prompt