Fixes

README.md

@@ -209,6 +209,20 @@ python -m olmocr.pipeline ./localworkspace --markdown --pdfs tests/gnarly_pdfs/*
 
 With the addition of the `--markdown` flag, results will be stored as markdown files inside of `./localworkspace/markdown/`. 
 
+#### Viewing Results
+
+The `./localworkspace/` workspace folder will then have both [Dolma](https://github.com/allenai/dolma) and markdown files (if using `--markdown`).
+
+
+```bash
+cat localworkspace/markdown/olmocr-sample.md 
+```
+
+```
+olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models
+...
+```
+
 ### Using an Inference Provider or External Server
 
 If you have a vLLM server already running elsewhere (or any inference platform implementing the OpenAI API), you can point olmOCR to use it instead of spawning a local instance:
@@ -241,20 +255,6 @@ Notes on arguments
 - Other arguments work the same as with local inference
 
 
-#### Viewing Results
-
-The `./localworkspace/` workspace folder will then have both [Dolma](https://github.com/allenai/dolma) and markdown files (if using `--markdown`).
-
-
-```bash
-cat localworkspace/markdown/olmocr-sample.md 
-```
-
-```
-olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models
-...
-```
-
 ### Multi-node / Cluster Usage
 
 If you want to convert millions of PDFs, using multiple nodes running in parallel, then olmOCR supports

olmocr/bench/synth/mine_html_templates.py

@@ -1,7 +1,5 @@
 import argparse
 import asyncio
-import base64
-import tempfile
 import glob
 import hashlib
 import json
@@ -10,6 +8,7 @@ import os
 import random
 import re
 import subprocess
+import tempfile
 import uuid
 from collections import defaultdict
 from typing import Dict, List
@@ -37,13 +36,7 @@ total_output_tokens = 0
 def get_git_commit_hash():
     """Get the current git commit hash, if available."""
     try:
-        result = subprocess.run(
+        result = subprocess.run(["git", "rev-parse", "HEAD"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=True)
-            ["git", "rev-parse", "HEAD"],
-            stdout=subprocess.PIPE,
-            stderr=subprocess.PIPE,
-            text=True,
-            check=True
-        )
         return result.stdout.strip()
     except (subprocess.CalledProcessError, FileNotFoundError):
         # Git not available or not a git repository
@@ -427,7 +420,7 @@ async def generate_html_from_image(client, image_base64):
         )
 
         # Check if response was complete
-        if hasattr(analysis_response, 'stop_reason') and analysis_response.stop_reason != 'end_turn':
+        if hasattr(analysis_response, "stop_reason") and analysis_response.stop_reason != "end_turn":
             print(f"Warning: Analysis response incomplete (stop_reason: {analysis_response.stop_reason})")
             return None
 
@@ -472,7 +465,7 @@ async def generate_html_from_image(client, image_base64):
         )
 
         # Check if response was complete
-        if hasattr(initial_response, 'stop_reason') and initial_response.stop_reason != 'end_turn':
+        if hasattr(initial_response, "stop_reason") and initial_response.stop_reason != "end_turn":
             print(f"Warning: Initial HTML response incomplete (stop_reason: {initial_response.stop_reason})")
             return None
 
@@ -492,7 +485,6 @@ async def generate_html_from_image(client, image_base64):
             print("Warning: No HTML code block found in initial response")
             return None
 
