diff --git a/tests/test_sglang.py b/tests/test_sglang.py
index c07e27a..0e94184 100644
--- a/tests/test_sglang.py
+++ b/tests/test_sglang.py
@@ -132,6 +132,9 @@ class TestHuggingFaceModel(unittest.IsolatedAsyncioTestCase):
                 json.dump(config_data, cfout)
 
         self.tokenizer = AutoTokenizer.from_pretrained(model_cache_dir, trust_remote_code=True)
+        self.image_token_id = self.tokenizer.encode("<|image_pad|>")[0]
+
+
         self.model = Qwen2VLForConditionalGeneration.from_pretrained(model_cache_dir, torch_dtype=torch.bfloat16, trust_remote_code=True).eval()
         self.processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-7B-Instruct")
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
@@ -149,13 +152,13 @@ class TestHuggingFaceModel(unittest.IsolatedAsyncioTestCase):
                 target_anchor_text_len=6000,
             )
 
+        messages = query["messages"]
+  
        # Apply chat template to get the text
         text = self.processor.apply_chat_template(
             query["messages"], tokenize=False, add_generation_prompt=True
         )
 
-        print(text)
-
         image_url = query["messages"][0]["content"][1]["image_url"]["url"]
 
         # Remove the "data:image/png;base64," prefix
@@ -175,6 +178,14 @@ class TestHuggingFaceModel(unittest.IsolatedAsyncioTestCase):
             return_tensors="pt",
         )
 
+        image_indices = [
+            idx
+            for idx, token in enumerate(inputs["input_ids"][0])
+            if token.item() == self.image_token_id
+        ]
+
+        print("IMAGE INDICES", image_indices)
+
         print(f"image_grid_thw - {inputs['image_grid_thw'].shape} {inputs['image_grid_thw']}")
         print(f"pixel_values - {inputs['pixel_values'].shape} {inputs['pixel_values'].detach().cpu().numpy()}")
         np.save('/root/pixel_values.npy', inputs['pixel_values'].detach().cpu().numpy())
@@ -224,6 +235,8 @@ class TestHuggingFaceModel(unittest.IsolatedAsyncioTestCase):
                 [inputs['attention_mask'], torch.ones((1, 1), dtype=inputs['attention_mask'].dtype).to(self.device)], dim=-1
             )
 
+        print(self.tokenizer.decode(generated_tokens))
+
         # Now take all the input ids and run them through sglang as a comparison
         async with AsyncClient(timeout=600) as session:
             query["temperature"] = 0.0