diff --git a/olmocr/bench/runners/run_nanonetsocr.py b/olmocr/bench/runners/run_nanonetsocr.py
index ea707d3..22d8579 100644
--- a/olmocr/bench/runners/run_nanonetsocr.py
+++ b/olmocr/bench/runners/run_nanonetsocr.py
@@ -1,10 +1,12 @@
 import base64
-import tempfile
 import os
 import re
-from PIL import Image
-from transformers import AutoTokenizer, AutoProcessor, AutoModelForImageTextToText
+import tempfile
+
 import torch
+from PIL import Image
+from transformers import AutoModelForImageTextToText, AutoProcessor, AutoTokenizer
+
 from olmocr.data.renderpdf import render_pdf_to_base64png
 
 _model = None
@@ -12,30 +14,26 @@ _tokenizer = None
 _processor = None
 _device = None
 
+
 def load_model(model_path: str = "nanonets/Nanonets-OCR-s"):
     global _model, _tokenizer, _processor, _device
-    
-    if _model is None:        
+
+    if _model is None:
         _device = "cuda" if torch.cuda.is_available() else "cpu"
         _model = AutoModelForImageTextToText.from_pretrained(
-            model_path, 
-            torch_dtype="auto", 
-            device_map="auto"
+            model_path,
+            torch_dtype="auto",
+            device_map="auto",
             # attn_implementation="flash_attention_2"
         )
         _model.eval()
         _tokenizer = AutoTokenizer.from_pretrained(model_path)
         _processor = AutoProcessor.from_pretrained(model_path)
-    
+
     return _model, _tokenizer, _processor
 
-async def run_nanonetsocr(
-    pdf_path: str,
-    page_num: int = 1,
-    model_path: str = "nanonets/Nanonets-OCR-s",
-    max_new_tokens: int = 4096,
-    **kwargs
-) -> str:
+
+async def run_nanonetsocr(pdf_path: str, page_num: int = 1, model_path: str = "nanonets/Nanonets-OCR-s", max_new_tokens: int = 4096, **kwargs) -> str:
     """
     Convert page of a PDF file to markdown using NANONETS-OCR.
 
@@ -48,47 +46,42 @@ async def run_nanonetsocr(
     Returns:
         str: The OCR result in markdown format.
     """
-    
+
     model, tokenizer, processor = load_model(model_path)
-    
-    image_base64 = render_pdf_to_base64png(
-        pdf_path, 
-        page_num=page_num, 
-        target_longest_image_dim=1024
-    )
-    
+
+    image_base64 = render_pdf_to_base64png(pdf_path, page_num=page_num, target_longest_image_dim=1024)
+
     with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as temp_file:
         image_data = base64.b64decode(image_base64)
         temp_file.write(image_data)
         temp_image_path = temp_file.name
-    
+
     try:
         image = Image.open(temp_image_path)
         prompt = """Extract the text from the above document as if you were reading it naturally. Return the tables in html format. Return the equations in LaTeX representation. If there is an image in the document and image caption is not present, add a small description of the image inside the <img></img> tag; otherwise, add the image caption inside <img></img>. Watermarks should be wrapped in brackets. Ex: <watermark>OFFICIAL COPY</watermark>. Page numbers should be wrapped in brackets. Ex: <page_number>14</page_number> or <page_number>9/22</page_number>. Prefer using ☐ and ☑ for check boxes."""
         messages = [
             {"role": "system", "content": "You are a helpful assistant."},
-            {"role": "user", "content": [
-                {"type": "image", "image": f"file://{temp_image_path}"},
-                {"type": "text", "text": prompt},
-            ]},
+            {
+                "role": "user",
+                "content": [
+                    {"type": "image", "image": f"file://{temp_image_path}"},
+                    {"type": "text", "text": prompt},
+                ],
+            },
         ]
         text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
-        
+
         inputs = processor(text=[text], images=[image], padding=True, return_tensors="pt", use_fast=True)
         inputs = inputs.to(model.device)
         with torch.no_grad():
-            output_ids = model.generate(
-                **inputs, 
-                max_new_tokens=max_new_tokens, 
-                do_sample=False
-            )
-        
-        generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
+            output_ids = model.generate(**inputs, max_new_tokens=max_new_tokens, do_sample=False)
+
+        generated_ids = [output_ids[len(input_ids) :] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
         output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
-        cleaned_text = re.sub(r'<page_number>.*?</page_number>', '', output_text[0])
-        
+        cleaned_text = re.sub(r"<page_number>\d+</page_number>", "", output_text[0])
+
         return cleaned_text
-        
+
     finally:
         try:
             os.unlink(temp_image_path)