-
         # Step 3: Render the initial HTML to PDF and then back to PNG for comparison
         # Create a temporary PDF file
         with tempfile.NamedTemporaryFile(suffix=".pdf", delete=False) as tmp_pdf:
@@ -520,15 +512,15 @@ async def generate_html_from_image(client, image_base64):
                 model="claude-sonnet-4-5-20250929",
                 max_tokens=40000,
                 temperature=1.0,
-                thinking={
+                thinking={"type": "enabled", "budget_tokens": 12000},
-                    "type": "enabled",
-                    "budget_tokens": 12000
-                },
                 messages=[
                     {
                         "role": "user",
                         "content": [
-                            {"type": "text", "text": "I'm going to show you two images:\n1. The original document\n2. How the HTML I generated renders\n\nPlease compare them carefully and provide a revised version of the HTML that better matches the original."},
+                            {
+                                "type": "text",
+                                "text": "I'm going to show you two images:\n1. The original document\n2. How the HTML I generated renders\n\nPlease compare them carefully and provide a revised version of the HTML that better matches the original.",
+                            },
                             {"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": image_base64}},
                             {"type": "text", "text": "Above is the ORIGINAL document."},
                             {"type": "image", "source": {"type": "base64", "media_type": "image/png", "data": rendered_image_base64}},
@@ -546,7 +538,7 @@ async def generate_html_from_image(client, image_base64):
                                 f"The webpage will be viewed at {png_width}x{png_height} pixels.\n\n"
                                 "Provide a REVISED version of the HTML that corrects any issues you identified. "
                                 "Make sure all important elements are visible and the layout matches the original as closely as possible.\n"
-                                "Output the complete revised HTML in a ```html code block."
+                                "Output the complete revised HTML in a ```html code block.",
                             },
                         ],
                     }
@@ -559,7 +551,7 @@ async def generate_html_from_image(client, image_base64):
                 refinement_response = await refinement_stream.get_final_message()
 
             # Check if refinement response was complete
-            if hasattr(refinement_response, 'stop_reason') and refinement_response.stop_reason != 'end_turn':
+            if hasattr(refinement_response, "stop_reason") and refinement_response.stop_reason != "end_turn":
                 print(f"Warning: Refinement response incomplete (stop_reason: {refinement_response.stop_reason})")
                 # Return initial HTML as fallback since it was complete
                 return initial_html
@@ -1022,7 +1014,6 @@ def generate_tests_from_html(html_content: str, pdf_id: str, page_num: int, rand
         # So add in the bulk of the test cases back in now
         tests.extend(table_tests)
 
-
     # Step 3: Generate TextPresenceTests and OrderingTests from markdown content
     # Convert HTML to markdown to get cleaner text for presence and ordering tests
     markdown_content = html_to_markdown_with_frontmatter(html_content)

olmocr/data/prepare_loc_transcripts.py

@@ -13,11 +13,12 @@ import time
 from concurrent.futures import ThreadPoolExecutor, as_completed
 from pathlib import Path
 from typing import Dict, Optional, Set, Tuple
-from olmocr.image_utils import convert_image_to_pdf_bytes
 
 import requests
 from tqdm import tqdm
 
+from olmocr.image_utils import convert_image_to_pdf_bytes
+
 
 def fix_image_url(url: str) -> str:
     """Fix image URL to use full resolution instead of percentage-based sizing."""

olmocr/data/prepare_national_archive_transcripts.py

@@ -99,6 +99,7 @@ from tqdm import tqdm
 
 from olmocr.image_utils import convert_image_to_pdf_bytes
 
+
 def download_image(url: str, output_path: Path, max_retries: int = 5) -> bool:
     """Download image from URL with exponential backoff retry logic."""
     for attempt in range(max_retries):

olmocr/data/prepare_workspace.py

@@ -113,7 +113,7 @@ def parse_jsonl_entry(entry: Dict) -> Optional[Dict]:
             "source_file": source_file,
             "metadata": metadata,
             "pdf_page_numbers": pdf_page_numbers,
-            "page_response_data": page_response_data
+            "page_response_data": page_response_data,
         }
     except Exception as e:
         logger.error(f"Error parsing JSONL entry: {e}")

olmocr/pipeline.py

@@ -1121,7 +1121,6 @@ async def main():
     )
     server_group.add_argument("--api_key", type=str, default=None, help="API key for authenticated remote servers (e.g., DeepInfra)")
 
-
     vllm_group = parser.add_argument_group(
         "VLLM arguments", "These arguments are passed to vLLM. Any unrecognized arguments are also automatically forwarded to vLLM."
     )
@@ -1133,7 +1132,6 @@ async def main():
     vllm_group.add_argument("--data-parallel-size", "-dp", type=int, default=1, help="Data parallel size for vLLM")
     vllm_group.add_argument("--port", type=int, default=30024, help="Port to use for the VLLM server")
 
-
     # Beaker/job running stuff
     beaker_group = parser.add_argument_group("beaker/cluster execution")
     beaker_group.add_argument("--beaker", action="store_true", help="Submit this job to beaker instead of running locally")