code: def __init__(
      self,
      model_size="7B",
      model_gen=1,
      device="",
      quantize=False,
      batch_size=1,
      max_batch_size=1,
      do_sample=False,
      temperature=1.0,
      checkpoint_path="",
      tokenizer_path="",
  ):
      super().__init__()
  
      if batch_size is None:
          batch_size = 1
      self.do_sample = do_sample
      self.temperature = temperature
      self._device = device
  
      assert isinstance(model_gen, int)
      assert isinstance(model_size, str)
      assert isinstance(batch_size, int)
      assert isinstance(checkpoint_path, str)
      assert isinstance(tokenizer_path, str)
  
      self.llama = LLaMa.build(
          checkpoint_path,
          tokenizer_path,
          model_gen,
          model_size,
          quantize,
      )
  
file_path: tinygrad/test/external/external_llama_eval.py
imports: from lm_eval.base import BaseLM
  from lm_eval import evaluator, tasks
  import torch, json, argparse
  from examples.llama import LLaMa
  from tinygrad.tensor import Tensor
  from tinygrad import Device
label_desc: Use image node_id 1 to create a new LLaMaAdaptor object from inherited base classes: BaseLM with example: obj = LLaMaAdaptor(model_size, model_gen, device, quantize, batch_size, max_batch_size, do_sample, temperature, checkpoint_path, tokenizer_path)