code: def test_small_model_integration_test(self):
      model_id = "llava-hf/vip-llava-7b-hf"
  
      model = VipLlavaForConditionalGeneration.from_pretrained(
          model_id, load_in_4bit=True
      )
      processor = AutoProcessor.from_pretrained(model_id)
  
      url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/compel-neg.png"
  
      image = Image.open(requests.get(url, stream=True).raw)
      prompt = "USER: <image>\nCan you please describe this image?\nASSISTANT:"
  
      inputs = processor(prompt, image, return_tensors="pt").to(
          torch_device, torch.float16
      )
  
      outputs = model.generate(**inputs, max_new_tokens=10)
  
      EXPECTED_OUTPUT = "USER: <image> \nCan you please describe this image?\nASSISTANT: The image features a brown and white cat sitting on"
      self.assertEqual(
          processor.decode(outputs[0], skip_special_tokens=True),
          EXPECTED_OUTPUT,
      )
  
imports: import gc
  import unittest
  import requests
  from transformers import AutoProcessor, VipLlavaConfig, VipLlavaForConditionalGeneration, is_torch_available, is_vision_available
  from transformers.testing_utils import require_bitsandbytes, require_torch, require_torch_gpu, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
label_desc: Use image node_id 3 for calling the VipLlavaForConditionalGenerationIntegrationTest obj's underlying member method code with example usage: obj.test_small_model_integration_test() without return types