diff --git a/olmocr/bench/runners/run_dotsocr.py b/olmocr/bench/runners/run_dotsocr.py
index ff8b410..24764a5 100644
--- a/olmocr/bench/runners/run_dotsocr.py
+++ b/olmocr/bench/runners/run_dotsocr.py
@@ -3,12 +3,11 @@ from io import BytesIO
 
 import torch
 from PIL import Image
-from transformers import AutoModelForCausalLM, AutoProcessor
 from qwen_vl_utils import process_vision_info
+from transformers import AutoModelForCausalLM, AutoProcessor
 
 from olmocr.data.renderpdf import render_pdf_to_base64png
 
-
 _model = None
 _processor = None
 
@@ -27,23 +26,13 @@ def load_model(model_name: str = "./weights/DotsOCR"):
     global _model, _processor
     if _model is None or _processor is None:
         _model = AutoModelForCausalLM.from_pretrained(
-            model_name,
-            torch_dtype=torch.bfloat16,
-            device_map="auto",
-            attn_implementation="flash_attention_2",
-            low_cpu_mem_usage=True,
-            trust_remote_code=True
+            model_name, torch_dtype=torch.bfloat16, device_map="auto", attn_implementation="flash_attention_2", low_cpu_mem_usage=True, trust_remote_code=True
         )
         _processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
     return _model, _processor
 
 
-def run_dotsocr(
-    pdf_path: str,
-    page_num: int = 1,
-    model_name: str = "./weights/DotsOCR",
-    target_longest_image_dim: int = 1024
-) -> str:
+def run_dotsocr(pdf_path: str, page_num: int = 1, model_name: str = "./weights/DotsOCR", target_longest_image_dim: int = 1024) -> str:
     """
     Convert page of a PDF file to structured layout information using DotsOCR.
 
@@ -71,25 +60,10 @@ def run_dotsocr(
     # Define the prompt for layout extraction
     prompt = """Extract the text content from this image."""
 
-    messages = [
-        {
-            "role": "user",
-            "content": [
-                {
-                    "type": "image",
-                    "image": image
-                },
-                {"type": "text", "text": prompt}
-            ]
-        }
-    ]
+    messages = [{"role": "user", "content": [{"type": "image", "image": image}, {"type": "text", "text": prompt}]}]
 
     # Preparation for inference
-    text = processor.apply_chat_template(
-        messages,
-        tokenize=False,
-        add_generation_prompt=True
-    )
+    text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
 
     image_inputs, video_inputs = process_vision_info(messages)
     inputs = processor(
@@ -105,17 +79,13 @@ def run_dotsocr(
     with torch.no_grad():
         generated_ids = model.generate(**inputs, max_new_tokens=4096)
 
-    generated_ids_trimmed = [
-        out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
-    ]
+    generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)]
 
-    output_text = processor.batch_decode(
-        generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
-    )
+    output_text = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)
 
     del inputs
     del generated_ids
     del generated_ids_trimmed
     torch.cuda.empty_cache()
 
-    return output_text[0] if output_text else ""
\ No newline at end of file
+    return output_text[0] if output_text else ""