diff --git a/Makefile b/Makefile
index 6e87a399a3..4b78f88edc 100644
--- a/Makefile
+++ b/Makefile
@@ -71,6 +71,11 @@ slow_tests_text_generation_example: test_installs
 slow_tests_image_to_text_example: test_installs
 	python -m pytest tests/test_image_to_text_example.py -v -s --token $(TOKEN)
 
+# Run visual question answering tests
+slow_tests_openclip_vqa_example: test_installs
+	python -m pip install -r examples/visual-question-answering/openclip_requirements.txt
+	python -m pytest tests/test_openclip_vqa.py
+
 slow_tests_fsdp: test_installs
 	python -m pytest tests/test_fsdp_examples.py -v -s --token $(TOKEN)
 
diff --git a/examples/visual-question-answering/run_openclip_vqa.py b/examples/visual-question-answering/run_openclip_vqa.py
index 698927dece..38548e5dcc 100644
--- a/examples/visual-question-answering/run_openclip_vqa.py
+++ b/examples/visual-question-answering/run_openclip_vqa.py
@@ -11,7 +11,7 @@
 import matplotlib.pyplot as plt
 import numpy
 import torch
-from open_clip import create_model_from_pretrained, get_tokenizer
+from open_clip import create_model_from_pretrained, get_tokenizer, model
 from PIL import Image
 
 from optimum.habana.transformers.modeling_utils import adapt_transformers_to_gaudi
@@ -49,7 +49,9 @@
         'pie_chart.png'
     ]
 
-def plot_images_with_metadata(images, metadata, output_dir, plot_name):
+def plot_images_with_metadata(images:list, metadata, output_dir: str, plot_name: str) -> None:
+    print(f"plottypes {type(images)} {type(metadata)} {type(output_dir)} {type(plot_name)}")
+
     num_images = len(images)
     fig, axes = plt.subplots(nrows=num_images, ncols=1, figsize=(5, 5 * num_images))
 
@@ -67,7 +69,7 @@ def plot_images_with_metadata(images, metadata, output_dir, plot_name):
     plt.savefig(f'{output_dir}/{plot_name}.png')
 
 
-def run_qa(model, images, texts, device):
+def run_qa(model: model, images: torch.Tensor, texts: torch.Tensor, device: torch.device) -> tuple:
     with torch.no_grad():
         image_features, text_features, logit_scale = model(images, texts)
         logits = (logit_scale * image_features @ text_features.t()).detach().softmax(dim=-1)
@@ -75,7 +77,7 @@ def run_qa(model, images, texts, device):
     return sorted_indices, logits
 
 
-def postprocess(args, sorted_indices, logits, topk):
+def postprocess(args: argparse.Namespace, sorted_indices: torch.Tensor, logits: torch.Tensor , topk: int) -> list:
     logits = logits.float().cpu().numpy()
     sorted_indices = sorted_indices.int().cpu().numpy()
     metadata_list = []
diff --git a/tests/test_openclip_vqa.py b/tests/test_openclip_vqa.py
index 412a411a4a..5b918ff7d8 100644
--- a/tests/test_openclip_vqa.py
+++ b/tests/test_openclip_vqa.py
@@ -18,6 +18,15 @@
             ("microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224", 1816)
         ],
     }
+else:
+    # Gaudi1 CI baselines
+    MODELS_TO_TEST = {
+        "bf16": [
+            ("laion/CLIP-ViT-g-14-laion2B-s12B-b42K", 550),
+            ("microsoft/BiomedCLIP-PubMedBERT_256-vit_base_patch16_224", 1200),
+         ],
+     }
+
 
 def _install_requirements():
         PATH_TO_EXAMPLE_DIR = Path(__file__).resolve().parent.parent / "examples"
@@ -64,7 +73,6 @@ def _test_openclip_vqa(
             results = json.load(fp)
 
         # Ensure performance requirements (throughput) are met
-        t= results["throughput"]
         assert results["throughput"] >= (2 - TIME_PERF_FACTOR) * baseline