code: def __init__(
      self,
      choices,
      model_path="meta-llama/Llama-2-7b-chat-hf",
      device="xpu",
      qtype="sym_int4",
  ):
      super(LlamaEvaluator, self).__init__(
          choices, model_path, device, qtype
      )
      self.tokenizer = LlamaTokenizer.from_pretrained(
          self.model_path, trust_remote_code=True
      )
      self.model = (
          AutoModelForCausalLM.from_pretrained(
              self.model_path,
              load_in_low_bit=self.qtype,
              optimize_model=True,
              use_cache=True,
              trust_remote_code=True,
          )
          .eval()
          .to(self.device)
      )
      self.generation_config = GenerationConfig(
          temperature=0.2,
          top_k=40,
          top_p=0.9,
          do_sample=True,
          num_beams=1,
          repetition_penalty=1.1,
          max_new_tokens=20,
      )
      self.sA_id = self.tokenizer.encode("A", add_special_tokens=False)[
          0
      ]
      self.sB_id = self.tokenizer.encode("B", add_special_tokens=False)[
          0
      ]
      self.sC_id = self.tokenizer.encode("C", add_special_tokens=False)[
          0
      ]
      self.sD_id = self.tokenizer.encode("D", add_special_tokens=False)[
          0
      ]
      self.A_id = self.tokenizer.encode("：A")[-1]
      self.B_id = self.tokenizer.encode("：B")[-1]
      self.C_id = self.tokenizer.encode("：C")[-1]
      self.D_id = self.tokenizer.encode("：D")[-1]
  
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 1 to create a new LlamaEvaluator object from inherited base classes: Evaluator with example: obj = LlamaEvaluator(choices, model_path, device, qtype)