class_docstr: A wrapper class for SimpleGPT, providing integration with the lm-evaluation-harness library.
class_docstr_tok: ["A","wrapper","class","for","SimpleGPT",",","providing","integration","with","the","lm-evaluation-harness","library","."]
code: def __init__(
      self,
      model: LLaMA,
      tokenizer,
      max_seq_length: Optional[int] = None,
  ):
      super().__init__()
      self._model = model
      self._tokenizer = tokenizer
      self._device = torch.device("cuda")
      self._max_seq_length = (
          2048 if max_seq_length is None else max_seq_length
      )
  
imports: import sys
  import time
  from pathlib import Path
  from typing import Optional
  import torch
  import torch._dynamo.config
  import torch._inductor.config
  import os
  import sys
  from sentencepiece import SentencePieceProcessor
  from model import LLaMA
  import lm_eval
  import main
  from generate import _load_model, encode_tokens, model_forward
label_desc: Use image node_id 1 to create a new SimpleGPTEvalWrapper object from inherited base classes: lm_eval.base with example: obj = SimpleGPTEvalWrapper(model, tokenizer, max_seq_length)