class_docstr: Test GPTQ model with exllamav2 kernel and desc_act=True (also known as act-order).
  More information on those arguments here:
  https://huggingface.co/docs/transformers/main_classes/quantization#transformers.GPTQConfig
class_docstr_tok: ["Test","GPTQ","model","with","exllamav2","kernel","and","desc_act=True","(","also","known","as","act-order",")",".","More","information","on","those","arguments","here",":","https",":","\/\/huggingface.co\/docs\/transformers\/main_classes\/quantization","#","transformers.GPTQConfig"]
docstr: Setup quantized model
docstr_tok: ["Setup","quantized","model"]
code: def setUpClass(cls):
      """
      Setup quantized model
      """
      cls.quantization_config = GPTQConfig(
          bits=4, exllama_config={"version": 2}
      )
      cls.quantized_model = AutoModelForCausalLM.from_pretrained(
          cls.model_name,
          torch_dtype=torch.float16,
          device_map={"": 0},
          quantization_config=cls.quantization_config,
      )
      cls.tokenizer = AutoTokenizer.from_pretrained(
          cls.model_name, use_fast=True
      )
  
imports: import tempfile
  import unittest
  import pytest
  from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig
  from transformers.testing_utils import is_torch_available, require_accelerate, require_auto_gptq, require_optimum, require_torch_gpu, require_torch_multi_gpu, slow
label_desc: Use image node_id 1 for calling the GPTQTestExllamaV2 obj's underlying member method code with example usage: obj.setUpClass(cls) without return types