class_docstr: A testing suite that makes sure that the PeftModel class is correctly integrated into the transformers library.
class_docstr_tok: ["A","testing","suite","that","makes","sure","that","the","PeftModel","class","is","correctly","integrated","into","the","transformers","library","."]
docstr: Simple test that tests if `add_adapter` works as expected
docstr_tok: ["Simple","test","that","tests","if","`","add_adapter","`","works","as","expected"]
code: def test_peft_add_adapter(self):
      """
      Simple test that tests if `add_adapter` works as expected
      """
      from peft import LoraConfig
  
      for model_id in self.transformers_test_model_ids:
          for (
              transformers_class
          ) in self.transformers_test_model_classes:
              model = transformers_class.from_pretrained(model_id).to(
                  torch_device
              )
  
              peft_config = LoraConfig(init_lora_weights=False)
  
              model.add_adapter(peft_config)
  
              self.assertTrue(
                  self._check_lora_correctly_converted(model)
              )
              # dummy generation
              _ = model.generate(
                  input_ids=torch.LongTensor(
                      [[0, 1, 2, 3, 4, 5, 6, 7]]
                  ).to(torch_device)
              )
  
imports: import os
  import tempfile
  import unittest
  from huggingface_hub import hf_hub_download
  from transformers import AutoModelForCausalLM, OPTForCausalLM
  from transformers.testing_utils import require_peft, require_torch, require_torch_gpu, slow, torch_device
  from transformers.utils import is_torch_available
label_desc: Use image node_id 6 for calling the PeftIntegrationTester obj's underlying member method code with example usage: obj.test_peft_add_adapter() without return types